ECOMAI: Ecological Motor Control and Predictive Maintenance using AI
Energy-Aware Speed Regulation in Electrical Drives: A Load-Agnostic Motor Control Approach Via Reinforcement Learning
Paper Presentation at the European Control Conference 2024
Bringing academic and industrial profession in the field of systems and control, and to promote scientific cooperation and exchanges, this is the goal of the European Control Conference.
We are delighted that within the ECOMAI context, the following paper contribution was presented by Steven Klotz from our consortiums partner Infineon:
The paper introduces a novel design of a reinforcement learning agent. Compared to application-tuned classical control methods, the agent demonstrated advanced capability to save energy, showcasing its potential for future applications.
Full Abstract: Energy-Aware Speed Regulation in Electrical Drives: A Load-Agnostic Motor Control Approach Via Reinforcement Learning
Authors
- Klotz, Steven Infineon Technologies AG
- Bucksch, Thorsten Infineon Technologies AG
- Goswami, Dip Eindhoven University of Technology
- Mueller-Gritschneder, Daniel TU Munich
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.
Read more about the conference which took place in Sweden from June 25 to 28, 2024.
Steven Klotz from Infineon represented the research topic of ECOMAI in the paper presentation at ecc24 in June, 24.
