Optimization of AR.Drone Position Control Using a Fuzzy Gain-Scheduled PID Approach

Abstract

Precision control of quadrotor unmanned aerial vehicles (UAVs), such as the AR.Drone, presents a significant challenge due to the inherently nonlinear and coupled dynamics of multi-rotor systems, actuator constraints, and unpredictable environmental disturbances. Traditional proportional-integral-derivative (PID) controllers, while widely used, often fail to achieve satisfactory performance under such nonlinear operating conditions, resulting in excessive overshoot, prolonged settling time, and sensitivity to external perturbations. This study proposes a hybrid fuzzy gain-scheduled PID control strategy that dynamically adjusts control gains in real time based on positional error and error rate, integrating the robustness of classical PID control with the adaptability of fuzzy logic. The methodology leverages fuzzy inference rules to modulate proportional, integral, and derivative gains according to system states, thereby enhancing transient response, minimizing steady-state error, and improving disturbance rejection.
A comprehensive system model of the AR.Drone, encompassing six degrees of freedom, coupled translational and rotational dynamics, and actuator constraints, serves as the foundation for controller design. The proposed control framework is implemented and validated through both simulation and experimental platforms. Simulated scenarios include hovering, step responses, and trajectory tracking under variable wind disturbances, while experimental trials utilize a commercially available AR.Drone 2.0 integrated with ROS-based telemetry for real-time measurement and feedback. Performance metrics, including root mean square error (RMSE), overshoot, settling time, and control effort, are rigorously analyzed and compared against conventional fixed-gain PID and adaptive backstepping controllers.
Results demonstrate that the fuzzy gain-scheduled PID significantly reduces positional RMSE, achieves faster settling times, and limits overshoot compared to conventional PID, with performance comparable to or exceeding adaptive backstepping strategies. Specifically, the hybrid controller effectively maintains stability under external disturbances, reduces actuator saturation events, and optimizes control energy utilization. Critical analysis indicates that the adaptive gain scheduling mitigates the limitations of classical PID in nonlinear UAV systems, while providing a computationally efficient alternative to complex adaptive controllers.
The study contributes to the literature by providing a practical, implementable framework for high-precision UAV position control, bridging the gap between classical PID simplicity and intelligent adaptive strategies. The proposed methodology has potential applications in aerial surveillance, autonomous delivery, environmental monitoring, and cooperative multi-UAV operations. Limitations, including sensitivity to fuzzy rule calibration and extreme environmental conditions, are discussed, with recommendations for future  research integrating model-free adaptation and multi-agent coordination.