Adaptive neural control of state delayed non‐linear systems with unmodelled dynamics and distributed time‐varying delays. Issue 12 (1st August 2014)

• Record Type: Journal Article
• Authors: Zhang, Tianping; Xia, Xiaonan; Zhu, Jiaming
• Is Part Of: IET control theory & applications. Volume 8:Issue 12(2014)
• Page Start: 1071
• Page End: 1082
• Publication Date: 2014-08-01
• Subjects: adaptive control -- neurocontrollers -- nonlinear systems -- time‐varying systems -- delays -- robust control; adaptive neural control -- unmodelled dynamics -- distributed time‐varying delays -- robust adaptive control -- strict feedback state delayed nonlinear systems -- radial basis function neural networks -- dynamic uncertainties -- dynamic signal -- state time‐varying delay -- distributed time‐varying delay uncertainties -- appropriate Lyapunov‐Krasovskii functionals -- Young's inequality -- semiglobal uniform ultimate boundedness -- closed loop system; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2013.0803
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : In this study, a robust adaptive control is proposed for a class of strict‐feedback state delayed non‐linear systems with unmodelled dynamics and distributed time‐varying delays using radial basis function neural networks. Dynamic uncertainties are dealt with using separation technique and introducing a dynamic signal. The terms including state time‐varying delay and distributed time‐varying delay uncertainties are compensated for by constructing appropriate Lyapunov–Krasovskii functionals. Using Young's inequality, only one learning parameter need to be tuned online at each step of recursion. It is proved that the proposed design method is able to guarantee semi‐global uniform ultimate boundedness of all signals in the closed‐loop system. Simulation results demonstrate the effectiveness of the proposed approach.