STRONG FEASIBILITY IN INPUT-MOVE-BLOCKING MODEL PREDICTIVE CONTROL

摘要

Time-invariant input-move-blocking regimes are used in many practical online model predictive control systems in order to reduce the computational complexity of the associated finite-horizon optimal control problem, and have been shown to be beneficial for offline model predictive control methods also. However, until now there exists no method to ensure strong feasibility. In this paper a least-restrictive method to enforce strong feasibility in time-invariant input-move-blocking model predictive control problems is proposed, where the state of the first prediction step is constrained to a novel type of controlled invariant set, called here a controlled invariant feasible set. An algorithm to determine maximal controlled invariant feasible sets is proposed. This algorithm is shown to be semi-decidable for the case of linear, time-invariant plants with time-invariant, polytopic state and control input constraint sets.