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Optimal input filters for iterative learning control systems with additive noises, random delays and data dropouts in both channels
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  • Lixun Huang,
  • Lijun Sun,
  • Tao Wang,
  • Weihua Liu,
  • Zhe Zhang,
  • Qiuwen Zhang
Lixun Huang
Zhengzhou University of Light Industry
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Lijun Sun
Henan University of Technology
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Tao Wang
Shanghai University
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Weihua Liu
Zhengzhou University of Light Industry
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Zhe Zhang
Zhengzhou University of Light Industry
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Qiuwen Zhang
Zhengzhou University of Light Industry
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Abstract

In wireless networked iterative learning control systems, the controller is separated from the plant, and additive noises, random delays and data dropouts arise in both sensor-to-controller and controller-to-actuator channels. In order to guarantee the convergence performance of such systems with the effect of these uncertainties, an input filter is designed based on a proportional iterative learning controller, so that updated inputs can be filtered at the actuator side. Specifically, two data transmission processes are first developed to describe the mix of those uncertainties in both channels by Bernoulli and Gaussian distributed variables with known distributions. Based on state augmentation, the two data transmission processes are further combined with the iterative learning process of controllers to build a unified filtering model. According to this unified model, an optimal filter is designed via the projection theory and implemented at the actuator side to filter the updated inputs in iteration domain. Moreover, the convergence performance of the filtering error covariance matrix is proved theoretically. Finally, some numerical results are given to illustrate the effectiveness of the proposed method.

Peer review status:UNDER REVIEW

22 Jan 2021Submitted to Mathematical Methods in the Applied Sciences
23 Jan 2021Assigned to Editor
23 Jan 2021Submission Checks Completed
29 Jan 2021Reviewer(s) Assigned