Volume 18, Issue 1 (5-2021)                   JSDP 2021, 18(1): 86-75 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Haghniaz Jahromi B, AlModarresi S M T, Hajebi P. Neural-Smith Predictor Method for Improvement of Networked Control Systems. JSDP. 2021; 18 (1) :86-75
URL: http://jsdp.rcisp.ac.ir/article-1-594-en.html
Yazd University
Abstract:   (361 Views)

Networked control systems (NCSs) are distributed control systems in which the nodes, including controllers, sensors, actuators, and plants are connected by a digital communication network such as the Internet. One of the most critical challenges in networked control systems is the stochastic time delay of arriving data packets in the communication network among the nodes. Using the Smith predictor as the controller is a common solution to overcome network time delay. Online and accurate modeling of the plant improves the performance of the networked control system, especially when the plant is nonlinear and has unknown parameters and time-variant behavior. In this paper, a novel controller, Neural-Smith predictor, is proposed, which firstly models plant using a perceptron neural network and secondly, another neural network is used as the core of signal processing of the controller. The parameters variation of the plant during time is considered online by the controller, and then the desired control signal is generated. The Integral of Time multiplied by the Absolut value of Error (ITAE) is a proper performance index for position control, so this index has been used to compare the results. Results of simulations show that NCS using the Neural-Smith predictor has better performance in comparison to the common Smith predictor and the novel compensation method using a modified communication disturbance observer (MCDOB) when the values of network time delay and variation of plant’s transfer function are excessive. For example, while the range of stochastic time delay is between 19 and 21 ms, the difference between the ITAE of controllers is 0.0004. This value increases to 0.027, while the range of stochastic time delay is between 910 and 930 ms.

Full-Text [PDF 1217 kb]   (145 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2018/05/13 | Accepted: 2021/03/1 | Published: 2021/05/22 | ePublished: 2021/05/22

1. [1] F. Du, W. Du, "Networked control systems with RBF neural network control and new Smith predictor," 4th IEEE Conference on Industrial Electronics and Applications, Xi'an, 2009, pp. 2744-2748. [DOI:10.1109/ICIEA.2009.5138702] [PMID]
2. [2] W . Du, F. Du, "New Smith Predictor and FRBF Neural Network Control for Networked Control Systems," Eighth IEEE/ACIS International Conference on Computer and Information Science, Shanghai, 2009, pp. 210-215. [DOI:10.1109/ICIS.2009.19]
3. [3] M. Mahmoud, M. Hamdan, "Fundamental Issues in Networked Control Systems," IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 5, 2018, pp. 902-922. [DOI:10.1109/JAS.2018.7511162]
4. [4] C. L. Lai, P. L Hsu, "Design the Remote Control System with the Time-delay Estimator and the Adaptive Smith Predictor," IEEE Transactions on Industrial Informatics, vol. 6, no. 1, 2010, pp. 73-80. [DOI:10.1109/TII.2009.2037917]
5. [5] A. C. Meruelo, D. M. Simpson, S. M. Veres, P. L. Newland, "Improved System Identification Using Artificial Neural Networks and Analysis of Individual Differences in Responses of an Identified Neuron," Neural Networks, vol. 75, 2016, pp. 56-65, [DOI:10.1016/j.neunet.2015.12.002] [PMID]
6. [6] Z. Tian, S. Li, Y. Wang, X. Wang, Q. Zhang, "The Time-delay Compensation Method for Networked Control System Based on Improved Fast Implicit GPC," International Journal of Control and Automation, vol. 9, no. 1, 2016, pp. 231-240. [DOI:10.14257/ijca.2016.9.1.21]
7. [7] F. Y. Wang, D. Liu, Networked Control Systems: Theory and Applications, Springer Publishing, 2008.
8. [8] Z. Xian-Ming, H. Qing-Long, Y. Xinghuo, "Survey on Recent Advances in Networked Control Systems," IEEE Transactions on Industrial Informatics, vol. 12, 2016, pp. 1740-1752. [DOI:10.1109/TII.2015.2506545]
9. [9] H.-C. Yi, H.-W. Kim, J.-Y Choi, "Design of Networked Control System with Discrete-time State Predictor over WSN," Journal of Advances in Computer Networks, vol. 2, no. 2, 2014, pp. 106-109. [DOI:10.7763/JACN.2014.V2.91]
10. [10] K.-E. You, L.-H. Xie, "Survey of Recent Progress in Networked Control Systems," Acta Automatica Sinica, vol. 39, no. 2, 2013, pp. 101-117. [DOI:10.1016/S1874-1029(13)60013-0]
11. [11] L. Zhang, H. Gao, O. Kaynak, "Network-induced Constraints in Networked Control Systems-A Survey," IEEE Transactions on Industrial Informatics, vol. 9, no. 1, 2013, pp. 403-416. [DOI:10.1109/TII.2012.2219540]
12. [12] T. Yamanaka, K. Yamada, R. Hotchi and R. Kubo, "Simultaneous Time-Delay and Data-Loss Compensation for Networked Control Systems With Energy-Efficient Network Interfaces," in IEEE Access, vol. 8, 2020, pp. 110082-110092. [DOI:10.1109/ACCESS.2020.3001293]
13. [13] P. Hajebi, S.M.T. AlModarresi, "Improvement of Networked Control Systems Performance Using Rotation in Fuzzy Logic Controller Rules." Journal of Signal and Data Processing (JSDP), vol. 11, no. 2, 2015, pp. 31- 42.

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing