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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Adaptive NN-Based Event-Triggered Containment Control for Unknown Nonlinear Networked Systems.

Yukan Zheng, Yuan-Xin Li, Wei-Wei Che

    IEEE Transactions on Neural Networks and Learning Systems
    |September 10, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces novel event-triggered control for multiagent systems, enhancing performance with adaptive neural networks. The approach ensures efficient control by updating controllers aperiodically, preventing Zeno behavior.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Multiagent systems face challenges in distributed control due to unknown nonlinearities and external disturbances.
    • Existing control methods often require continuous communication and computation, leading to resource inefficiency.

    Purpose of the Study:

    • To develop a distributed event-triggered containment control strategy for multiagent systems.
    • To address unknown nonlinearities and external disturbances using adaptive neural networks.
    • To reduce computational and communication load through aperiodic controller updates.

    Main Methods:

    • Utilizing composite distributed adaptive neural network (NN) event-triggering conditions.
    • Designing an event-triggered controller that updates aperiodically.
    • Applying NN-based adaptive control and event-triggered control strategies.
    • Proving the exclusion of Zeno behavior.

    Main Results:

    • Achieved uniform ultimate bounded containment control for multiagent systems.
    • Demonstrated significant savings in computation, resources, and transmission load.
    • Successfully excluded Zeno behavior, ensuring practical implementation.

    Conclusions:

    • The proposed distributed event-triggered containment control scheme is effective for multiagent systems.
    • The use of adaptive neural networks and aperiodic updates enhances control efficiency and resource management.
    • The method provides a robust solution for complex networked systems facing uncertainties.