A Machine Learning-Based Reference Governor for Nonlinear Systems With Application to Automotive Fuel Cells

The prediction-based nonlinear reference governor (PRG) is an add-on algorithm to enforce constraints on prestabilized nonlinear systems by modifying, whenever necessary, the reference signal. The implementation of PRG carries a heavy computational burden, as it may require multiple numerical simulations of the plant model at each sample time. To this end, this article proposes an alternative approach based on machine learning, where we first use a regression neural network (NN) to approximate the input-output map of the PRG from a set of training data. During the real-time operation, at each sample time, we use the trained NN to compute a nominal reference command, which may not be constraint admissible due to training errors and limited data. We adopt a novel sensitivity-based approach to minimally adjust the nominal reference while ensuring constraint enforcement. We thus refer to the resulting control strategy as the modified NN reference governor (MNN-RG), which is computationally more efficient than the PRG. The computational and theoretical properties of the MNN-RG are presented. Finally, the effectiveness and limitations of the proposed method are studied by applying it as a load governor for constraint management in automotive fuel cell (FC) systems through simulation-based case studies.