Filtered predictive control design using multi-objective optimization based on genetic algorithm for handling offset in chemical processes

The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection.