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Adaptive NN Optimal Consensus Fault-Tolerant Control for Stochastic Nonlinear Multiagent Systems.

Kewen Li, Yongming Li

    IEEE Transactions on Neural Networks and Learning Systems
    |August 25, 2021
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    Summary

    This study presents an adaptive neural network control for nonlinear multiagent systems, ensuring consensus tracking despite disturbances and faults. The developed fault-tolerant control algorithm guarantees system stability and performance.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Nonlinear multiagent systems (MASs) face challenges from stochastic disturbances and actuator faults.
    • Achieving optimal consensus tracking in such systems requires robust control strategies.
    • Existing methods may not adequately address both unknown dynamics and fault tolerance simultaneously.

    Purpose of the Study:

    • To develop an adaptive neural network (NN) optimal consensus tracking control for nonlinear MASs.
    • To address stochastic disturbances and time-varying actuator bias faults.
    • To ensure fault-tolerant control and maintain consensus.

    Main Methods:

    • Utilizing NNs to approximate unknown nonlinear dynamics.
    • Constructing a state identifier and a fault estimator for actuator bias faults.
    • Employing adaptive dynamic programming (ADP) for identifier-critic-actor framework.
    • Designing an adaptive NN optimal consensus fault-tolerant control algorithm.

    Main Results:

    • Demonstrated uniform ultimate boundedness (UUB) of all system signals in probability.
    • Ensured follower agents' states achieve consensus with the leader's state.
    • Validated the effectiveness of the proposed optimal consensus control scheme through simulations.

    Conclusions:

    • The developed adaptive NN optimal consensus fault-tolerant control is effective for nonlinear MASs.
    • The control scheme successfully handles stochastic disturbances and actuator bias faults.
    • The proposed method ensures system stability and achieves consensus tracking performance.