Complexity Reduction in Explicit MPC through Model Reduction

Abstract

In this paper we propose to use model reduction techniques to make explicit model predictive control possible and more attractive for a larger number of applications and for longer control horizons. The main drawback of explicit model predictive control is the large increase in controller complexity as the problem size increases. For this reason, the procedure is limited to applications with low-order models, a small number of constraints and/or short control horizons. The proposed use of model reduction techniques is demonstrated for several applications, among others for control of fuel cell breathing. In all applications, a significant reduction in controller complexity is achieved.