Nonperiodic and Periodic Event-Triggered Online H∞ Control for Constrained Nonlinear Systems

被引:0
|
作者
Wang, Ding [1 ,2 ,3 ,4 ]
Hu, Lingzhi [1 ,2 ,3 ,4 ]
Wang, Hua [5 ]
Qiao, Junfei [1 ,2 ,3 ,4 ]
机构
[1] Beijing Univ Technol, Sch Informat Sci & Technol, Beijing 100124, Peoples R China
[2] Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
[3] Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
[4] Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
[5] Zhengzhou Univ, Sch Comp Sci & Artificial Intelligence, Zhengzhou 450001, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive dynamic programming (ADP); constrained control; neural networks; nonperiodic and periodic event-triggered control (ETC); online H-infinity control; ADAPTIVE CRITIC DESIGNS; LINEAR-SYSTEMS; TIME; ALGORITHM;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article presents two new event-triggered control (ETC) schemes based on the online critic learning technique, which aims at tackling the optimal regulation problem of discrete-time constrained nonlinear systems with the disturbance input. First, a novel stability criterion condition is designed to obtain an initial admissible policy pair by using an offline iterative method under the time-triggered control framework. Then, starting from the stability of the constrained system, a nonperiodic ETC method and a periodic ETC method are developed by adopting an online learning algorithm. In addition, four kinds of neural networks are constructed for the implementation of the event-based online H-infinity optimal control strategy. Finally, two experimental examples with physical backgrounds are provided to illustrate the effectiveness and superiority of the developed schemes.
引用
收藏
页码:331 / 343
页数:13
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