A Periodic Approach to Dynamic Output Feedback MPC for Quasi-LPV Model

• 成果类型: Article
• 署名作者: Hu, Jianchen; Ding, Baocang
• 署名单位: Xi'an Jiaotong University; Chongqing University of Posts & Telecommunications
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3002162
• 发表日期: 2021
• 页码: 2257-2264
• 关键词: Output feedback; predictive models; uncertainty; optimization; Linear matrix inequalities; Electron tubes; Predictive control; Dynamic output feedback; linear parameter varying (LPV) model; model predictive control (MPC); periodic approach

摘要

This article considers dynamic output feedback model predictive control for quasi-linear parameter varying model with norm-bounded disturbance. The infinite horizon control moves are parameterized as the parameter-dependent dynamic output feedback (PDDOF) laws. In contrast to the traditional single-step approach where one PDDOF law is utilized, in this article a periodic approach where a sequence of PDDOF laws, which are invoked periodically in the future predictions, are applied. The regions of attraction for the periodic approach are considerably larger, and the control performance improved, as compared with the single-step approach. The recursive feasibility, and convergence of augmented state and output/input, are guaranteed. An illustrative example is given to show the effectiveness of this approach.