Adaptive output-feedback control for a class of nonlinear systems based on optimized backstepping technique

被引:4
作者
Shen, Fei [1 ]
Wang, Xinjun [2 ]
Li, Haotian [1 ]
Yin, Xinghui [1 ]
机构
[1] Hohai Univ, Sch Comp & Informat, Nanjing, Peoples R China
[2] Shandong Normal Univ, Coll Informat Sci & Engn, Jinan 250000, Peoples R China
关键词
actor-critic architecture; neural networks (NNs); optimized backstepping (OB); output-feedback control; reinforcement learning (RL);
D O I
10.1002/acs.3397
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, an adaptive output-feedback control for a class of strict-feedback nonlinear systems is developed based on optimized backstepping technique. Neural networks are utilized to approximate unknown functions, while a state observer is designed to estimate the unmeasurable system state signals. Since the presented optimized control scheme requires training the adaptive parameters for reinforcement learning (RL), it will be more challenging for designing control algorithms and deriving the adaptive update rates. In general, optimization control is designed based on the solution of Hamilton-Jacobi-Bellman equation, but solving the equation is very difficult due to the inherent nonlinearity and intractability. So, RL strategy of actor-critic architecture is used. According to the Lyapunov stability theory, it is proved that all signals of the closed-loop systems are semi-global uniformly ultimately bounded. Finally, the results of the simulation cases are provided to show the effectiveness of the designed controller scheme.
引用
收藏
页码:1077 / 1097
页数:21
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