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A data-driven feedforward control combining feedforward tuning and cascaded iterative learning control
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AI
DOI：10.1016/j.measurement.2025.117414.png)
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Abstract
En 中文
Feedforward control can effectively enhance the accuracy of control systems. This paper introduces a data-driven feedforward control method that combines iterative feedforward parameter tuning with cascaded iterative learning control (CILC). The proposed approach employs a feedforward parameterization technique with an input shaping filter (CFT) to obtain an optimal feedforward controller, effectively eliminating errors induced by the reference trajectory and significantly enhancing extrapolation capability for trajectory variations. CILC builds upon standard iterative learning control (ILC) by incorporating an external iteration loop, which more fully utilizes the ability of ILC to suppress repetitive disturbances in the system, and further improve control accuracy. The proposed method integrates the flexibility of iterative feedforward tuning with the high tracking accuracy of CILC and is validated through theoretical analysis and simulation. Additionally, experiments conducted on a wafer stage confirm the effectiveness and practical value of this approach.
Keywords:
Feedforward control
Data-driven control
Wafer stage