Embedding Current Constraints in Reinforcement Learning for Electric Motor Control

Abstract

Robotic and automotive platforms increasingly rely on electric motors, demanding sophisticated control strategies. Handing over the optimization process for critical objectives such as disturbance rejection and energy efficiency to autonomous systems can significantly streamline the design effort. In this work, we propose an actor-critic reinforcement learning framework designed to optimize both speed regulation and energy efficiency through its reward function, while explicitly embedding hardware constraints into the training loss function. Specifically, we introduce a novel formulation that encodes violations of physical control constraints directly into the actor loss function, thereby integrating application-specific hardware limitations into the learning process. We demonstrate the effectiveness of this approach by comparing it against classical reward-shaping based methods. Our results show significant improvements in constraint handling and compliance during training and operational deployment. This study addresses key challenges associated with deploying model-free neural network-based control strategies, contributing to enhanced safety and reliability in reinforcement learning-derived control policies.