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Adaptive control for mobile robot using wavelet networks.

C R de Sousa1, E M Hemerly, R H Galvao

  • 1Dept. of Syst. & Control, Instituto Tecnologico de Aeronautica, Sao Jose Dos Campos.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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This study enhances mobile robot adaptive control using neural and wavelet networks. The controller online learns robot dynamics, adapting parameters and structure without prior knowledge, ensuring stability.

Area of Science:

  • Robotics
  • Control Theory
  • Artificial Intelligence

Background:

  • Adaptive control of mobile robots is crucial for autonomous navigation.
  • Existing methods often require prior knowledge of robot dynamics and network training.
  • Handling unmodeled dynamics and disturbances remains a challenge in robust robot control.

Purpose of the Study:

  • To improve adaptive control of mobile robots using neural and wavelet networks.
  • To develop a controller that learns robot dynamics online, adapting parameters and structure.
  • To provide a stability proof encompassing unmodeled dynamics and disturbances.

Main Methods:

  • Utilizing wavelet networks for adaptive control of mobile robots.
  • Employing the second method of Lyapunov for stability analysis.

Related Experiment Videos

  • Implementing online adaptation of wavelet network parameters and architecture.
  • Simulating the controller with parameters from the Magellan mobile robot.

    • Main Results:

    • The proposed controller demonstrates stability in the presence of unmodeled dynamics and disturbances.
    • All parameters within the wavelet networks are adapted online.
    • The wavelet network architecture is automatically adjusted online.
    • Simulation results validate the effectiveness of the adaptive control strategy.

    Conclusions:

    • The developed adaptive control method enhances mobile robot performance by learning dynamics online.
    • The controller's ability to adapt parameters and structure ensures robustness.
    • This approach eliminates the need for prior dynamic knowledge and network pre-training, simplifying implementation.