# Real-time System Identification Using Deep Learning for Linear Processes with Application to Unmanned Aerial Vehicles

arXiv: https://arxiv.org/abs/2004.08603 · HTML: https://mlsanity.com/paper/2004.08603/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Disentangled Generative Models for Robust Prediction of System Dynamics](https://mlsanity.com/paper/2108.11684/) (2021)
- [Predicting Physical Object Properties from Video](https://mlsanity.com/paper/2206.00930/) (2022)
- [Dynamical System Parameter Identification using Deep Recurrent Cell Networks](https://mlsanity.com/paper/2107.02427/) (2021)

## Abstract (arXiv)

This paper proposes a novel parametric identification approach for linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT). The proposed methodology utilizes MRFT to reveal distinguishing frequencies about an unknown process; which are then passed to a trained DL model to identify the underlying process parameters. The presented approach guarantees stability and performance in the identification and control phases respectively, and requires few seconds of observation data to infer the dynamic system parameters. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented methodology. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.