REINFORCEMENT LEARNING IN MECHATRONIC SYSTEMS: A CASE STUDY ON DC MOTOR CONTROL

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 de ployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A con troller based on reinforcement learning was developed and evaluated in com parison 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, particu larly 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 pro cesses, 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 intelli gence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems