Improved frequency domain iterative learning control applied to trajectory tracking

Zhu, Junhan; Cai, Zhihao; Zhao, Jiang; Wang, Yingxun

doi:10.1109/ascc.2017.8287168

Cited by 2 publications

(1 citation statement)

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“…Although there existed an updating for the learning law in [21], this proposed algorithm could not achieve an unbiased estimation of the inverse model and its estimation would certainly be affected by the noise. Additionally, there are some ILC methods designed in the frequency domain [22]- [24], but their learning laws were calculated based on the nominal plant model which would probably result in a limitation on tracking performance due to the model error.…”

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confidence: 99%

Frequency-Domain Data-Driven Adaptive Iterative Learning Control Approach: With Application to Wafer Stage

Yang

Zanchetta

et al. 2021

IEEE Trans. Ind. Electron.

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The feedforward control is becoming increasingly important in ultra-precision stages. However, the conventional model-based methods can not achieve expected performance in new-generation stages since it is hard to obtain the accurate plant model due to the complicated stage dynamical properties. To tackle this problem, this paper develops a model-free data-driven adaptive iterative learning approach that is designed in the frequency-domain. Explicitly, the proposed method utilizes the frequency-response data to learn and update the output of the feedforward controller online, which benefits that both the structure and parameters of the plant model are not required. An unbiased estimation method for the frequency response of the closed-loop system is proposed and proved through the theoretical analysis. Comparative experiments on a linear motor confirm the effectiveness and superiority of the proposed method, and show that it has the ability to avoid the performance deterioration caused by the model mismatch with the increasing iterative trials.Index Terms-adaptive ILC, data-driven, frequency domain, feedforward control, linear motor. I. INTRODUCTIONN OWADAYS, the ultra-precision motion stages have been widely applied in many nanoscale manufacturing industries like IC manufacturing [1]. The precision motion stage has Manuscript

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confidence: 99%