An active set solver for input-constrained robust receding horizon control

An efficient optimization procedure is proposed for computing a receding horizon control law for linear systems with constrained control inputs and additive disturbances. The procedure uses an active set method to solve the dynamic programming problem associated with the min-max optimization of a predicted cost. The active set at the solution is determined at each sampling instant as a function of the current system state using the first-order necessary conditions for optimality. The computational complexity of each iteration is linear in the length of the prediction horizon. We discuss conditions for stability and bounds on state and input l²-norms in closed loop operation.