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    This study presents a new method for autonomous surface vehicles (ASVs) to follow paths in a set time. The algorithms ensure cooperative path following despite sensor limitations and system uncertainties.

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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Marine Engineering

    Background:

    • Underactuated autonomous surface vehicles (ASVs) face challenges including lack of velocity sensors, unmodeled dynamics, and actuator saturation.
    • Cooperative path following (CPF) is crucial for coordinated multi-vehicle operations but is complicated by these limitations.

    Purpose of the Study:

    • To investigate practical prescribed-time (PT) cooperative path following (CPF) for underactuated ASVs.
    • To develop algorithms that overcome sensor limitations and system uncertainties for synchronized path following within a specified time.

    Main Methods:

    • Designed a practical prescribed-time velocity observer (PTVO) to estimate unmeasurable velocities.
    • Developed a cooperative guidance law using aperiodic intermittent communication for kinematic control.
    • Designed an aperiodic intermittent neural network (NN) controller for dynamic control, addressing unmodeled dynamics and actuator saturation.
    • Constructed an intermittent adaptive law to estimate NN weights, reducing complexity.

    Main Results:

    • The PTVO successfully estimated unmeasurable velocity information.
    • The cooperative guidance law enabled synchronized path following with reduced communication load.
    • The NN controller effectively handled unmodeled dynamics and actuator saturation.
    • The closed-loop system demonstrated convergence to a residual set within the prescribed time interval.

    Conclusions:

    • The proposed algorithms effectively achieve prescribed-time cooperative path following for underactuated ASVs.
    • The methods address practical challenges like sensor limitations and actuator saturation.
    • Numerical simulations validated the effectiveness and robustness of the developed algorithms.