A Novel Model Predictive Runge–Kutta Neural Network Controller for Nonlinear MIMO Systems

Abstract

In this paper, a novel model predictive Runge–Kutta neural network (RK-NN) controller based on Runge–Kutta model is proposed for nonlinear MIMO systems. The proposed adaptive controller structure incorporates system model which provides to approximate the K-step ahead future behaviour of the controlled system, nonlinear controller where Runge–Kutta neural network (RK-NN) controller is directly deployed and adjustment mechanism based on Levenberg–Marquardt optimization method so as to optimize the weights of the Runge–Kutta neural network (RK-NN) controller. RBF neural network is employed as constituent network in order to identify the changing rates of the controller dynamics. So, the learning ability of RBF neural network and Runge Kutta integration method are combined in the MIMO nonlinear controller block. The control performance of the proposed MIMO RK-NN controller has been examined via simulations performed on a nonlinear three tank system and Van de Vusse benchmark system for different cases, and the obtained results indicate that the RK-NN controller and Runge–Kutta model achieve good control and modeling performances for nonlinear MIMO dynamical systems.