Uncertainty Modelling and Robust Observer Synthesis using the Koopman Operator

This repository contains the companion code for Uncertainty Modelling and Robust Observer Synthesis using the Koopman Operator. All the code required to generate the paper's plots from raw data is included here.

The regression methods detailed in the paper are implemented using pykoop, the authors' Koopman operator identification library.

This software relies on doit to automate experiment execution and plot generation.

Requirements

This software is compatible with Linux, macOS, and Windows. It was developed on Arch Linux with Python 3.12.6, while the experiments used in the corresponding paper were run on Windows 10 with Python 3.10.9. The pykoop library supports any version of Python above 3.7.12. You can install Python from your package manager or from the official website.

Installation

To clone the repository, run

$ git clone git@github.com:decargroup/robust_koopman_observer.git

The recommended way to use Python is through a virtual environment. Create a virtual environment (in this example, named venv) using

$ virtualenv venv

Activate the virtual environment with¹

$ source ./venv/bin/activate

To use a specific version of Python in the virtual environment, instead use

$ source ./venv/bin/activate --python <PATH_TO_PYTHON_BINARY>

If the virtual environment is active, its name will appear at the beginning of your terminal prompt in parentheses:

(venv) $

To install the required dependencies in the virtual environment, including pykoop, run

(venv) $ pip install -r ./requirements.txt

The LMI solver used, MOSEK, requires a license to use. You can request personal academic license here. You will be emailed a license file which must be placed in ~/mosek/mosek.lic².

Usage

To automatically generate all the plots used in the paper, first download the Quantifying Manufacturing Variation in Motor Drives dataset from the Federated Research Data Repository and place it in a directory called dataset/ in the root of the repository. The raw/ and preprocessed/ directories of the dataset should be placed directly inside the dataset/ directory. The command ls ./dataset should show

example.py  preprocessed  preprocess.py  raw  README.md  requirements.txt

Once the dataset is downloaded, run

(venv) $ doit

in the repository root. This command will preprocess the raw data located in dataset/, run all the required experiments, and generate figures, placing all the results in a directory called build/.

To execute just one task and its dependencies, run

(venv) $ doit <TASK_NAME>

To see a list of all available task names, run

(venv) $ doit list --all

For example, to generate only the Koopman uncertainty plots, run

(venv) $ doit plot_uncertainty:koopman

If you have a pre-built copy of build/ or other build products, doit will think they are out-of-date and try to rebuild them. To prevent this, run

(venv) $ doit reset-dep

after placing the folders in the right locations. This will force doit to recognize the build products as up-to-date and prevent it from trying to re-generate them. This is useful when moving the build/ directory between machines.

Repository Layout

The files and folders of the repository are described here:

Path	Description
dataset/	Motor drive dataset must be downloaded here.
build/	Generated by doit. Contains all doit build products.
figures/	Generated by doit. Contains all the paper plots.
dodo.py	Describes all of doit's tasks, like a Makefile.
actions.py	Contains the actual implementations of the doit tasks.
obs_syn.py	Module containing observer synthesis code.
onesine.py	Module containing sinusoidal Koopman lifting functions.
tf_cover.py	Module containing code to bound transfer function residuals.
LICENSE	Repository license
requirements.txt	Contains the required Python packages and versions.
README.md	This file.

Footnotes

1. On Windows, use > \venv\Scripts\activate. ↩

2. On Windows, place the license in C:\Users\<USER>\mosek\mosek.lic. ↩