Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics

Authors:
Dan Wang;Jialiang Huang;Weiyao Lan;Xiaoqiang Li
Affiliations:
Marine Engineering College, Dalian Maritime University, Dalian 116026, PR China;Dalian Maritime University, Dalian 116026, PR China and Jimei University, Xiamen 361021, PR China;Department of Automation, Xiamen University, Xiamen 361005, PR China;Dalian Maritime University, Dalian 116026, PR China
Venue:
Mathematics and Computers in Simulation
Year:
2009

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Abstract

A neural network-based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input-output models with an unknown nonlinear function and unmodeled dynamics. By on-line approximating the unknown nonlinear functions and unmodeled dynamics by radial basis function (RBF) networks, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. It is proved that with the proposed control law, the closed-loop system is stable and the tracking error converges to zero in the presence of unmodeled dynamics and unknown nonlinearity. A simulation example is presented to demonstrate the method.