arXiv Machine Learning June 2, 2026 By Julian Langschwert, Georg Schaefer, Jakob Rehrl, Stefan Huber, Simon Hirlaender

Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems

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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.

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