Adaptive neural network asymptotical tracking control for an uncertain nonlinear system with input quantisation. Issue 9 (4th July 2018)

• Record Type: Journal Article
• Authors: Sun, Haibin; Hou, Linlin; Zong, Guangdeng
• Is Part Of: International journal of systems science. Volume 49:Issue 9(2018)
• Page Start: 1974
• Page End: 1984
• Publication Date: 2018-07-04
• Subjects: Neural network -- sliding mode differentiator -- asymptotical tracking -- input quantisation -- uncertain nonlinear system; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2018.1481240
• Languages: English
• ISSNs: 0020-7721

Abstract

Abstract : In this paper, the problem of adaptive neural network asymptotical tracking is investigated for a class of nonlinear system with unknown function, external disturbances and input quantisation. Based on neural network technique, an adaptive asymptotical tracking controller is provided for an uncertain nonlinear system via backstepping method. In order to reduce complexity of the control algorithm in the backstepping design process, a sliding mode differentiator is employed to estimate the virtual control law and only two parameters need to be estimated via adaptive control technique. The stability of the closed-loop system is analysed by using Lyapunov function method and zero-tracking error performance is obtained in the presence of unknown nonlinear function, external disturbances and input quantisation. Finally, an application example is employed to demonstrate the effectiveness of the proposed scheme.