AI RESEARCH
Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
arXiv CS.LG
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ArXi:2606.00059v1 Announce Type: cross 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.