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<p>This paper investigates the integration of an Interval Type-2 fuzzy system (IT2 FS), optimized through the modified shark smell optimization (MSSO) algorithm, into the control of an 8-cable-driven parallel robot (8-CDPR) to ensure precise position tracking. The Interval Type-2 sets, specifically designed to control four bottom cables and four top cables, are constructed based on system characteristics and inspired by human reasoning. The MSSO algorithm is redesigned from the original shark smell optimization (SSO) by incorporating a dedicated objective function and gradient descent to tune the output consequents of the IT2 FS. The modified Type-2 fuzzy logic controller (MT2 FLC), developed on the IT2 FS framework with MSSO, leverages the inherent strengths of the IT2 FS and further improves the control performance under disturbances caused by variations in the end-effector mass and uncertainties arising from the expansion of the base workspace. Simulation results validate that the proposed control strategy achieves the desired position and outperforms the non-optimized Type-2 fuzzy logic controller under disturbances and uncertainties.</p>

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Optimized Interval Type-2 Fuzzy Control Using Modified Shark Smell Algorithm for Precision Trajectory Tracking of 8-Cable-Driven Parallel Robots

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Duc-Huy Nguyen,
  • Thi-Van-Anh Nguyen

摘要

This paper investigates the integration of an Interval Type-2 fuzzy system (IT2 FS), optimized through the modified shark smell optimization (MSSO) algorithm, into the control of an 8-cable-driven parallel robot (8-CDPR) to ensure precise position tracking. The Interval Type-2 sets, specifically designed to control four bottom cables and four top cables, are constructed based on system characteristics and inspired by human reasoning. The MSSO algorithm is redesigned from the original shark smell optimization (SSO) by incorporating a dedicated objective function and gradient descent to tune the output consequents of the IT2 FS. The modified Type-2 fuzzy logic controller (MT2 FLC), developed on the IT2 FS framework with MSSO, leverages the inherent strengths of the IT2 FS and further improves the control performance under disturbances caused by variations in the end-effector mass and uncertainties arising from the expansion of the base workspace. Simulation results validate that the proposed control strategy achieves the desired position and outperforms the non-optimized Type-2 fuzzy logic controller under disturbances and uncertainties.