Directionality of Model Predictive Control and Its Effect on the Stability of Unstable Systems

Existing methods of designing stable predictive controller fail to stabilize the closed loop system for unstable processes even though the required conditions are satisfied. We show that the main reason is the decrease of directionality of the predictive model (and not the model itself) in the controller. In consequence, a MPC control design method is proposed in which some of the cost function weightings are used to compensate directionality. The remaining weightings are set to guarantee the stability of the closed loop system. By studying different examples it is shown that using the proposed method, the closed loop stability of minimum or non-minimum phase processes with repetitive unstable poles are achieved.