终端(电信)
计算机科学
控制理论(社会学)
不平等
事件(粒子物理)
鲁棒控制
模型预测控制
数学
数学优化
控制(管理)
控制系统
工程类
人工智能
物理
计算机网络
数学分析
电气工程
量子力学
作者
Li Deng,Zhan Shu,Tongwen Chen
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-8
标识
DOI:10.1109/tac.2024.3357417
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI