# Disturbance Rejection-Guarded Learning for Vibration Suppression of Two-Inertia Systems

arXiv: https://arxiv.org/abs/2404.10240 · HTML: https://mlsanity.com/paper/2404.10240/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2024.

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

- [Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning](https://mlsanity.com/paper/2410.06570/) (2024)

## Abstract (arXiv)

Model uncertainty presents significant challenges in vibration suppression of multi-inertia systems, as these systems often rely on inaccurate nominal mathematical models due to system identification errors or unmodeled dynamics. An observer, such as an extended state observer (ESO), can estimate the discrepancy between the inaccurate nominal model and the true model, thus improving control performance via disturbance rejection. The conventional observer design is memoryless in the sense that once its estimated disturbance is obtained and sent to the controller, the datum is discarded. In this research, we propose a seamless integration of ESO and machine learning. On one hand, the machine learning model attempts to model the disturbance. With the assistance of prior information about the disturbance, the observer is expected to achieve faster convergence in disturbance estimation. On the other hand, machine learning benefits from an additional assurance layer provided by the ESO, as any imperfections in the machine learning model can be compensated for by the ESO. We validated the effectiveness of this novel learning-for-control paradigm through simulation and physical tests on two-inertial motion control systems used for vibration studies.