Introducing robustness in model predictive control with multiple models and switching

Introducing robustness in model predictive control with multiple modelsand switching

Key words:  Receding horizon control  Time-varying horizon  Multiple models  Switching  Tracking  Robustness

摘要:

Abstract:

Model predictive control is model-based. Therefore, the procedure is inherently not robust to modelling uncertainties. Further, a crucial design parameter is the prediction horizon. Only offline procedures to estimate an upper bound of the optimal value of this parameter are available. These procedures are computationally intensive andmodel-based. Besides, a single choice of this horizon is perhaps not the best option at all time instants. This is especially true when the control objective is to track desired trajectories. In this paper,we resolve the issue by a time-varying horizon achieved by switching betweenmultiplemodel-predictive controllers. The stability of the overall system is discussed. In addition, an introduction of multiple models to handle modelling uncertainties makes the overall system robust. The improvement in performance is demonstrated through several examples.