Adaptive Neural Network Control for a Class of Nonlinear Systems With Function Constraints on States

被引：243

作者：

Liu, Yan-Jun ^{[1
]}

Zhao, Wei ^{[2
]}

Liu, Lei ^{[1
]}

Li, Dapeng ^{[3
]}

Tong, Shaocheng ^{[1
]}

Chen, C. L. Philip ^{[4
,5
]}

机构：

[1] Liaoning Univ Technol, Coll Sci, Jinzhou 121001, Peoples R China

[2] Liaoning Univ Technol, Sch Elect Engn, Jinzhou 121001, Peoples R China

[3] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

[4] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China

[5] Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Nonlinear systems; Time-varying systems; Artificial neural networks; Adaptive control; Backstepping; Tools; MIMO communication; full state constraints; neural networks (NNs); nonlinear systems; TRACKING CONTROL; PRESCRIBED PERFORMANCE; APPROXIMATION; OBSERVER; DESIGN;

D O I：

10.1109/TNNLS.2021.3107600

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this article, the problem of tracking control for a class of nonlinear time-varying full state constrained systems is investigated. By constructing the time-varying asymmetric barrier Lyapunov function (BLF) and combining it with the backstepping algorithm, the intelligent controller and adaptive law are developed. Neural networks (NNs) are utilized to approximate the uncertain function. It is well known that in the past research of nonlinear systems with state constraints, the state constraint boundary is either a constant or a time-varying function. In this article, the constraint boundaries both related to state and time are investigated, which makes the design of control algorithm more complex and difficult. Furthermore, by employing the Lyapunov stability analysis, it is proven that all signals in the closed-loop system are bounded and the time-varying full state constraints are not violated. In the end, the effectiveness of the control algorithm is verified by numerical simulation.

引用

页码：2732 / 2741

页数：10

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