A Controller Design based on Iterative Learning method and Model Predictive Control for a nonlinear process system

Abstract

This paper proposes a controller using model predictive control and iterative learning control algorithm for a class of nonlinear process. A predictive control model computes system outputs, and accurate prediction is the desired purpose. Model predictive controller relies on dynamic models of a system, linear empirical models are mostly derived from system identification. In order to control a system accurately, an accurate model is needed. These control purposes can be achievable by using iterative learning algorithm. This type of modeling is applied for the first time in this paper for a nonlinear process. Iterative learning algorithm improves the performance of the processes that perform the same motion or operation repeatedly. This proposed method is capable of improving the accuracy of a model and the robustness of this method in presence of repetitive disturbance. The rejection of repetitive disturbance and deterministic modeling error which caused from repetitive disturbances by iterative learning algorithm has been proved. The proposed method is used for controlling liquid level in two-tank. The simulation results show the effectiveness of the proposed method.