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Optimized neural network based sliding mode control for quadrotors with disturbances.

Ping Li1, Zhe Lin1, Hong Shen1

  • 1College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China.

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|March 24, 2021
PubMed
Summary

This study introduces an optimized sliding mode control (SMC) strategy for quadrotors using radial basis function neural networks (RBFNNs) to handle unknown disturbances. Particle Swarm Optimization (PSO) further enhances control performance for smoother trajectory tracking.

Keywords:
disturbanceparticle swarm optimization (PSO)quadrotorradial basis function neural network (RBFNN)sliding mode control (SMC)

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence in Engineering
  • Aerospace Engineering

Background:

  • Quadrotors are susceptible to unknown external disturbances affecting their stability and trajectory tracking.
  • Traditional control methods struggle to compensate for these unpredictable dynamics.
  • Accurate control is crucial for applications like aerial surveillance, delivery, and exploration.

Purpose of the Study:

  • To develop an advanced control strategy for quadrotors capable of mitigating unknown disturbances.
  • To enhance trajectory tracking accuracy and smoothness using intelligent control techniques.
  • To validate the effectiveness of the proposed control method through comparative analysis.

Main Methods:

  • Modeling the quadrotor dynamics, explicitly including unknown external disturbances.
  • Implementing a sliding mode control (SMC) framework for position and attitude stabilization.
  • Utilizing optimized radial basis function neural networks (RBFNNs) to approximate unknown controller uncertainties.
  • Applying the Particle Swarm Optimization (PSO) algorithm to minimize approximation errors and tune RBFNN parameters.
  • Proving the convergence of state tracking errors theoretically.

Main Results:

  • The proposed RBFNN-based SMC strategy effectively compensates for unknown quadrotor disturbances.
  • The integration of Particle Swarm Optimization (PSO) significantly improves controller performance.
  • Comparative analysis demonstrates that the PSO-optimized approach yields quicker and smoother trajectory tracking compared to the non-optimized version.
  • Theoretical analysis confirms the convergence of state tracking errors, ensuring system stability.

Conclusions:

  • The optimized RBFNN-based SMC strategy offers a robust and effective solution for quadrotor control under unknown disturbances.
  • Particle Swarm Optimization is a valuable tool for enhancing the performance of neural network-based controllers in dynamic systems.
  • The proposed method significantly advances the state-of-the-art in quadrotor trajectory control, enabling more reliable autonomous flight.