Adaptive neural output feedback tracking control for a class of nonlinear systems. Issue 11 (18th August 2019)

• Record Type: Journal Article
• Authors: Han, Yu-Qun; Zhu, Shan-Liang; Duan, De-Yu; Yang, Shu-Guo
• Is Part Of: International journal of systems science. Volume 50:Issue 11(2019)
• Page Start: 2088
• Page End: 2101
• Publication Date: 2019-08-18
• Subjects: Neural network -- nonlinear system -- unmeasurable states -- adaptive tracking control -- dynamic surface control; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2019.1645918
• Languages: English
• ISSNs: 0020-7721

Abstract

Abstract : In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.