# Disturbance Estimation and Rejection for High-Precision Multirotor Position Control

## TL;DR

This paper compares Model Predictive and PID controllers with disturbance estimators for multirotor drones, demonstrating their effectiveness in rejecting wind and ground effects while considering computational constraints.

## Contribution

It introduces and experimentally evaluates MPC and PID controllers with Extended and Unscented Kalman filters for disturbance rejection in multirotor systems.

## Key findings

- MPC with disturbance estimation outperforms PID in strong wind conditions.
- UKF provides more accurate disturbance estimates than Extended Kalman Filter.
- All algorithms can operate within the computational limits of small multirotor platforms.

## Abstract

Many multirotor Unmanned Aerial Systems applications have a critical need for precise position control in environments with strong dynamic external disturbances such as wind gusts or ground and wall effects. Moreover, to maximize flight time, small multirotor platforms have to operate within strict constraints on payload and thus computational performance. In this paper, we present the design and experimental comparison of Model Predictive and PID multirotor position controllers augmented with a disturbance estimator to reject strong wind gusts up to 12 m/s and ground effect. For disturbance estimation, we compare Extended and Unscented Kalman filtering. In extensive in- and outdoor flight tests, we evaluate the suitability of the developed control and estimation algorithms to run on a computationally constrained platform. This allows to draw a conclusion on whether potential performance improvements justify the increased computational complexity of MPC for multirotor position control and UKF for disturbance estimation.