Title :
Adaptive neural control for a class of nonlinearly parametric time-delay systems
Author :
Ho, Daniel W C ; Li, Junmin ; Niu, Yugang
Author_Institution :
Dept. of Math., City Univ. of Hong Kong, China
fDate :
5/1/2005 12:00:00 AM
Abstract :
In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.
Keywords :
Lyapunov methods; adaptive control; closed loop systems; control nonlinearities; delay systems; neurocontrollers; nonlinear control systems; state feedback; uncertain systems; wavelet transforms; adaptive closed loop system; adaptive neural control; integral type Lyapunov Krasovskii functional; state feedback; time delay nonlinear system; unknown nonlinearities; wavelet neural network online approximation; Adaptive control; Adaptive systems; Backstepping; Control systems; Linear feedback control systems; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear systems; Programmable control; Adaptive neural control; nonlinear time-delay system; wavelet neural network (WNN); Algorithms; Computer Simulation; Feedback; Linear Models; Neural Networks (Computer); Nonlinear Dynamics; Numerical Analysis, Computer-Assisted; Signal Processing, Computer-Assisted; Stochastic Processes;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.844907
