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  • 1School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

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Summary
This summary is machine-generated.

This study introduces an adaptive neural network (ANN) controller with feature augmentation (FA) for quadrotors, enhancing disturbance estimation and learning rate for stable flight control.

Keywords:
adaptive neural network (ANN)feature augmentation (FA)input-to-state stable (ISS)quadrotorstate predictor (SP)

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Quadrotor control is challenged by unknown internal and external disturbances.
  • Existing adaptive neural network (ANN) controllers require improved learning accuracy and rate.
  • Feature augmentation (FA) and state prediction (SP) offer potential solutions for enhanced ANN performance.

Purpose of the Study:

  • To design an adaptive neural network (ANN) controller for quadrotors.
  • To improve the learning accuracy and rate of the ANN controller using feature augmentation (FA) and a state predictor (SP).
  • To ensure the stability of the closed-loop control system.

Main Methods:

  • An adaptive neural network (ANN) controller with a two-component structure (position and attitude sub-controllers) was designed.
  • Feature augmentation (FA) was implemented to enhance the ANN's ability to learn data characteristics.
  • A state predictor (SP) was introduced to anticipate state errors and optimize the ANN's learning rate.

Main Results:

  • The proposed ANN controller effectively estimates unknown disturbance terms in quadrotors.
  • Feature augmentation (FA) improved the learning accuracy of the ANN.
  • The state predictor (SP) successfully increased the learning rate of the ANN.
  • Stability analysis confirmed the closed-loop system is input-to-state stable (ISS).

Conclusions:

  • The developed adaptive neural network (ANN) controller with feature augmentation (FA) and state prediction (SP) demonstrates superior performance for quadrotor control.
  • The controller effectively handles unknown disturbances, ensuring system stability.
  • Validated through simulations and experimental platforms, the proposed control algorithm offers a robust solution for quadrotor applications.