Model Predictive Control of Linear Time-Varying Systems with Bounded Disturbances Using a Dynamic Terminal Policy

This paper presents a computationally efficient model predictive control algorithm applicable for linear time varying systems with bounded disturbances. The proposed algorithm is based on the use of an optimized dynamic terminal control law that makes a good control performance possible with an online optimization over relatively smaller horizon lengths. In particular, the use of a dynamic terminal policy allows the optimization of the terminal disturbance invariant feasible set offline so that an upper bound of the terminal cost along with the finite horizon cost can be minimized online with reduced computations. Simulation results show that the proposed control scheme ensures a good performance with significantly reduced online computational complexity as compared to other schemes that use a static feedback control as the terminal control law.