kq-lmpc-quadrotor 1.0.5

Koopman-based Linear MPC for Quadrotor Control

🚁 kq_lmpc_quadrotor: Koopman MPC for Quadrotors Derived From Analytically Derived Koopman Embeddings

A complete Python package for real-time Koopman-based Linear Model Predictive Control (LMPC) of quadrotors.

First hardware-deployed real-time Linear MPC for quadrotors derived using Koopman operator theory, no data required.
Fast QP ⚡ Comparable Performance like NMPC 🎯 Runs on embedded hardware 💻✅

✅ KQ-LMPC = Koopman Lift + Convex MPC + Real-Time Control

A unified Python framework:

• Koopman lifting without machine learning
• Convex linear MPC that runs in real time
• Provable stability + hardware deployability

---

🌟 Key Features

✅ Analytical Koopman lifting with generalizable observables
    → No neural networks, no training, no data fitting required

✅ Data-free Koopman-lifted LTI + LPV models
    → Derived directly from SE(3) quadrotor dynamics using Lie algebra structure

✅ Real-time Linear MPC (LMPC)
    → Solved as a single convex QP termed KQ-LMPC
    → < 10 ms solve time on Jetson NX / embedded hardware

✅ Trajectory tracking on SE(3)
    → Provable controllability in lifted Koopman space

✅ Closed-loop robustness guarantees
    → Input-to-state practical stability (I-ISpS)

✅ Hardware-ready integration
    → Works with PX4 Offboard Mode, ROS2, MAVSDK, MAVROS

✅ Drop-in MPC module
    → for both KQ-LMPC, NMPC with acados on Python.

---

🧠 Paper

This work is based on:

"Real-Time Linear MPC for Quadrotors on SE(3): An Analytical Koopman-based Realization"
IEEE Robotics and Automation Letters (RA-L), 2025
Santosh Rajkumar, Chengyu Yang, Yuliang Gu, Sheng Cheng, Naira Hovakimyan, Debdipta Goswami
[Paper PDF] • [ArXiv] • [Video Demos]

If you use this repository, please cite us 🙏

@article{rajkumar2025kqlmpc,
  title={Real-Time Linear MPC for Quadrotors on SE(3): An Analytical Koopman-based Realization},
  author={Rajkumar, Santosh and Yang, Chengyu and Gu, Yuliang and Cheng, Sheng and Hovakimyan, Naira and Goswami, Debdipta},
  journal={IEEE Robotics and Automation Letters},
  year={2025}
}

🔧 Installation

*Virtual environment recommended

Install from PyPI (recommended):

pip install kq-lmpc-quadrotor

Install from source

git clone https://github.com/santoshrajkumar/kq-lmpc-quadrotor.git
cd kq-lmpc-quadrotor
pip install -e .

⚡ Quick Demo

lqr_demo

kqlmpc_demo

⚙️ Note: This package uses the acados toolchain for fast MPC.
Please ensure that acados is installed and its Python interface is configured before running the demo/examples with MPC.
Installation guide: https://docs.acados.org/installation/index.html

Python interface: https://docs.acados.org/python_interface/index.html

OS requirement: Linux/Mac (Not tested on Windows).

Ensure that LD_LIBRARY_PATH is set correctly (DYLD_LIBRARY_PATHon MacOS).

Ensure that ACADOS_SOURCE_DIR is set correctly.