Energy-Aware Speed Regulation in Electrical Drives: A Load-Agnostic Motor Control Approach via Reinforcement Learning

Abstract

Robotic and automotive platforms are rapidly expanding in features and are incorporating more and more electric motor components. Consequently, the energy efficiency of motor control systems emerges as a major design challenge. The process of formulating and fine-tuning specialized speed regulation strategies for each application becomes progressively more laborious and expensive. A reinforcement learning agent specialized in electrical motor dynamics, capable of generalizing across a wide range of possible end-use applications, presents a promising and convenient solution. In this article, we introduce a novel design of a reinforcement learning agent, grounded in time series analysis, intended for application-agnostic electric motor control that optimizes both speed regulation and energy efficiency. Trained on the motor's internal dynamics, the agent provides operating point-specific control inputs, eliminating the need for manual tuning and application system-identification. Compared to application tuned classical control methods, the agent exhibited on-par or improved speed regulation performance and demonstrated advanced capability to save energy, showcasing its potential for future applications.