Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
arXiv:2606.00059v1 Announce Type: new 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 achieves competitive estimation accuracy across all three identified parameters, outperforming classical baselines while incurring only 0.75% safety violations.
[2606.00059] Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
[Submitted on 19 May 2026]
Title:Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
View a PDF of the paper titled Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems, by Julian Langschwert and Georg Schaefer and Jakob Rehrl and Stefan Huber and Simon Hirlaender
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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 achieves competitive estimation accuracy across all three identified parameters, outperforming classical baselines while incurring only 0.75% safety violations.
Comments: Accepted at DEXA AI4IP 2026
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2606.00059 [cs.RO]
(or arXiv:2606.00059v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.00059
arXiv-issued DOI via DataCite
Submission history
From: Julian Langschwert [view email] [v1] Tue, 19 May 2026 11:39:49 UTC (16 KB)
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