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Adaptive Super-Twisting Tracking for Uncertain Robot Manipulators Based on the Event-Triggered Algorithm.

Yajun Ma1,2, Hui Zhao3, Tao Li4

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an event-triggered adaptive control for robot manipulators, enhancing stability and performance. This method reduces computational load by updating control signals only when necessary, ensuring efficient operation.

Keywords:
Zeno free executionadaptive super-twistingevent-triggered controlrobot manipulator

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Area of Science:

  • Robotics
  • Control Systems
  • Nonlinear Dynamics

Background:

  • Robot manipulators are susceptible to external uncertainties and disturbances, impacting their performance and stability.
  • Traditional periodic control methods can be computationally intensive and inefficient for uncertain systems.

Purpose of the Study:

  • To develop an event-triggered control scheme for uncertain robot manipulators.
  • To enhance system-tracking performance and guarantee robust stability.
  • To reduce computational cost compared to traditional control methods.

Main Methods:

  • An event-triggered adaptive super-twisting control (ETASTC) method was proposed for multivariable second-order nonlinear systems.
  • The control signal is updated based on system stability requirements, avoiding frequent periodic execution.
  • Zeno-free execution of the triggering sequence was rigorously proven.

Main Results:

  • The proposed ETASTC method ensures system-tracking performance and robust stability for uncertain robot manipulators.
  • Numerical simulations and experimental studies demonstrated significant savings in computational cost.
  • The method successfully maintained the desired performance of the robot system.

Conclusions:

  • The event-triggered adaptive super-twisting control is an effective strategy for uncertain robot manipulator systems.
  • This approach offers a computationally efficient alternative to traditional periodic control methods.
  • The ETASTC method guarantees both performance and stability while minimizing resource utilization.