Decentralized state feedback control for interconnected systems with application to power systems

2016 IEEE 55th Conference on Decision and Control (CDC)

et al. 2016

This paper is devoted to the problem of designing a sparsely distributed sliding mode control for networked systems. Indeed, this note employs a distributed sliding mode control framework by exploiting (some of) other subsystems' information to improve the performance of each local controller so that it can widen the applicability region of the given scheme. To do so, different from the traditional schemes in the literature, a novel approach is proposed to design the sliding surface, in which the level of required control effort is taken into account during the sliding surface design based on the H 2 control. We then use this novel scheme to provide an innovative less-complex procedure that explores sparse control networks to satisfy the underlying control objective. Besides, the proposed scheme to design the sliding surface makes it possible to avoid unbounded growth of control effort during the sparsification of the control network structure. Illustrative examples are presented to show the effectiveness of the proposed approach. keywords Networked control systems, H 2 -based optimal sparse sliding mode control, distributed control systems, linear matrix inequality.

Section: )mentioning

confidence: 99%

“…Recently, the issue of designing a control network with minimum communication links has been studied in the literature [21,23,24,22,14,15,16]. As an illustration, [14] proposes a non-convex condition which is solved numerically by exploiting a convex reweighted 1 norm approximation.…”

Section: Introductionmentioning

confidence: 99%

ℋ<inf>2</inf>-based optimal sparse sliding mode control for networked control systems

2016 IEEE 55th Conference on Decision and Control (CDC)

et al. 2016

“…Another alternative to avoid solving a combinatorial problem is to consider a multi-objective problem of controller structure and control law co-design by incorporating secondary cost functions, which promotes sparsity of the controller, into a main cost function, which represents a performance specification of the closed-loop system (see e.g. Lin, Fardad, andJovanovic (2011) andSchuler, Münz, andAllgöwer (2014)). …”

Section: Introductionmentioning

confidence: 99%

A framework for optimal actuator/sensor selection in a control system

International Journal of Control

Savkin

et al. 2017

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 closedloop 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 multiobjective 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.keywords Simultaneous actuator/sensor selection, regularisation, row/columnwise sparsification, linear matrix inequality, dynamic output feedback.

“…[9] and [10]) is to solve e.g. the H 2 (or the H ∞ ) problem, by incorporating a sparsity promoting penalty function to the objective function.…”

Section: Introductionmentioning

confidence: 99%

Optimal actuator/sensor selection through dynamic output feedback

2016 IEEE 55th Conference on Decision and Control (CDC)

Savkin

2016

Abstract-This paper is devoted to the problem of optimal selection of a subset of available actuators/sensors through a multi-channel H 2 dynamic output feedback controller for continuous linear time invariant systems. Incorporating two extra terms for penalizing the number of actuators and sensors into the optimization objective function, we develop an iterative process to identify the favorable row/column-wise sparse DOF gains. Employing the identified structure, we solve the constructed row/column structured multi-channel H 2 DOF problem in order to derive a gain that exploits optimum number of sensors/actuators by which the closed-loop stability is maintained and the performance degradation of the closedloop system is restricted. Through an example we demonstrate the remarkable performance and broad applicability of the proposed approach.Index Terms-Simultaneous actuator/sensor selection, multichannel H 2 row-column-sparse dynamic output feedback (DOF) problem, linear matrix inequality. I. INTRODUCTIONThe number of components (actuators or sensors) in modern control systems can be very large, and hence, it is often not very feasible to manually find a subset of all available components to meet a specific control objective. Hence, the problem of selecting a configuration of actuators (sensors) from the set of all available actuators (sensors), while the control performance remains in an acceptable level compared to the non-sparse performance, is a well-known problem in the literature of control theory; see e.g. [1]-[6]. This problem can equivalently be considered as the design of a row (column) sparse feedback gain for the underling system. This paper aims to develop a unified framework to systematically design a sparse row-wise and columnwise dynamic output feedback (DOF) gain via convex optimization, while satisfying multi-channel H 2 performance specifications. One immediate application of this issue will be in the over-actuated (over-sensed) systems [7]. It is known that one method for reconfiguration strategy of fault tolerant control is usually to build an over-actuated (over-sensed) system first and then design a nominal controller using some of the available components. Hence whenever a fault happens in the system, the configuration of the control system is changed by using some of the redundant components in order to attain the nominal control objective [8].This problem leads to a difficult combinatorial optimization problem. There are a large number of investigations in