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Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning.

Wei He, Yiting Dong

    IEEE Transactions on Neural Networks and Learning Systems
    |April 1, 2017
    PubMed
    Summary
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    This study introduces adaptive fuzzy neural network (NN) control with impedance learning for robots. It ensures stable tracking performance despite unknown dynamics and state constraints in uncertain environments.

    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Robots operating in uncertain environments face challenges from unknown dynamics and state constraints.
    • Effective control strategies are crucial for robots interacting with compliant, unpredictable surroundings.

    Purpose of the Study:

    • To develop an adaptive fuzzy neural network (NN) control strategy for constrained robots.
    • To address unknown system dynamics, state constraints, and uncertain environment interactions.
    • To ensure stable and accurate trajectory tracking for the robot.

    Main Methods:

    • A fuzzy NN learning algorithm was developed to identify uncertain robot dynamics without prior knowledge.
    • Impedance learning was integrated to manage robot-environment interaction and guide desired destinations.

    Related Experiment Videos

  • Barrier Lyapunov functions were employed to enforce state constraints during control.

    • Main Results:

    • The proposed control scheme guarantees closed-loop system stability using Lyapunov's stability theory.
    • Accurate tracking performance is achieved even with unknown dynamics and state constraints.
    • Simulation studies validated the effectiveness of the adaptive fuzzy NN control approach.

    Conclusions:

    • The adaptive fuzzy NN control with impedance learning provides a robust solution for constrained robot control.
    • The method successfully handles unknown dynamics, state constraints, and uncertain environmental interactions.
    • This approach enhances robot performance and reliability in complex operational settings.