Adaptive tracking for periodically time-varying and nonlinearly parameterized systems using multilayer neural networks.
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.
Chen
Weisheng
W
Department of Applied Mathematics, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China. wshchen@126.com
Jiao
Licheng
L
D000465:Algorithms; D003198:Computer Simulation; D005240:Feasibility Studies; D005583:Fourier Analysis; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors