Robust MPC for Open-Loop Unstable Systems Using A New Data-Based System Representation

We propose a robust model predictive control (MPC) approach for open-loop unstable systems with initially measured input-state data. Compared with a behavioral approach used in the existing data-driven MPC, a data-based system representation which is better to describe the original system is constructed to replace the classic model to predict future behaviors, leading to the reduction of computational burden and the improvement of the model mismatch between the prediction model and the original plant. Accordingly, the terminal ingredients dependent on this data-based representation are designed. Simulation results are provided to show the effectiveness of the proposed approach.