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Title: Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process

Abstract

In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a RBF neural network (NN) with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop sysyem, a geometric analysis based ILC method is adopted in the outer loop. Lastly, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.

Authors:
 [1];  [1];  [2];  [3]
  1. Northeastern Univ., Liaoning (China)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. State Key Laboratory of Synthetical Automation for Process Industries (China)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1482213
Report Number(s):
PNNL-SA-127287
Journal ID: ISSN 0278-0046
Grant/Contract Number:  
61290323, 61473064; AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Translations on Industrial Electronics
Additional Journal Information:
Journal Name: IEEE Translations on Industrial Electronics; Journal ID: ISSN 0278-0046
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Probability density function (PDF); stochastic distribution control (SDC); iterative learning control (ILC); geometric analysis; fiber length distribution (FLD); refining process

Citation Formats

Li, Ming jie, Zhou, Ping, Wang, Hong, and Chai, Tianyou. Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process. United States: N. p., 2018. Web. doi:10.1109/TIE.2018.2879293.
Li, Ming jie, Zhou, Ping, Wang, Hong, & Chai, Tianyou. Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process. United States. doi:10.1109/TIE.2018.2879293.
Li, Ming jie, Zhou, Ping, Wang, Hong, and Chai, Tianyou. Wed . "Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process". United States. doi:10.1109/TIE.2018.2879293.
@article{osti_1482213,
title = {Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process},
author = {Li, Ming jie and Zhou, Ping and Wang, Hong and Chai, Tianyou},
abstractNote = {In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a RBF neural network (NN) with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop sysyem, a geometric analysis based ILC method is adopted in the outer loop. Lastly, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.},
doi = {10.1109/TIE.2018.2879293},
journal = {IEEE Translations on Industrial Electronics},
issn = {0278-0046},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
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