predictive control with probability constraints for linear stochastic systems

作     者：Jiwei Li Dewei Li Yugeng Xi 

作者机构：Department of Automation Shanghai Jiao Tong University Shanghai 200240 People's Republic of China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai 200240 People's Republic of China 

出 版 物：《IET Control Theory & Applications》 (IET控制论与应用)

年 卷 期：2017年第11卷第4期

页      面：557-566页

学科分类：0808[工学-电气工程] 08[工学] 

主　　题：H∞ control predictive control linear systems stochastic systems closed loop systems probability linear matrix inequalities linear matrix inequalities disturbed stochastic systems probabilistic invariance multistep sets closed-loop H∞ performance stochastic model predictive control linear stochastic systems probability constraints H∞ predictive control 

摘      要：This study develops stochastic model predictive control that guarantees the recursive feasibility of the closed-loop performance. Multi-step sets with scaling parameters are proposed to contain the uncertain system trajectory. Meanwhile, by extending the probabilistic invariance to disturbed stochastic systems, we formulate probabilistic constraints as linear matrix inequalities. We show that the introduced scaling parameters enhance the feasibility of predictive control and reduce the conservatism of the constraint satisfaction. The designed control algorithm is recursively feasible and stabilises the system in the mean-square sense. A simplified algorithm further reduces much computational burden and makes the proposed approach more practical.