Koopman-based data-free lifting for quadrotor dynamics on SE(3)
Project description
quadrotor_koopman_datafree
A lightweight Python package for Koopman-based data-free lifting of quadrotor dynamics on SE(3). This package analytically constructs lifted linear representations of quadrotor dynamics without requiring any trajectory data or machine learning. It supports both NumPy (numerical) and CasADi (symbolic) versions of the Koopman lifting maps for control and Model Predictive Control (MPC) applications.
✨ Key Features
- ✅ Koopman state lifting on SE(3) for quadrotors
- ✅ Analytical (data-free) Koopman operators
- ✅ Compatible with linear MPC, LQR, and optimal control
- ✅ Supports CasADi symbolic modeling
- ✅ Includes utilities for state conversion, noise, rotation matrices
- ✅ Maps between actual and lifted control spaces (
u ↔ U,u_tilde) - ✅ Clean and modular implementation
📦 Installation
From PyPI:
pip install quadrotor_koopman_datafree
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