A novel approach for performance improvement in Model Predictive Safety Certification with output tracking

Abstract While learning‐based controllers demonstrate impressive performance, a significant challenge remains the lack of safety guarantees. Existing safety approaches often involve separating the optimization of the objective and constraint certification, which introduces a Model Predictive Safety Certification (MPSC) scheme. Unfortunately, this isolation frequently compromises performance, even though it ensures constraint satisfaction. This paper introduces a novel Multi‐Step MPSC scheme that combines Predictive Functional Control to track the reference output generated by the learning‐based controller, along with the utilization of a robust optimizer. The goal of this innovation is to introduce a method that confirms the system's safety while enhancing the performance of the MPSC. A numerical case study is provided and compared with the studies conducted by other researchers. The simulation results validate the efficiency and robustness of the proposed approach.