Model Predictive Control with Multi-Objective Cost Function considering Stabilization and Linear Cost Optimization

Almost all control problem are essentially multi-objective optimization problems which include such items as closed loop stabilization and economic cost optimization As a dynamic optimizer for the process operation, a new Model Predictive Control (MPC) algorithm based on the multi-objective optimization problem is formulated and analyzed. It uses an extended cost function including both conventional penalty terms for control errors or fluctuations, and newly introduced linear cost terms for output and/or input variables. The effectiveness of the proposed method, from the viewpoint of economic cost, is confirmed with some simulation studies, especially in the case of rapid change of the process operating conditions.