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3,500 |
Multi-Sensor Fuzzy Data Fusion Using Sensors with Different Characteristics
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eess.SY
|
This paper proposes a new approach to multi-sensor data fusion. It suggests
that aggregation of data from multiple sensors can be done more efficiently
when we consider information about sensors' different characteristics. Similar
to most research on effective sensors' characteristics, especially in control
systems, our focus is on sensors' accuracy and frequency response. A rule-based
fuzzy system is presented for fusion of raw data obtained from the sensors that
have complement characteristics in accuracy and bandwidth. Furthermore, a fuzzy
predictor system is suggested aiming for extreme accuracy which is a common
need in highly sensitive applications. Advantages of our proposed sensor fusion
system are shown by simulation of a control system utilizing the fusion system
for output estimation.
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electrics
|
3,501 |
Cluster Synchronization of Coupled Systems with Nonidentical Linear Dynamics
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eess.SY
|
This paper considers the cluster synchronization problem of generic linear
dynamical systems whose system models are distinct in different clusters. These
nonidentical linear models render control design and coupling conditions highly
correlated if static couplings are used for all individual systems. In this
paper, a dynamic coupling structure, which incorporates a global weighting
factor and a vanishing auxiliary control variable, is proposed for each agent
and is shown to be a feasible solution. Lower bounds on the global and local
weighting factors are derived under the condition that every interaction
subgraph associated with each cluster admits a directed spanning tree. The
spanning tree requirement is further shown to be a necessary condition when the
clusters connect acyclically with each other. Simulations for two applications,
cluster heading alignment of nonidentical ships and cluster phase
synchronization of nonidentical harmonic oscillators, illustrate essential
parts of the derived theoretical results.
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electrics
|
3,502 |
Global stabilization of multiple integrators by a bounded feedback with constraints on its successive derivatives
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eess.SY
|
In this paper, we address the global stabilization of chains of integrators
by means of a bounded static feedback law whose p first time derivatives are
bounded. Our construction is based on the technique of nested saturations
introduced by Teel. We show that the control amplitude and the maximum value of
its p first derivatives can be imposed below any prescribed values. Our results
are illustrated by the stabilization of the third order integrator on the
feedback and its first two derivatives.
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electrics
|
3,503 |
Stochastic Battery Model for Aggregation of Thermostatically Controlled Loads
|
cs.SY
|
The potential of demand side as a frequency reserve proposes interesting
opportunity in handling imbalances due to intermittent renewable energy
sources. This paper proposes a novel approach for computing the parameters of a
stochastic battery model representing the aggregation of Thermostatically
Controlled Loads (TCLs). A hysteresis based non-disruptive control is used
using priority stack algorithm to track the reference regulation signal. The
parameters of admissible ramp-rate and the charge limits of the battery are
dynamically calculated using the information from TCLs that is the status
(on/off), availability and relative temperature distance till the switching
boundary. The approach builds on and improves on the existing research work by
providing a straight-forward mechanism for calculation of stochastic parameters
of equivalent battery model. The effectiveness of proposed approach is
demonstrated by a test case having a large number of residential TCLs tracking
a scaled down real frequency regulation signal.
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electrics
|
3,504 |
PI(D) tuning for Flight Control Systems via Incremental Nonlinear Dynamic Inversion
|
cs.SY
|
Previous results reported in the robotics literature show the relationship
between time-delay control (TDC) and proportional-integral-derivative control
(PID). In this paper, we show that incremental nonlinear dynamic inversion
(INDI) - more familiar in the aerospace community - are in fact equivalent to
TDC. This leads to a meaningful and systematic method for PI(D)-control tuning
of robust nonlinear flight control systems via INDI. We considered a
reformulation of the plant dynamics inversion which removes effector blending
models from the resulting control law, resulting in robust model-free control
laws like PI(D)-control.
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electrics
|
3,505 |
Robust Power System Dynamic State Estimator with Non-Gaussian Measurement Noise: Part II--Implementation and Results
|
cs.SY
|
This paper is the second of a two-part series that discusses the
implementation issues and test results of a robust Unscented Kalman Filter
(UKF) for power system dynamic state estimation with non-Gaussian synchrophasor
measurement noise. The tuning of the parameters of our Generalized
Maximum-Likelihood-type robust UKF (GM-UKF) is presented and discussed in a
systematic way. Using simulations carried out on the IEEE 39-bus system, its
performance is evaluated under different scenarios, including i) the occurrence
of two different types of noises following thick-tailed distributions, namely
the Laplace or Cauchy probability distributions for real and reactive power
measurements; ii) the occurrence of observation and innovation outliers; iii)
the occurrence of PMU measurement losses due to communication failures; iv)
cyber attacks; and v) strong system nonlinearities. It is also compared to the
UKF and the Generalized Maximum-Likelihood-type robust iterated EKF (GM-IEKF).
Simulation results reveal that the GM-UKF outperforms the GM-IEKF and the UKF
in all scenarios considered. In particular, when the system is operating under
stressed conditions, inducing system nonlinearities, the GM-IEKF and the UKF
diverge while our GM-UKF does converge. In addition, when the power measurement
noises obey a Cauchy distribution, our GM-UKF converges to a state estimate
vector that exhibits a much higher statistical efficiency than that of the
GM-IEKF; by contrast, the UKF fails to converge. Finally, potential
applications and future work of the proposed GM-UKF are discussed in concluding
remarks section.
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electrics
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3,506 |
Online Simultaneous State and Parameter Estimation
|
eess.SY
|
In this paper, a concurrent learning based adaptive observer is developed for
a class of second-order nonlinear time-invariant systems with uncertain
dynamics. The developed technique results in uniformly ultimately bounded state
and parameter estimation errors. As opposed to persistent excitation which is
required for parameter convergence in traditional adaptive control methods, the
developed technique only requires excitation over a finite time interval to
achieve parameter convergence. Simulation results in both noise-free and noisy
environments are presented to validate the design.
|
electrics
|
3,507 |
A Graphical Characterization of Structurally Controllable Linear Systems with Dependent Parameters
|
cs.SY
|
One version of the concept of structural controllability defined for
single-input systems by Lin and subsequently generalized to multi-input systems
by others, states that a parameterized matrix pair $(A, B)$ whose nonzero
entries are distinct parameters, is structurally controllable if values can be
assigned to the parameters which cause the resulting matrix pair to be
controllable. In this paper the concept of structural controllability is
broadened to allow for the possibility that a parameter may appear in more than
one location in the pair $(A, B)$. Subject to a certain condition on the
parameterization called the "binary assumption", an explicit graph-theoretic
characterization of such matrix pairs is derived.
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electrics
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3,508 |
Variation Evolving for Optimal Control Computation, a Compact Way
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eess.SY
|
A compact version of the variation evolving method (VEM) is developed in the
primal variable space for optimal control computation. Following the idea that
originates from the Lyapunov continuous-time dynamics stability theory in the
control field, the optimal solution is analogized to the stable equilibrium
point of a dynamic system and obtained asymptotically through the variation
motion. With the introduction of a virtual dimension, namely the variation
time, the evolution partial differential equation (EPDE), which seeks the
optimal solution with a theoretical guarantee, is developed for the optimal
control problem (OCP) with free terminal states, and the equivalent optimality
conditions with no employment of costates are established in the primal space.
These conditions show that the optimal feedback control law is generally not
analytically available because the optimal control is related to the future
states. Since the derived EPDE is suitable to be computed with the
semi-discrete method in the field of PDE numerical calculation, the optimal
solution may be obtained by solving the resulting finite-dimensional
initial-value problem (IVP).
|
electrics
|
3,509 |
Small Satellite Constellation Separation using Linear Programming based Differential Drag Commands
|
cs.SY
|
We study the optimal control of an arbitrarily large constellation of small
satellites operating in low Earth orbit. Simulating the lack of on-board
propulsion, we limit our actuation to the use of differential drag maneuvers to
make in-plane changes to the satellite orbits. We propose an efficient method
to separate a cluster of satellites into a desired constellation shape while
respecting actuation constraints and maximizing the operational lifetime of the
constellation. By posing the problem as a linear program, we solve for the
optimal drag commands for each of the satellites on a daily basis with a
shrinking-horizon model predictive control approach. We then apply this control
strategy in a nonlinear orbital dynamics simulation with a simple, varying
atmospheric density model. We demonstrate the ability to control a cluster of
100+ satellites starting at the same initial conditions in a circular low Earth
orbit to form an equally spaced constellation (with a relative angular
separation error tolerance of one-tenth a degree). The constellation separation
task can be executed in 71 days, a time frame that is competitive for the
state-of-the-practice. This method allows us to trade the time required to
converge to the desired constellation with a sacrifice in the overall
constellation lifetime, measured as the maximum altitude loss experienced by
one of the satellites in the group after the separation maneuvers.
|
electrics
|
3,510 |
An Elementary Introduction to Kalman Filtering
|
eess.SY
|
Kalman filtering is a classic state estimation technique used in application
areas such as signal processing and autonomous control of vehicles. It is now
being used to solve problems in computer systems such as controlling the
voltage and frequency of processors.
Although there are many presentations of Kalman filtering in the literature,
they usually deal with particular systems like autonomous robots or linear
systems with Gaussian noise, which makes it difficult to understand the general
principles behind Kalman filtering. In this paper, we first present the
abstract ideas behind Kalman filtering at a level accessible to anyone with a
basic knowledge of probability theory and calculus, and then show how these
concepts can be applied to the particular problem of state estimation in linear
systems. This separation of concepts from applications should make it easier to
understand Kalman filtering and to apply it to other problems in computer
systems.
|
electrics
|
3,511 |
A New Approach To Estimate The Collision Probability For Automotive Applications
|
eess.SY
|
We revisit the computation of a probability of collision in the context of
automotive collision avoidance (also referred to as conflict detection in other
contexts). After reviewing existing approaches to the definition and
computation of a collision probability we argue that the question "What is the
probability of collision within the next three seconds?" can be answered on the
basis of a collision probability rate.
Using results on level crossings for vector stochastic processes we derive a
general expression for the upper bound of the distribution of the collision
probability rate. This expression is valid for arbitrary prediction models
including process noise.
We demonstrate in several examples that distributions obtained by large-scale
Monte-Carlo simulations obey this bound and in many cases approximately
saturate the bound. We derive an approximation for the distribution of the
collision probability rate that can be computed on an embedded platform. An
upper bound of the probability of collision is then obtained by one-dimensional
numerical integration over the time period of interest.
A straightforward application of this method applies to the collision of an
extended object with a second point-like object. Using an abstraction of the
second object by salient points of its boundary we propose an application of
this method to two extended objects with arbitrary orientation.
Finally, the distribution of the collision probability rate is compared to
approximations of time-to-collision distributions for one-dimensional motions
that have been obtained previously.
|
electrics
|
3,512 |
The Strategic LQG System: A Dynamic Stochastic VCG Framework for Optimal Coordination
|
eess.SY
|
The classic Vickrey-Clarke-Groves (VCG) mechanism ensures incentive
compatibility, i.e., that truth-telling of all agents is a dominant strategy,
for a static one-shot game. However, in a dynamic environment that unfolds over
time, the agents' intertemporal payoffs depend on the expected future controls
and payments, and a direct extension of the VCG mechanism is not sufficient to
guarantee incentive compatibility. In fact, it does not appear to be feasible
to construct mechanisms that ensure the dominance of dynamic truth-telling for
agents comprised of general stochastic dynamic systems. The contribution of
this paper is to show that such a dynamic stochastic extension does exist for
the special case of Linear-Quadratic-Gaussian (LQG) agents with a careful
construction of a sequence of layered payments over time. For a set of LQG
agents, we propose a modified layered version of the VCG mechanism for payments
that decouples the intertemporal effect of current bids on future payoffs, and
prove that truth-telling of dynamic states forms a dominant strategy if system
parameters are known and agents are rational.
An important example of a problem needing such optimal dynamic coordination
of stochastic agents arises in power systems where an Independent System
Operator (ISO) has to ensure balance of generation and consumption at all time
instants, while ensuring social optimality. The challenge is to determine a
bidding scheme between all agents and the ISO that maximizes social welfare,
while taking into account the stochastic dynamic models of agents, since
renewable energy resources such as solar/wind are stochastic and dynamic in
nature, as are consumptions by loads which are influenced by factors such as
local temperatures and thermal inertias of facilities.
|
electrics
|
3,513 |
Data-Driven Participation Factors for Nonlinear Systems Based on Koopman Mode Decomposition
|
cs.SY
|
This paper develops a novel data-driven technique to compute the
participation factors for nonlinear systems based on the Koopman mode
decomposition. Provided that certain conditions are satisfied, it is shown that
the proposed technique generalizes the original definition of the linear
mode-in-state participation factors. Two numerical examples are provided to
demonstrate the performance of our approach: one relying on a canonical
nonlinear dynamical system, and the other based on the two-area four-machine
power system. The Koopman mode decomposition is capable of coping with a large
class of nonlinearity, thereby making our technique able to deal with
oscillations arising in practice due to nonlinearities while being fast to
compute and compatible with real-time applications.
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electrics
|
3,514 |
Temporal Logic Verification of Stochastic Systems Using Barrier Certificates
|
cs.SY
|
This paper presents a methodology for temporal logic verification of
discrete-time stochastic systems. Our goal is to find a lower bound on the
probability that a complex temporal property is satisfied by finite traces of
the system. Desired temporal properties of the system are expressed using a
fragment of linear temporal logic, called safe LTL over finite traces. We
propose to use barrier certificates for computations of such lower bounds,
which is computationally much more efficient than the existing
discretization-based approaches. The new approach is discretization-free and
does not suffer from the curse of dimensionality caused by discretizing state
sets. The proposed approach relies on decomposing the negation of the
specification into a union of sequential reachabilities and then using barrier
certificates to compute upper bounds for these reachability probabilities. We
demonstrate the effectiveness of the proposed approach on case studies with
linear and polynomial dynamics.
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electrics
|
3,515 |
Robust path-following control for articulated heavy-duty vehicles
|
eess.SY
|
Path following and lateral stability are crucial issues for autonomous
vehicles. Moreover, these problems increase in complexity when handling
articulated heavy-duty vehicles due to their poor manoeuvrability, large sizes
and mass variation. In addition, uncertainties on mass may have the potential
to significantly decrease the performance of the system, even to the point of
destabilising it. These parametric variations must be taken into account during
the design of the controller. However, robust control techniques usually
require offline adjustment of auxiliary tuning parameters, which is not
practical, leading to sub-optimal operation. Hence, this paper presents an
approach to path-following and lateral control for autonomous articulated
heavy-duty vehicles subject to parametric uncertainties by using a robust
recursive regulator. The main advantage of the proposed controller is that it
does not depend on the offline adjustment of tuning parameters. Parametric
uncertainties were assumed to be on the payload, and an $\mathcal{H}_{\infty}$
controller was used for performance comparison. The performance of both
controllers is evaluated in a double lane-change manoeuvre. Simulation results
showed that the proposed method had better performance in terms of robustness,
lateral stability, driving smoothness and safety, which demonstrates that it is
a very promising control technique for practical applications.
|
electrics
|
3,516 |
Reachability Analysis Using Dissipation Inequalities For Uncertain Nonlinear Systems
|
eess.SY
|
We propose a method to outer bound forward reachable sets on finite horizons
for uncertain nonlinear systems with polynomial dynamics. This method makes use
of time-dependent polynomial storage functions that satisfy appropriate
dissipation inequalities that account for time-varying uncertain parameters, L2
disturbances, and perturbations characterized by integral quadratic constraints
(IQCs) with both hard and soft factorizations. In fact, to our knowledge, this
is the first result introducing IQCs to reachability analysis, thus allowing
for various types of uncertainty, including unmodeled dynamics. The generalized
S-procedure and Sum-of-Squares techniques are used to derive algorithms with
the goal of finding the tightest outer bound with a desired shape. Both
pedagogical and practically motivated examples are presented, including a
7-state F-18 aircraft model.
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electrics
|
3,517 |
On asymptotic characterization of destabilizing switching signals for switched linear systems
|
eess.SY
|
This paper deals with classes of (de)stabilizing switching signals for
switched systems. Most of the available conditions for stability of switched
systems are sufficient in nature, and consequently, their violation does not
conclude instability of a switched system. The study of instability is,
however, important for obvious reasons. Our contributions are twofold: Firstly,
we propose a class of switching signals under which a continuous-time switched
linear system is unstable. Our characterization of instability depends solely
on the asymptotic behaviour of frequency of switching, frequency of transition
between subsystems, and fraction of activation of subsystems. Secondly, we show
that our class of destabilizing switching signals is a strict subset of the
class of switching signals that does not satisfy asymptotic characterization of
stability recently proposed in the literature. This observation identifies a
gap between asymptotic characterizations of stabilizing and destabilizing
switching signals for switched linear systems. The main apparatus for our
analysis is multiple Lyapunov-like functions.
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electrics
|
3,518 |
MAP moving horizon estimation for threshold measurements with application to field monitoring
|
eess.SY
|
The paper deals with state estimation of a spatially distributed system given
noisy measurements from pointwise-in-time-and-space threshold sensors spread
over the spatial domain of interest. A Maximum A posteriori Probability (MAP)
approach is undertaken and a Moving Horizon (MH) approximation of the MAP
cost-function is adopted. It is proved that, under system linearity and
log-concavity of the noise probability density functions, the proposed MH-MAP
state estimator amounts to the solution, at each sampling interval, of a convex
optimization problem. Moreover, a suitable centralized solution for large-scale
systems is proposed with a substantial decrease of the computational
complexity. The latter algorithm is shown to be feasible for the state
estimation of spatially-dependent dynamic fields described by Partial
Differential Equations (PDE) via the use of the Finite Element (FE) spatial
discretization method. A simulation case-study concerning estimation of a
diffusion field is presented in order to demonstrate the effectiveness of the
proposed approach. Quite remarkably, the numerical tests exhibit a
noise-assisted behavior of the proposed approach in that the estimation
accuracy results optimal in the presence of measurement noise with non-null
variance.
|
electrics
|
3,519 |
Model predictive control for transient frequency regulation of power networks
|
eess.SY
|
This paper introduces a control strategy to simultaneously achieve asymptotic
stabilization and transient frequency regulation of power networks. The control
command is generated by iteratively solving an open-loop control cost
minimization problem with stability and transient frequency constraints. To
deal with the non-convexity of the stability constraint, we propose a
convexification strategy that uses a reference trajectory based on the system's
current state. We also detail how to employ network partitions to implement the
proposed control strategy in a distributed way, where each region only requires
system information from neighboring regions to execute its controller.
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electrics
|
3,520 |
Descriptor system techniques and software tools
|
eess.SY
|
The role of the descriptor system representation as basis for reliable
numerical computations for system analysis and synthesis, and in particular,
for the manipulation of rational matrices, is discussed and available robust
numerical software tools are described.
|
electrics
|
3,521 |
Compositional Abstraction of Large-Scale Stochastic Systems: A Relaxed Dissipativity Approach
|
eess.SY
|
In this paper, we propose a compositional approach for the construction of
finite abstractions (a.k.a. finite Markov decision processes (MDPs)) for
networks of discrete-time stochastic control subsystems that are not
necessarily stabilizable. The proposed approach leverages the interconnection
topology and a notion of finite-step stochastic storage functions, that
describes joint dissipativity-type properties of subsystems and their
abstractions, and establishes a finite-step stochastic simulation function as a
relation between the network and its abstraction. To this end, we first develop
a new type of compositionality conditions which is less conservative than the
existing ones. In particular, using a relaxation via a finite-step stochastic
simulation function, it is possible to construct finite abstractions such that
stabilizability of each subsystem is not necessarily required. We then propose
an approach to construct finite MDPs together with their corresponding
finite-step storage functions for general discrete-time stochastic control
systems satisfying an incremental passivablity property. We also construct
finite MDPs for a particular class of nonlinear stochastic control systems. To
demonstrate the effectiveness of the proposed results, we apply our results on
three different case studies.
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electrics
|
3,522 |
Solving Power System Differential Algebraic Equations Using Differential Transformation
|
eess.SY
|
This paper proposes a novel non-iterative method to solve power system
differential algebraic equations (DAEs) using the differential transformation,
a mathematical tool that can obtain power series coefficients by transformation
rules instead of calculating high order derivatives and has proved to be
effective in solving state variables of nonlinear differential equations in our
previous study. This paper further solves non-state variables, e.g. current
injections and bus voltages, directly with a realistic DAE model of power
grids. These non-state variables, nonlinearly coupled in network equations, are
conventionally solved by numerical methods with time-consuming iterations, but
their differential transformations are proved to satisfy formally linear
equations in this paper. Thus, a non-iterative algorithm is designed to
analytically solve all variables of a power system DAE model with ZIP loads.
From test results on a Polish 2383-bus system, the proposed method demonstrates
fast and reliable time performance compared to traditional numerical methods.
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electrics
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3,523 |
Vulnerability Assessment of N-1 Reliable Power Systems to False Data Injection Attacks
|
eess.SY
|
This paper studies the vulnerability of large-scale power systems to false
data injection (FDI) attacks through their physical consequences. Prior work
has shown that an attacker-defender bi-level linear program (ADBLP) can be used
to determine the worst-case consequences of FDI attacks aiming to maximize the
physical power flow on a target line. Understanding the consequences of these
attacks requires consideration of power system operations commonly used in
practice, specifically real-time contingency analysis (RTCA) and security
constrained economic dispatch (SCED). An ADBLP is formulated with detailed
assumptions on attacker's knowledge, and a modified Benders' decomposition
algorithm is introduced to solve such an ADBLP. The vulnerability analysis
results presented for the synthetic Texas system with 2000 buses show that
intelligent FDI attacks can cause post-contingency overflows.
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electrics
|
3,524 |
Verification of C-detectability Using Petri Nets
|
eess.SY
|
Detectability describes the property of an system whose current and the
subsequent states can be uniquely determined after a finite number of
observations. In this paper, we relax detectability to C-detectability that
only requires a given set of crucial states can be distinguished from other
states. Four types of C-detectability: strong C-detectability, weak
C-detectability, periodically strong C-detectability, and periodically weak
C-detectability are defined in the framework of labeled Petri nets, which have
larger modeling power than finite automata. Moreover, based on the notion of
basis markings, the approaches are developed to verify the four C-detectability
of a bounded labeled Petri net system. Without computing the whole reachability
space and without enumerating all the markings consistent with an observation,
the proposed approaches are more efficient.
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electrics
|
3,525 |
Multi-agent estimation and filtering for minimizing team mean-squared error
|
eess.SY
|
Motivated by estimation problems arising in autonomous vehicles and
decentralized control of unmanned aerial vehicles, we consider multi-agent
estimation and filtering problems in which multiple agents generate state
estimates based on decentralized information and the objective is to minimize a
coupled mean-squared error which we call \emph{team mean-square error}. We call
the resulting estimates as minimum team mean-squared error (MTMSE) estimates.
We show that MTMSE estimates are different from minimum mean-squared error
(MMSE) estimates. We derive closed-form expressions for MTMSE estimates, which
are linear function of the observations where the corresponding gain depends on
the weight matrix that couples the estimation error. We then consider a
filtering problem where a linear stochastic process is monitored by multiple
agents which can share their observations (with delay) over a communication
graph. We derive expressions to recursively compute the MTMSE estimates. To
illustrate the effectiveness of the proposed scheme we consider an example of
estimating the distances between vehicles in a platoon and show that MTMSE
estimates significantly outperform MMSE estimates and consensus Kalman
filtering estimates.
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electrics
|
3,526 |
On the Existence of a Fixed Spectrum for a Multi-channel Linear System: A Matroid Theory Approach
|
cs.SY
|
Conditions for the existence of a fixed spectrum \{i.e., the set of fixed
modes\} for a multi-channel linear system have been known for a long time. The
aim of this paper is to reestablish one of these conditions using a new and
transparent approach based on matroid theory.
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electrics
|
3,527 |
A Negotiation-based Right-of-way Assignment Strategy to Ensure Traffic Safety and Efficiency in Lane Change
|
eess.SY
|
It is widely acknowledged that verifying the safety of autonomous driving
strategies requires a substantial body of simulation testing and road testing.
In recent years, the formal safety methods represented by
Responsibility-Sensitive Safety (RSS) have encouraged low-cost autonomous
driving safety research, benefitting from its accurate assessment of safety and
clear division of responsibilities. However, how to maintain traffic efficiency
while ensuring safety remains a challenge. To address this problem, this paper
proposes a formulized negotiation-based lane-changing strategy that makes a
trade-off between safety and efficiency. Both theoretical analysis and
numerical experimental results shows that compared to RSS, our strategy can
noticeably improve the success rate of changing lanes on the premise of safety.
|
electrics
|
3,528 |
Robust simultaneous stabilization and decoupling of unstable adversely coupled uncertain resource constraints plants of a nano air vehicle
|
eess.SY
|
The plants of nano air vehicles (NAVs) are generally unstable, adversely
coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited
sensing and computational capabilities. Hence, these vehicles need a single
controller referred to as Robust Simultaneously Stabilizing Decoupling (RSSD)
output feedback controller that achieves simultaneous stabilization, desired
decoupling, robustness, and performance for a finite set of unstable
multi-input-multi-output adversely coupled uncertain plants. To synthesize a
RSSD output feedback controller, a new method that is based on a central plant
is proposed in this paper. Given a finite set of plants for simultaneous
stabilization, we considered a plant in this set that has the smallest maximum
$v-$gap metric as the central plant. Following this, the sufficient condition
for the existence of a simultaneous stabilizing controller associated with such
a plant is described. The decoupling feature is then appended to this
controller using the properties of the eigenstructure assignment method.
Afterward, the sufficient conditions for the existence of a RSSD output
feedback controller are obtained. Using these sufficient conditions, a new
optimization problem for the synthesis of a RSSD output feedback controller is
formulated. To solve this optimization problem, a new genetic algorithm based
offline iterative algorithm is developed. The effectiveness of this iterative
algorithm is then demonstrated by generating a RSSD controller for a fixed-wing
nano air vehicle. The performance of this controller is validated through
numerical and hardware-in-the-loop simulations.
|
electrics
|
3,529 |
Optimal Storage Arbitrage under Net Metering using Linear Programming
|
eess.SY
|
We formulate the optimal energy arbitrage problem for a piecewise linear cost
function for energy storage devices using linear programming (LP). The LP
formulation is based on the equivalent minimization of the epigraph. This
formulation considers ramping and capacity constraints, charging and
discharging efficiency losses of the storage, inelastic consumer load and local
renewable generation in presence of net-metering which facilitates selling of
energy to the grid and incentivizes consumers to install renewable generation
and energy storage. We consider the case where the consumer loads, electricity
prices, and renewable generations at different instances are uncertain. These
uncertain quantities are predicted using an Auto-Regressive Moving Average
(ARMA) model and used in a model predictive control (MPC) framework to obtain
the arbitrage decision at each instance. In numerical results we present the
sensitivity analysis of storage performing arbitrage with varying ramping
batteries and different ratio of selling and buying price of electricity.
|
electrics
|
3,530 |
Decentralized Multi-target Tracking in Urban Environments: Overview and Challenges
|
eess.SY
|
In multi-target tracking, sensor control involves dynamically configuring
sensors to achieve improved tracking performance. Many of these techniques
focus on sensors with memoryless states (e.g., waveform adaptation, beam
scheduling, and sensor selection), lending themselves to computationally
efficient control strategies. Mobile sensor control for multi-target tracking,
however, is significantly more challenging due to the complexity of the
platform state dynamics. This platform complexity necessitates high-fidelity,
non-myopic control strategies in order to achieve strong tracking performance
while maintaining safe operation. These sensor control techniques are
particularly important in non-cooperative urban surveillance applications
including person of interest, vehicle, and unauthorized UAV interdiction. In
this overview paper, we highlight the current state of the art in mobile sensor
control for multi-target tracking in urban environments. We use this
application to motivate the need for closer collaboration between the
information fusion, tracking, and control research communities across three
challenge areas relevant to the urban surveillance problem.
|
electrics
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3,531 |
Passive Multi-Target Tracking Using the Adaptive Birth Intensity PHD Filter
|
eess.SY
|
Passive multi-target tracking applications require the integration of
multiple spatially distributed sensor measurements to distinguish true tracks
from ghost tracks. A popular multi-target tracking approach for these
applications is the particle filter implementation of Mahler's probability
hypothesis density (PHD) filter, which jointly updates the union of all target
state space estimates without requiring computationally complex
measurement-to-track data association. Although this technique is attractive
for implementation in computationally limited platforms, the performance
benefits can be significantly overshadowed by inefficient sampling of the
target birth particles over the region of interest. We propose a multi-sensor
extension of the adaptive birth intensity PHD filter described in (Ristic,
2012) to achieve efficient birth particle sampling driven by online sensor
measurements from multiple sensors. The proposed approach is demonstrated using
distributed time-difference-of-arrival (TDOA) and
frequency-difference-of-arrival (FDOA) measurements, in which we describe exact
techniques for sampling from the target state space conditioned on the
observations. Numerical results are presented that demonstrate the increased
particle density efficiency of the proposed approach over a uniform birth
particle sampler.
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electrics
|
3,532 |
Obstructed Target Tracking in Urban Environments
|
eess.SY
|
Accurate tracking in urban environments necessitates target birth, survival,
and detection models that quantify the impact of terrain and building geometry
on the sequential estimation procedure. Current efforts assume that target
trajectories are limited to fixed paths, such as road networks. In these
settings a single airborne platform with a downward-facing camera is capable of
fully observing a target, outside of a few obstructed regions that can be
determined a priori (e.g. tunnels). However, many practical target types are
not necessarily restricted to road networks and thus require knowledge of
azimuthal shadowed regions to the sensor. In this paper, we propose the
integration of geospatial data for an urban environment into a particle filter
realization of a random finite set target tracking algorithm. Specifically, we
use 3D building polygons to compute the azimuthal shadowed regions with respect
to deployed sensor location. The particle filter predict and update steps are
modified such that (1) target births are assumed to occur in line-of-sight
(LOS) regions, (2) targets do not move into obstructions, (3) true target
detections only occur in LOS regions. The localization error performance
improvement for a single target Bernoulli filter under these modifications is
presented using freely available building vector data of New York City.
|
electrics
|
3,533 |
Learning Pugachev's Cobra Maneuver for Tail-sitter UAVs Using Acceleration Model
|
eess.SY
|
The Pugachev's cobra maneuver is a dramatic and demanding maneuver requiring
the aircraft to fly at extremely high Angle of Attacks (AOA) where stalling
occurs. This paper considers this maneuver on tail-sitter UAVs. We present a
simple yet very effective feedback-iterative learning position control
structure to regulate the altitude error and lateral displacement during the
maneuver. Both the feedback controller and the iterative learning controller
are based on the aircraft acceleration model, which is directly measurable by
the onboard accelerometer. Moreover, the acceleration model leads to an
extremely simple dynamic model that does not require any model identification
in designing the position controller, greatly simplifying the implementation of
the iterative learning control. Real-world outdoor flight experiments on the
"Hong Hu" UAV, an aerobatic yet efficient quadrotor tail-sitter UAV of
small-size, are provided to show the effectiveness of the proposed controller.
|
electrics
|
3,534 |
Compositional Abstraction-based Synthesis of General MDPs via Approximate Probabilistic Relations
|
eess.SY
|
We propose a compositional approach for constructing abstractions of general
Markov decision processes using approximate probabilistic relations. The
abstraction framework is based on the notion of $\delta$-lifted relations,
using which one can quantify the distance in probability between the
interconnected gMDPs and that of their abstractions. This new approximate
relation unifies compositionality results in the literature by incorporating
the dependencies between state transitions explicitly and by allowing abstract
models to have either finite or infinite state spaces. Accordingly, one can
leverage the proposed results to perform analysis and synthesis over abstract
models, and then carry the results over concrete ones. To this end, we first
propose our compositionality results using the new approximate probabilistic
relation which is based on lifting. We then focus on a class of stochastic
nonlinear dynamical systems and construct their abstractions using both model
order reduction and space discretization in a unified framework. We provide
conditions for simultaneous existence of relations incorporating the structure
of the network. Finally, we demonstrate the effectiveness of the proposed
results by considering a network of four nonlinear dynamical subsystems
(together 12 dimensions) and constructing finite abstractions from their
reduced-order versions (together 4 dimensions) in a unified compositional
framework. We benchmark our results against the compositional abstraction
techniques that construct both infinite abstractions (reduced-order models) and
finite MDPs in two consecutive steps. We show that our approach is much less
conservative than the ones available in the literature.
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electrics
|
3,535 |
Feedback Linearization for Quadrotors UAV
|
eess.SY
|
In the paper "Control Design for UAV Quadrotors via Embedded Model Control"
[1], the authors designed a complete control unit for a UAV Quadrotor, based on
the Embedded Model Control (EMC) methodology, in combination with the Feedback
Linearization (FL); when applied to non-linear systems. Specifically, [1]
proposes to use the FL as a novel way to design the internal model for the EMC
state and disturbance predictor. To support the treatise in [1], in this report
the feedback-linearized model of the UAV quadrotor leveraged in [1] is
step-by-step derived.
|
electrics
|
3,536 |
Optimal In-field Routing for Full and Partial Field Coverage with Arbitrary Non-Convex Fields and Multiple Obstacle Areas
|
eess.SY
|
Within the context of optimising the logistics in agriculture this paper
relates to optimal in-field routing for full and partial field coverage with
arbitrary non-convex fields and multiple obstacle areas. It is distinguished
between nine different in-field routing tasks: two for full-field coverage,
seven for partial-field coverage and one for shortest path planning between any
two vertices of the transition graph. It differentiates between equal or
different start and end vertices for a task, coverage of only a subset of
vertices, and a subset of edges or combinations. The proposed methods are
developed primarily for applying sprays and fertilisers with larger operating
widths and with fields where there is unique headland path. Partial field
coverage where, e.g., only a specific subset of edges has to be covered is
relevant for precision agriculture and also for optimised logistical operation
of smaller-sized machinery with limited loading capacities. The result of this
research is the proposition of two compatible algorithms for optimal full and
partial field coverage path planning, respectively. These are evaluated on
three real-world fields to demonstrate their characteristics and computational
efficiency.
|
electrics
|
3,537 |
Data-Driven Model Predictive Control with Stability and Robustness Guarantees
|
eess.SY
|
We propose a robust data-driven model predictive control (MPC) scheme to
control linear time-invariant (LTI) systems. The scheme uses an implicit model
description based on behavioral systems theory and past measured trajectories.
In particular, it does not require any prior identification step, but only an
initially measured input-output trajectory as well as an upper bound on the
order of the unknown system. First, we prove exponential stability of a nominal
data-driven MPC scheme with terminal equality constraints in the case of no
measurement noise. For bounded additive output measurement noise, we propose a
robust modification of the scheme, including a slack variable with
regularization in the cost. We prove that the application of this robust MPC
scheme in a multi-step fashion leads to practical exponential stability of the
closed loop w.r.t. the noise level. The presented results provide the first
(theoretical) analysis of closed-loop properties, resulting from a simple,
purely data-driven MPC scheme.
|
electrics
|
3,538 |
Lyapunov Differential Equation Hierarchy and Polynomial Lyapunov Functions for Switched Linear Systems
|
eess.SY
|
This work studies the problem of searching for homogeneous polynomial
Lyapunov functions for stable switched linear systems. Specifically, we show an
equivalence between polynomial Lyapunov functions for systems of this class and
quadratic Lyapunov functions for a related hierarchy of Lyapunov differential
equations. This creates an intuitive procedure for checking the stability
properties of switched linear systems and a computationally competitive
algorithm is presented for generating high-order homogeneous polynomial
Lyapunov functions in this manner. Additionally, we provide a comparison
between polynomial Lyapunov functions generated with our proposed approach and
polynomial Lyapunov functions generated with a more traditional sum-of-squares
based approach.
|
electrics
|
3,539 |
A hierarchical Lyapunov-based cascade adaptive control scheme for lower-limb exoskeleton
|
eess.SY
|
This paper proposes a hierarchical Lyapunov-based adaptive cascade control
scheme for a lower-limb exoskeleton with control saturation. The proposed
approach is composed by two control levels with cascade structure. At the
higher layer of the structure, a Lyapunov-based back-stepping regulator
including adaptive estimation of uncertain parameters and friction force is
designed for the leg dynamics, to minimize the deviation of the joint position
and its reference value. At the lower layer, a Lyapunov-based neural network
adaptive controller is in charge of computing control action for the hydraulic
servo system, to follow the force reference computed at the high level, also to
compensate for model uncertainty, nonlinearity, and control saturation. The
proposed approach shows to be capable in minimizing the interaction torque
between machine and human, and suitable for possible imprecise models. The
robustness of the closed-loop system is discussed under input constraint.
Simulation experiments are reported, which shows that the proposed scheme is
effective in imposing smaller interaction torque with respect to PD controller,
and in control of models with uncertainty and nonlinearity.
|
electrics
|
3,540 |
Adaptive Resource Management for a Virtualized Computing Platform within Edge Computing
|
eess.SY
|
In virtualized computing platforms, energy consumption is related to the
computing-plus-communication processes. However, most of the proposed energy
consumption models and energy saving solutions found in literature consider
only the active Virtual Machines (VMs), thus the overall operational energy
expenditure is usually related to solely the computation process. To address
this shortcoming, in this paper we consider a computing-plus-communication
energy model, within the Multi-access Edge Computing (MEC) paradigm, and then
put forward a combination of a traffic engineering- and MEC Location
Service-based online server management algorithm with Energy Harvesting (EH)
capabilities, called Automated Resource Controller for Energy-aware Server
(ARCES), for autoscaling and reconfiguring the computing-plus-communication
resources. The main goal is to minimize the overall energy consumption, under
hard per-task delay constraints (i.e., Quality of Service (QoS)). ARCES jointly
performs (i) a short-term server demand and harvested solar energy forecasting,
(ii) VM soft-scaling, workload and processing rate allocation and lastly, (iii)
switching on/off of transmission drivers (i.e., fast tunable lasers) coupled
with the location-aware traffic scheduling. Our numerical results reveal that
ARCES achieves on average energy savings of 69%, and an energy consumption
ranging from 31%-45%and from 21%-25% at different values of per-VM
reconfiguration cost, with respect to the case where no energy management is
applied.
|
electrics
|
3,541 |
A Passivity Interpretation of Energy-Based Forced Oscillation Source Location Methods
|
eess.SY
|
This paper develops a systematic framework for analyzing how low frequency
forced oscillations propagate in electric power systems. Using this framework,
the paper shows how to mathematically justify the so-called Dissipating Energy
Flow (DEF) forced oscillation source location technique. The DEF's specific
deficiencies are pinpointed, and its underlying energy function is analyzed via
incremental passivity theory. This analysis is then used to prove that there
exists no passivity transformation (i.e. quadratic energy function) which can
simultaneously render all components of a lossy classical power system passive.
The paper goes on to develop a simulation-free algorithm for predicting the
performance of the DEF method in a generalized power system, and it analyzes
the passivity of three non-classical load and generation components. The
proposed propagation framework and performance algorithm are both tested and
illustrated on the IEEE 39-bus New England system and the WECC 179-bus system.
|
electrics
|
3,542 |
A steady-state stability analysis of uniform synchronous power grid topologies
|
eess.SY
|
Motter et al. derived a real-valued master stability function which
determines whether and to what degree a given power grid is asymptotically
stable. Stright and Edrington adopted certain uniformity assumptions on a
grid's components and demonstrated how differences in topologies obtained using
these components can affect the stabilities of the resulting grids. Building on
this work, we show via simulations the physical significance of stability as
opposed to instability. We show that for stable topologies, increased stability
can correspond to decreased generator torque ripple. We also describe how some
elementary changes in grid topology can affect stability values. Known
stability values for certain abstract circulant grids are used to quantify
stability enhancement as interconnection density increases.
|
electrics
|
3,543 |
Memory Augmented Neural Network Adaptive Controller for Strict Feedback Nonlinear Systems
|
eess.SY
|
In this work, we consider the adaptive nonlinear control problem for strict
feedback nonlinear systems, where the functions that determine the dynamics of
the system are completely unknown. We assume that certain upper bounds for the
functions $g_i$s of the system are known. The objective of the control design
is to design an adaptive controller that can adapt to changes in the unknown
functions that are even abrupt. We propose a novel backstepping memory
augmented NN (MANN) adaptive control method for the control of strict feedback
non-linear systems. Here, each NN, in the backstepping NN adaptive controller,
is augmented with an external working memory. The NN can write relevant
information to its working memory and later retrieve them to modify its output,
thus providing it with the capability to leverage past learned information
effectively and improve its speed of learning. We propose a specific design for
this external memory interface and show that the proposed control design
achieves bounded stability for the closed loop system. We also provide
substantial numerical evidence showing that the proposed memory augmentation
improves the speed of learning by a significant margin.
|
electrics
|
3,544 |
Game-Theoretic Mixed $H_2/H_{\infty}$ Control with Sparsity Constraint for Multi-agent Networked Control Systems
|
eess.SY
|
Multi-agent networked control systems (NCSs) are often subject to model
uncertainty and are limited by large communication cost, associated with
feedback of data between the system nodes. To provide robustness against model
uncertainty and to reduce the communication cost, this paper investigates the
mixed $H_2/H_{\infty}$ control problem for NCS under the sparsity constraint.
First, proximal alternating linearized minimization (PALM) is employed to solve
the centralized social optimization where the agents have the same optimization
objective. Next, we investigate a sparsity-constrained noncooperative game,
which accommodates different control-performance criteria of different agents,
and propose a best-response dynamics algorithm based on PALM that converges to
an approximate Generalized Nash Equilibrium (GNE) of this game. A special case
of this game, where the agents have the same $H_2$ objective, produces a
partially-distributed social optimization solution. We validate the proposed
algorithms using a network with unstable node dynamics and demonstrate the
superiority of the proposed PALM-based method to a previously investigated
sparsity-constrained mixed $H_2/H_{\infty}$ controller.
|
electrics
|
3,545 |
Dynamic Control of Functional Splits for Energy Harvesting Virtual Small Cells: a Distributed Reinforcement Learning Approach
|
eess.SY
|
In this paper, we propose a network scenario where the baseband processes of
the virtual small cells powered solely by energy harvesters and batteries can
be opportunistically executed in a grid-connected edge computing server,
co-located at the macro base station site. We state the corresponding energy
minimization problem and propose multi-agent Reinforcement Learning (RL) to
solve it. Distributed Fuzzy Q-Learning and Q-Learning on-line algorithms are
tailored for our purposes. Coordination among the multiple agents is achieved
by broadcasting system level information to the independent learners. The
evaluation of the network performance confirms that coordination via
broadcasting may achieve higher system level gains than un-coordinated
solutions and cumulative rewards closer to the off-line bounds. Finally, our
analysis permits to evaluate the benefits of continuous state/action
representation for the learning algorithms in terms of faster convergence,
higher cumulative reward and more adaptation to changing environments.
|
electrics
|
3,546 |
Ensemble Consider Kalman Filtering
|
eess.SY
|
In this paper, the ensemble consider Kalman filter is proposed to mitigate
the negative effects of uncertain parameters in nonlinear dynamic and
measurement models. The ensemble Kalman filter can avoid using the Jacobian
matrices and reduce the computational complexity, the unknown parameters of the
models still are not considered. By incorporating the statistics of the
uncertain parameters into the state estimation formulations and using an
augmented-state approach, the ensemble integration is reset by resampling the
ensemble members in the new step, and the EnCKF algorithm is derived. Two
numerical simulations show that the presented EnCKF can mitigate the negative
effects of the uncertain parameters.
|
electrics
|
3,547 |
A Chance-Constrained Stochastic Electricity Market
|
eess.SY
|
Efficiently accommodating uncertain renewable resources in wholesale
electricity markets is among the foremost priorities of market regulators in
the US, UK and EU nations. However, existing deterministic market designs fail
to internalize the uncertainty and their scenario-based stochastic extensions
are limited in their ability to simultaneously maximize social welfare and
guarantee non-confiscatory market outcomes in expectation and per each
scenario. This paper proposes a chance-constrained stochastic market design,
which is capable of producing a robust competitive equilibrium and
internalizing uncertainty of the renewable resources in the price formation
process. The equilibrium and resulting prices are obtained for different
uncertainty assumptions, which requires using either linear (restrictive
assumptions) or second-order conic (more general assumptions) duality in the
price formation process. The usefulness of the proposed stochastic market
design is demonstrated via the case study carried out on the 8-zone ISO New
England testbed.
|
electrics
|
3,548 |
Flight Control for UAV Loitering Over a Ground Target with Unknown Maneuver
|
eess.SY
|
This paper proposes a flight controller for an unmanned aerial vehicle (UAV)
to loiter over a ground moving target (GMT). We are concerned with the scenario
that the stochastically time-varying maneuver of the GMT is unknown to the UAV,
which renders it challenging to estimate the GMT's motion state. Assuming that
the state of the GMT is available, we first design a discrete-time Lyapunov
vector field for the loitering guidance and then design a discrete-time
integral sliding mode control (ISMC) to track the guidance commands. By
modeling the maneuver process as a finite-state Markov chain, we propose a
Rao-Blackwellised particle filter (RBPF), which only requires a few number of
particles, to simultaneously estimate the motion state and the maneuver of the
GMT with a camera or radar sensor. Then, we apply the principle of certainty
equivalence to the ISMC and obtain the flight controller for completing the
loitering task. Finally, the effectiveness and advantages of our controller are
validated via simulations.
|
electrics
|
3,549 |
Distributed $H_\infty$ Estimation Resilient to Biasing Attacks
|
eess.SY
|
We consider the distributed $H_\infty$ estimation problem with an additional
requirement of resilience to biasing attacks. An attack scenario is considered
where an adversary misappropriates some of the observer nodes and injects
biasing signals into observer dynamics. The paper proposes a procedure for the
derivation of a distributed observer which endows each node with an attack
detector which also functions as an attack compensating feedback controller for
the main observer. Connecting these controlled observers into a network results
in a distributed observer whose nodes produce unbiased robust estimates of the
plant. We show that the gains for each controlled observer in the network can
be computed in a decentralized fashion, thus reducing vulnerability of the
network.
|
electrics
|
3,550 |
A Global Solution Method for Decentralized Multi-Area SCUC and Savings Allocation Based on MILP Value Functions
|
eess.SY
|
To address the issue that Lagrangian dual function based algorithms cannot
guarantee convergence and global optimality for decentralized multi-area
security constrained unit commitment (M-SCUC) problems, a novel decomposition
and coordination method using MILP (mixed integer linear programming) value
functions is proposed in this paper. Each regional system operator sets the
tie-line power injections as variational parameters in its regional SCUC model,
and utilizes a finite algorithm to generate a MILP value function, which
returns the optimal generation cost for any given interchange scheduling. With
the value functions available from all system operators, theoretically, a
coordinator is able to derive a globally optimal interchange scheduling. Since
power exchanges may alter the financial position of each area considerably from
what it would have been via scheduling independently, we then propose a fair
savings allocation method using the values functions derived above and the
Shapley value in cooperative game theory. Numerical experiments on a two-area
12-bus system and a three-area 457-bus system are carried out. The validness of
the value functions based method is verified for the decentralized M-SCUC
problems. The outcome of savings allocation is compared with that of the
locational marginal cost based method.
|
electrics
|
3,551 |
A Dual-level Model Predictive Control Scheme for Multi-timescale Dynamical Systems--Extended Version
|
eess.SY
|
So far, many control algorithms have been developed for singularly perturbed
systems. However, in many industrial processes, enforcing closed-loop fast-slow
dynamics for peculiarly non-separable ones is a prior request and a crucial
issue to be resolved. Aiming at the above problem, this paper presents two
dual-level model predictive control (MPC) algorithms for two-timescale
dynamical systems with unknown bounded disturbance and input constraint. The
proposed algorithms, each one composed of two regulators working in slow and
fast time scales, are designed to generate closed-loop separable dynamics at
the high and low levels. As a key feature, the proposed algorithms are not only
suitable for singularly perturbed systems, but also capable of imposing
separable closed-loop performance for dynamics that are non-separable and
strongly coupled. The recursive feasibility and convergence properties are
proven under suitable assumptions. The simulation results on controlling a
Boiler Turbine (BT) system, including the comparisons with other classic
controllers are reported, which show the effectiveness of the proposed
algorithms.
|
electrics
|
3,552 |
Target Encirclement with any Smooth Pattern Using Range-only Measurements
|
eess.SY
|
This paper proposes a coordinate-free controller to drive a mobile robot to
encircle a target at unknown position by only using range measurements.
Different from the existing works, a backstepping based controller is proposed
to encircle the target with zero steady-state error for any desired smooth
pattern. Moreover, we show its asymptotic exponential convergence under a fixed
set of control parameters, which are independent of the initial distance to the
target. The effectiveness and advantages of the proposed controller are
validated via simulations.
|
electrics
|
3,553 |
LIDAR-Assisted Exact Output Regulation for Load Mitigation in Wind Turbines
|
eess.SY
|
Optimising wind turbine performance involves maximising energy harvesting
while seeking to minimise load fatigues on the tower structure, blades and
rotor. To improve turbine control performance, wind preview measurement
technologies such as Light Detection And Ranging (LIDAR) have been a point of
interest for researchers in recent years. In this paper, we explore the
application of a classical control methodology known as Exact Output Regulation
(EOR) for improving the control performance of a LIDAR-enhanced wind turbine.
The EOR controller is designed to achieve the rejection of known input
disturbances, while also ensuring the system output tracks a desired reference
signal. The controller is comprised of a state feedback controller together
with a feedforward gain. The LIDAR wind preview information is used to obtain a
low-order exosystem for modeling wind dynamics. This wind exosystem is used to
obtain the feedforward gain matrix that enables the EOR controller to
effectively reject the input disturbance and achieve the desired reference
tracking. Extensive simulations of the EOR controller with a broad range of
wind speeds in both partial load and full load operating regions are performed
on the full nonlinear aero-elastic model of the National Renewable Energy
Laboratory (NREL) 5 MW reference wind turbine. For performance comparisons, we
also implement a baseline torque controller and a commonly used feedforward
control method known as Disturbance Accommodation Control (DAC). The results
show that, in comparison with a baseline and DAC controller, the EOR controller
can provide substantially improved reduction of fatigue loads and smoother
power output, without compromising energy production levels.
|
electrics
|
3,554 |
An Integral Formulation and Convex Hull Pricing for Unit Commitment
|
eess.SY
|
Reducing uplift payments has been a challenging problem for most wholesale
markets in US. The main difficulty comes from the unit commitment discrete
decision makings. Recently convex hull pricing has shown promises to reduce the
uplift payments. However, it has been intractable to obtain the optimal convex
hull price. In this paper, we describe an innovative approach to decide the
optimal convex hull price by simply solving a linear program. We also provide
an example to illustrate the calculation process. The final computational
experiments on a revised IEEE-118 bus system verify the cost effectiveness by
utilizing our proposed approach.
|
electrics
|
3,555 |
Computationally Efficient Distributed Multi-sensor Fusion with Multi-Bernoulli Filter
|
eess.SY
|
This paper proposes a computationally efficient algorithm for distributed
fusion in a sensor network in which multi-Bernoulli (MB) filters are locally
running in every sensor node for multi-target tracking. The generalized
Covariance Intersection (GCI) fusion rule is employed to fuse multiple MB
random finite set densities. The fused density comprises a set of fusion
hypotheses that grow exponentially with the number of Bernoulli components.
Thus, GCI fusion with MB filters can become computationally intractable in
practical applications that involve tracking of even a moderate number of
objects. In order to accelerate the multi-sensor fusion procedure, we derive a
theoretically sound approximation to the fused density. The number of fusion
hypotheses in the resulting density is significantly smaller than the original
fused density. It also has a parallelizable structure that allows multiple
clusters of Bernoulli components to be fused independently. By carefully
clustering Bernoulli components into isolated clusters using the GCI divergence
as the distance metric, we propose an alternative to build exactly the
approximated density without exhaustively computing all the fusion hypotheses.
The combination of the proposed approximation technique and the fast clustering
algorithm can enable a novel and fast GCIMB fusion implementation. Our analysis
shows that the proposed fusion method can dramatically reduce the computational
and memory requirements with small bounded L1-error. The Gaussian mixture
implementation of the proposed method is also presented. In various numerical
experiments, including a challenging scenario with up to forty objects, the
efficacy of the proposed fusion method is demonstrated.
|
electrics
|
3,556 |
Resilient Leader-Follower Consensus with Time-Varying Leaders in Discrete-Time Systems
|
eess.SY
|
The problem of consensus in the presence of adversarially behaving agents has
been studied extensively in the literature. The proposed algorithms typically
guarantee that the consensus value lies within the convex hull of initial
normal agents' states. In leader-follower consensus problems however, the
objective for normally behaving agents is to track a time-varying reference
state that may take on values outside of this convex hull. In this paper we
present a method for agents with discrete-time dynamics to resiliently track a
set of leaders' common time-varying reference state despite a bounded subset of
the leaders and followers behaving adversarially. The efficacy of our results
are demonstrated through simulations.
|
electrics
|
3,557 |
Herding an Adversarial Swarm in an Obstacle Environment
|
eess.SY
|
This paper studies a defense approach against a swarm of adversarial agents.
We employ a closed formation (`StringNet') of defending agents around the
adversarial agents to restrict their motion and guide them to a safe area while
navigating in an obstacle-populated environment. Control laws for forming the
StringNet and guiding it to a safe area are developed, and the stability of the
closed-loop system is analyzed formally. The adversarial swarm is assumed to
move as a flock in the presence of rectangular obstacles. Simulation results
are provided to demonstrate the efficacy of the approach.
|
electrics
|
3,558 |
Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs
|
eess.SY
|
Several algorithms in prior literature have been proposed which guarantee
consensus of normally behaving agents in a network that may contain
adversarially behaving agents. These algorithms guarantee that the consensus
value lies within the convex hull of initial normal agents' states, with the
exact consensus value possibly being unknown. In leader-follower consensus
problems however, the objective is for normally behaving agents to track a
reference state that may take on values outside of this convex hull. In this
paper we present methods for agents in time-varying graphs with discrete-time
dynamics to resiliently track a reference state propagated by a set of leaders
despite a bounded subset of the leaders and followers behaving adversarially.
Our results are demonstrated through simulations.
|
electrics
|
3,559 |
Newton's Method and Differential Dynamic Programming for Unconstrained Nonlinear Dynamic Games
|
eess.SY
|
Dynamic games arise when multiple agents with differing objectives control a
dynamic system. They model a wide variety of applications in economics,
defense, energy systems and etc. However, compared to single-agent control
problems, the computational methods for dynamic games are relatively limited.
As in the single-agent case, only specific dynamic games can be solved exactly,
so approximation algorithms are required. In this paper, we show how to extend
a recursive Newton's algorithm and the popular differential dynamic programming
(DDP) for single-agent optimal control to the case of full-information non-zero
sum dynamic games. In the single-agent case, the convergence of DDP is proved
by comparison with Newton's method, which converges locally at a quadratic
rate. We show that the iterates of Newton's method and DDP are sufficiently
close for the DDP to inherit the quadratic convergence rate of Newton's method.
We also prove both methods result in an open-loop Nash equilibrium and a local
feedback $O(\epsilon^2)$-Nash equilibrium. Numerical examples are provided.
|
electrics
|
3,560 |
On the Local Input-Output Stability of Event-Triggered Control Systems
|
cs.SY
|
This paper studies performance preserving event design in nonlinear
event-based control systems based on a local L2-type performance criterion.
Considering a finite gain local L2-stable disturbance driven continuous-time
system, we propose a triggering mechanism so that the resulting sampled-data
system preserves similar disturbance attenuation local L2-gain property. The
results are applicable to nonlinear systems with exogenous disturbances bounded
by some Lipschitz-continuous function of state. It is shown that an
exponentially decaying function of time, combined with the proposed triggering
condition, extends the inter-event periods. Compared to the existing works,
this paper analytically estimates the increase in intersampling periods at
least for an arbitrary period of time. We also propose a so-called discrete
triggering condition to quantitatively find the improvement in inter-event
times at least for an arbitrary number of triggering iterations. Illustrative
examples support the analytically derived results.
|
electrics
|
3,561 |
Adaptive polytopic estimation for nonlinear systems under bounded disturbances using moving horizon
|
eess.SY
|
This paper introduces an adaptive polytopic estimator design for nonlinear
systems under bounded disturbances combining moving horizon and dual estimation
techniques. It extends the moving horizon estimation results for LTI systems to
polytopic LPV systems. The design and necessary conditions to guarantee the
robust stability and convergence to the true state and parameters for the case
of bounded disturbances and convergence to the true system and state are given
for the vanishing disturbances.
|
electrics
|
3,562 |
Simultaneous state estimation and control for nonlinear systems subject to bounded disturbances
|
cs.SY
|
In this work, we address the output--feedback control problem for nonlinear
systems under bounded disturbances using a moving horizon approach. The
controller is posed as an optimization-based problem that simultaneously
estimates the state trajectory and computes future control inputs. It minimizes
a criterion that involves finite forward and backward horizon with respect the
unknown initial state, measurement noises and control input variables and it is
maximized with respect the unknown future disturbances. Although simultaneous
state estimation and control approaches are already available in the
literature, the novelty of this work relies on linking the lengths of the
forward and backward windows with the closed-loop stability, assuming
detectability and decoding sufficient conditions to assure system
stabilizability. Simulation examples are carried out to compare the performance
of simultaneous and independent estimation and control approaches as well as to
show the effects of simultaneously solving the control and estimation problems.
|
electrics
|
3,563 |
Probabilistic model predictive safety certification for learning-based control
|
eess.SY
|
Reinforcement learning (RL) methods have demonstrated their efficiency in
simulation environments. However, many applications for which RL offers great
potential, such as autonomous driving, are also safety critical and require a
certified closed-loop behavior in order to meet safety specifications in the
presence of physical constraints. This paper introduces a concept, called
probabilistic model predictive safety certification (PMPSC), which can be
combined with any RL algorithm and provides provable safety certificates in
terms of state and input chance constraints for potentially large-scale
systems. The certificate is realized through a stochastic tube that safely
connects the current system state with a terminal set of states, that is known
to be safe. A novel formulation in terms of a convex receding horizon problem
allows a recursively feasible real-time computation of such probabilistic
tubes, despite the presence of possibly unbounded disturbances. A design
procedure for PMPSC relying on bayesian inference and recent advances in
probabilistic set invariance is presented. Using a numerical car simulation,
the method and its design procedure are illustrated by enhancing a simple RL
algorithm with safety certificates.
|
electrics
|
3,564 |
Validating Coordination Schemes between Transmission and Distribution System Operators using a Laboratory-Based Approach
|
eess.SY
|
The secure operation of future power systems will rely on better coordination
between transmission system and distribution system operators. Increasing
integration of renewables throughout the whole system is challenging the
traditional operation. To tackle this problem, the SmartNet project proposes
and evaluates five different coordination schemes between system operators
using three benchmark scenarios from Denmark, Italy, and Spain. In the project,
field tests in each of the benchmark countries are complemented with a number
of laboratory validation tests, to cover scenarios that cannot be tested in
field trials. This paper presents the outcome of these laboratory tests. Three
tests are shown, focusing on controller validation, analysis of communication
impacts, and how well price-based controls can integrate with the SmartNet
coordination schemes. The results demonstrate important indications for the
field tests and also show some of the limitations with the current
implementations of the coordinations schemes.
|
electrics
|
3,565 |
A Receding Horizon Framework for Autonomy in Unmanned Vehicles
|
eess.SY
|
In this article we present a unified framework based on receding horizon
techniques that can be used to design the three tasks (guidance, navigation and
path-planning) which are involved in the autonomy of unmanned vehicles. This
tasks are solved using model predictive control and moving horizon estimation
techniques, which allows us to include physical and dynamical constraints at
the design stage, thus leading to optimal and feasible results. In order to
demonstrate the capabilities of the proposed framework, we have used Gazebo
simulator in order to drive a Jackal unmanned ground vehicle (UGV) along a
desired path computed by the path-planning task. The results we have obtained
are successful as the estimation and guidance errors are small and the Jackal
UGV is able to follow the desired path satisfactorily and it is also capable to
avoid the obstacles which are in its way.
|
electrics
|
3,566 |
Design of distributed guidance laws for multi-UAV cooperative attacking a moving target based on reducing surrounding area
|
eess.SY
|
In this paper, two cooperative guidance laws based on the area around the
target of multiattackers are designed to deal with the problem of cooperative
encirclement or simultaneous attack in the case of known target acceleration
and unknown target acceleration. Multi-attacker communication network only
needs to contain a directed spanning tree, and does not require all attackers
to observe the target information, where at least one can observe the target.
The components along the attacker-target line of sight in the novel guidance
laws can reduce the relative remaining distance between the attacker and the
target at the same speed, thus completing simultaneous attack and avoiding the
calculation of the remaining time. The components of the guidance laws
perpendicular to the attacker-target line of sight can make the normal overload
of relative motion zero, so that the trajectory will not be distorted and the
collision problem within the attacker group can be avoided. The simulation
results verify the practicability of the novel guidance laws.
|
electrics
|
3,567 |
Computer-aided modelling of complex physical systems with BondGraphTools
|
eess.SY
|
BondGraphTools is a Python library for scripted modelling of complex
multi-physics systems. In contrast to existing modelling solutions,
BondGraphTools is based upon the well established bond graph methodology,
provides a programming interface for symbolic model composition, and is
intended to be used in conjunction with the existing scientific Python
toolchain. Here we discuss the design, implementation and use of
BondGraphTools, demonstrate how it can be used to accelerate systems modelling
with an example from optomechanics, and comment on current and future
applications in cross-domain modelling, particularly in systems biology.
|
electrics
|
3,568 |
Nested Reinforcement Learning Based Control for Protective Relays in Power Distribution Systems
|
eess.SY
|
This paper envisions a new control architecture for the protective relay
setting in future power distribution systems. With deepening penetration of
distributed energy resources at the end users level, it has been recognized as
a key engineering challenge to redesign the protective relays in the future
distribution system. Conceptually, these protective relays are the discrete
ON/OFF control devices at the end of each branch and node in a power network.
The key technical difficulty lies in how to set up the relay control logic so
that the protection could successfully differentiate heavy load and faulty
operating conditions. This paper proposes a new nested reinforcement learning
approach to take advantage of the structural properties of distribution
networks and develop a new set of training methods for tuning the protective
relays.
|
electrics
|
3,569 |
Sliding Mode Control Techniques and Artificial Potential Field for Dynamic Collision Avoidance in Rendezvous Maneuvers
|
eess.SY
|
The paper considers autonomous rendezvous maneuver and proximity operations
of two spacecraft in presence of obstacles. A strategy that combines guidance
and control algorithms is analyzed. The proposed closed-loop system is able to
guarantee a safe path in a real environment, as well as robustness with respect
to external disturbances and dynamic obstacles. The guidance strategy exploits
a suitably designed Artificial Potential Field (APF), while the controller
relies on Sliding Mode Control (SMC), for both position and attitude tracking
of the spacecraft. As for the position control, two different first order SMC
methods are considered, namely the component-wise and the simplex-based control
techniques. The proposed integrated guidance and control strategy is validated
by extensive simulations performed with a six degree-of-freedom (DOF) orbital
simulator and appears suitable for real-time control with minimal on-board
computational effort. Fuel consumption and control effort are evaluated,
including different update frequencies of the closed-loop software.
|
electrics
|
3,570 |
Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics
|
eess.SY
|
Iterative trajectory optimization techniques for non-linear dynamical systems
are among the most powerful and sample-efficient methods of model-based
reinforcement learning and approximate optimal control. By leveraging
time-variant local linear-quadratic approximations of system dynamics and
reward, such methods can find both a target-optimal trajectory and time-variant
optimal feedback controllers. However, the local linear-quadratic assumptions
are a major source of optimization bias that leads to catastrophic greedy
updates, raising the issue of proper regularization. Moreover, the approximate
models' disregard for any physical state-action limits of the system causes
further aggravation of the problem, as the optimization moves towards
unreachable areas of the state-action space. In this paper, we address the
issue of constrained systems in the scenario of online-fitted stochastic linear
dynamics. We propose modeling state and action physical limits as probabilistic
chance constraints linear in both state and action and introduce a new
trajectory optimization technique that integrates these probabilistic
constraints by optimizing a relaxed quadratic program. Our empirical
evaluations show a significant improvement in learning robustness, which
enables our approach to perform more effective updates and avoid premature
convergence observed in state-of-the-art algorithms.
|
electrics
|
3,571 |
Preference-based Energy Exchange in a Network of Microgrids
|
eess.SY
|
Peer-to-peer energy trading is emerging as a new paradigm that in the near
future may disrupt conventional electricity markets and heavily affect energy
exchanges in networks of microgrids. In this paper, a preference mechanism is
considered to compute optimal energy exchanges in a network of microgrids with
or without the supervision of the distribution system operator, and the
alternating direction method of multipliers is adopted for its distributed
solution. The effect of the preference mechanism on the resulting power flow in
the network is further studied and discussed for realistic case studies.
Results show that a desired power flow in the network of interconnected
microgrids can be achieved with different preference values locally chosen or
imposed by the system operator. In particular, appropriate preferences may be
used to give rise to different clusters of microgrids and reduce energy
exchanges between different clusters.
|
electrics
|
3,572 |
Combining Learning and Model Based Control via Discrete-Time Chen-Fliess Series
|
eess.SY
|
A learning control system is presented suitable for control affine nonlinear
plants based on discrete-time Chen-Fliess series and capable of incorporating
knowledge of a given physical model. The underlying noncommutative algebraic
and combinatorial structures needed to realize the multivariable case are also
described. The method is demonstrated using a two-input, two-output
Lotka-Volterra system.
|
electrics
|
3,573 |
Distributed Optimal Guidance Laws for Multiple Unmanned Aerial Vehicles Attacking A Moving Target
|
eess.SY
|
In this paper, two cooperative guidance laws based on two-point boundary
value are designed to deal with the problem of cooperative encirclement and
simultaneous attack under condition of both known target acceleration and
unknown target acceleration. The only requirement for the multi-attacker
communication network is that it contains a directed spanning tree. The
guidance laws can function properly as long as at least one attacker can
observed the target. The acceleration components along the attacker-target line
of sight in the novel guidance laws can reduce the relative remaining distance
between each of the attackers and the target at the same speed, thus completing
simultaneous attack and avoiding the calculation of the remaining time. The
components of the guidance laws perpendicular to the attacker-target line of
sight can make the normal overload of relative motion zero, so that the
trajectory will be smooth and the collision problem within the attacker can be
avoided. Simulation results verified the practicability of the novel guidance
laws.
|
electrics
|
3,574 |
Beyond Phasors: Continuous-Spectrum Modeling of Power Systems using the Hilbert Transform
|
eess.SY
|
Modern power systems are at risk of largely reducing the inertia of
generation assets and prone to experience extreme dynamics. The consequence is
that, during electromechanical transients triggered by large contingencies,
transmission of electrical power may take place in a broad spectrum well beyond
the single fundamental component. Traditional modeling approaches rely on the
phasor representation derived from the Fourier Transform (FT) of the signal
under analysis. During large transients, though, FT-based analysis may fail to
accurately identify the fundamental component parameters, in terms of
amplitude, frequency and phase. In this paper, we propose an alternative
approach relying on the Hilbert Transform (HT), that, in view of the
possibility to identify the whole spectrum, enables the tracking of signal
dynamics. We compare FT- and HT-based approaches during representative
operating conditions, i.e., amplitude modulations, frequency ramps and step
changes, in synthetic and real-world datasets. We further validate the
approaches using a contingency analysis on the IEEE 39-bus.
|
electrics
|
3,575 |
$H_{\infty}$-Control of Grid-Connected Converters: Design, Objectives and Decentralized Stability Certificates
|
eess.SY
|
The modern power system features high penetration of power converters due to
the development of renewables, HVDC, etc. Currently, the controller design and
parameter tuning of power converters heavily rely on rich engineering
experience and extrapolation from a single converter system, which may lead to
inferior performance or even instabilities under variable grid conditions. In
this paper, we propose an $H_{\infty}$-control design framework to provide a
systematic way for the robust and optimal control design of power converters.
We discuss how to choose weighting functions to achieve anticipated and robust
performance with regards to multiple control objectives. Further, we show that
by a proper choice of the weighting functions, the converter can be
conveniently specified as grid-forming or grid-following in terms of
small-signal dynamics. Moreover, this paper first proposes a decentralized
stability criterion based on the small gain theorem, which enables us to
guarantee the global small-signal stability of a multi-converter system through
local control design of the power converters. We provide high-fidelity
nonlinear simulations and hardware-in-the-loop (HIL) real-time simulations to
illustrate the effectiveness of our method.
|
electrics
|
3,576 |
The Branching-Course MPC Algorithm for Maritime Collision Avoidance
|
eess.SY
|
This article presents a new algorithm for short-term maritime collision
avoidance (COLAV) named the branching-course MPC (BC-MPC) algorithm. The
algorithm is designed to be robust with respect to noise on obstacle estimates,
which is a significant source of disturbance when using exteroceptive sensors
such as e.g. radars for obstacle detection and tracking. Exteroceptive sensors
do not require vessel-to-vessel communication, which enables COLAV toward
vessels not equipped with e.g. automatic identification system (AIS)
transponders, in addition to increasing the robustness with respect to faulty
information which may be provided by other vessels. The BC-MPC algorithm is
compliant with rules 8 and 17 of the International Regulations for Preventing
Collisions at Sea (COLREGs), and favors maneuvers following rules 13-15. This
results in a COLREGs-aware algorithm which can ignore rules 13-15 when
necessary. The algorithm is experimentally validated in several full-scale
experiments in the Trondheimsfjord in 2017 using a radar-based system for
obstacle detection and tracking. The COLAV experiments show good performance in
compliance with the desired algorithm behavior.
|
electrics
|
3,577 |
Smart Charging Benefits in Autonomous Mobility on Demand Systems
|
eess.SY
|
In this paper, we study the potential benefits from smart charging for a
fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD)
services. We first consider a profit-maximizing platform operator who makes
decisions for routing, charging, rebalancing, and pricing for rides based on a
network flow model. Clearly, each of these decisions directly influence the
fleet's smart charging potential; however, it is not possible to directly
characterize the effects of various system parameters on smart charging under a
classical network flow model. As such, we propose a modeling variation that
allows us to decouple the charging and routing problems faced by the operator.
This variation allows us to provide closed-form mathematical expressions
relating the charging costs to the maximum battery capacity of the vehicles as
well as the fleet operational costs. We show that investing in larger battery
capacities and operating more vehicles for rebalancing reduces the charging
costs, while increasing the fleet operational costs. Hence, we study the
trade-off the operator faces, analyze the minimum cost fleet charging strategy,
and provide numerical results illustrating the smart charging benefits to the
operator.
|
electrics
|
3,578 |
Distributed Design of Robust Kalman Filters over Corrupted Channels
|
eess.SY
|
We study distributed filtering for a class of uncertain systems over
corrupted communication channels. We propose a distributed robust Kalman filter
with stochastic gains, through which upper bounds of the conditional mean
square estimation errors are calculated online. We present a robust collective
observability condition, under which the mean square error of the distributed
filter is proved to be uniformly upper bounded if the network is strongly
connected. For better performance, we modify the filer by introducing a
switching fusion scheme based on a sliding window. It provides a smaller upper
bound of the conditional mean square error. Numerical simulations are provided
to validate the theoretical results and show that the filter scales to large
networks.
|
electrics
|
3,579 |
Hybrid Collision Avoidance for ASVs Compliant with COLREGs Rules 8 and 13-17
|
eess.SY
|
This paper presents a three-layered hybrid collision avoidance (COLAV) system
for autonomous surface vehicles, compliant with rules 8 and 13-17 of the
International Regulations for Preventing Collisions at Sea (COLREGs). The COLAV
system consists of a high-level planner producing an energy-optimized
trajectory, a model predictive control based mid-level COLAV algorithm
considering moving obstacles and the COLREGs, and the branching-course model
predictive control algorithm for short-term COLAV handling emergency situations
in accordance with the COLREGs. Previously developed algorithms by the authors
are used for the high-level planner and short-term COLAV, while we in this
paper further develop the mid-level algorithm to make it comply with COLREGs
rules 13-17. This includes developing a state machine for classifying obstacle
vessels using a combination of the geometrical situation, the distance and time
to the closest point of approach (CPA) and a new CPA-like measure. The
performance of the hybrid COLAV system is tested through numerical simulations
for three scenarios representing a range of different challenges, including
multi-obstacle situations with multiple simultaneously active COLREGs rules,
and also obstacles ignoring the COLREGs. The COLAV system avoids collision in
all the scenarios, and follows the energy-optimized trajectory when the
obstacles do not interfere with it.
|
electrics
|
3,580 |
Development of a novel matrix-based methodology for system engineering: A case study
|
eess.SY
|
Developing a structured method for analyzing various aspects of a system
requires a novel methodology. This study is aimed at developing such as
methodology through combining two major matrix methods, namely, Design
Structure Matrix (DSM) and Interface Structure Matrix (ISM). Through this
paper, a business process modeling method is applied to turn a real work
project to a process model. Then that process model is written in two various
matrix forms of DSM and ISM. These two matrices are analyzed by two types of
algorithm for extracting activity levels and sub-processes. In the end, a Mixed
Matrix Model (MMM) is built upon these activity levels and sub-processes, which
can be used as a framework for the engineering of real-world systems.
|
electrics
|
3,581 |
Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicle in Mixed Traffic Conditions
|
eess.SY
|
Managed lanes, such as a dedicated lane for connected and automated vehicles
(CAVs), can provide not only technological accommodation but also desired
market incentives for road users to adopt CAVs in the near future. In this
paper, we investigate traffic flow characteristics with two configurations of
the managed lane across different market penetration rates and quantify the
benefits from the perspectives of lane-level headway distribution, fuel
consumption, communication density, and overall network performance. The
results highlight the benefits of implementing managed lane strategies for
CAVs: 1) a dedicated CAV lane significantly extends the stable region of the
speed-flow diagram and yields a greater road capacity. As the result shows, the
highest flow rate is 3,400 vehicles per hour per lane at 90% market penetration
rate with one CAV lane; 2) the concentration of CAVs in one lane results in a
narrower headway distribution (with smaller standard deviation) even with
partial market penetration; 3) a dedicated CAV lane is also able to eliminate
duel-bell-shape distribution that is caused by the heterogeneous traffic flow;
and 4) a dedicated CAV lane creates a more consistent CAV density, which
facilitates communication activity and decreases the probability of packet
dropping.
|
electrics
|
3,582 |
Discrete Event Simulation of Driver's Routing Behavior Rule at a Road Intersection
|
eess.SY
|
Several factors influence traffic congestion and overall traffic dynamics.
Simulation modeling has been utilized to understand the traffic performance
parameters during traffic congestions. This paper focuses on driver behavior of
route selection by differentiating three distinguishable decisions, which are
shortest distance routing, shortest time routing and less crowded road routing.
This research generated 864 different scenarios to capture various traffic
dynamics under collective driving behavior of route selection. Factors such as
vehicle arrival rate, behaviors at system boundary and traffic light phasing
were considered. The simulation results revealed that shortest time routing
scenario offered the best solution considering all forms of interactions among
the factors. Overall, this routing behavior reduces traffic wait time and total
time (by 69.5% and 65.72%) compared to shortest distance routing.
|
electrics
|
3,583 |
Multi-object Tracking in Unknown Detection Probability with the PMBM Filter
|
eess.SY
|
This paper focuses on the joint multi-object tracking (MOT) and the estimate
of detection probability with the \emph{Poisson multi-Bernoulli mixture} (PMBM)
filter. In a majority of multi-object scenarios, the knowledge of detection
probability is usually uncertain, which is often estimated offline from the
training data. In such cases, online filtering is not allowed or believable,
otherwise, significate parameter mismatch will result in biased estimates
(state and cardinality of objects). Consequently, the ability of adaptively
estimating the detection probability is essential in practice. In this paper,
we detail how the detection probability can be estimated accompanied with the
state estimates. Besides, closed-form solutions to the proposed method are
derived by approximating the intensity of Poisson random finite set (RFS) to a
Beta-Gaussian mixture form and density of Bernoulli RFS to a single
Beta-Gaussian form. Simulation results show the effectiveness and superiority
of the proposed method.
|
electrics
|
3,584 |
Self-triggered stabilizing controllers for linear continuous-time systems
|
eess.SY
|
Self-triggered control is an improvement on event-triggered control methods.
Unlike the latter, self-triggered control does not require monitoring the
behavior of the system constantly. Instead, self-triggered algorithms predict
the events at which the control law has to be updated before they happen,
relying on system model and past information.\\ In this work, we present a
self-triggered version of an event-triggered control method in which events are
generated when a pseudo-Lyapunov function (PLF) associated with the system
increases up to a certain limit. This approach has been shown to considerably
decrease the communications between the controller and the plant, while
maintaining system stability. To predict the intersections between the PLF and
the upper limit, we use a simple and fast root-finding algorithm. The algorithm
mixes the global convergence properties of the bisection and the fast
convergence properties of the Newton-Raphson method. \\ Moreover, to ensure the
convergence of the method, the initial iterate of the algorithm is found
through a minimization algorithm.
|
electrics
|
3,585 |
Generalized Proportional Allocation Policies for Robust Control of Dynamical Flow Networks
|
eess.SY
|
We study a robust control problem for dynamical flow networks. In the
considered dynamical models, traffic flows along the links of a transportation
network --modeled as a capacited multigraph-- and queues up at the nodes,
whereby control policies determine which incoming queues at a node are to be
allocated service simultaneously, within some predetermined scheduling
constraints. We first prove a fundamental performance limitation by showing
that for a dynamical flow network to be stabilizable by some control policy it
is necessary that the exogenous inflows belong to a certain stability region,
that is determined by the network topology, link capacities, and scheduling
constraints. Then, we introduce a family of distributed controls, referred to
as Generalized Proportional Allocation (GPA) policies, and prove that they
stabilize a dynamical transportation network whenever the exogenous inflows
belong to such stability region. The proposed GPA control policies are
decentralized and fully scalable as they rely on local feedback information
only. Differently from previously studied maximally stabilizing control
strategies, the GPA control policies do not require any global information
about the network topology, the exogenous inflows, or the routing, which makes
them robust to demand variations and unpredicted changes in the link capacities
or the routing decisions. Moreover, the proposed GPA control policies also take
into account the overhead time while switching between services. Our
theoretical results find one application in the control of urban traffic
networks with signalized intersections, where vehicles have to queue up at
junctions and the traffic signal controls determine the green light allocation
to the different incoming lanes.
|
electrics
|
3,586 |
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids
|
eess.SY
|
This paper presents a supervised multi-agent safe policy learning (SMAS-PL)
method for optimal power management of networked microgrids (MGs) in
distribution systems. While conventional reinforcement learning (RL) algorithms
are black-box decision models that could fail to satisfy grid operational
constraints, our proposed method is constrained by AC power flow equations and
other operational limits. Accordingly, the training process employs the
gradient information of operational constraints to ensure that the optimal
control policy functions generate safe and feasible decisions. Furthermore, we
have developed a distributed consensus-based optimization approach to train the
agents' policy functions while maintaining MGs' privacy and data ownership
boundaries. After training, the learned optimal policy functions can be safely
used by the MGs to dispatch their local resources, without the need to solve a
complex optimization problem from scratch. Numerical experiments have been
devised to verify the performance of the proposed method.
|
electrics
|
3,587 |
Small-Signal Stability Analysis for Droop-Controlled Inverter-based Microgrids with Losses and Filtering
|
eess.SY
|
An islanded microgrid supplied by multiple distributed energy resources
(DERs) often employs droop-control mechanisms for power sharing. Because
microgrids do not include inertial elements, and low pass filtering of noisy
measurements introduces lags in control, droop-like controllers may pose
significant stability concerns. This paper aims to understand the effects of
droop-control on the small-signal stability and transient response of the
microgrid. Towards this goal, we present a compendium of results on the
small-signal stability of droop-controlled inverter-based microgrids with
heterogeneous loads, which distinguishes: (1) lossless vs. lossy networks; (2)
droop mechanisms with and without filters, and (3) mesh vs. radial network
topologies. Small-signal and transient characteristics are also studied using
multiple simulation studies on IEEE test systems
|
electrics
|
3,588 |
Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing's type
|
eess.SY
|
Parametric studies for dynamic systems are of high interest to detect
instability domains. This prediction can be demanding as it requires a refined
exploration of the parametric space due to the disrupted mechanical behavior.
In this paper, an efficient surrogate strategy is proposed to investigate the
behavior of an oscillator of Duffing's type in combination with an
elasto-plastic friction force model. Relevant quantities of interest are
discussed. Sticking time is considered using a machine learning technique based
on Gaussian processes called kriging. The largest Lyapunov exponent is proposed
as an efficient indicator of non-regular behavior. This indicator is estimated
using a perturbation method. A dedicated adaptive kriging strategy for
classification called MiVor is utilized and appears to be highly proficient in
order to detect instabilities over the parametric space and can furthermore be
used for complex response surfaces in multi-dimensional parametric domains.
|
electrics
|
3,589 |
A novel passivity based controller for a piezoelectric beam
|
eess.SY
|
This paper presents a new passivity property for distributed piezoelectric
devices with integrable port-variables. We present two new control
methodologies by exploiting the integrability property of the port-variables.
The derived controllers have a Proportional-Integral (PI) like structure.
Finally, we present the simulation results and in-depth analysis on the tuning
gains explaining their transient and steady-state behaviors.
|
electrics
|
3,590 |
A Soft-switched Fast Cell-to-Cell Voltage Equalizer for Electrochemical Energy Storage
|
eess.SY
|
Batteries are connected in series to meet the voltage requirement in many
applications. A voltage equalizer circuit is necessary to ensure that none of
the batteries is over-charged or over-discharged.
A novel fast soft-switched cell-to-cell voltage equalizer topology is
proposed in this work. This topology can transfer charge from multiple
over-charged batteries to multiple under-charged batteries simultaneously
avoiding any unnecessary charging or discharging of a battery to achieve fast
voltage equalization. The proposed circuit topology and modulation method
ensure zero voltage switching under all battery conditions. The circuit
operation and soft-switching are analyzed and experimentally verified with a
four battery voltage equalizer prototype. The prototype is tested with a
battery bank and a hybrid ultra-capacitor bank, and a high conversion
efficiency is verified.
|
electrics
|
3,591 |
A Reconfigurable Solar Photovoltaic Grid-Tied Inverter Architecture for Enhanced Energy Access in Backup Power Applications
|
eess.SY
|
In this paper, a photovoltaic (PV) reconfigurable grid-tied inverter (RGTI)
scheme is proposed. Unlike a conventional GTI that ceases operation during a
power outage, the RGTI is designed to act as a regular GTI in the on-grid mode
but it is reconfigured to function as a DC-DC charge-controller that continues
operation during a grid outage. During this period, the RGTI is tied to the
battery-bank of an external UPS based backup power system to augment it with
solar power. Such an operation in off-grid mode without employing communication
with the UPS is challenging, as the control of RGTI must not conflict with the
battery management system of the UPS. The hardware and control design aspects
of this requirement are discussed in this paper. A battery emulation control
scheme is proposed for the RGTI that facilitates seamless functioning of the
RGTI in parallel with the physical UPS battery to reduce its discharge current.
A system-level control scheme for overall operation and power management is
presented to handle the dynamic variations in solar irradiation and UPS loads
during the day, such that the battery discharge burden is minimized. The design
and operation of the proposed RGTI system are independent of the external UPS
and can be integrated with an UPS supplied by any manufacturer. Experimental
results on a 4~kVA hardware setup validate the proposed RGTI concept, its
operation and control.
|
electrics
|
3,592 |
Decentralized Dynamic State Estimation in Microgrids
|
eess.SY
|
This paper proposes a decentralized dynamic state estimation scheme for
microgrids. The approach employs the voltage and current measurements in the
dq0 reference frame through phasor synchronization to be able to exclude
orthogonal functions from their relationship formulas. Based on that premise,
we utilize a Kalman filter to dynamically estimate states of microgrids. The
decoupling of measurement values to state and input vectors reduces the
computational complexity. The Kalman filter considers the process noise
covariances, which are modified with respect to the covariance of measured
input values. Theoretical analysis and simulation results are provided for
validation.
|
electrics
|
3,593 |
Backward Reachability for Polynomial Systems on A Finite Horizon
|
eess.SY
|
A method is presented to obtain an inner-approximation of the backward
reachable set (BRS) of a given target tube, along with an admissible controller
that maintains trajectories inside this tube. The proposed optimization
algorithms are formulated as nonlinear optimization problems, which are
decoupled into tractable subproblems and solved by an iterative algorithm using
the polynomial S-procedure and sum-of-squares techniques. This framework is
also extended to uncertain nonlinear systems with L_2 disturbances and
L_{\infty} parametric uncertainties. The effectiveness of the method is
demonstrated on several nonlinear robotics and aircraft systems with control
saturation.
|
electrics
|
3,594 |
Simulation Credibility Assessment Methodology with FPGA-based Hardware-in-the-loop Platform
|
eess.SY
|
Electronic control systems are becoming more and more complicated, which
makes it difficult to test them sufficiently only through experiments.
Simulation is an efficient way in the development and testing of complex
electronic systems, but the simulation results are always doubtful by people
due to the lack of credible simulation platforms and assessment methods. This
paper proposes a credible simulation platform based on real-time FPGA-based
hardware-in-the-loop (HIL) simulation, and then an assessment method is
proposed to quantitatively assess its simulation credibility. By using the FPGA
to simulate all sensor chips, the simulation platform can ensure that the
tested electronic system maintains the same hardware and software operating
environment in both simulations and experiments, which makes it possible to
perform the same tests in the simulation platform and the real experiment to
compare and analyze the simulation errors. Then, the testing methods and
assessment indices are proposed to assess the simulation platform from various
perspectives, such as performance, time-domain response, and frequency-domain
response. These indices are all normalized to the same scale (from 0 to 1) and
mapped to a uniform assessment criterion, which makes it convenient to compare
and synthesize different assessment indices. Finally, an overall assessment
index is proposed by combining all assessment indices obtained from different
tests to assess the simulation credibility of the whole simulation platform.
The simulation platform and the proposed assessment method are applied to a
multicopter system, where the effectiveness and practicability are verified by
simulations and experiments.
|
electrics
|
3,595 |
Distributed Integer Balancing under Weight Constraints in the Presence of Transmission Delays and Packet Drops
|
eess.SY
|
We consider the distributed weight balancing problem in networks of nodes
that are interconnected via directed edges, each of which is able to admit a
positive integer weight within a certain interval, captured by individual lower
and upper limits. A digraph with positive integer weights on its (directed)
edges is weight-balanced if, for each node, the sum of the weights of the
incoming edges equals the sum of the weights of the outgoing edges. In this
work, we develop a distributed iterative algorithm which solves the integer
weight balancing problem in the presence of arbitrary (time-varying and
inhomogeneous) time delays that might affect transmissions at particular links.
We assume that communication between neighboring nodes is bidirectional, but
unreliable since it may be affected from bounded or unbounded delays (packet
drops), independently between different links and link directions. We show
that, even when communication links are affected from bounded delays or
occasional packet drops (but not permanent communication link failures), the
proposed distributed algorithm allows the nodes to converge to a set of weight
values that solves the integer weight balancing problem, after a finite number
of iterations with probability one, as long as the necessary and sufficient
circulation conditions on the lower and upper edge weight limits are satisfied.
Finally, we provide examples to illustrate the operation and performance of the
proposed algorithms.
|
electrics
|
3,596 |
Controlling Power and Virtual Inertia from Storage for Frequency Response
|
eess.SY
|
Nowadays, power imbalance happens more frequently due to the more integration
of renewable energy sources. Energy storage is a kind of devices that can
charge energy at one time and discharge energy at another time. This function
makes that storage is widely envolved into promoting power balance of power
system. Besides this function, storage can also emulate virtual inertia to
respond to frequency deviations in the system. This work provides a generalized
optimization framework to analyze how to control power and virtual inertia from
storage to participate in frequency response when a large disturbance happens.
Centralized and distributed model predictive control is employed here, and case
study verifies the effectiveness of our optimization framework.
|
electrics
|
3,597 |
Short-term ASV Collision Avoidance with Static and Moving Obstacles
|
eess.SY
|
This article considers collision avoidance (COLAV) for both static and moving
obstacles using the branching-course model predictive control (BC-MPC)
algorithm, which is designed for use by autonomous surface vehicles (ASVs). The
BC-MPC algorithm originally only considered COLAV of moving obstacles, so in
order to make the algorithm also be able to avoid static obstacles, we
introduce an extra term in the objective function based on an occupancy grid.
In addition, other improvements are made to the algorithm resulting in
trajectories with less wobbling. The modified algorithm is verified through
full-scale experiments in the Trondheimsfjord in Norway with both virtual
static obstacles and a physical moving obstacle. A radar-based tracking system
is used to detect and track the moving obstacle, which enables the algorithm to
avoid obstacles without depending on vessel-to-vessel communication. The
experiments show that the algorithm is able to simultaneously avoid both static
and moving obstacles, while providing clear and readily observable maneuvers.
The BC-MPC algorithm is compliant with rules 8, 13 and 17 of the the
International Regulations for Preventing Collisions at Sea (COLREGs), and
favors maneuvers following rules 14 and 15.
|
electrics
|
3,598 |
Safety Control with Preview Automaton
|
eess.SY
|
This paper considers the problem of safety controller synthesis for systems
equipped with sensor modalities that can provide preview information. We
consider switched systems where switching mode is an external signal for which
preview information is available. In particular, it is assumed that the sensors
can notify the controller about an upcoming mode switch before the switch
occurs. We propose preview automaton, a mathematical construct that captures
both the preview information and the possible constraints on switching signals.
Then, we study safety control synthesis problem with preview information. An
algorithm that computes the maximal invariant set in a given mode-dependent
safe set is developed. These ideas are demonstrated on two case studies from
autonomous driving domain.
|
electrics
|
3,599 |
Approximate Nonlinear Regulation via Identification-Based Adaptive Internal Models
|
eess.SY
|
This paper concerns the problem of adaptive output regulation for
multivariable nonlinear systems in normal form. We present a regulator
employing an adaptive internal model of the exogenous signals based on the
theory of nonlinear Luenberger observers. Adaptation is performed by means of
discrete-time system identification schemes, in which every algorithm
fulfilling some optimality and stability conditions can be used. Practical and
approximate regulation results are given relating the prediction capabilities
of the identified model to the asymptotic bound on the regulated variables,
which become asymptotic whenever a "right" internal model exists in the
identifier's model set. The proposed approach, moreover, does not require
"high-gain" stabilization actions.
|
electrics
|
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