Design and implementation of control algorithms for a tethered UAV

• Main Author: Zhao, Lingxuan
• Other Authors: Xie Lihua
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/171840
• Institution: Nanyang Technological University

This dissertation focuses on the control algorithms design and optimization techniques for tethered drones, with the primary objective of enhancing trajectory control stability. The research encompasses dynamic modeling of tethered drones, a survey of Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC), as well as the application of neural networks in this domain. The study begins with the dynamic modeling of tethered drones, laying the foundation for subsequent control algorithm development. A comprehensive survey of various control methods is conducted. Additionally, the research emphasizes the application of neural networks, leveraging their data-driven approach to capture the complex dynamics of the system, considering factors such as uncertain tether length in the air and ground friction. Based on the research findings, a novel control algorithm is proposed. This algorithm involves observing the state of the tether connecting to the drone over a certain time period and utilizing a neural network model to predict the force exerted by the tether on the aircraft in the next time step. Finally, this predicted force is compensated within the underlying flight control system, enabling accurate trajectory tracking of the tethered drone. The effectiveness of the proposed algorithm is evaluated through simulations using five different 3D flight trajectories. The results demonstrate a significant reduction in tracking errors, with an average decrease of 110.84% compared to baseline control methods. This highlights the efficacy of incorporating neural network-based disturbance compensation in improving trajectory tracking performance. In conclusion, this research emphasizes the significance of control algorithms and optimization techniques for tethered drones. The proposed approach, integrating neural network-based disturbance compensation, exhibits promising results in enhancing trajectory tracking performance. These research findings contribute to the field of tethered drone control and provide valuable insights for future investigations.