Constrained receding horizon predictive control for systems with disturbances

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Abstract
A receding horizon predictive control method for systems with input constraints and disturbances is proposed. A polyhedral feasible set of states which is invariant with respect to a given state feedback control law is derived in the presence of bounded disturbances. The proposed predicted control algorithm deploys a strategy in which the current state is steered into the polyhedral invariant feasible set within a finite number N of feasible control moves, despite the presence of disturbances. The future control moves over the horizon N are represented as the sum of the state feedback control and a perturbation; the perturbation term provides the degrees of freedom with which to enlarge the stabilizable set of initial states. The control algorithm is formulated in linear matrix inequalities so that it can be solved using semidefinite programming.

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