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<p>In this paper, an adaptive sliding mode control (SMC) scheme with fractional integral design is proposed for Euler-Lagrange system (ELS) subject to disturbances. The adaptive predefined-time fractional integral SMC (APISMC) scheme is developed based on a fractional-order integral nonsingular terminal sliding mode, incorporating a predefined-time stability framework to guarantee rapid and predictable convergence. An adaptive scheme is employed to adjust the control gain online in response to system uncertainties and external disturbances without requiring prior knowledge of their bounds. Lyapunov-based analysis is conducted to prove the predefined-time convergence of the tracking errors and stability of the closed-loop system. Numerical simulations on a two-link manipulator demonstrate the effectiveness of the proposed control strategy in achieving accurate trajectory tracking and strong robustness to disturbances.</p>

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Adaptive Predefined-Time Fractional Order Integral Sliding Mode Control for Euler-Lagrange System with External Disturbances

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Zhenya Zhang,
  • Bo Xiao,
  • Tao Han,
  • Yuan Tan,
  • Xi-Sheng Zhan

摘要

In this paper, an adaptive sliding mode control (SMC) scheme with fractional integral design is proposed for Euler-Lagrange system (ELS) subject to disturbances. The adaptive predefined-time fractional integral SMC (APISMC) scheme is developed based on a fractional-order integral nonsingular terminal sliding mode, incorporating a predefined-time stability framework to guarantee rapid and predictable convergence. An adaptive scheme is employed to adjust the control gain online in response to system uncertainties and external disturbances without requiring prior knowledge of their bounds. Lyapunov-based analysis is conducted to prove the predefined-time convergence of the tracking errors and stability of the closed-loop system. Numerical simulations on a two-link manipulator demonstrate the effectiveness of the proposed control strategy in achieving accurate trajectory tracking and strong robustness to disturbances.