neuroflight

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.