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<p>Repetitive motion represents one of the most common tasks in piezo-driven fast tool servo (FTS) systems, which are employed to track periodic trajectories for ultra-precision manufacturing of micro/nano-structures. However, the tracking control of such systems faces significant challenges due to complex disturbances and system uncertainties. To address these issues, this paper proposes a composite control scheme that integrates a feedforward iterative learning control (ILC) structure with disturbance compensation, aiming to improve the tracking performance of FTS systems under iteration-varying disturbances. An enhanced ILC strategy is first developed to learn feedforward control signals for tracking periodic references and compensating repetitive disturbances. By incorporating a dead-zone nonlinear function into the learning law, the learning gains are adjusted according to the amplitude of the error signal, which enables rapid attenuation of large iteration-invariant disturbances while maintaining robustness against iteration-varying disturbances. Furthermore, a disturbance observer (DOB) is introduced to estimate and compensate iteration-varying disturbances. The convergence of the proposed control scheme is rigorously analyzed, and design guidelines are provided. Comparative experiments conducted on an FTS prototype consistently confirm the effectiveness of the proposed method, demonstrating good tracking performance and strong anti-disturbance capability.</p>

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Enhanced Iterative Learning Control with Disturbance Observer for Trajectory Tracking of Fast Tool Servo Systems

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Yajie Jing,
  • Lingchen Meng,
  • Pengbo Liu,
  • Zhiming Zhang,
  • Fei Wang,
  • Guangchun Xiao,
  • Peng Yan

摘要

Repetitive motion represents one of the most common tasks in piezo-driven fast tool servo (FTS) systems, which are employed to track periodic trajectories for ultra-precision manufacturing of micro/nano-structures. However, the tracking control of such systems faces significant challenges due to complex disturbances and system uncertainties. To address these issues, this paper proposes a composite control scheme that integrates a feedforward iterative learning control (ILC) structure with disturbance compensation, aiming to improve the tracking performance of FTS systems under iteration-varying disturbances. An enhanced ILC strategy is first developed to learn feedforward control signals for tracking periodic references and compensating repetitive disturbances. By incorporating a dead-zone nonlinear function into the learning law, the learning gains are adjusted according to the amplitude of the error signal, which enables rapid attenuation of large iteration-invariant disturbances while maintaining robustness against iteration-varying disturbances. Furthermore, a disturbance observer (DOB) is introduced to estimate and compensate iteration-varying disturbances. The convergence of the proposed control scheme is rigorously analyzed, and design guidelines are provided. Comparative experiments conducted on an FTS prototype consistently confirm the effectiveness of the proposed method, demonstrating good tracking performance and strong anti-disturbance capability.