Newton-Raphson Flow for PX4
Control
Python
JAX
Research
A research-grade quadrotor controller backed by three peer-reviewed papers.
NR Flow reframes trajectory tracking as an optimization problem solved at each timestep by the Newton-Raphson method — yielding fast, accurate control that is more computationally efficient than NMPC while matching or exceeding its tracking performance. The result: a controller that runs comfortably on a Raspberry Pi 4 onboard a real quadrotor.
Integral Control Barrier Functions (I-CBFs) are baked in to handle actuation limits without discontinuous switching, and all computations are JAX JIT-compiled for real-time deployment.
Key features:
- Newton-Raphson iterative Jacobian inversion for feedback linearization
- Integral CBF safety constraints on actuator inputs (enabled by default, configurable)
- JAX JIT-compilation for real-time performance on resource-constrained hardware
- Structured CLI for simulation and hardware deployment across multiple trajectory types
- Optional CSV logging with automatic analysis notebook generation
- Demonstrated on a Holybro X500V2 + Raspberry Pi 4
Academic foundation:
| Venue | Year |
|---|---|
| American Control Conference (ACC) | 2024 |
| IEEE Transactions on Control Systems Technology (TCST) | 2025 |
| IEEE Transactions on Robotics (TRO) | 2025 |
Built with: Python · JAX · ROS 2 · PX4