Systematic Online Tuning of Multirotor UAVs for Accurate Trajectory Tracking Under Wind Disturbances and In-Flight Dynamics Changes

TL;DR

This paper presents a systematic, model-based controller tuning method for multirotor UAVs that maintains accurate trajectory tracking under wind disturbances and in-flight changes, using deep neural network identification and feedback linearization.

Contribution

It introduces a novel DNN-MRFT based tuning approach with feedback linearization for UAV control, enabling adaptive, robust performance in uncertain environments.

Findings

Achieved RMSE of 3.59 cm in aggressive trajectories under wind.

Demonstrated robustness to aerodynamic changes and payload variations.

Low discrepancy between simulation and real-world results.

Abstract

The demand for accurate and fast trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) have grown recently due to advances in UAV avionics technology and application domains. In many applications, the multirotor UAV is required to accurately perform aggressive maneuvers in challenging scenarios like the presence of external wind disturbances or in-flight payload changes. In this paper, we propose a systematic controller tuning approach based on identification results obtained by a recently developed Deep Neural Networks with the Modified Relay Feedback Test (DNN-MRFT) algorithm. We formulate a linear equivalent representation suitable for DNN-MRFT using feedback linearization. This representation enables the analytical investigation of different controller structures and tuning settings, and captures the non-linearity trends of the system. With this approach, the trade-off between performance and robustness in design was made possible which is convenient for the design of controllers of UAVs operating in uncertain environments. We demonstrate that our approach is adaptive and robust through a set of experiments, where accurate trajectory tracking is maintained despite significant changes to the UAV aerodynamic characteristics and the application of wind disturbance. Due to the model-based system design, it was possible to obtain low discrepancy between simulation and experimental results which is beneficial for potential use of the proposed approach for real-time model-based planning and fault detection tasks. We obtained RMSE of $3.59 \; cm$ when tracking aggressive trajectories in the presence of strong wind, which is on par with state-of-the-art.