A Runge-Kutta Model Based Adaptive Predictive Controller for Nonlinear Systems

The aim of this paper is to illustrate the application of the previously proposed [1] Runge-Kutta (RK) model based nonlinear model predictive control method to the Continuously Stirred Tank Reactor (CSTR) system. The method utilizes the so-called Runge-Kutta model of a continuous-time nonlinear system as an approximate discrete model and employes it in the Model Predictive Control (GPC) loop. In the proposed method, the RK model is not only used for control purposes, but also it is utilized for state estimation in the Extended Kalman Filter (EKF) framework and on-line parameter adaptation. The results show the effectiveness of the method in control, state estimation and parameter adaptation tasks.