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Updated: Oct 3, 2025

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Research on Robot Fuzzy Neural Network Motion System Based on Artificial Intelligence.

Jie Hu1

  • 1Suzhou University, Institute of Physical Education, Suzhou 234000, Anhui, China.

Computational Intelligence and Neuroscience
|February 21, 2022
PubMed
Summary

A novel intelligent controller using a self-learning interval type-II fuzzy neural network enhances industrial robot adaptability. This system improves trajectory tracking accuracy and stability, even with system uncertainties, for better robot performance.

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Industrial robots require adaptive and robust motion controllers to handle uncertainties.
  • Conventional controllers often struggle with complex dynamics and environmental changes.
  • Intelligent control offers a promising approach to enhance robot performance.

Purpose of the Study:

  • To propose an intelligent controller for industrial robots based on a self-learning interval type-II fuzzy neural network.
  • To enhance the adaptability, trajectory tracking accuracy, and robustness of robot motion control.
  • To verify the effectiveness of the proposed controller and a reusable particle swarm optimal motion planning method.

Main Methods:

  • Development of a parallel intelligent controller integrating an interval type-II fuzzy neural network and a PD controller.
  • Design of the interval type-II fuzzy neural network using a slave design method and a dual sequence symmetric trapezoidal membership function arrangement.
  • Implementation of a parametric self-learning algorithm based on sliding mode control theory for online parameter adjustment and stability analysis using Lyapunov's theorem.

Main Results:

  • Simulations demonstrated significant improvements in trajectory tracking accuracy and robustness for a Delta parallel robot under system uncertainty.
  • Experimental validation confirmed the intelligent controller's effectiveness in enhancing robot trajectory tracking accuracy and stability.
  • The reusable particle swarm optimal motion planning method efficiently solved complex robot motion planning problems online.

Conclusions:

  • The self-learning interval type-II fuzzy neural network controller provides high adaptability and improved performance for industrial robot motion control.
  • The proposed controller is effective in overcoming system uncertainties and ensuring stable and accurate trajectory tracking.
  • The integrated approach of intelligent control and optimal motion planning offers a robust solution for complex robotic applications.