https://github.com/cadojo/replicated-roa-analysis
Replicated nonlinear region of attraction (ROA) analysis for NASA's Generic Transport Model (GTM).
https://github.com/cadojo/replicated-roa-analysis
control-systems control-theory dynamics nonlinear-control python3 scientific-computing
Last synced: over 1 year ago
JSON representation
Replicated nonlinear region of attraction (ROA) analysis for NASA's Generic Transport Model (GTM).
- Host: GitHub
- URL: https://github.com/cadojo/replicated-roa-analysis
- Owner: cadojo
- Created: 2020-06-17T22:04:53.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2022-01-30T18:51:45.000Z (over 4 years ago)
- Last Synced: 2025-01-16T20:23:06.999Z (over 1 year ago)
- Topics: control-systems, control-theory, dynamics, nonlinear-control, python3, scientific-computing
- Language: Jupyter Notebook
- Homepage:
- Size: 10.2 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Replicated Region of Attraction Analysis
## Summary
This repository contains a [report](/Report/Carpinelli_ROA_Estimation.pdf), and [Jupyter Notebook](/Code/Carpinelli%20-%20ROA%20Estimation.pdf) summarizing (and at times, replicating) nonlinear region of attraction analysis completed by [Chakraborty et al](https://www.sciencedirect.com/science/article/abs/pii/S0967066110002595). This project was completed as part of the University of Maryland Aerospace Engineering Department's Applied Nonlinear Control course (ENAE 743). The end result has been placed here; the full repository history is in a private coursework repository.
## Background
NASA's GTM is a high-fidelity radio controlled model aircraft, which is used to research off-nominal flight control conditions. Chakraborty et al summarized the longitudinal state dynamics of NASA's GTM, approximated the dynamics as low-order polynomials, and then utilized existing sum-of-squares optimization software (such as [SOSTOOLS](https://www.cds.caltech.edu/sostools/)) to efficiently approximate the maximum elliptical subset of a trim condition's region of attraction.
As part of a Spring 2020 ENAE 743 final project, Chakraborty et al's results were summarized. First, the results were informally summarized and partially implemented with Python code in a [Jupyter Notebook](/Code/Carpinelli%20-%20ROA%20Estimation.pdf). Then, the results (replicated and not) were formally summarized in a [report](/Report/Carpinelli_ROA_Estimation.pdf).