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A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

Chen Shen1,*, Youping Chen1, Bing Chen1, Jingming Xie1
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
* Corresponding Author: Chen Shen. Email: .

Computers, Materials & Continua 2019, 61(1), 379-397. https://doi.org/10.32604/cmc.2019.04883

Abstract

Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed. As such, the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system. This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations. The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties. Thus, the controller can reduce the need for manual adjustments. The controller applies nonlinear generalized predictive control to generate an adaptive control signal for attaining robust performance. A wavelet-based neural network model is adopted as the prediction model with high prediction precision and time-frequency localization characteristics. Online training is utilized to predict uncertain system dynamics by tuning the wavelet neural network parameters and the controller parameters adaptively. The performance of the controller algorithm is verified by both simulation, and in a real-time practical application involving a single-input single-output double-zone sliver drafting system used in textiles manufacturing. Both the simulation and practical results demonstrate an excellent control performance in terms of the mean thickness and coefficient of variation of output slivers, which verifies the effectiveness of this approach in improving the long-term uniformity of slivers.

Keywords

Continuous material processing, wavelet neural network (WNN), nonlinear generalized predictive control (NGPC), auto-leveling system

Cite This Article

C. Shen, Y. Chen, B. Chen and J. Xie, "A compensation controller based on a nonlinear wavelet neural network for continuous material processing operations," Computers, Materials & Continua, vol. 61, no.1, pp. 379–397, 2019.

Citations




This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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