Event-Triggered Robust MPC With Terminal Inequality Constraints: A Data-Driven Approach

• 成果类型: Article
• 署名作者: Deng, Li; Shu, Zhan; Chen, Tongwen
• 署名单位: University of Alberta
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3357417
• 发表日期: 2024
• 页码: 4773-4780
• 关键词: Optimization; trajectory; predictive models; mathematical models; Stochastic processes; Stability criteria; COSTS; Data-driven control; event-triggered robust MPC; input-to-state stability; terminal inequality constraint

摘要

An event-triggered robust model predictive control (MPC) design is proposed for unknown systems using initially measured input-output data. A terminal inequality constraint is developed for the MPC optimization problem without any prior identification, resulting in a larger feasible region and a lower bound for the prediction horizon when compared with a terminal equality constraint. An event-triggered scheme associated with a local controller is designed to trigger the solution of the data-driven MPC optimization problem when necessary, leading to the reduction of resource consumption. Under mild conditions, recursive feasibility and input-to-state stability are guaranteed theoretically. Simulation results are provided to show the effectiveness of the proposed approach.