Adaptive Dynamic Surface Neural Control of Pure-Feedback Nonlinear Time-Delay Systems
This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function theorem and mean value theorem overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid the explosion of complexity in the backstepping design. The unknown time-delay functions are compensated for using Lyapunov-Krasovskii functionals, RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced signi.cantly, and semi-global uniform ultimate boundedness of all the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed scheme.
Pure-Feedback Systems Nonlinear Time-Delay Systems Dynamic Surface Control Neural Network Adaptive Control
Min Wang
College of Automation, South China University of Technology, Guangzhou 510641, China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
2031-2036
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）