Technical Brief

An Interactive Real-Time SCADA Platform With Customizable Virtual Instruments for Cloud Control Systems

[+] Author and Article Information
Haoran Tan

School of Information Science and Engineering,
Central South University,
Changsha 410083, Hunan, China
e-mail: tanhaoran@csu.edu.cn

Zhiwu Huang

School of Information Science and Engineering,
Central South University,
Changsha 410083, Hunan, China
e-mail: hzw@csu.edu.cn

Min Wu

School of Automation,
China University of Geosciences,
Wuhan 430074, Hubei, China;
Hubei Key Laboratory of Advanced Control and Intelligent
Automation for Complex Systems,
Wuhan 430074, Hubei, China
e-mail: wumin@cug.edu.cn

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received May 18, 2018; final manuscript received October 29, 2018; published online December 12, 2018. Assoc. Editor: Huiping Li.

J. Dyn. Sys., Meas., Control 141(4), 044501 (Dec 12, 2018) (8 pages) Paper No: DS-18-1244; doi: 10.1115/1.4041977 History: Received May 18, 2018; Revised October 29, 2018

This paper studies the design and implementation of an interactive real-time cloud supervisory control and data acquisition (SCADA) platform. The platform relying on C# and client/server architecture provides full support for data supervision of the cloud control system (CCS). Users are allowed to design supervisory interfaces by dynamically creating and customizing virtual instruments, which are seamlessly integrated into the platform by reconstructing it. Both the scalar and matrix data from different cloud nodes are supported for supervising simultaneously in real-time through receiving data asynchronously. The user can tune the parameters of the CCS online via duplex channels based on the transmission control protocol/internet protocol (IP). To overcome the disturbance of network delays to data display, a stable data and real-time data communication scheme are proposed. All the supervised data can be stored in separate files for further analysis. Finally, the online simulation and experiment are provided to demonstrate the feasibility of the designed SCADA platform.

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.


Wang, L. , and Ranjan, R. , 2015, “ Processing Distributed Internet of Things Data in Clouds,” IEEE Cloud Comput., 2(1), pp. 76–80.
Atzori, L. , Iera, A. , and Morabito, G. , 2010, “ The Internet of Things: A Survey,” Comput. Networks, 1(15), pp. 2787–2805.
Zhou, Y. , Zhang, D. , and Xiong, N. , 2017, “ Post-Cloud Computing Paradigms: A Survey and Comparison,” Tsinghua Sci. Technol., 22(6), pp. 714–732.
Xia, Y. , 2012, “ From Networked Control Systems to Cloud Control Systems,” Control Conference, Hefei, China, July 25–27, pp. 5878–5883. https://ieeexplore.ieee.org/document/6390971
Xia, Y. , 2015, “ Cloud Control Systems,” IEEE/CAA J. Autom. Sin., 2(2), pp. 134–142.
Xia, Y. , Qin, Y. , Zhai, D. H. , and Chai, S. , 2016, “ Further Results on Cloud Control Systems,” Sci. China Inf. Sci., 59(7), pp. 1–5.
Ma, L. , Xia, Y. , Ali, Y. , and Zhan, Y. , 2017, “ Engineering Problems in Initial Phase of Cloud Control System,” 36th Chinese Control Conference, Dalian, China, July 26–28, pp. 7892–7896.
Kamalapurkar, R. , Fischer, N. , Obuz, S. , and Dixon, W. E. , 2016, “ Time-Varying Input and State Delay Compensation for Uncertain Nonlinear Systems,” IEEE Trans. Autom. Control, 61(3), pp. 834–839.
Zhang, X. M. , Han, Q. L. , and Yu, X. , 2016, “ Survey on Recent Advances in Networked Control Systems,” IEEE Trans. Ind. Inf., 12(5), pp. 1740–1752.
Li, Y. , Tan, C. , and Liu, G. , 2016, “ Output Consensus of Networked Multi-Agent Systems With Time-Delay Compensation Scheme,” J. Franklin Inst., 353(4), pp. 917–935.
Pang, Z. , Liu, G. , Zhou, D. , and Sun, D. , 2017, “ Data-Driven Control With Input Design-Based Data Dropout Compensation for Networked Nonlinear Systems,” IEEE Trans. Control Syst. Technol., 25(2), pp. 628–636.
Albattat, A. , Gruenwald, B. , and Yucelen, T. , 2017, “ Design and Analysis of Adaptive Control Systems Over Wireless Networks,” ASME J. Dyn. Syst., Meas., Control, 139(7), p. 074501.
Ward, J. S. , and Barker, A. , 2014, “ Observing the Clouds: A Survey and Taxonomy of Cloud Monitoring,” J. Cloud Comput., 3(1), pp. 1–30.
Aceto, G. , Botta, A. , Donato, W. D. , and Pescapè, A. , 2013, “ Cloud Monitoring: A Survey,” Comput. Networks, 57(9), pp. 2093–2115.
Abrahao, S. , and Insfran, E. , 2017, “ Models@runtime for Monitoring Cloud Services in Google App Engine,” IEEE World Congress on Services, Honolulu, HI, June 25–30, pp. 30–35.
Zou, D. , Zhang, W. , Qiang, W. , Xiang, G. , Yang, L. T. , Jin, H. , and Hu, K. , 2013, “ Design and Implementation of a Trusted Monitoring Framework for Cloud Platforms,” Future Gener. Comput. Syst., 29(8), pp. 2092–2102.
Dong, B. , Lee, C. , Chiu, Y. , and Huang, Y. , 2017, “ An Intelligent Embedded Cloud Monitoring System Design,” International Automatic Control Conference (CACS), Pingtung, Taiwan, Nov. 12–15, pp. 1–4.
Abawajy, J. H. , and Hassan, M. M. , 2017, “ Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System,” IEEE Commun. Mag., 55(1), pp. 48–53.
Zhang, W. , Tomizuka, M. , and Byl, N. , 2016, “ A Wireless Human Motion Monitoring System for Smart Rehabilitation,” ASME J. Dyn. Syst., Meas., Control, 138(11), p. 111004.
Adissi, M. O. , Filho, A. C. L. , Gomes, R. D. , Silva, D. M. G. B. , and Belo, F. A. , 2017, “ Implementation and Deployment of an Intelligent Industrial Wireless System for Induction Motor Monitoring,” ASME J. Dyn. Syst., Meas., Control, 139(12), p. 124502.
Tautz-Weinert, J. , and Watson, S. J. , 2017, “ Using SCADA Data for Wind Turbine Condition Monitoring—A Review,” IET Renewable Power Gener., 11(4), pp. 382–394.
Pingali, S. , 2017, “ Cloud Computing and Crowdsourcing for Monitoring Lakes in Developing Countries,” IEEE International Conference on Cloud Computing in Emerging Markets, Bangalore, India, Oct. 19–21, pp. 161–163.
Roopaei, M. , Rad, P. , and Choo, K. K. R. , 2017, “ Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging,” IEEE Cloud Comput., 4(1), pp. 10–15.
Chen, Q. , Ahmed, Q. , Rizzoni, G. , and Qiu, M. , 2016, “ Design and Evaluation of Model-Based Health Monitoring Scheme for Automated Manual Transmission,” ASME J. Dyn. Syst., Meas., Control, 138(10), p. 101011.
Wang, J. , and Zhang, Y. , 2013, “ Monitoring System of Machine Tools Based on the Intouch,” International Conference on Mechanical and Automation Engineering, Jiujang, China, July 21–23, pp. 70–72.
Yangang, X. , Han, W. , Xingqi, L. , and Qiang, H. , 2010, “ Monitor System Design for Machine Electric Spindle Based on MCGS,” J. Networks, 5(12), pp. 248–252.
Toru, N. , Yasuhiro, K. , and Ahmed, K. , 2018, “ Consensus-Based Cooperative Formation Control for Multiquadcopter System With Unidirectional Network Connections,” ASME J. Dyn. Syst., Meas., Control, 140(4), p. 044502.
Liu, G. , and Zhang, S. , 2018, “ A Survey on Formation Control of Small Satellites,” Proc. IEEE, 106(3), pp. 440–457.
An, B. R. , and Liu, G. P. , 2012, “ Networked Real-Time Controller Based on PC/104,” Intelligent Control and Automation, Beijing, China, July 6–8, pp. 831–834.
Tan, H. , Wu, M. , and Huang, Z. , 2016, “ Coordinated Control for Multi-Agent Systems Based on Networked Predictive Control Schemes,” Control and Decision Conference, Yinchuan, China, May 28–30, pp. 2530–2535.
Pang, Z. , and Liu, G. , 2010, “ Model-Based Recursive Networked Predictive Control,” IEEE International Conference on Systems Man and Cybernetics, Istanbul, Turkey, Oct. 10–13, pp. 1665–1670.


Grahic Jump Location
Fig. 1

Architecture of the cloud SCADA platform

Grahic Jump Location
Fig. 2

Main interface of the cloud SCADA platform

Grahic Jump Location
Fig. 5

Structure of data communication channels

Grahic Jump Location
Fig. 4

The process of instruction delivery

Grahic Jump Location
Fig. 3

The process of packet reception and display

Grahic Jump Location
Fig. 6

The timelines of data transmission process

Grahic Jump Location
Fig. 7

Internet-based CCSs

Grahic Jump Location
Fig. 8

Supervisory interface for the Internet-based multi-agent coordinative control simulation

Grahic Jump Location
Fig. 9

Intranet-based multimotor coordinative control test rig

Grahic Jump Location
Fig. 10

Supervisory interface for the Intranet-based multimotor coordinative control experiment



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In