Robust MPC Tuning by Quadratic Weights Online Estimation of the Cost Function through Extended Kalman Filter

The following paper presents an improved self-tuned Model-based Predictive Controller (MPC) with constraints through an Extended Kalman Filter (EKF). The algorithm is tested in a Continuous Stirred Tank Reactor (CSTR) process for temperature and molar concentration regulations. The proposed method was developed from a previous work [1], where the MPC was tuned from error and control weights of equal value. A posterior analysis demonstrated that the MPC tuning was not robust. Sometimes it generated negative weight values due to the EKF convergence and the algorithm cannot always assure a global minimum value at performance index. Therefore, this work considers this problem and always generates positive weights which assures a minimum global value in the cost function of the MPC.