Adaptive backstepping tracking control of a car with n trailers based on RBF neural network. Issue 2 (22nd October 2019)

• Record Type: Journal Article
• Authors: Jin, Zengke; Liang, Zhenying; Guo, Pengfei; Zheng, Mingwen
• Is Part Of: Asian journal of control. Volume 23:Issue 2(2021)
• Page Start: 824
• Page End: 834
• Publication Date: 2019-10-22
• Subjects: adaptive -- backstepping -- mobile robot -- RBF neural network -- tracking control; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.2255
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: An adaptive control scheme based on a radial basis function neural network (RBFNN) is proposed for the kinematic model of a car with n trailers. Firstly, a chained system for a kinematic model of a car with n trailers is introduced, then the tracking error system is given. Secondly, a RBFNN controller is designed using the backstepping method and adaptive method, in which neural networks (NNs) are used to approximate the unknown functions in the error system. By using Lyapunov stability theory, the adaptive law of network weights is designed. This method ensures the stability of the error system. Simulation results are provided to verify the effectiveness of the approach.