Model Reference Adaptive Control Based on Neural Network

摘要

In this paper, an approach to model reference adaptive control based on neural networks is pro- posed for a class of nonlinear dynamical systems. The controller structure can employ a radial basis function network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using σ-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neigh-borhood of zero, whose size is evaluated and depends on the approximation error of the neural network. Simulation results showing the feasibility and performance of the proposed approach are given.