Adaptive control of drone by rejection of disturbances

Abstract

Quadrotor or Unmanned Aerial Vehicles (UAVs), a popular type, use four propellers for flight and are gaining popularity due to their versatility and ease of use. Interest in controlling UAVs has significantly increased recently. This work focuses on the control and trajectory planning challenges of quadrotors. While many studies address disturbances and faults, the inherent underactuation (four inputs controlling six degrees of freedom) makes precise control and trajectory tracking difficult, particularly in complex scenarios. The research aims to improve quadrotor control in challenging environments. The Newton-Euler method is used in this work to develop the quadrotor's dynamic model. Then, an exploration using Dyna-Q reinforcement learning for autonomous quadrotor navigation in complex environments. The algorithm allows the quadrotor to learn optimal flight paths through trial and error. In addition, this thesis presents an in-depth investigation into improving the autonomy and control capabilities of quadrotors. The focus is on developing and implementing various linear and nonlinear control strategies to regulate the behavior of quadrotor UAVs. Each control strategy is carefully adjusted and fine-tuned to achieve the desired dynamic response and stability during quadrotor flight. Following that, we provide a comparison of the designed controllers. It then focuses on comparing the performance of fractional-order PID (FOPID) and sliding mode control (SMC) for trajectory tracking, emphasizing robustness against disturbances and nonlinearities. Furthermore, the research introduces an intelligent trajectory planning system using Dyna-Q learning to enable autonomous navigation and obstacle avoidance in complex environments, enhancing quadrotor adaptability and responsiveness for various applications. Extensive simulations validate the proposed control strategies and trajectory planning. Overall, this study contributes significantly to the field of quadrotor control and autonomy, providing valuable insights and solutions for improving flight stability and enabling secure and efficient operations in a variety of real-world scenarios.