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<p>This paper investigates input–output constraints adaptive fuzzy control strategy with cooperative optimization approach of the gain and time-varying nonlinear disturbance observer for manipulator systems. First, the static control gain strategies cannot simultaneously optimize system performances during both dynamic and steady-state stages. To address this problem, a novel cooperative optimization approach of the gain (COG) based on tracking error is proposed to replace the traditional static gain strategies. Second, a time-varying nonlinear disturbance observer (NDO) is proposed to accurately estimate variable disturbances and mitigate harmful observation peak at the initial stage of manipulator tracking. Furthermore, an auxiliary system and an asymmetric time-varying barrier Lyapunov function are used to ensure that the inputs and outputs of the system remain within predefined constraints. Notably, the traditional backstepping control relies on precise model information. To minimize the impact of model uncertainties on tracking performance, an adaptive fuzzy control is employed to design the controller, eliminating the need for precise model information. Finally, the effectiveness of the proposed input–output constraints adaptive fuzzy control strategy with COG and time-varying NDO is verified and analyzed through comparative experiments on a two-joint manipulator platform.</p>

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Disturbance observer-based constrained adaptive fuzzy control for manipulator: a cooperative optimization approach of gain

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Qing Yang,
  • Haisheng Yu,
  • Xiangxiang Meng,
  • Wenqian Yu,
  • Qingkun Guo

摘要

This paper investigates input–output constraints adaptive fuzzy control strategy with cooperative optimization approach of the gain and time-varying nonlinear disturbance observer for manipulator systems. First, the static control gain strategies cannot simultaneously optimize system performances during both dynamic and steady-state stages. To address this problem, a novel cooperative optimization approach of the gain (COG) based on tracking error is proposed to replace the traditional static gain strategies. Second, a time-varying nonlinear disturbance observer (NDO) is proposed to accurately estimate variable disturbances and mitigate harmful observation peak at the initial stage of manipulator tracking. Furthermore, an auxiliary system and an asymmetric time-varying barrier Lyapunov function are used to ensure that the inputs and outputs of the system remain within predefined constraints. Notably, the traditional backstepping control relies on precise model information. To minimize the impact of model uncertainties on tracking performance, an adaptive fuzzy control is employed to design the controller, eliminating the need for precise model information. Finally, the effectiveness of the proposed input–output constraints adaptive fuzzy control strategy with COG and time-varying NDO is verified and analyzed through comparative experiments on a two-joint manipulator platform.