Improving the Performance of Robust MPC Using the Perturbation on Control Input Strategy Based on Nominal Performance Cost

Abstract

Abstract This paper proposes a strategy to improve the control performance of robust MPC by using a sequence of free control inputs. For a given feedback gain which asymptotically stabilizes the closed-loop system, a sequence of free control inputs is calculated by minimizing the nominal performance cost over the perturbation horizon N . The nominal model of the process is included in the controller design in order to improve control performance. The optimization problem at each time step is formulated as the convex optimization problem involving linear matrix inequalities (LMI). The proposed strategy can be applied to the feedback gains derived from both a single Lyapunov function and parameter dependent Lyapunov function. For both cases, robust stability is proved to be guaranteed. The controller design is illustrated with an example in chemical process. The results show that the proposed strategy can significantly improve control performance of the conventional robust MPC.