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<p>Parametric uncertainties and unknown external loads in an electro-hydraulic actuator system inevitably degrade both tracking performance and closed-loop stability. This study investigates a composite observer-based backstepping control scheme with tracking error constraints to enhance position tracking performance in the presence of disturbances. In the developed framework, extended sliding mode observer and linear extended state observer are employed to simultaneously estimate both unmeasurable states and modeling uncertainties. The integration of a barrier Lyapunov function with dynamic surface control is effective in avoiding both the violation of tracking error constraints while circumventing the computational burden inherent in traditional backstepping iterations. Specifically, first-order low-pass filters within the dynamic surface control framework are utilized to obtain filtered virtual control laws and their derivatives, thereby eliminating the explosion of complexity often encountered in the backstepping scheme. Furthermore, a data-driven flow feedforward compensator is implemented to reduce the reliance on feedback controller. Comparative simulation and experimental results demonstrate the effectiveness of the proposed algorithm, showing rapid convergence of the position tracking error.</p>

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Dynamic surface control based on composite observer for electro-hydraulic systems with error constraints

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Xiaofan Lyu,
  • Pingjiang Wang,
  • Shinan Lin,
  • Deyu Su,
  • Chong Zhu,
  • Xiaoyu Wang

摘要

Parametric uncertainties and unknown external loads in an electro-hydraulic actuator system inevitably degrade both tracking performance and closed-loop stability. This study investigates a composite observer-based backstepping control scheme with tracking error constraints to enhance position tracking performance in the presence of disturbances. In the developed framework, extended sliding mode observer and linear extended state observer are employed to simultaneously estimate both unmeasurable states and modeling uncertainties. The integration of a barrier Lyapunov function with dynamic surface control is effective in avoiding both the violation of tracking error constraints while circumventing the computational burden inherent in traditional backstepping iterations. Specifically, first-order low-pass filters within the dynamic surface control framework are utilized to obtain filtered virtual control laws and their derivatives, thereby eliminating the explosion of complexity often encountered in the backstepping scheme. Furthermore, a data-driven flow feedforward compensator is implemented to reduce the reliance on feedback controller. Comparative simulation and experimental results demonstrate the effectiveness of the proposed algorithm, showing rapid convergence of the position tracking error.