Adaptive neural control for a class of nonlinearly parametric time-delay systems.
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.
Ho
Daniel W C
DW
Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong. madaniel@cityu.edu.hk
Li
Junmin
J
Niu
Yugang
Y
D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes