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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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