Control in unmanned aerial vehicles

Quadcopter technology has significantly progressed, yet achieving precise control and stable flight in varying conditions remains a challenge. Current control systems, particularly Proportional-Integral-Derivative (PID) controllers, struggle with the dynamic nature of quadcopter flight, often failing to maintain desired attitudes and positions with precision. The integration of PID controllers with advanced estimation algorithms like the Extended Kalman Filter (EKF) is a potential solution, promising to enhance control by balancing responsiveness and stability against unpredictable factors like wind gusts, system dynamics, and sensor inaccuracies. This study aims to investigate the efficacy of integrating PID controllers with the EKF in quadcopter flight control. The research will explore how these systems' combined strengths might improve precision, adaptability, and autonomy in flight control. The study will focus on implementing a PID controller for error minimization between desired trajectories and actual flight paths, and evaluate the EKF's role in improving state estimation accuracy by fusing data from multiple sensors. The integration aims to create a robust control system, adaptable to environmental changes and capable of complex maneuvers like indoor navigation, obstacle avoidance, and tracking moving targets.