Adaptive Tracking for Periodically Time-Varying and Nonlinearly Parameterized Systems Using Multilayer Neural Networks

• Published in: IEEE transactions on neural networks Vol. 21; no. 2; pp. 345 - 351
• Main Authors: Chen, Weisheng, Jiao, Licheng
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2010; Institute of Electrical and Electronics Engineers
• Subjects: Adaptive control; Adaptive systems; Algorithms; Applied sciences; Artificial intelligence; Backstepping; Computer science; control theory; systems; Computer Simulation; Connectionism. Neural networks; Control systems; dynamic surface control (DSC); Dynamical systems; Exact sciences and technology; Feasibility Studies; Fourier Analysis; Fourier series; Fourier series expansion (FSE); integral-type Lyapunov function (ILF); Multi-layer neural network; multilayer neural network (MNN); Multilayers; Neural networks; Neural Networks (Computer); Nonlinear control systems; Nonlinear Dynamics; nonlinearly parameterized systems; periodically time-varying disturbances; Programmable control; Time Factors; Time varying systems; Tracking errors; Uncertainty; Closed loop; Backstepping; Tracking; Control synthesis; integral-type Lyapunov function (ILF); multilayer neural network (MNN); periodically time-varying disturbances; Neural network; Fourier series; Adaptive control; Modeling; Adaptive method; Time varying system; dynamic surface control (DSC); nonlinearly parameterized systems; Fourier series expansion (FSE); Distributed control; Series expansion; Tracking error; Feasibility; Lyapunov function; Multilayer network

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...