Radial basis function 회로망을 이용한 새로운 신경망 선형화 제어구조

Abstract

To control nonlinear chemical processes, a new neural linearizing control scheme is proposed. This is a hybrid of a radial basis function(RBF) network and a linear controller, thus the control action applied to the process is the sum of both control actions. Firstly, to train the RBF network a linear reference model is determined by analyzing the past operating data of the process. Then, the training of the RBF network is iteratively performed to minimize the diggerence between outputs of the process and the linear reference model. As a result, the apparent dynamics of the process added by the RBF network becomes similar to that of the linear reference model. After training, the orifinal nonlinear control provlem changes to a linear one, and the closed-loop control performance is improved by using the optimum tuning parameters of the linear controller for the linear dynamics. The proposed control scheme performs control and training simultaneously, and shows a good control performance for nonlinear chemical processes.