Nonlinear Model Predictive Control for pH Neutralization Process based on SOMA Algorithm

Abstract

Abstract-In this work, the pH neutralization process is described by a neural network Wiener (NNW) model. A nonlinear Model Predictive Control (NMPC) is established for the considered process. The main difficulty that can be encountered in NMPC is solving the optimization problem at each sampling time to determine an optimal solution in finite time. The aim of this paper is the use of global optimization method to solve the NMPC minimization problem. Therefore, we propose in this work, to use the Self Organizing Migrating Algorithm (SOMA) to solve the presented optimization problem. This algorithm proves its efficiency to determine the optimal control sequence with a lower computation time. Then the NMPC is compared to adaptive PID controller, where we propose to use the SOMA algorithm to formulate the PID in order to determine the optimal parameters of the PID. The performances of the two controllers based on the SOMA algorithm are tested on the pH neutralization process.

pH Neutralization Process Control based on Active Disturbance Rejection Control

“…In the field of industrial applications, the PID controller is considered as the most popular controller types, but it has been unable to satisfy the growing demand for efficiency, speed, and accuracy. Nonlinear control techniques also have been applied [9][10][11][12][13][14][15][16][17][18][19][20][21]. Other works involve intelligent control technique [22,23].…”