# Learning Pugachev's Cobra Maneuver for Tail-sitter UAVs Using Acceleration Model

## TL;DR

This paper introduces a simple acceleration-based feedback-iterative learning control method for tail-sitter UAVs to perform the challenging Pugachev's cobra maneuver, validated through outdoor experiments.

## Contribution

It proposes a novel acceleration model-based control framework that simplifies implementation and enhances maneuver performance for tail-sitter UAVs.

## Key findings

- Effective altitude and lateral control during the maneuver
- Successful outdoor flight experiments demonstrating maneuver execution
- Simplified control design without model identification

## Abstract

The Pugachev's cobra maneuver is a dramatic and demanding maneuver requiring the aircraft to fly at extremely high Angle of Attacks (AOA) where stalling occurs. This paper considers this maneuver on tail-sitter UAVs. We present a simple yet very effective feedback-iterative learning position control structure to regulate the altitude error and lateral displacement during the maneuver. Both the feedback controller and the iterative learning controller are based on the aircraft acceleration model, which is directly measurable by the onboard accelerometer. Moreover, the acceleration model leads to an extremely simple dynamic model that does not require any model identification in designing the position controller, greatly simplifying the implementation of the iterative learning control. Real-world outdoor flight experiments on the "Hong Hu" UAV, an aerobatic yet efficient quadrotor tail-sitter UAV of small-size, are provided to show the effectiveness of the proposed controller.