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Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.

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pybounds

Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.

PyPI version

Introduction

This repository provides a minimal working example demonstrating how to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise.

Installing

The package can be installed by cloning the repo and running python setup.py install from inside the home pybounds directory.

Alternatively using pip

pip install pybounds

Notebook examples

For a simple system

For a more complex system

Citation

If you use the code or methods from this package, please cite the following paper:

Benjamin Cellini, Burak Boyacioglu, Stanley David Stupski, and Floris van Breugel. Discovering and exploiting active sensing motifs for estimation with empirical observability. (2024) bioRxiv.

Related packages

This repository is the evolution of the EISO repo (https://github.com/BenCellini/EISO), and is intended as a companion to the repository directly associated with the paper above.

License

This project utilizes the MIT LICENSE. 100% open-source, feel free to utilize the code however you like.

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Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.

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