Further results on cloud control systems

Abstract

This paper is devoted to further investigating the cloud control systems (CCSs). The benefits and challenges of CCSs are provided. Both new research results of ours and some typical work made by other researchers are presented. It is believed that the CCSs can have huge and promising effects due to their potential advantages.

This is a preview of subscription content, access via your institution.

References

  1. 1

    Atzori L, Iera A, Morabito G. The internet of things: a survey. Comput Netw, 2010, 54: 2787–2805

    Article  MATH  Google Scholar 

  2. 2

    Lee E A. Cyber physical systems: Design challenges. In: Proceedings of the 11th IEEE International Symposium on Object Oriented Real-Time Distributed Computing, Orlando, 2008. 363–369

    Google Scholar 

  3. 3

    Armbrust M, Fox A, Griffith R, et al. A view of cloud computing. Commun ACM, 2010, 53: 50–58

    Article  Google Scholar 

  4. 4

    Ji C Q, Li Y, Qiu W M, et al. Big data processing in cloud computing environments. In: Proceedings of the 12th International Symposium on Pervasive Systems, Algorithms and Networks, San Marcos, 2012. 17–23

    Google Scholar 

  5. 5

    Xia Y Q. From networked control systems to cloud control systems. In: Proceedings of the 31st Chinese Control Conference, Hefei, 2012. 5878–5883

    Google Scholar 

  6. 6

    Xia Y Q. Cloud control systems. IEEE/CAA J Automat Sin, 2015, 2: 134–142

    MathSciNet  Article  Google Scholar 

  7. 7

    Wang T, Gao H J, Qiu J B. A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans Neural Netw, 2016, 27: 416–425

    MathSciNet  Article  Google Scholar 

  8. 8

    Zhang J H, Lin Y J, Shi P. Output tracking control of networked control systems via delay compensation controllers. Automatica, 2015, 57: 85–92

    MathSciNet  Article  MATH  Google Scholar 

  9. 9

    Li H F, Liu H T, Li J Q. Workflow scheduling algorithm based on control structure reduction in cloud environment. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Diego, 2014. 2587–2592

    Google Scholar 

  10. 10

    Li H F, Gao X C, Di Y J. SLA-aware resource reservation management in cloud workflows. In: Proceedings of the 27th Chinese Control and Decision Conference, Qingdao, 2015. 4226–4231

    Google Scholar 

  11. 11

    Chang S T, Wang Y J, Liu L, et al. Reentry trajectory optimization based on differential evolution. Int J Comput Electr Automat Control Inf Eng, 2011, 5: 855–859

    Google Scholar 

  12. 12

    Zhang Q Z, Liu C J, Yang B, et al. Reentry trajectory planning optimization based on ant colony algorithm. In: Proceedings of IEEE International Conference on Robotics and Biomimetics, Sanya, 2007. 1064–1068

    Google Scholar 

  13. 13

    Kehoe B, Patil S, Abbeel P, et al. A survey of research on cloud robotics and automation. IEEE Trans Autom Sci Eng, 2015, 12: 398–409

    Article  Google Scholar 

  14. 14

    Ericson K, Pallickara S, Anderson C W. Analyzing electroencephalograms using cloud computing techniques. In: Proceedings of the 2nd International Conference on Cloud Computing Technology and Science, Athens, 2010. 185–192

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yuanqing Xia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xia, Y., Qin, Y., Zhai, DH. et al. Further results on cloud control systems. Sci. China Inf. Sci. 59, 073201 (2016). https://doi.org/10.1007/s11432-016-5586-9

Download citation

Keywords

  • cloud control systems
  • cloud computing
  • cyber-physical systems
  • networked control systems
  • big data