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<p>This paper presents an adaptive saturated integral sliding mode controller (ASISMC) integrated with an adaptive extended state observer (AESO) to address the challenges of nonlinear time-varying characteristics, parameter uncertainties, and external disturbances in valve positioner systems (VPS). To begin with, the VPS dynamic model is established by combining the valve positioner model, the control valve model, and the Euler-Lagrange model, where the uncertain parameters are estimated using the adaptive laws. Moreover, a tracking differentiator (TD) is implemented to ensure smooth and continuous signal processing. The AESO with structural self-regulation capability is also developed to estimate system perturbations and uncertainties effectively. Meanwhile, the proposed ASISMC is designed using adaptive laws based on the backstepping technique, further enhanced by setting saturation constraints on the integral sliding film surface. Finally, experimental validation confirms the superiority of the proposed approach over existing methods in improving control precision, response velocity, and disturbance rejection capability.</p>

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Adaptive saturated integral sliding mode control for valve positioner system via an AESO

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Yan Shi,
  • Shijian Zhao,
  • Mingze Sun,
  • Zhibo Sun,
  • Shaofeng Xu,
  • Yushan Ma,
  • Yixuan Wang

摘要

This paper presents an adaptive saturated integral sliding mode controller (ASISMC) integrated with an adaptive extended state observer (AESO) to address the challenges of nonlinear time-varying characteristics, parameter uncertainties, and external disturbances in valve positioner systems (VPS). To begin with, the VPS dynamic model is established by combining the valve positioner model, the control valve model, and the Euler-Lagrange model, where the uncertain parameters are estimated using the adaptive laws. Moreover, a tracking differentiator (TD) is implemented to ensure smooth and continuous signal processing. The AESO with structural self-regulation capability is also developed to estimate system perturbations and uncertainties effectively. Meanwhile, the proposed ASISMC is designed using adaptive laws based on the backstepping technique, further enhanced by setting saturation constraints on the integral sliding film surface. Finally, experimental validation confirms the superiority of the proposed approach over existing methods in improving control precision, response velocity, and disturbance rejection capability.