Adaptive Neural Network Dynamic Surface Control for Perturbed Nonlinear Time-delay Systems

作     者：Geng Ji 

作者机构：School of Mathematics and Information Engineering Taizhou University Linhai 317000 PRC 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2012年第9卷第2期

页      面：135-141页

核心收录：

学科分类：080801[工学-电机与电器] 0808[工学-电气工程] 08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 080201[工学-机械制造及其自动化] 

主　　题：Adaptive control dynamic surface control neural network nonlinear time delay system stability analysis. 

摘      要：This paper proposes an adaptive neural network control method for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function neural networks are used to approximate unknown intermediate control signals. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control technique is used to overcome the problem of ＂explosion of complexity＂ in backstepping design procedure. In addition, the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system is proved. A main advantage of the proposed controller is that both problems of ＂curse of dimensionality＂ and ＂explosion of complexity＂ are avoided simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the approach.