Adaptive fractional-order non-singular terminal sliding mode control for omnidirectional quadrotors based on WRBF neural network
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a robust control strategy for tilt-rotor quadrotors using a unified Fractional-Order Nonsingular Terminal Sliding Mode Controller (FONTSMC) and adaptive Wavelet Radial Basis Function (WRBF) network. The novel approach enhances trajectory tracking accuracy and stability under disturbances for unmanned aerial vehicle (UAV) operations.
Area Of Science
- Robotics and Control Systems
- Aerospace Engineering
- Artificial Intelligence
Background
- Tilt-rotor quadrotors face challenges in trajectory tracking due to uncertainties and disturbances.
- Existing control strategies often lack robustness and precise handling of complex dynamics.
Purpose Of The Study
- To develop a novel, robust six-degree-of-freedom (6-DOF) trajectory tracking control strategy for tilt-rotor quadrotors.
- To enhance control performance under unstructured uncertainties and external disturbances.
Main Methods
- Co-design of a Fractional-Order Nonsingular Terminal Sliding Mode Controller (FONTSMC) with an adaptive Wavelet Radial Basis Function (WRBF) neural network.
- Integration of a Mexican Hat wavelet activation function for improved approximation and noise robustness.
- Implementation of a parallel control structure with Moore-Penrose pseudo-inverse for actuator allocation.
Main Results
- The WRBF network accurately estimates and compensates for uncertainties, ensuring fast finite-time convergence without singularity.
- The Mexican Hat wavelet improved learning speed and noise robustness over Gaussian RBF networks.
- Simulations confirmed superior tracking accuracy, convergence speed, and smoother response compared to conventional methods.
Conclusions
- The proposed unified FONTSMC and WRBF framework provides a robust and efficient solution for 6-DOF trajectory tracking in tilt-rotor quadrotors.
- The controller demonstrates significant advantages in handling uncertainties and disturbances, crucial for complex unmanned aerial vehicle (UAV) applications.
- The study validates the effectiveness of the integrated control architecture for advanced robotic flight control.
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