Neuroflight interesting

rickyg32 · January 30, 2019, 11:13pm

This is an article about Neuroflight…a change to Beta Flight. Curious if this is a real thing and if Arducopter would ever go that way.

http://www.multirotorguide.com/news/neuroflight-flight-control-firmware-with-ai/

cala2 · January 31, 2019, 1:58am

Looks than another step from autotune that was a great step

james_pattison · January 31, 2019, 2:20am

I’m curious as to how they fit a firmware that they say is 913kb onto a board that has 512kb flash…

Nando · February 1, 2019, 10:45am

I think it’s very interesting!
Could be an improvement over the current autotune. How easy would it be to copy-paste some of that into ardupilot?

Giorgio_Rinolfi · February 5, 2019, 11:29am

Really interesting and promising!!

github.com

wil3/neuroflight/blob/v3.3.x-neuroflight/README.md

![Neuroflight](https://github.com/wil3/neuroflight/raw/v3.3.x-neuroflight/logo.png)

## WARNING 
This is experimental firmware, use at your own risk! This repo is intended for academic
and research
purposes. 

There is still a significant amount of research required to validate the
behavior of neuro-flight controllers. If the neural network is not trained properly, instabilities and unpredictable behaviors will occur.  

## Neuroflight: Next Generation Flight Control Firmware

Neuroflight is the first open source neuro-flight controller software (firmware) for remotely piloting multi-rotors and fixed wing aircraft. Neuroflight's primary focus is to provide optimal flight performance.  

Neuroflight aims to address limitations in PID control used in Betaflight
through the use of neural network flight control (neuro-flight control). Neuro-flight control has been actively researched for more than a decade. In contrast to traditional control algorithms, neuro-flight control has the ability to *adapt*, *plan*, and *learn*. To account for dynamic changes Betaflight has introduced gain scheduling to increase the I gain when certain conditions are met, for example low voltages or high throttle (anti-gravity). On the other hand, neuro-flight control learns the true underlying dynamics of the aircraft allowing for optimal control depending on the current aircraft state. For example neuro-flight control has the potential to learn the batteries discharge rates to dynamically adjust control signal outputs accordingly.  The goal of this work is to provide the community with a
stable platform to innovate and advance development of neuro-flight control design for drones, and to take a step towards
making neuro-flight controllers mainstream. For further details refer to our
[preprint](https://wfk.io/docs/neuroflight.pdf) and please use the following BibTex
entry to cite our work,

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iampete · February 5, 2019, 1:39pm

I have done a fair bit of work with neural networks, I cant see much advantage in this particular implementation. Maybe something that could scale the PID terms so you can keep them within a reasonable range. PID’s have the advantage that your initial guess can in most cases still fly the vehicle and be tuned up using autotune in 30mins or so. I think he mentions 10,000,000 2 second iterations for the training. It must run much faster than real time but even so will be many hours. And you still have to hope your simulated copter is close enough to your real one.

i think there is more scope for neural networks to be applied for attitude estimation in place of the ekf, you could go from raw sensor data direct to quaternions. But it would be hard to get it to cope with all the possible combinations of sensors and data rates.

soldierofhell · June 8, 2021, 10:13pm

Has anyone seen a continuation of this work? I guess this Betaflight fork is no longer active. This could also indicate that indeed this approach in typical cases doesn’t bring much value. But recently I have an issue where control is delayed (so called dead time) and here PIDs just can’t work. I’m thinking about similar approch but with model-based RL. So far I managed to build PX4 with TFLite for Microcontrollers and plan to experiments on it (can’t use ArduPilot because I don’t know know how waf works, CMake is straightforward here).