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Event-Triggered Consensus Control for Networked Underactuated Robotic Systems.

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    This study introduces novel event-triggered control algorithms for networked underactuated robotic systems, ensuring stable states and preventing Zeno behaviors while minimizing communication load for improved system performance.

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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Networked Systems

    Background:

    • Underactuated robotic systems present unique control challenges due to limited actuators.
    • Networked control systems require efficient communication strategies to manage data flow.
    • Event-triggered control offers a promising approach to reduce communication load.

    Purpose of the Study:

    • To develop novel event-triggered control algorithms for networked underactuated robotic systems.
    • To ensure consensus, stability, and avoid Zeno behaviors under fixed and switched communication networks.
    • To optimize the trade-off between communication load and system performance.

    Main Methods:

    • Design of distributed event-triggered control algorithms for fixed and switched networks.
    • Establishment of sufficient criteria for system stability and performance.
    • Development of a distributed sampled-data rule for efficient state updates.
    • Extension of results to other sampled-data control algorithms.

    Main Results:

    • Guaranteed convergence of active states and boundedness of passive actuator velocities.
    • Exclusion of Zeno behaviors in networked underactuated robotic systems.
    • Demonstrated trade-off optimization between communication load and system performance for fixed networks.
    • Validation of efficient state updating and reduced control costs under switched networks.

    Conclusions:

    • The proposed event-triggered control algorithms effectively manage networked underactuated robotic systems.
    • Novel criteria ensure stability and performance while minimizing communication.
    • The methods offer significant advantages for practical implementation and reduced computational burden.