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Related Experiment Video

Updated: Jan 3, 2026

Preparation and 3D Tracking of Catalytic Swimming Devices
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Model-Based Event-Triggered Tracking Control of Underactuated Surface Vessels With Minimum Learning Parameters.

Yingjie Deng, Xianku Zhang, Namkyun Im

    IEEE Transactions on Neural Networks and Learning Systems
    |November 26, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces event-triggered control (ETC) for underactuated surface vessels (USV), reducing data transmission by only updating control laws when needed. This adaptive model approach enhances tracking performance while managing system uncertainties.

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    Area of Science:

    • Marine Engineering
    • Control Systems Theory
    • Robotics

    Background:

    • Underactuated Surface Vessels (USV) require efficient control strategies to manage complex dynamics.
    • Traditional continuous control methods lead to high communication loads and computational complexity.
    • Event-Triggered Control (ETC) offers a promising alternative by reducing data transmission frequency.

    Purpose of the Study:

    • To develop a model-based Event-Triggered Control (ETC) scheme for the tracking control of underactuated surface vessels (USV).
    • To reduce communication traffic between sensors and controllers by eliminating continuous state acquisition.
    • To enhance control system robustness against internal and external uncertainties.

    Main Methods:

    • An adaptive model is employed, updated only when the triggering condition is violated.
    • Neural Networks (NNs) approximate system uncertainties, utilizing Minimum Learning Parameters (MLPs) to reduce computational load.
    • Dynamic Surface Control (DSC) is integrated to ensure smooth control law transitions and avoid complexity explosion.
    • Impulsive dynamic systems and Lyapunov functions are used to guarantee system stability (SGUUB).

    Main Results:

    • The proposed ETC scheme significantly reduces communication load compared to traditional methods.
    • Adaptive laws for MLPs are devised, with updates occurring only at triggering instants.
    • An adaptive triggering condition is derived, incorporating a dead-zone operator to prevent the Zeno phenomenon.
    • Simulations validate the effectiveness of the proposed control scheme, demonstrating robust tracking performance.

    Conclusions:

    • The developed model-based ETC with adaptive NNs and DSC provides an efficient and robust solution for USV tracking control.
    • The approach effectively minimizes communication overhead and computational complexity.
    • Guaranteed semiglobal uniformly ultimate boundedness (SGUUB) of error signals confirms the stability and reliability of the control system.