A novel nonlinear model predictive control design based on a hybrid particle swarm optimization-sequential quadratic programming algorithm: application to an evaporator system

Abstract

This paper proposes a hybrid algorithm by combining particle swarm optimization (PSO) with sequential quadratic programming (SQP) to handle the optimization part of the nonlinear model predictive control (NMPC). In the proposed method, a nonlinear model of the plant is directly applied to predict the future behaviour of the system for a certain horizon. In each sampling time a constrained nonlinear optimization (CNO) problem has to be solved in order to find the optimizing input sequence with predefined length. These solutions minimize the errors between the predicted states and the desired ones of the system. Only the first optimizing input is applied to the system and the rest of the sequence is discarded. The whole prediction and optimization must be repeated for the next upcoming steps. The evolutionary and heuristic PSO algorithm in cooperation with the powerful local minimizing SQP algorithm can quickly find these optimizing solutions and also can satisfy the constrains. The evaporation process due to its nonlinear dynamics and the existence of disturbances affecting the evaporator is considered to evaluate the performance of the proposed method. The simulation results show that the controller designed via the proposed approach can make the system track the reference commands and satisfy the restrictions properly. Moreover, based on the simulation results it can be seen that this approach is more efficient in comparison with the NMPC method, in which only the SQP algorithm has been utilized for optimization, and also the linear model predictive control method.