A Predictive Safety Filter for Safe Learning of Optimal Operation of Permanent Magnet Synchronous Motors

This paper deals with the design of a predictive safety filter which enables safe learning of optimal operating points for permanent magnet synchronous motors. It is shown that the proposed safety filter can be implemented in real-time on a rapid prototyping hardware using a tailored fast alternating minimization algorithm implementation. The effectiveness of the safety filter is shown when learning optimal operating points for a permanent magnet synchronous motor at the test bench using Bayesian optimization.