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This paper presents an adaptive terminal sliding mode control method for industrial robot manipulators. The proposed approach ensures finite-time error convergence without requiring prior knowledge of the bounds of uncertainties and external disturbances. This is accomplished by employing an RBF neural network and an adaptive control algorithm to estimate the upper limits of these uncertainties. Additionally, the controller eliminates chattering effects while maintaining robustness and accuracy. The stability of the control algorithm is rigorously validated using Lyapunov theory. The proposed controller has been tested through simulations on a three-degree-of-freedom robot to demonstrate its effectiveness. The simulation results confirm the controller's ability to handle uncertainties and disturbances effectively, highlighting its potential for real-world industrial applications.

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Adaptive Terminal Sliding Mode Control Using RBF Neural Network for Industrial Robot Manipulators

  • 会议论文

  • 发表日期：

    2025年

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  • Le Van Chuong,
  • Ho Sy Phuong,
  • Duong Dinh Tu,
  • Phan Van Du,
  • Dang Thai Son,
  • Mai The Anh,
  • Dinh Van Nam,
  • Ta Hung Cuong,
  • Phan Van Vy

摘要

This paper presents an adaptive terminal sliding mode control method for industrial robot manipulators. The proposed approach ensures finite-time error convergence without requiring prior knowledge of the bounds of uncertainties and external disturbances. This is accomplished by employing an RBF neural network and an adaptive control algorithm to estimate the upper limits of these uncertainties. Additionally, the controller eliminates chattering effects while maintaining robustness and accuracy. The stability of the control algorithm is rigorously validated using Lyapunov theory. The proposed controller has been tested through simulations on a three-degree-of-freedom robot to demonstrate its effectiveness. The simulation results confirm the controller's ability to handle uncertainties and disturbances effectively, highlighting its potential for real-world industrial applications.