SCIENCE CHINA Information Sciences, Volume 59 , Issue 7 : 073201(2016) https://doi.org/10.1007/s11432-016-5586-9

Further results on cloud control systems

More info
  • ReceivedApr 11, 2016
  • AcceptedMay 10, 2016
  • PublishedJun 20, 2016



National Basic Research Program of China(973)


National Natural Science Foundation of China(61225015)

National Natural Science Foundation of China(61105092)

National Natural Science Foundation of China(61422102)

Beijing Natural Science Foundation(4161001)

Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61321002)



This work was supported by National Basic Research Program of China (973) (Grant No. 2012CB720000), National Natural Science Foundation of China (Grant Nos. 61225015, 61105092, 61422102), Beijing Natural Science Foundation (Grant No. 4161001), and Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 61321002).


[1] Atzori L, Iera A, Morabito G. The internet of things: a survey. Comput Netw, 2010, 542787-2805 CrossRef Google Scholar

[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] Armbrust M, Fox A, Griffith R, et al. A view of cloud computing. Commun ACM, 2010, 5350-58 Google Scholar

[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] 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] Xia Y Q. Cloud control systems. IEEE/CAA J Automat Sin, 2015, 2134-142 CrossRef Google Scholar

[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, 27416-425 CrossRef Google Scholar

[8] Zhang J H, Lin Y J, Shi P. Output tracking control of networked control systems via delay compensation controllers. Automatica, 2015, 5785-92 CrossRef Google Scholar

[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] 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] 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, 5855-859 Google Scholar

[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] Kehoe B, Patil S, Abbeel P, et al. A survey of research on cloud robotics and automation. IEEE Trans Autom Sci Eng, 2015, 12398-409 CrossRef Google Scholar

[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


Contact and support