Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on...

Area of Science:

• Robotics and Control Systems
• Aerospace Engineering
• Autonomous Systems

Background:

• Controller tuning is crucial for Unmanned Aerial Vehicle (UAV) performance and stability.
• Existing methods often require manual intervention or complex system models.
• Onboard, autonomous tuning during flight presents significant challenges.

Purpose of the Study:

• To develop a novel, fully autonomous autotuning method for UAV flight controllers.
• To enable controller parameter optimization directly during flight operations.
• To achieve precise control performance without requiring a UAV dynamics model.

Main Methods:

• An autotuning approach utilizing a Fibonacci-search optimization technique within bootstrap sequences.
• Controller performance is guided by a user-defined cost function evaluating tracking errors or performance indices.
• The method relies solely on periodical measurements from basic onboard sensors.

Main Results:

• The proposed method successfully identified locally-best controller parameters autonomously during flight.
• Experimental verification in real-world outdoor conditions demonstrated high robustness against environmental disturbances like wind.
• The approach exhibited low computational complexity and easy deployability.

Conclusions:

• The novel autotuning method offers an efficient and robust solution for onboard UAV controller optimization.
• This technique simplifies the process of achieving precise flight control for UAVs.
• The method's independence from a dynamics model and its resilience to disturbances make it highly practical.