Model-Predictive Control for Markovian Jump Systems Under Asynchronous Scenario: An Optimizing Prediction Dynamics Approach

• 成果类型: Article
• 署名作者: Zhang, Bin; Song, Yan
• 署名单位: University of Shanghai for Science & Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3164832
• 发表日期: 2022
• 页码: 4900-4907
• 关键词: Heuristic algorithms; Perturbation methods; optimization; Hidden Markov models; predictive models; Prediction algorithms; uncertainty; Asynchronous modes; hidden Markov models (HMMs); Markovian jump systems (MJSs); mean-square stability; model-predictive control (MPC); optimizing prediction dynamics (OPD)

摘要

This article is concerned with the model-predictive control (MPC) problem based on optimizing prediction dynamics (OPD) for a class of discrete-time Markovian jump systems with polytopic uncertainties and hard constraints, where a hidden Markov model is constructed to tackle the asynchronous problem between the system modes and the controller modes. A new MPC strategy with OPD is put forward to achieve a nice tradeoff among the online computation burden, the initial feasible region, and the control performance. The main idea of the proposed strategy is twofold: 1) the terminal constraint set and the corresponding detected-mode-dependent state feedback gain are determined by an offline min-max problem and 2) a dynamic perturbation is introduced into the control law to enlarge the feasible region, where estimator gains are derived offline with the aid of the matrix factorization technique, and the dynamic controller state is designed online to steer the system state belonging to the initial feasible region into the terminal constraint set. Finally, a simulation example regarding the dc motor device system is provided to validate the effectiveness of the proposed method.