Injection Speed Optimization Based on Improved Generalized Predictive Control

Jia Bao; Haiyang Hu

doi:doi:10.11648/j.eas.20220706.11

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Injection Speed Optimization Based on Improved Generalized Predictive Control

Jia Bao, Haiyang Hu

Published in Engineering and Applied Sciences (Volume 7, Issue 6)

Received: 28 October 2022 Accepted: 14 November 2022 Published: 29 November 2022

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Abstract

Injection molding is a typical nonlinear system, in which there is a need for high-precision control of injection velocity to produce sophisticated products. In view of the shortcomings in control precision of existing control systems, this paper proposes an improved generalized predictive control (GPC) model for high-precision injection velocity control. The velocity response curves are studied and corresponding control action coefficients under step disturbance with different velocity constants are determined based on the characteristics of curves. To overcome large overshoot and insufficient accuracy when controlling large delay processes, the softening factor is changed to a dynamic softening factor and the initial value of reference trajectory is determined with a new manner. To verify the performance of the propsed model, extensive simulation and experimental analysis are conducted considering parameters including horizon length, prediction horizon length, control horizon length, control weighting factor and softening coefficient. The resultsreveal that the improved GPC model achieves fairly high accuracy for the control of injection velocity, the errors is controlled within 0.05 cm/s, which can meet the injection precision requirement of actual injection molding machines. Moreover, the model can guarantee the starting and finishing ends of prediction horizon to overcome the over-regulation occurring in high precision control with other algorithms, meanwhile, the model also improves the control response velocity.

Published in	Engineering and Applied Sciences (Volume 7, Issue 6)
DOI	10.11648/j.eas.20220706.11
Page(s)	77-84
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Generalized Predictive Control, Nonlinear System, Velocity Control, Control Precision

References

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APA Style

Jia Bao, Haiyang Hu. (2022). Injection Speed Optimization Based on Improved Generalized Predictive Control. Engineering and Applied Sciences, 7(6), 77-84. https://doi.org/10.11648/j.eas.20220706.11

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ACS Style

Jia Bao; Haiyang Hu. Injection Speed Optimization Based on Improved Generalized Predictive Control. Eng. Appl. Sci. 2022, 7(6), 77-84. doi: 10.11648/j.eas.20220706.11

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AMA Style

Jia Bao, Haiyang Hu. Injection Speed Optimization Based on Improved Generalized Predictive Control. Eng Appl Sci. 2022;7(6):77-84. doi: 10.11648/j.eas.20220706.11

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@article{10.11648/j.eas.20220706.11,
  author = {Jia Bao and Haiyang Hu},
  title = {Injection Speed Optimization Based on Improved Generalized Predictive Control},
  journal = {Engineering and Applied Sciences},
  volume = {7},
  number = {6},
  pages = {77-84},
  doi = {10.11648/j.eas.20220706.11},
  url = {https://doi.org/10.11648/j.eas.20220706.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20220706.11},
  abstract = {Injection molding is a typical nonlinear system, in which there is a need for high-precision control of injection velocity to produce sophisticated products. In view of the shortcomings in control precision of existing control systems, this paper proposes an improved generalized predictive control (GPC) model for high-precision injection velocity control. The velocity response curves are studied and corresponding control action coefficients under step disturbance with different velocity constants are determined based on the characteristics of curves. To overcome large overshoot and insufficient accuracy when controlling large delay processes, the softening factor is changed to a dynamic softening factor and the initial value of reference trajectory is determined with a new manner. To verify the performance of the propsed model, extensive simulation and experimental analysis are conducted considering parameters including horizon length, prediction horizon length, control horizon length, control weighting factor and softening coefficient. The resultsreveal that the improved GPC model achieves fairly high accuracy for the control of injection velocity, the errors is controlled within 0.05 cm/s, which can meet the injection precision requirement of actual injection molding machines. Moreover, the model can guarantee the starting and finishing ends of prediction horizon to overcome the over-regulation occurring in high precision control with other algorithms, meanwhile, the model also improves the control response velocity.},
 year = {2022}
}

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TY  - JOUR
T1  - Injection Speed Optimization Based on Improved Generalized Predictive Control
AU  - Jia Bao
AU  - Haiyang Hu
Y1  - 2022/11/29
PY  - 2022
N1  - https://doi.org/10.11648/j.eas.20220706.11
DO  - 10.11648/j.eas.20220706.11
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 77
EP  - 84
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20220706.11
AB  - Injection molding is a typical nonlinear system, in which there is a need for high-precision control of injection velocity to produce sophisticated products. In view of the shortcomings in control precision of existing control systems, this paper proposes an improved generalized predictive control (GPC) model for high-precision injection velocity control. The velocity response curves are studied and corresponding control action coefficients under step disturbance with different velocity constants are determined based on the characteristics of curves. To overcome large overshoot and insufficient accuracy when controlling large delay processes, the softening factor is changed to a dynamic softening factor and the initial value of reference trajectory is determined with a new manner. To verify the performance of the propsed model, extensive simulation and experimental analysis are conducted considering parameters including horizon length, prediction horizon length, control horizon length, control weighting factor and softening coefficient. The resultsreveal that the improved GPC model achieves fairly high accuracy for the control of injection velocity, the errors is controlled within 0.05 cm/s, which can meet the injection precision requirement of actual injection molding machines. Moreover, the model can guarantee the starting and finishing ends of prediction horizon to overcome the over-regulation occurring in high precision control with other algorithms, meanwhile, the model also improves the control response velocity.
VL  - 7
IS  - 6
ER  -

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Author Information

Jia Bao

Science Technology Department of Zhejiang Province, Hangzhou, China
Haiyang Hu

College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

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APA Style

Jia Bao, Haiyang Hu. (2022). Injection Speed Optimization Based on Improved Generalized Predictive Control. Engineering and Applied Sciences, 7(6), 77-84. https://doi.org/10.11648/j.eas.20220706.11

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ACS Style

Jia Bao; Haiyang Hu. Injection Speed Optimization Based on Improved Generalized Predictive Control. Eng. Appl. Sci. 2022, 7(6), 77-84. doi: 10.11648/j.eas.20220706.11

Copy | Download

AMA Style

Jia Bao, Haiyang Hu. Injection Speed Optimization Based on Improved Generalized Predictive Control. Eng Appl Sci. 2022;7(6):77-84. doi: 10.11648/j.eas.20220706.11

Copy | Download

@article{10.11648/j.eas.20220706.11,
  author = {Jia Bao and Haiyang Hu},
  title = {Injection Speed Optimization Based on Improved Generalized Predictive Control},
  journal = {Engineering and Applied Sciences},
  volume = {7},
  number = {6},
  pages = {77-84},
  doi = {10.11648/j.eas.20220706.11},
  url = {https://doi.org/10.11648/j.eas.20220706.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20220706.11},
  abstract = {Injection molding is a typical nonlinear system, in which there is a need for high-precision control of injection velocity to produce sophisticated products. In view of the shortcomings in control precision of existing control systems, this paper proposes an improved generalized predictive control (GPC) model for high-precision injection velocity control. The velocity response curves are studied and corresponding control action coefficients under step disturbance with different velocity constants are determined based on the characteristics of curves. To overcome large overshoot and insufficient accuracy when controlling large delay processes, the softening factor is changed to a dynamic softening factor and the initial value of reference trajectory is determined with a new manner. To verify the performance of the propsed model, extensive simulation and experimental analysis are conducted considering parameters including horizon length, prediction horizon length, control horizon length, control weighting factor and softening coefficient. The resultsreveal that the improved GPC model achieves fairly high accuracy for the control of injection velocity, the errors is controlled within 0.05 cm/s, which can meet the injection precision requirement of actual injection molding machines. Moreover, the model can guarantee the starting and finishing ends of prediction horizon to overcome the over-regulation occurring in high precision control with other algorithms, meanwhile, the model also improves the control response velocity.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Injection Speed Optimization Based on Improved Generalized Predictive Control
AU  - Jia Bao
AU  - Haiyang Hu
Y1  - 2022/11/29
PY  - 2022
N1  - https://doi.org/10.11648/j.eas.20220706.11
DO  - 10.11648/j.eas.20220706.11
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 77
EP  - 84
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20220706.11
AB  - Injection molding is a typical nonlinear system, in which there is a need for high-precision control of injection velocity to produce sophisticated products. In view of the shortcomings in control precision of existing control systems, this paper proposes an improved generalized predictive control (GPC) model for high-precision injection velocity control. The velocity response curves are studied and corresponding control action coefficients under step disturbance with different velocity constants are determined based on the characteristics of curves. To overcome large overshoot and insufficient accuracy when controlling large delay processes, the softening factor is changed to a dynamic softening factor and the initial value of reference trajectory is determined with a new manner. To verify the performance of the propsed model, extensive simulation and experimental analysis are conducted considering parameters including horizon length, prediction horizon length, control horizon length, control weighting factor and softening coefficient. The resultsreveal that the improved GPC model achieves fairly high accuracy for the control of injection velocity, the errors is controlled within 0.05 cm/s, which can meet the injection precision requirement of actual injection molding machines. Moreover, the model can guarantee the starting and finishing ends of prediction horizon to overcome the over-regulation occurring in high precision control with other algorithms, meanwhile, the model also improves the control response velocity.
VL  - 7
IS  - 6
ER  -

Copy | Download