Pareto Optimal Tuning of Adaptive PID Controller using Model Predictive Strategy

In this paper, the gains of PID controller are obtained online using Model Predictive Control (MPC). In fact MPC tries to tune the PID-controller parameters by predicting system’s behavior in some time steps ahead. In this way, the nonlinear differential equation of system is approximated by a linear polynomial with unknown parameters. These unknown parameters are obtained using genetic algorithm to minimize the deviation between the real plant and approximated model. Moreover, multi-objective approach has been used to capture the parameters of MPC which are prediction horizon, control horizon and weight factor to minimize simultaneously two objective functions that are control effort and Integral time absolute error (ITAE) of the system response. Results mentioned at the end, obviously declare that the proposed method surpasses conventional MPC and PID-tuning method.