Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems

Authors

Jing Li, College of Weapons Engineering, Naval University of Engineering, WuHan 430033, ChinaFollow
Taotao Zhang, College of Weapons Engineering, Naval University of Engineering, WuHan 430033, China
Kai Jin, College of Weapons Engineering, Naval University of Engineering, WuHan 430033, ChinaFollow
Shengzhi Yuan, College of Weapons Engineering, Naval University of Engineering, WuHan 430033, China
Zilong Zha, College of Weapons Engineering, Naval University of Engineering, WuHan 430033, China

Abstract

Abstract: A time-varying RBF neural network with time-varying properties is firstly proposed, and its approximation theorem is obtained. For a class of nonlinear systems with non-parametric time-varying uncertainties, the proposed time-varying RBF neural network is used to approximate the time-varying uncertainties, and the controller is designed by making use of Lyapunov stability theory and adaptive iterative learning control techniques. We obtain the stability theorem of the designed controller. The simulation results verify the effectiveness of the time-varying neural network and the correctness of the controller design scheme.

Recommended Citation

Li, Jing; Zhang, Taotao; Jin, Kai; Yuan, Shengzhi; and Zha, Zilong (2023) "Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems," Journal of System Simulation: Vol. 35: Iss. 10, Article 14.
DOI: 10.16182/j.issn1004731x.joss.23-FZ0796E
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss10/14

First Page

2223

Last Page

2236

CLC

TP273

Recommended Citation

Li Jing, Zhang Taotao, Jin Kai, et al. Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems[J]. Journal of System Simulation, 2023, 35(10): 2223-2236.

Corresponding Author

Jin kai

DOI

10.16182/j.issn1004731x.joss.23-FZ0796E