A Study on Model Reference Adaptive Control Using Neural Networks.

概要

論文の詳細を見る
In this paper, a new scheme to control nonlinear systems is proposed. A back-propagation neural network is applied to a nonlinear model reference adaptive control. Traditional model reference adaptive control techniques can only deal with linear systems or some special nonlinear systems. The back-propagation neural networks have the capability to learn arbitrary nonlinearity and are easy to apply to adaptive control applications. But, to construct a controller by a only neural network is very hard, because the charge of neural network is large and the learning speed of neural network become very slowly. Thus, for reducing the charge of neural network a scheme combining back-propagation neural networks with traditional model reference adaptive control techniques is proposed. Traditional model reference adaptive control is mainly employed to control the linearity of system, and the back-propagation neural network is employed as compensation of nonlinearity. Here, the employing traditional model reference adaptive control technique is dead-zone method. Moreover, in this paper the trasient response of the system become better by setting norm upper bound of a neural network compensator. Simulation results show that this scheme is validity.
公益社団法人計測自動制御学会の論文