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Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.

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This study introduces a shared control scheme using a self-learning neuro-fuzzy (SLNF) controller and an obstacle avoidance controller (OAC) to improve brain-computer interface (BCI) navigation for mobile robots. The new method enhances safety and control performance, achieving a 94.29% task completion rate.

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Area of Science:

  • Neuroscience
  • Robotics
  • Artificial Intelligence

Background:

  • Brain-computer interfaces (BCIs) enable direct brain-device communication, aiding individuals with neurological disorders.
  • Existing BCIs for mobile robots face safety and control limitations.
  • Electroencephalography (EEG) signals are converted into control commands for BCIs.

Purpose of the Study:

  • To propose a shared control scheme to enhance the safety and control performance of brain-controlled mobile robots.
  • To integrate a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC).
  • To improve the robustness and reliability of BCIs in real-world applications.

Main Methods:

  • Developed a shared control scheme combining a self-learning neuro-fuzzy (SLNF) controller and an obstacle avoidance controller (OAC).
  • The SLNF controller tracks user intentions while the OAC ensures robot safety.
  • Implemented a model-free SLNF controller capable of online parameter updates to mitigate disturbances.

Main Results:

  • The proposed SLNF controller achieved a 94.29% task completion rate, outperforming Direct BCI (79.29%) and Fuzzy-PID (92.86%).
  • Average task completion time was reduced to 85.31 seconds, compared to 92.01 seconds for Direct BCI and 86.16 seconds for Fuzzy-PID.
  • Demonstrated reduced settling time and overshoot, indicating enhanced control performance and robustness.

Conclusions:

  • The proposed shared control scheme significantly improves the safety, control performance, and robustness of brain-controlled mobile robots.
  • The SLNF controller effectively handles user intentions and external disturbances, leading to higher task success rates.
  • This approach offers a promising solution for enhancing independence and mobility for individuals with neurological impairments.