Menu
Publications
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2021-21-5-646-652
Improving the quality of network management of technological processes
Read the full article ';
Article in Russian
For citation:
Abstract
For citation:
Emelyanov A.E., Sukhanova N.V. Improving the quality of network management of technological processes. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 5, pp. 646–652 (in Russian). doi: 10.17586/2226-1494-2021-21-5-646-652
Abstract
Modern control systems use digital networks for data transmission. Such systems have random delays and loss of data packets. The aim of the research is to study the impact of data buffering on the quality of process control focusing on systems with a limited buffer volume for data packets by simulation modeling and compensation of this influence using the Smith predictor. A distinctive feature of the proposed solution is the compensation for accidental delay. To improve the network management quality of technological processes, the authors proposed to use the Smith predictor. The Smith predictor includes a model of the object and a buffer for data packets. The buffer is used to generate a random delay time. Its operation is determined by the mode of data transmission over the network channel. The simulation of the functioning of the network control system was performed in the Simulink environment of the Matlab system. The novelty of the developed simulation model lies in the fact that its development is based on modeling the time break of the information flow. The simulation was carried out for the volumes of data packet buffers ranging from 1 to 5 and the probability of data transmission ranging from 0.9 to 0.4. The results of the study proved that the use of the Smith predictor to compensate for random delay significantly increases the quality of transients of network control systems. It is shown that the use of the Smith predictor significantly improves the quality of network systems. The developed simulation models can be used in the design of new networked control systems and in the modernization of the systems already used in practice.
Keywords: network control, buffer, random delay, packet loss, Smith predictor, simulation modeling
References
References
1. Reyes-Yanes A., Martinez P., Ahmad R. Real-time growth rate and fresh weight estimation for little gem romaine lettuce in aquaponic grow beds. Computers and Electronics in Agriculture, 2020, vol. 179, pp. 105827. https://doi.org/10.1016/j.compag.2020.105827
2. Narushin V.G., Romanov M.N., Lu G., Cugley J., Griffin D.K. Digital imaging assisted geometry of chicken eggs using Hügelschäffer's model. Biosystems Engineering, 2020, vol. 197, pp. 45–55. https://doi.org/10.1016/j.biosystemseng.2020.06.008
3. Povarov V., Danilov A., Burkovsky V., Gusev K. Data support system for controlling decentralised nuclear power industry facilities through uninterruptible condition monitoring. MATEC Web of Conferences, 2018, vol. 161, pp. 02012. https://doi.org/10.1051/matecconf/201816102012
4. Akashi S., Ishii H., Cetinkaya A. Self-triggered control with tradeoffs in communication and computation. Automatica, 2018, vol. 94, pp. 373–380. https://doi.org/10.1016/j.automatica.2018.04.028
5. Li H., Yan W., Shi Y. Triggering and control codesign in self-triggered model predictive control of constrained systems: With guaranteed performance. IEEE Transactions on Automatic Control, 2018, vol. 63, no. 11, pp. 4008–4015. https://doi.org/10.1109/TAC.2018.2810514
6. Zietkiewicz J., Horla D., Owczarkowski A. Sparse in the time stabilization of a bicycle robot model: Strategies for event- and self-triggered control approaches. Robotics, 2018, vol. 7, no. 4, pp. 77. https://doi.org/10.3390/robotics7040077
7. Hua M-D., Hamel T., Morin P., Samson C. Introduction to feedback control of underactuated VTOL vehicles: a review of basic control design ideas and principles. IEEE Control Systems, 2013, vol. 33, no. 1, pp. 61–75. https://doi.org/10.1109/MCS.2012.2225931
8. Hua M.-D., Hamel T., Morin P., Samson C. Automatica Control of VTOL vehicles with thrust-tilting augmentation. Automatica, 2015, vol. 52, pp. 1–7. https://doi.org/10.1016/j.automatica.2014.10.129
9. Nguyen L.-H., Hua M.-D., Hamel T. A nonlinear control approach for trajectory tracking of slender-body axisymmetric underactuated underwater vehicles. Proc. 18th European Control Conference (ECC), 2019, pp. 4053–4060. https://doi.org/10.23919/ECC.2019.8795880
10. Hua M.-D., Trumpf J., Hamel T., Mahony R., Morin P. Nonlinear observer design on SL(3) for homography estimation by exploiting point and line correspondences with application to image stabilization. Automatica, 2020, vol. 115, pp. 108858. https://doi.org/10.1016/j.automatica.2020.108858
11. You K-Y., Xie L-H. Survey of recent progress in networked control systems. Zidonghua Xuebao/Acta Automatica Sinica, 2013, vol. 39, no. 2, pp. 101–118. https://doi.org/10.3724/SP.J.1004.2013.00101
12. Chen X., Hao F. Periodic event-triggered state-feedback and output-feedback control for linear systems. International Journal of Control, Automation and Systems, 2015, vol. 13, no. 4, p. 779–787. https://doi.org/10.1007/s12555-013-0318-z
13. Kravets O.Ja., Choporov O.N. The problems and peculiarities of modelling integrated systems of heterogeneous traffic service. Journal of Siberian Federal University - Mathematics and Physics, 2018, vol. 11, no. 5, pp. 581–587. http://dx.doi.org/10.17516/1997-1397-2018-11-5-581-587
14. Kravets O.J., Ryzhkov A.P., Krasnovskiy E.E. Modelling heterogeneous data transmission systems based on queueing system networks. International Journal of Advanced Trends in Computer Science and Engineering, 2020, vol. 9, no. 4, pp. 6393–6399. https://doi.org/10.30534/ijatcse/2020/323942020
15. Kravets O.J., Shaytura S.V., Minitaeva A.M., Atlasov I.V. Analysis of routing processes in telecommunication networks with unsteady flows using Markov processes. IOP Conference Series: Materials Science and Engineering, 2020, vol. 862, no. 5, pp. 05205. http://dx.doi.org/10.1088/1757-899X/862/5/052005
16. Provotorov V.V., Raijhelgauz L.B., Fedotov A.A., Makarova S.N., Kravets O.J. Outrunning planning by network management in Industry 4.0 concept. IOP Conference Series: Materials Science and Engineering, 2020, vol. 862, no. 4, pp. 042011. https://doi.org/10.1088/1757-899X/862/4/042011
17. Wu H., Lou L., Chen C.-C., Hirche S., Kuhnlenz K. Cloud-based networked visual servo control. IEEE Transactions on Industrial Electronics, 2013, vol. 60, no. 2, pp. 554–566. https://doi.org/10.1109/TIE.2012.2186775
18. Liu K., Selivanov A., Fridman E. Survey on time-delay approach to networked control. Annual Reviews in Control, 2019, vol. 48, pp. 57–79. https://doi.org/10.1016/j.arcontrol.2019.06.005
19. Zhao Y.-B., Liu G.-P., Kang Y., Yu L. Packet-Based Control for Networked Control Systems: A Co-Design Approach. Springer, 2017, 184 p. http://dx.doi.org/10.1007/978-981-10-6250-6
20. Zhang D., Shi P., Wang Q.-G., Yu L. Analysis and synthesis of networked control systems: A survey of recent advances and challenges. ISA Transactions, 2017, vol. 66, pp. 376–392. https://doi.org/10.1016/j.isatra.2016.09.026
21. Kravets O.Ya., Choporov O.N., Bolnokin V.E. Mathematical models and algorithmization of monitoring control an affiliated network in maintenance service distributed organizations. Quality - Access to Success, 2018, vol. 19, no. 167, pp. 68–72.
22. Kravets O.J., Abramov G.V., Beletskaja S.J. Generalization of the mechanisms of cross-correlation analysis in the case of a multivariate time series. IOP Conference Series: Materials Science and Engineering, 2017, vol. 173, no. 1, pp. 012009. http://dx.doi.org/10.1088/1757-899X/173/1/012009
23. Kucakin M.A., Lapko A.N., Lebedenko E.V., Ryabokon V.V. On the question of verification of the adequacy of the imitation model of the decentralized management system of the process of network planning based on intellectual autonomous agents. Information Systems and Technologies, 2019, no. 1(111), pp. 30–36. (in Russian)
24. Lebedenko E.V., Minaychev A.A. Processing system model of multiservice data of high-speed trunk channels with non-stationary load. Telecommunications, 2017, no. 8, pp. 27–29. (in Russian)
25. Abramov G.V., Emelyanov A.E., Ivashin A.L. Identification of applicability area of mathematical model of network control system functioning in asynchronous mode during data transfer via multiple access channel. Proc. of the WMSCI 2011: The 15th World Multi-Conference on Systemics, Cybernetics and Informatics, 2011, vol. 3, pp. 199–202.
26. Abramov G.V., Emel'yanov A.E., Kolbaya K.Ch. Distribution law evaluation for demand service time in information system with multiple access to the data link. Automation and Remote Control, 2012, vol. 73, no. 1, pp. 181–185. http://dx.doi.org/10.1134/S000511791201016X
27. Abramov G.V., Avcinov I.A., Emelyanov A.E., Sukhanova N.V. Application of computer simulation models in the study of the impact of data buffering on the quality of control in network systems. Journal of Physics: Conference Series, 2019, vol. 1278, no. 1, pp. 012004. http://dx.doi.org/10.1088/1742-6596/1278/1/012004
28. Pheng S., Xiaonan L., Lav R., Wang Z., Jiang Z. Robust speed control for networked DC motor system. International Journal of Advanced Computer Science and Applications, 2020, vol. 11, no. 5, pp. 10–17. https://doi.org/10.14569/IJACSA.2020.0110502
29. Wu Y., Wu Y., Zhao Y. An enhanced predictive control structure for networked control system with random time delays and packet dropouts. Proc. 3rd International Conference on Information Science and Control Engineering (ICISCE), 2016, pp. 834–838. https://doi.org/10.1109/ICISCE.2016.182