EVENT-TRIGGERED OPTIMAL CONTROL OF COMPLETELY UNKNOWN NONLINEAR SYSTEMS VIA IDENTIFIER-CRITIC LEARNING

被引：1

作者：

Peng, Zhinan ^{[1
,2
]}

Zhang, Zhiquan ^{[3
]}

Luo, Rui ^{[1
]}

Kuang, Yiqun ^{[1
]}

Hu, Jiangping ^{[1
,4
]}

Cheng, Hong ^{[1
]}

Ghosh, Bijoy Kumar ^{[1
,5
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China

[2] Univ Elect Sci & Technol China, Inst Elect & Informat Engn, Dongguan 523808, Peoples R China

[3] Univ Penn, Sch Engn & Appl Sci, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA

[4] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst Huzhou, Huzhou 313001, Peoples R China

[5] Texas Tech Univ, Dept Math & Stat, Lubbock, TX 79409 USA

来源：

KYBERNETIKA | 2023年 / 59卷 / 03期

基金：

中国博士后科学基金;

关键词：

optimal control; unknown nonlinear system; adaptive dynamic programming; identifier-critic neural networks; event-triggered mechanism; MULTIAGENT SYSTEMS; TRACKING;

D O I：

10.14736/kyb-2023-3-0365

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes an online identifier-critic learning framework for event-triggered optimal control of completely unknown nonlinear systems. Unlike classical adaptive dynamic programming (ADP) methods with actor-critic neural networks (NNs), a filter-regression-based approach is developed to reconstruct the unknown system dynamics, and thus avoid the dependence on an accurate system model in the control design loop. Meanwhile, NN adaptive laws are designed for the parameter estimation by using only the measured system state and input data, and facilitate the identifier-critic NN design. The convergence of the adaptive laws is analyzed. Furthermore, in order to reduce state sampling frequency, two kinds of aperiodic sampling schemes, namely static and dynamic event triggers, are embedded into the proposed optimal control design. Finally, simulation results are presented to demonstrate the effectiveness of the proposed event-triggered optimal control strategy.

引用

页码：365 / 391

页数：27