Quasi-Linear Recurrent Neural Network based Identification and Predictive Control

Abstract

In this paper, aiming at the cumbersome solution of control law in neural network predictive control algorithm, a quasi-linear neural network identification and predictive control algorithm is proposed. The recurrent neural network is embedded into the quasi-linear model, which can be viewed as a quasi-ARX model macroscopically. In the quasi-linear recurrent neural network predictive control, the solution of the control law only need one-step derivation, which can greatly simplify the solution process of control law. At the same time, the quasi-linear recurrent neural network can effectively restrain the over-fitting problem in the identification process. Theoretical analysis and simulations are given to prove the simplicity and effectiveness of the proposed computing method.