Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers.

Area of Science:

• Robotics and Control Systems
• Aerospace Engineering
• Artificial Intelligence

Background:

• Unmanned aerial vehicles (UAVs) require robust stability control for autonomous operations.
• Conventional proportional integral derivative (PID) controllers face challenges in adapting to varying mission requirements.
• Maintaining precise attitude and position control is critical for UAV maneuverability and mission success.

Purpose of the Study:

• To develop a novel fuzzy-gain scheduling mechanism for adaptive PID control in UAVs.
• To enhance the stability and trajectory tracking capabilities of UAVs.
• To create a robust and simple control strategy effective under uncertainties and external disturbances.

Main Methods:

• Implementation of a fuzzy-gain scheduling strategy to dynamically adjust PID controller parameters.
• Integration of the proposed control system with the Robot Operating System (ROS) and flight control unit.
• Comparative analysis of the proposed controller against conventional PID controllers.

Main Results:

• The fuzzy-gain scheduled PID controller demonstrated successful trajectory tracking for UAVs.
• The proposed approach exhibited superior performance compared to conventional PID controllers, especially in the presence of noise.
• The position controller showed resilience, with minimal impact from altitude errors (2% lower error).

Conclusions:

• The fuzzy-gain scheduling mechanism offers an effective, simple, and robust solution for UAV attitude and position stabilization.
• This adaptive control strategy enhances UAV performance in dynamic and uncertain environments.
• The developed system shows significant potential for improving autonomous UAV flight control.