Abstract

The talk is concerned with a novel robust model predictive control synthesis method termed parameterized tube model predictive control. This novel proposal supersedes the state of the art robust model predictive control algorithms including the several generations of tube model predictive control as well as robust model predictive control utilizing the so–called disturbance affine control policy. Our method allows uniquely for simultaneous online optimization of state and control tubes as well as the corresponding control policy via single linear/quadratic programme. The employed parameterization of the state and control tubes and the associated induced control policy offers greater generality compared to competing methods while it retains the desirable degree of computational tractability. Under rather mild and natural assumptions, the proposed parameterized tube model predictive control provides a–priori guarantees of the desirable strong system theoretic properties including relevant set invariance and robust stability properties. We also deliver several illustrative examples demonstrating clear advantages over the state of the art robust model predictive control synthesis methods.