Advanced Policy Learning Near-Optimal Regulation

被引：0
|
作者：
Ding Wang [1 ,2 ]
Xiangnan Zhong [1 ,3 ]
机构：
[1] IEEE
[2] the Faculty of Information Technology, Beijing University of Technology, and also with the Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology
[3] the Department of Electrical Engineering, University of North Texas
来源：
IEEE/CAA Journal of Automatica Sinica | 2019年 / 6卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Adaptive critic algorithm; learning control; neural approximation; nonaffine dynamics; optimal regulation;
D O I：
暂无
中图分类号：
O232 [最优控制];
学科分类号：
070105 ; 0711 ; 071101 ; 0811 ; 081101 ;
摘要：
Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.
引用
收藏
页码：743 / 749
页数：7
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