Steven Klotz from Infineon representing ECOMAI at the ecc24 conference in Stockholm
Bringing academic and industrial profession in the field of systems and control together, and to promote scientific cooperation and exchanges, this is the goal of the European Control Conference.
We are delighted that within the ECOMAI context, Steven Klotz vom Infineon was invited to present the paper.
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.
Authors:
- Klotz Steven, Infineon Technologies AG
- Bucksch Thorsten, Infineon Technologies AG
- Goswami, Dip, Eindhoven University of Technology
- Mueller-Gritschneder Daniel, TU Munich
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.
Download a short version of the Paper here.
Read more about the conference which took place in Sweden from June 25 to 28, 2024.