TITLE:
Reinforcement Learning in Mechatronic Systems: A Case Study on DC Motor Control
AUTHORS:
Alexander Nüßgen, Alexander Lerch, René Degen, Marcus Irmer, Martin de Fries, Fabian Richter, Cecilia Boström, Margot Ruschitzka
KEYWORDS:
Artificial Intelligence in Product Development, Mechatronic Systems, Reinforcement Learning for Control, System Integration and Verification, Adaptive Optimization Processes, Knowledge-Based Engineering
JOURNAL NAME:
Circuits and Systems,
Vol.16 No.1,
January
20,
2025
ABSTRACT: The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.