Feasibility in predictive control of constrained linear systems: the output feedback case

This paper addresses model-based predictive regulation for a linear discrete-time system in the presence of unknown but bounded disturbances, partial state information and state/control constraints. The proposed nonlinear dynamic compensator uses a set-valued estimator, which recursively updates the membership set of the plant state, along with a receding-horizon regulator which selects on-line the control variable depending upon the current state membership set. It is shown that the overall scheme preserves feasibility if this is assumed from the outset, and hence guarantees closed-loop stability and constraint fulfilment. These properties rely on exact set-membership estimation. A simple approximation scheme which avoids set-membership estimation but preserves stability is also proposed and the relative performance/complexity tradeoffs are discussed. Simulation results demonstrate the effectiveness of the proposed method. Copyright (C) 2002 John Wiley Sons, Ltd.