Model-Predictive Control With Generalized Zone Tracking

Abstract

In this paper, we propose a new framework for model-predictive control (MPC) with generalized zone tracking. The proposed zone MPC tracks a generalized target set of system state and input which is not necessarily control-invariant. In this context, the classical MPC theory no longer applies because the target zone may not be stable in the sense of Lyapunov. We extend LaSalle's invariance principle and develop new theories for stability analysis of zone MPC. It is proved that under the zone MPC design, the system converges to the maximal control invariant set in the target zone. Sufficient conditions for asymptotic stability of the maximal control-invariant set are also discussed. By tracking the generalized target zone, the proposed zone MPC is able to: (i) yield smaller zone tracking errors than all existing methods which essentially track some steady-state subset of the target zone, and (ii) allow more admissible operations and release more degrees of freedom to achieve other economic objectives. Further discussions are made on extending the prediction horizon of the zone MPC based on an auxiliary control law as well as handling a secondary economic objective via a second-step economic optimization.