# Feature Augmentation-Based Adaptive Neural Network Control for Quadrotors

## TL;DR

This paper introduces an adaptive neural network controller for quadrotors that improves learning accuracy and stability using feature augmentation and a state predictor.

## Contribution

The novelty lies in combining feature augmentation with adaptive neural networks and a state predictor for enhanced quadrotor control.

## Key findings

- The proposed controller improves learning accuracy through feature augmentation.
- The state predictor increases the learning rate of the neural network.
- The closed-loop system is proven to be input-to-state stable.

## Abstract

In this article, an adaptive neural network (ANN) controller based on feature augmentation (FA) is designed for quadrotors. The proposed controller consists of two components: a position sub-controller and an attitude sub-controller. We use the ANN to estimate unknown internal and external disturbance terms within quadrotors. To improve the learning accuracy of the ANN, we design an FA structure, which enables networks to more effectively learn the characteristics in the data. To increase the learning rate of the ANN, a state predictor (SP) is proposed to anticipate the state errors, which subsequently updates the learning rate of the ANN. Based on stability analysis, we prove that the closed-loop system is input-to-state stable (ISS). Finally, the effectiveness of our proposed control algorithm is demonstrated by comparing it with related control algorithms on both the MATLAB R2020a/Simulink simulation platform and a quadrotor experimental platform.