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Iterative Learning Control Without Resetting Conditions of an Algorithm Based on a Finite-Time Zeroing Neural

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Summary

This study introduces a novel robotic manipulator control method, the non-resetting iterative learning control without resetting conditions based on a finite-time zeroing neural network (NRCILC-FTZNN), for precise trajectory tracking. The new approach significantly reduces tracking errors and improves convergence speed, even with external disturbances.

Keywords:
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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Robotic manipulators often face challenges with trajectory tracking accuracy due to external disturbances.
  • Existing iterative learning control (ILC) methods may require specific reset conditions, limiting their practical application in repetitive tasks.

Purpose of the Study:

  • To design an advanced iterative learning control strategy for robotic manipulators that operates without reset conditions.
  • To enhance trajectory tracking performance by mitigating external disturbances and improving convergence speed.

Main Methods:

  • Development of a finite-time zeroing neural network (FTZNN) to effectively eliminate external disturbances.
  • Proposal of a non-resetting iterative learning control without resetting conditions (NRCILC) framework that integrates FTZNN.
  • The NRCILC-FTZNN automatically provides initial state values, removing the need for reset conditions in each iteration.

Main Results:

  • The proposed NRCILC-FTZNN method achieved significant reductions in trajectory-tracking errors, with mean absolute error (MAE) improvements of 46.89% and 63.29% compared to other control schemes.
  • Tracking errors converged to zero in fewer iterations than conventional methods.
  • Simulations confirmed the robustness and effectiveness of the NRCILC-FTZNN scheme in robotic manipulator systems operating under disturbances.

Conclusions:

  • The NRCILC-FTZNN is a highly effective control strategy for robotic manipulators, offering superior trajectory tracking performance.
  • The elimination of reset conditions and the integration of FTZNN provide a robust and efficient solution for repetitive tasks with external disturbances.