state_observers

Package with some state observers

README

State Observers Library 🚀

Overview

The State Observers Library provides a set of classes for implementing state observer algorithms, including Luenberger observers and Kalman filters. These observers are widely used in control systems for estimating the internal state variables of a dynamic system from measurements of its outputs.

The library is designed to be modular and extensible, with a base StateObserver class that can be extended to implement various state estimation algorithms. It also integrates with ROS 2 through pluginlib, allowing for dynamic loading and configuration of observers and their parameters.

Features ✨

Modular Design: A base StateObserver class provides common functionality, making it easy to implement new observers.
Luenberger Observer: Implements a Luenberger observer for linear systems.
Kalman Filter: Implements a discrete-time Kalman Filter for linear systems.
ROS 2 Pluginlib Integration: Observers and their parameter classes are available as ROS 2 plugins.
Parameter Classes: Separate parameter classes manage configuration parameters for observers.

Classes and Plugins

Observer Classes

StateObserver: Abstract base class providing the interface and common functionality for state observers.
Luenberger: Inherits from StateObserver. Implements the Luenberger observer algorithm for linear systems.
KalmanFilter: Inherits from StateObserver. Implements the Kalman Filter algorithm for linear systems.

Parameter Classes

StateObserverParam: Base class for managing common parameters, such as state-space matrices and the initial state vector.
LuenbergerParam: Inherits from StateObserverParam. Adds the observer gain matrix L specific to the Luenberger observer.
KalmanFilterParam: Inherits from StateObserverParam. Adds process noise covariance Q, measurement noise covariance R, and initial error covariance P0 specific to the Kalman Filter.

ROS 2 Plugins

The library provides ROS 2 pluginlib plugins for dynamic loading:

Parameter Plugins (state_observers_params_plugins.xml):

<library path="state_observers">
  <class type="state_observer::LuenbergerParam" base_class_type="state_observer::StateObserverParam">
    <description>This is a plugin for Luenberger Param ROS2 Loader</description>
  </class>
  <class type="state_observer::KalmanFilterParam" base_class_type="state_observer::StateObserverParam">
    <description>This is a plugin for Kalman Filter Param ROS2 Loader</description>
  </class>
</library>

Installation

To use the State Observers Library in your ROS 2 workspace, clone the repository and build it using colcon:

cd ~/ros2_ws/src
git clone https://github.com/JRL-CARI-CNR-UNIBS/state_observers.git
cd ~/ros2_ws
colcon build

Contributing 🤝

Contributions to this project are welcome! You can contribute by reporting bugs, suggesting new features, or submitting pull requests.

How to Contribute

Report Bugs: Use the issue tracker to report bugs.
Suggest Features: Propose new features or enhancements.
Submit Pull Requests: Fork the repository and submit pull requests for your contributions.

TODO List

[x] Base class of state observer, Luenberger, Kalman Filter
[ ] Create a class to represent a dynamic system (general and linear) in state-space format that contains A, B, C, D matrices (or f, g, etc. functions) with update methods, and pass it to state observer classes as input (not directly A, B, C, D)
[ ] Implement Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter, etc.
[x] Test State Observer Base Class
[ ] Test Luenberger, Kalman Filter, and new observers
[x] Set up GitHub Workflows for Continuous Integration (CI)
[x] Implement ROS 2 Pluginlib support for StateObserver, Luenberger, KalmanFilter, and their parameter classes

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.