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Adaptive control of mobile robots using a neural network.

C de Sousa Júnior1, E M Hermerly

  • 1Department of Systems and Control, Instituto Tecnológico de Aeronáutica, Brazil. celso_de_sousa_junior@hotmail.com

International Journal of Neural Systems
|September 28, 2001
PubMed
Summary

This study introduces a novel neural network control for mobile robots, enabling real-time adaptation without prior training. The approach effectively handles navigation challenges like model uncertainties and disturbances.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Mobile robot navigation requires robust control strategies.
  • Traditional methods often struggle with real-time adaptation to uncertainties.
  • Online learning offers a promising alternative for dynamic environments.

Purpose of the Study:

  • To propose a Neural Network-based control approach for mobile robots.
  • To enable on-line weight adaptation without prior learning.
  • To address uncertainties and disturbances in robot navigation.

Main Methods:

  • Development of a Neural Network control architecture.
  • Implementation of on-line weight adaptation laws.
  • Simulation of various robot navigation scenarios.

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Main Results:

  • Demonstrated successful on-line weight adaptation.
  • Validated the control approach under model uncertainties.
  • Showcased effectiveness in the presence of disturbances.

Conclusions:

  • The proposed Neural Network control is effective for mobile robot navigation.
  • On-line adaptation enhances robustness in uncertain environments.
  • The method provides a viable solution for real-time robot control.