Compound Adaptive Fuzzy Quantized Control for Quadrotor and Its Experimental Verification.

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Quadrotors exhibit underactuation and strong coupling, complicating precise position and attitude control.
• Real-time control systems often require low communication rates, posing challenges for control precision due to quantizer nonlinearities.

Purpose of the Study:

• To develop a precise position and attitude tracking control scheme for quadrotors.
• To address the challenges of underactuation, strong coupling, and quantizer nonlinearities in quadrotor control systems.

Main Methods:

• A fuzzy approximator-based compound adaptive fuzzy quantized control scheme is proposed.
• Combines output-feedback control for position and state-feedback control for attitude.
• Employs adaptive fuzzy dynamic surface control (DSC) to overcome the 'explosion of complexity' in backstepping.

Main Results:

• Effectively mitigates nonlinearities introduced by quantizers, improving control precision at low communication rates.
• Achieves L∞ tracking performance, ensuring prompt convergence of attitude signals to desired trajectories.
• Validates the proposed control scheme's effectiveness through experiments on the Quanser Qball-X4 quadrotor platform.

Conclusions:

• The proposed adaptive fuzzy quantized control scheme enhances quadrotor tracking precision and overcomes underactuation.
• The integration of DSC and adaptive fuzzy logic effectively manages system complexities and nonlinearities.
• Experimental results confirm the practical viability and effectiveness of the developed control strategy.