Research of Different Flight Controls and Methods for Quadrotor Aircraft

The rapid development of unmanned aerial vehicle (UAV) technology, particularly quadrotors, has led to their widespread use in small- to medium-sized applications. This paper provides a comprehensive overview of key flight control strategies for quadrotor UAVs, with a focus on their application in dynamic and complex environments, such as urban areas with tall buildings or forests with dense vegetation. The study discusses traditional methods such as Proportional-Integral-Derivative (PID) control, which is widely used for its simplicity, alongside more advanced techniques like Model Predictive Control (MPC), Adaptive Control, and Active Disturbance Rejection Control (ADRC). Additionally, data-driven approaches such as Deep Reinforcement Learning (DRL) are examined for their ability to adapt to unpredictable conditions, such as sudden changes in wind patterns or unexpected obstacles. Each method is analyzed in terms of its effectiveness in enhancing stability, tracking accuracy, and disturbance rejection, while also highlighting their respective computational requirements and limitations. Future trends, including the integration of hybrid control strategies and advancements in computational power, are explored to improve real-time decision-making and autonomy in UAVs. As the demand for UAVs grows in industries requiring precise and stable flight, further research into these control methods will be critical for advancing quadrotor technology.