A new input/output constrained model predictive control with frequency domain tuning technique and its application to an ethylene plant

Recently model predictive control (MPC) has attracted attention as a practical process control technique. In this paper, we outline the features of MPC and propose a new MPC method derived from the modification of generalized predictive control (GPC). Firstly, a Kalman filter based predictor is introduced in order to improve the robustness of the predictor against noises. Secondly, a time-dependent weighting factor is introduced into the MPC´s quadratic type cost function, in order to improve the transient response characteristics. Furthermore, the cost function is extended by adding a new term related to the reference tracking error of the manipulation variables. Thirdly, a parameter tuning method is proposed that adjusts the weighting factors in the cost function considering robust stability of the control system. Finally, the proposed MPC method with and without constraint conditions that are the upper/lower limits and rate limits for both manipulation variables and process control variables, is formulated. An application study of the MPC method to an ethylene plant´s dynamic simulator is described