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

Steven Klotz, Thorsten Bucksch, Dip Goswami, Daniel Mueller-Gritschneder

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.