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

arXiv: https://arxiv.org/abs/2606.00059 · HTML: https://mlsanity.com/paper/2606.00059/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 1 of 1)

- [D-Optimality-Guided Reinforcement Learning for Efficient Open-Loop Calibration of a 3-DOF Ankle Rehabilitation Robot](https://mlsanity.com/paper/2601.15707/) (2026)

## Built on by (most cited first, 1 of 1)

- [Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System](https://mlsanity.com/paper/2607.03125/) (2026)

## 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.