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Improved fast non-singular adaptive super-twisting sliding mode control based on radial basis function neural network

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This study introduces a novel neural network-based adaptive control for robot joints, enhancing precision and disturbance rejection. The improved fast non-singular super-twisting control ensures robust performance in complex robotic applications.

Keywords:
Adaptive gainNon-singularRadial basis function neural networkRobot joint moduleSuper-twisting sliding mode control

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Robot joint modules require precise control for complex tasks.
  • Nonlinear friction and stiffness present significant challenges in robotic control.
  • Existing control schemes often struggle with singularity and external disturbances.

Purpose of the Study:

  • To develop an improved fast non-singular adaptive super-twisting control scheme for robot joint modules.
  • To enhance trajectory tracking accuracy and disturbance rejection capabilities.
  • To address precise control issues in robotic systems using neural network-based compensation.

Main Methods:

  • Established a second-order state-space model of robot joint modules using Lagrangian energy equations.
  • Proposed an improved fast non-singular terminal sliding surface to avoid singularity and accelerate convergence.
  • Designed a radial basis function neural network compensator for uncertain model factors and an adaptive switching control law.

Main Results:

  • The proposed control scheme demonstrated superior trajectory tracking performance under various reference trajectories.
  • Effective disturbance rejection capabilities were shown in the presence of external disturbances.
  • Simulation and experimental results validated the effectiveness and robustness of the control strategy.

Conclusions:

  • The novel neural network-based adaptive super-twisting control scheme significantly improves the precise control of robot joint modules.
  • The method offers enhanced robustness against model uncertainties and external disturbances.
  • The practical applicability for engineering is improved due to adaptive disturbance rejection without precise information.