mlsanityData to 14 Oct 2026

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

arXiv 2606.00059 ↗First posted 19 May 2026cs.RO cs.LGcited by 1

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 4 references that are in the map. First posted on arXiv 19 May 2026.

Builds on

1 of 4 references.

References · 1

  1. 2026D-Optimality-Guided Reinforcement Learning for Efficient Open-Loop Calibration of a 3-DOF Ankle Rehabilitation Robotcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter Systemcited by 0

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