# Modeling, Identification and Control of Model Jet Engines for Jet Powered Robotics

arXiv: https://arxiv.org/abs/1909.13296 · HTML: https://mlsanity.com/paper/1909.13296/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Symbolic Regression on Sparse and Noisy Data with Gaussian Processes](https://mlsanity.com/paper/2309.11076/) (2023)
- [Koopman Eigenfunction-Based Identification and Optimal Nonlinear Control of Turbojet Engine](https://mlsanity.com/paper/2505.10438/) (2025)
- [Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation](https://mlsanity.com/paper/2111.06742/) (2021)
- [Learning Aerodynamics for the Control of Flying Humanoid Robots](https://mlsanity.com/paper/2506.00305/) (2025)
- [Koopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine](https://mlsanity.com/paper/2604.01730/) (2026)

## Abstract (arXiv)

The paper contributes towards the modeling, identification, and control of model jet engines. We propose a nonlinear, second order model in order to capture the model jet engines governing dynamics. The model structure is identified by applying sparse identification of nonlinear dynamics, and then the parameters of the model are found via gray-box identification procedures. Once the model has been identified, we approached the control of the model jet engine by designing two control laws. The first one is based on the classical Feedback Linearization technique while the second one on the Sliding Mode control. The overall methodology has been verified by modeling, identifying and controlling two model jet engines, i.e. P100-RX and P220-RXi developed by JetCat, which provide a maximum thrust of 100 N and 220 N, respectively.