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Related Experiment Video

Updated: Sep 13, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Improved Adaptive Sliding Mode Control Using Quasi-Convex Functions and Neural Network-Assisted Time-Delay Estimation

Jin Woong Lee1, Jae Min Rho2, Sun Gene Park2

  • 1Department of ICT Convergence Engineering, Soonchunhyang University, Asan 31538, Republic of Korea.

Sensors (Basel, Switzerland)
|July 30, 2025
PubMed
Summary

This study introduces an adaptive sliding mode control for robotic manipulators, using neural networks and a novel continuous gain function to reduce chattering and ensure stability. The method effectively suppresses vibrations and guarantees performance in robotic systems.

Keywords:
adaptive sling mode controlneural networksrobotic manipulatortime-delay controluniform ultimate boundedness

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Robotic manipulators require precise control for complex tasks.
  • Traditional control methods often suffer from chattering and instability.
  • Existing time-delay estimation (TDE) techniques can have errors that impact performance.

Purpose of the Study:

  • To develop an adaptive sliding mode control (SMC) strategy for robotic manipulators.
  • To enhance time-delay estimation (TDE) using neural networks and mitigate TDE errors.
  • To introduce a quasi-convex function-based continuous control gain to suppress chattering.

Main Methods:

  • Implemented an adaptive SMC strategy with a neural network-enhanced TDE.
  • Utilized radial basis function neural networks with a damped weight update law to compensate for TDE errors.
  • Proposed a quasi-convex function-based continuous gain to replace traditional switching gains.

Main Results:

  • The proposed continuous gain function effectively suppressed the chattering phenomenon.
  • The adaptive control strategy guaranteed uniform ultimate boundedness, ensuring system stability.
  • Simulation and experimental results validated the effectiveness of the proposed method.

Conclusions:

  • The novel adaptive sliding mode control strategy enhances robotic manipulator performance by reducing chattering.
  • The integration of neural networks for TDE compensation and quasi-convex functions for gain control offers a robust solution.
  • The study demonstrates a significant advancement in stable and precise robotic control.