Journal Article

UAV Model-based Flight Control with Artificial Neural Networks: A Survey

Gu, Weibin, Rizzo, Alessandro, Valavanis, Kimon P., Rutherford, Matthew J.

2020

Overview

Model-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes.

• Publisher: Springer Netherlands,Springer,Springer Nature B.V
• Subject: Artificial Intelligence / Artificial neural networks / Comparative studies / Control / Control systems design / Controllers / Drone aircraft / Electrical Engineering / Engineering / Flight control systems / Literature reviews / Mathematical models / Mechanical Engineering / Mechatronics / Model accuracy / Neural networks / Robotics / Surveys / System identification / Unmanned aerial vehicles