Optimized tracking control using reinforcement learning strategy for a class of nonlinear systems

被引:12
作者
Yang, Xue [1 ]
Li, Bin [1 ]
机构
[1] Qilu Univ Technol, Shandong Acad Sci, Sch Math & Stat, Jinan 250353, Peoples R China
基金
中国国家自然科学基金;
关键词
Lyapunov function; neural networks (NNs); nonlinear systems; optimal control; reinforcement learning (RL); CONSTRAINED OPTIMAL-CONTROL; BACKSTEPPING CONTROL; ALGORITHM; DESIGN; STATE;
D O I
10.1002/asjc.2866
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 paper is to develop a simplified optimized tracking control using reinforcement learning (RL) strategy for a class of nonlinear systems. Since the nonlinear control gain function is considered in the system modeling, it is challenging to extend the existing RL-based optimal methods to the tracking control. The main reasons are that these methods' algorithm are very complex; meanwhile, they also require to meet some strict conditions. Different with these exiting RL-based optimal methods that derive the actor and critic training laws from the square of Bellman residual error, which is a complex function consisting of multiple nonlinear terms, the proposed optimized scheme derives the two RL training laws from negative gradient of a simple positive function, so that the algorithm can be significantly simplified. Moreover, the actor and critic in RL are constructed by employing neural network (NN) to approximate the solution of Hamilton-Jacobi-Bellman (HJB) equation. Finally, the feasibility of the proposed method is demonstrated in accordance with both Lyapunov stability theory and simulation example.
引用
收藏
页码:2095 / 2104
页数:10
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