Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
arXiv:2606.00059v1 Announce Type: cross Abstract: Informative excitation signals are critical for accurate system identification of mechatronic systems, yet classical system identification (SI) approaches require expert knowledge and hand-crafted signal design to respect hardware safety constraints, limiting their generalizability. We propose a reinforcement learning (RL) agent that learns optimal excitation signals for a Quanser Aero 2 testbed while autonomously enforcing safety constraints through reward shaping. Evaluated across 10 independent training seeds, our comprehensive agent achieve
The increasing complexity of mechatronic systems and the growing capabilities of reinforcement learning make this a timely advancement for automated system identification.
This development allows for more accurate and safer system identification, crucial for the reliable deployment of advanced robotic and automated systems, reducing the need for specialized human expertise.
The process of designing excitation signals for system identification can now be automated and optimized by AI, leading to more efficient and robust system modeling.
- · Robotics manufacturers
- · Automation companies
- · AI/ML researchers
- · Advanced manufacturing
- · Traditional system identification consultants
- · Manual signal design methodologies
Mechatronic systems will be identified and calibrated more rapidly and accurately, accelerating development cycles.
The improved reliability of these systems could lead to broader and faster adoption of complex automated technologies in critical applications.
This could enable more sophisticated and adaptable autonomous systems that can self-optimize and self-diagnose in real-time, reducing operational costs.
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Read at arXiv cs.LG