Date of Defense
2-5-2025 10:00 AM
Location
F1-1043
Document Type
Thesis Defense
Degree Name
Master of Science in Mechanical Engineering (MSME)
College
COE
Department
Mechanical and Aerospace Engineering
First Advisor
Dr. Tarek Dief
Keywords
Quadrotor, Real-time flight path tracking, PID, Dynamic environments, Autonomous navigation
Abstract
This thesis presents the design and development of an advanced real-time flight path tracking control system for a quadrotor, specifically aimed at accurately following a moving ground object such as a rover. The primary focus is on addressing challenges associated with precise tracking in dynamic and unpredictable environments, including real-time communication between the drone and the rover, rapid response to environmental changes, and maintaining control of the quadrotor’s altitude, yaw, pitch, and roll.
To achieve these objectives, the study explores the application of mathematical models based on ordinary differential equations, incorporating delay elements to simulate the real-time behaviour of the system. The control framework integrates Proportional-Integral-Derivative (PID) control techniques along with machine learning algorithms to optimize the quadrotor’s performance in adjusting its flight path dynamically. The system is tested through experimental setups where the quadrotor tracks a rover in real-time, continuously receiving positional data and responding to changes in the rover’s speed and direction with minimal latency.
The results demonstrate significant improvements in tracking accuracy, altitude stability, and attitude control, with the quadrotor maintaining close alignment with the rover's movements even under varying environmental conditions. The experimental outcomes emphasize the critical role of robust communication between the quadrotor and the rover, as well as the importance of adaptive control mechanisms to ensure reliable performance in environments featuring obstacles, uneven terrain, or weather fluctuations.
This research contributes to the broader field of (UAV) control systems by providing a comprehensive solution that enhances tracking accuracy through the integration of real-time data processing, adaptive control strategies, and obstacle detection. The findings have practical implications for a wide range of applications, including autonomous navigation, search and rescue operations, surveillance, and infrastructure inspection, where precise and reliable tracking of moving objects is essential.
Included in
DESIGN OF REAL-FLIGHT-PATH TRACKING CONTROL OF QUADROTOR
F1-1043
This thesis presents the design and development of an advanced real-time flight path tracking control system for a quadrotor, specifically aimed at accurately following a moving ground object such as a rover. The primary focus is on addressing challenges associated with precise tracking in dynamic and unpredictable environments, including real-time communication between the drone and the rover, rapid response to environmental changes, and maintaining control of the quadrotor’s altitude, yaw, pitch, and roll.
To achieve these objectives, the study explores the application of mathematical models based on ordinary differential equations, incorporating delay elements to simulate the real-time behaviour of the system. The control framework integrates Proportional-Integral-Derivative (PID) control techniques along with machine learning algorithms to optimize the quadrotor’s performance in adjusting its flight path dynamically. The system is tested through experimental setups where the quadrotor tracks a rover in real-time, continuously receiving positional data and responding to changes in the rover’s speed and direction with minimal latency.
The results demonstrate significant improvements in tracking accuracy, altitude stability, and attitude control, with the quadrotor maintaining close alignment with the rover's movements even under varying environmental conditions. The experimental outcomes emphasize the critical role of robust communication between the quadrotor and the rover, as well as the importance of adaptive control mechanisms to ensure reliable performance in environments featuring obstacles, uneven terrain, or weather fluctuations.
This research contributes to the broader field of (UAV) control systems by providing a comprehensive solution that enhances tracking accuracy through the integration of real-time data processing, adaptive control strategies, and obstacle detection. The findings have practical implications for a wide range of applications, including autonomous navigation, search and rescue operations, surveillance, and infrastructure inspection, where precise and reliable tracking of moving objects is essential.