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

TL;DR

This paper introduces a machine learning-based reference governor for nonlinear systems, significantly reducing computational load while maintaining constraint enforcement, demonstrated through automotive fuel cell system simulations.

Contribution

It proposes a novel neural network approach to approximate and replace the computationally intensive PRG, enabling real-time constraint management in nonlinear systems.

Findings

MNN-RG reduces computational time compared to traditional PRG.

The method effectively enforces constraints in automotive fuel cell simulations.

The approach has limitations related to training data and approximation errors.

Abstract

The prediction-based nonlinear reference governor (PRG) is an add-on algorithm to enforce constraints on pre-stabilized 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 paper 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 neural network reference governor (MNN-RG), which is significantly more computationally efficient than the PRG. The computational and theoretical properties of 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 systems through simulation-based case studies.