Distributed stochastic MPC for linear systems with probabilistic constraints and quantisation. Issue 3 (1st February 2020)

• Record Type: Journal Article
• Authors: Zhao, Guanglei; Yang, Shida
• Is Part Of: IET control theory & applications. Volume 14:Issue 3(2020)
• Page Start: 396
• Page End: 404
• Publication Date: 2020-02-01
• Subjects: control nonlinearities -- stochastic systems -- stochastic processes -- predictive control -- optimal control -- linear systems -- constraint handling -- delays -- closed loop systems -- stability -- time‐varying systems -- networked control systems; distributed stochastic MPC -- linear systems -- probabilistic constraints -- distributed stochastic model predictive control algorithm design -- networked subsystems -- stochastic disturbances -- network‐induced imperfections -- time‐varying delays -- quantisation process -- probabilistic constraint handling strategy -- novel DSMPC implementation algorithm -- probabilistic distribution -- determinate constraints -- closed‐loop system; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2019.0030
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : This study investigates the distributed stochastic model predictive control (DSMPC) algorithm design for a class of networked subsystems with stochastic disturbances and coupled probabilistic constraints, differently from the existing work, the network‐induced imperfections such as time‐varying delays and quantisation process are considered. A probabilistic constraint handling strategy and a novel DSMPC implementation algorithm are presented. In addition, according to the probabilistic distribution of the uncertainties, the probabilistic constraints are transformed into a set of determinate constraints. Although the closed‐loop system is affected by the time‐varying delays and quantisation process, the quadratic stability of the closed‐loop system and the feasibility of the optimisation problem can still be guaranteed with the developed algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.