Title :
Adaptive Tracking for Periodically Time-Varying and Nonlinearly Parameterized Systems Using Multilayer Neural Networks
Author :
Chen, Weisheng ; Jiao, Licheng
Author_Institution :
Dept. of Appl. Math., Xidian Univ., Xi´´an, China
Abstract :
This brief addresses the problem of designing adaptive neural network tracking control for a class of strict-feedback systems with unknown time-varying disturbances of known periods which nonlinearly appear in unknown functions. Multilayer neural network (MNN) and Fourier series expansion (FSE) are combined into a novel approximator to model each uncertainty in systems. Dynamic surface control (DSC) approach and integral-type Lyapunov function (ILF) technique are combined to design the control algorithm. The ultimate uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the feasibility of control scheme proposed in this brief.
Keywords :
Fourier series; Lyapunov methods; adaptive control; feedback; multilayer perceptrons; neurocontrollers; nonlinear control systems; time-varying systems; Fourier series expansion; adaptive tracking control; dynamic surface control; integral-type Lyapunov function; multilayer neural networks; nonlinear system; strict feedback systems; time-varying system; uniform boundedness; Backstepping; Fourier series expansion (FSE); dynamic surface control (DSC); integral-type Lyapunov function (ILF); multilayer neural network (MNN); nonlinearly parameterized systems; periodically time-varying disturbances; Algorithms; Computer Simulation; Feasibility Studies; Fourier Analysis; Neural Networks (Computer); Nonlinear Dynamics; Time Factors;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2009.2038999