8,489 research outputs found
New advances in H∞ control and filtering for nonlinear systems
The main objective of this special issue is to
summarise recent advances in H∞ control and filtering
for nonlinear systems, including time-delay, hybrid and
stochastic systems. The published papers provide new
ideas and approaches, clearly indicating the advances
made in problem statements, methodologies or applications
with respect to the existing results. The special
issue also includes papers focusing on advanced and
non-traditional methods and presenting considerable
novelties in theoretical background or experimental
setup. Some papers present applications to newly
emerging fields, such as network-based control and
estimation
Linearly Solvable Stochastic Control Lyapunov Functions
This paper presents a new method for synthesizing stochastic control Lyapunov
functions for a class of nonlinear stochastic control systems. The technique
relies on a transformation of the classical nonlinear Hamilton-Jacobi-Bellman
partial differential equation to a linear partial differential equation for a
class of problems with a particular constraint on the stochastic forcing. This
linear partial differential equation can then be relaxed to a linear
differential inclusion, allowing for relaxed solutions to be generated using
sum of squares programming. The resulting relaxed solutions are in fact
viscosity super/subsolutions, and by the maximum principle are pointwise upper
and lower bounds to the underlying value function, even for coarse polynomial
approximations. Furthermore, the pointwise upper bound is shown to be a
stochastic control Lyapunov function, yielding a method for generating
nonlinear controllers with pointwise bounded distance from the optimal cost
when using the optimal controller. These approximate solutions may be computed
with non-increasing error via a hierarchy of semidefinite optimization
problems. Finally, this paper develops a-priori bounds on trajectory
suboptimality when using these approximate value functions, as well as
demonstrates that these methods, and bounds, can be applied to a more general
class of nonlinear systems not obeying the constraint on stochastic forcing.
Simulated examples illustrate the methodology.Comment: Published in SIAM Journal of Control and Optimizatio
Maximum Entropy/Optimal Projection (MEOP) control design synthesis: Optimal quantification of the major design tradeoffs
The underlying philosophy and motivation of the optimal projection/maximum entropy (OP/ME) stochastic modeling and reduced control design methodology for high order systems with parameter uncertainties are discussed. The OP/ME design equations for reduced-order dynamic compensation including the effect of parameter uncertainties are reviewed. The application of the methodology to several Large Space Structures (LSS) problems of representative complexity is illustrated
Stabilization of Networked Control Systems with Sparse Observer-Controller Networks
In this paper we provide a set of stability conditions for linear
time-invariant networked control systems with arbitrary topology, using a
Lyapunov direct approach. We then use these stability conditions to provide a
novel low-complexity algorithm for the design of a sparse observer-based
control network. We employ distributed observers by employing the output of
other nodes to improve the stability of each observer dynamics. To avoid
unbounded growth of controller and observer gains, we impose bounds on their
norms. The effects of relaxation of these bounds is discussed when trying to
find the complete decentralization conditions
Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems
In many Cyber-Physical Systems, we encounter the problem of remote state
estimation of geographically distributed and remote physical processes. This
paper studies the scheduling of sensor transmissions to estimate the states of
multiple remote, dynamic processes. Information from the different sensors have
to be transmitted to a central gateway over a wireless network for monitoring
purposes, where typically fewer wireless channels are available than there are
processes to be monitored. For effective estimation at the gateway, the sensors
need to be scheduled appropriately, i.e., at each time instant one needs to
decide which sensors have network access and which ones do not. To address this
scheduling problem, we formulate an associated Markov decision process (MDP).
This MDP is then solved using a Deep Q-Network, a recent deep reinforcement
learning algorithm that is at once scalable and model-free. We compare our
scheduling algorithm to popular scheduling algorithms such as round-robin and
reduced-waiting-time, among others. Our algorithm is shown to significantly
outperform these algorithms for many example scenarios
Value of Information in Feedback Control
In this article, we investigate the impact of information on networked
control systems, and illustrate how to quantify a fundamental property of
stochastic processes that can enrich our understanding about such systems. To
that end, we develop a theoretical framework for the joint design of an event
trigger and a controller in optimal event-triggered control. We cover two
distinct information patterns: perfect information and imperfect information.
In both cases, observations are available at the event trigger instantly, but
are transmitted to the controller sporadically with one-step delay. For each
information pattern, we characterize the optimal triggering policy and optimal
control policy such that the corresponding policy profile represents a Nash
equilibrium. Accordingly, we quantify the value of information
as the variation in the cost-to-go of the system given
an observation at time . Finally, we provide an algorithm for approximation
of the value of information, and synthesize a closed-form suboptimal triggering
policy with a performance guarantee that can readily be implemented
Distributed Robust Set-Invariance for Interconnected Linear Systems
We introduce a class of distributed control policies for networks of
discrete-time linear systems with polytopic additive disturbances. The
objective is to restrict the network-level state and controls to user-specified
polyhedral sets for all times. This problem arises in many safety-critical
applications. We consider two problems. First, given a communication graph
characterizing the structure of the information flow in the network, we find
the optimal distributed control policy by solving a single linear program.
Second, we find the sparsest communication graph required for the existence of
a distributed invariance-inducing control policy. Illustrative examples,
including one on platooning, are presented.Comment: 8 Pages. Submitted to American Control Conference (ACC), 201
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