17,040 research outputs found
A framework for optimal actuator/sensor selection in a control system
© 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group. When dealing with large-scale systems, manual selection of a subset of components (sensors/actuators), or equivalently identification of a favourable structure for the controller, that guarantees a certain closed-loop performance, is not very feasible. This paper is dedicated to the problem of concurrent optimal selection of actuators/sensors which can equivalently be considered as the structure identification for the controller. In the context of a multi-channel H 2 dynamic output feedback controller synthesis, we formulate and analyse a framework in which we incorporate two extra terms for penalising the number of actuators and sensors into the variational formulations of controller synthesis problems in order to induce a favourable controller structure. We then develop an explicit scheme as well as an iterative process for the purpose of dealing with the multi-objective problem of controller structure and control law co-design. It is also stressed that the immediate application of the proposed approach lies within the fault accommodation stage of a fault tolerant control scheme. By two numerical examples, we demonstrate the remarkable performance of the proposed approach
Self-Tuning Network Control Architectures with Joint Sensor and Actuator Selection
We formulate a mathematical framework for designing a self-tuning network
control architecture, and propose a computationally-feasible greedy algorithm
for online architecture optimization. In this setting, the locations of active
sensors and actuators in the network, as well as the feedback control policy
are jointly adapted using all available information about the network states
and dynamics to optimize a performance criterion. We show that the case with
full-state feedback can be solved with dynamic programming, and in the
linear-quadratic setting, the optimal cost functions and policies are piecewise
quadratic and piecewise linear, respectively. Our framework is extended for
joint sensor and actuator selection for dynamic output feedback control with
both control performance and architecture costs. For large networks where
exhaustive architecture search is prohibitive, we describe a greedy heuristic
for actuator selection and propose a greedy swapping algorithm for joint sensor
and actuator selection. Via numerical experiments, we demonstrate a dramatic
performance improvement of greedy self-tuning architectures over fixed
architectures. Our general formulation provides an extremely rich and
challenging problem space with opportunities to apply a wide variety of
approximation methods from stochastic control, system identification,
reinforcement learning, and static architecture design for practical
model-based control.Comment: 12 pages, submitted to IEEE-TCNS. arXiv admin note: text overlap with
arXiv:2301.0669
On Submodularity and Controllability in Complex Dynamical Networks
Controllability and observability have long been recognized as fundamental
structural properties of dynamical systems, but have recently seen renewed
interest in the context of large, complex networks of dynamical systems. A
basic problem is sensor and actuator placement: choose a subset from a finite
set of possible placements to optimize some real-valued controllability and
observability metrics of the network. Surprisingly little is known about the
structure of such combinatorial optimization problems. In this paper, we show
that several important classes of metrics based on the controllability and
observability Gramians have a strong structural property that allows for either
efficient global optimization or an approximation guarantee by using a simple
greedy heuristic for their maximization. In particular, the mapping from
possible placements to several scalar functions of the associated Gramian is
either a modular or submodular set function. The results are illustrated on
randomly generated systems and on a problem of power electronic actuator
placement in a model of the European power grid.Comment: Original arXiv version of IEEE Transactions on Control of Network
Systems paper (Volume 3, Issue 1), with a addendum (located in the ancillary
documents) that explains an error in a proof of the original paper and
provides a counterexample to the corresponding resul
LQG Control and Sensing Co-Design
We investigate a Linear-Quadratic-Gaussian (LQG) control and sensing
co-design problem, where one jointly designs sensing and control policies. We
focus on the realistic case where the sensing design is selected among a finite
set of available sensors, where each sensor is associated with a different cost
(e.g., power consumption). We consider two dual problem instances:
sensing-constrained LQG control, where one maximizes control performance
subject to a sensor cost budget, and minimum-sensing LQG control, where one
minimizes sensor cost subject to performance constraints. We prove no
polynomial time algorithm guarantees across all problem instances a constant
approximation factor from the optimal. Nonetheless, we present the first
polynomial time algorithms with per-instance suboptimality guarantees. To this
end, we leverage a separation principle, that partially decouples the design of
sensing and control. Then, we frame LQG co-design as the optimization of
approximately supermodular set functions; we develop novel algorithms to solve
the problems; and we prove original results on the performance of the
algorithms, and establish connections between their suboptimality and
control-theoretic quantities. We conclude the paper by discussing two
applications, namely, sensing-constrained formation control and
resource-constrained robot navigation.Comment: Accepted to IEEE TAC. Includes contributions to submodular function
optimization literature, and extends conference paper arXiv:1709.0882
Performance bounds for optimal feedback control in networks
Many important complex networks, including critical infrastructure and
emerging industrial automation systems, are becoming increasingly intricate
webs of interacting feedback control loops. A fundamental concern is to
quantify the control properties and performance limitations of the network as a
function of its dynamical structure and control architecture. We study
performance bounds for networks in terms of optimal feedback control costs. We
provide a set of complementary bounds as a function of the system dynamics and
actuator structure. For unstable network dynamics, we characterize a tradeoff
between feedback control performance and the number of control inputs, in
particular showing that optimal cost can increase exponentially with the size
of the network. We also derive a bound on the performance of the worst-case
actuator subset for stable networks, providing insight into dynamics properties
that affect the potential efficacy of actuator selection. We illustrate our
results with numerical experiments that analyze performance in regular and
random networks
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