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Prescribed-time adaptive event-triggered control for robot manipulators based on command filtering.

Yongling Xia1, Yanbin Liu1, Weichao Sun2

  • 1School of Harbin Institute of Technology, Harbin 150000, China.

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|May 23, 2025
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Summary
This summary is machine-generated.

This study introduces a new control method for uncertain robotic manipulators, ensuring stability within a set time. The approach uses neural networks and event triggers to manage uncertainties and save resources.

Keywords:
Command filterEvent-triggeredNeural networksPrescribed-time

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Robotic manipulator control is challenged by model uncertainties and external disturbances.
  • Achieving precise control within a predefined time frame is crucial for many applications.
  • Existing methods often suffer from complexity explosion and filter errors.

Purpose of the Study:

  • To develop a novel prescribed-time adaptive event-triggered control scheme for uncertain manipulator systems.
  • To address model uncertainties and external disturbances effectively.
  • To ensure stability within a user-defined settling time without the complexity explosion problem.

Main Methods:

  • Utilized neural networks for handling manipulator model uncertainties.
  • Developed a piecewise function for a sufficient condition of prescribed-time stability.
  • Implemented an error compensation strategy to manage filter errors.
  • Introduced an adaptive estimation strategy for external disturbances.
  • Incorporated an event-trigger mechanism to optimize communication resources.

Main Results:

  • A novel prescribed-time adaptive event-triggered control scheme was successfully proposed.
  • The method decouples convergence domain and settling time into preset parameters.
  • The approach avoids the 'explosion of complexity' and compensates for filter errors.
  • External disturbances are adaptively compensated, and communication resources are saved.
  • Simulation results demonstrated the superiority of the proposed control approach.

Conclusions:

  • The proposed control scheme achieves prescribed-time stability for uncertain manipulator systems.
  • The method effectively handles model uncertainties, external disturbances, and filter errors.
  • The event-triggered mechanism enhances communication efficiency, making it suitable for real-world applications.