Runge–Kutta model-based adaptive predictive control mechanism for non-linear processes

Abstract

This paper proposes a novel non-linear model predictive control mechanism for non-linear systems. The idea behind the mechanism is that the so-called Runge–Kutta model of a continuous-time non-linear system can be regarded as an approximate discrete model and employed in a generalized predictive control loop for prediction and derivative calculation purposes. Additionally, the Runge–Kutta model of the system is used for state estimation in the extended Kalman filter framework and online parameter adaptation. The proposed method has been tested on two different non-linear systems. Simulation results have revealed the effectiveness of the proposed method for different cases.