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<p>This paper presents a novel shared control framework for a <i>Snake-like Soft Robot</i> (SSR) inspired by the dynamic modeling of a 2 <i>Spherical-Prismatic-Spherical</i> (2S<Emphasis Type="Underline">P</Emphasis>S) parallel robot. The SSR is modeled as a parallel mechanism with two actuated limbs, reducing actuator count and cost without compromising motion capabilities. Then, the nonlinear dynamics of the robot are transformed into a <i>State-Dependent Coefficient</i> (SDC) parameterization form, enabling the design of a shared control scheme based on the <i>Hamilton-Jacobi-Bellman</i> (HJB) theory. The control architecture integrates multiple sub-controllers, including <i>Time-Delay Estimation</i> (TDE), <i>Pole-Placement Control</i> (PPC), <i>Adaptive Sliding Mode Control</i> (ASMC), and a suboptimal controller, whose contributions are dynamically adjusted through predefined sharing functions. The effectiveness of the proposed controller is validated through stability analysis based on Lyapunov’s second method, followed by extensive simulations and experimental results. The findings confirm that the shared control framework enhances tracking accuracy and robustness against external disturbances and model uncertainties, outperforming existing control strategies. The study highlights the potential of shared control for robotic systems.</p>

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Enhanced maneuverability in small-scale soft robot: a shared control framework with model-based sub-controllers derived from parallel robot dynamics

  • 期刊论文

  • 发表日期：

    2025年8月25日

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  • Hanie Marufkhani,
  • Mohammad A. Khosravi,
  • Farzaneh Abdollahi,
  • Mohammad Zareinejad

摘要

This paper presents a novel shared control framework for a Snake-like Soft Robot (SSR) inspired by the dynamic modeling of a 2 Spherical-Prismatic-Spherical (2SPS) parallel robot. The SSR is modeled as a parallel mechanism with two actuated limbs, reducing actuator count and cost without compromising motion capabilities. Then, the nonlinear dynamics of the robot are transformed into a State-Dependent Coefficient (SDC) parameterization form, enabling the design of a shared control scheme based on the Hamilton-Jacobi-Bellman (HJB) theory. The control architecture integrates multiple sub-controllers, including Time-Delay Estimation (TDE), Pole-Placement Control (PPC), Adaptive Sliding Mode Control (ASMC), and a suboptimal controller, whose contributions are dynamically adjusted through predefined sharing functions. The effectiveness of the proposed controller is validated through stability analysis based on Lyapunov’s second method, followed by extensive simulations and experimental results. The findings confirm that the shared control framework enhances tracking accuracy and robustness against external disturbances and model uncertainties, outperforming existing control strategies. The study highlights the potential of shared control for robotic systems.