A partial multiparametric optimization strategy to improve the computational performance of model predictive control

Abstract

Determining the optimal manipulated action for large scale model predictive control formulations requires significant computational overhead. It has been demonstrated that the offline, explicit solution provided by multiparametric programming has the capacity to greatly improve the online computational performance of MPC strategies. For large scale problems, developing and deploying the full multiparametric solution remains an open challenge. In this work, a partial multiparametric solution is utilized to improve the initialization procedure for a hot start strategy. The hot start strategy provides an improved technique for determining the optimal solution of large scale MPC formulations, and the partial multiparametric solution ensures the initialization is suitable under varying conditions. The efficacy of the proposed strategy is verified on randomly generated large scale MPC problems.