A Neural Network-based Multi-timestep Command Governor for Nonlinear Systems with Constraints

TL;DR

This paper introduces a neural network-based multi-timestep command governor (NN-MCG) for nonlinear systems, significantly reducing computational load while maintaining constraint enforcement and system performance.

Contribution

It proposes a neural network approximation of the MCG control law with a sensitivity-based adjustment to ensure constraints, offering a computationally efficient alternative.

Findings

NN-MCG is much faster than traditional MCG.

Nearly identical constraint management performance achieved.

Effective in automotive fuel cell load governor application.

Abstract

The multi-timestep command governor (MCG) is an add-on algorithm that enforces constraints by modifying, at each timestep, the reference command to a pre-stabilized control system. The MCG can be interpreted as a Model-Predictive Control scheme operating on the reference command. The implementation of MCG on nonlinear systems carries a heavy computational burden as it requires solving a nonlinear program with multiple decision variables at each timestep. This paper proposes a less computationally demanding alternative, based on approximating the MCG control law using a neural network (NN) trained on offline data. However, since the NN output may not always be constraint-admissible due to training errors, its output is adjusted using a sensitivity-based method. We thus refer to the resulting control strategy as the neural network-based MCG (NN-MCG). As validation, the proposed controller is applied as a load governor for constraint management in an automotive fuel cell system. It is shown that the proposed strategy is significantly more computationally efficient than the traditional MCG, while achieving nearly identical performance if the NN is well-trained.