工业网络系统的感知-传输-控制一体化:挑战和进展

关新平; 陈彩莲; 杨博; 华长春; 吕玲; 朱善迎

doi:10.16383/j.aas.c180484

工业网络系统的感知-传输-控制一体化:挑战和进展

doi: 10.16383/j.aas.c180484

关新平^1,2,,
陈彩莲^1,2, ,,
杨博^1,2,,
华长春^3,,
吕玲^1,2,,
朱善迎^1,2,

1.
上海交通大学自动化系上海 200240
2.
系统控制与信息处理教育部重点实验室上海 200240
3.
燕山大学电气工程学院秦皇岛 066004

基金项目:

aa上海市自然科学基金aaa 18ZR1419900

国家自然科学基金 61603251

国家自然科学基金 61573245

国家自然科学基金 61731012

国家自然科学基金 61633017

国家自然科学基金 61622307

国家自然科学基金 61521063

详细信息

作者简介:
关新平  IEEE/CAA Fellow.上海交通大学讲席教授, 系统控制与信息处理教育部重点实验室主任.国家杰出青年基金获得者, 教育部长江学者特聘教授.1999年获哈尔滨工业大学博士学位.主要研究方向为工业网络系统设计, 控制与优化, 智能工厂中无线网络及应用.E-mail:xpguan@sjtu.edu.cn

杨博  上海交通大学自动化系教授.主要研究方向为能源网络和无线网络的博弈论分析和优化.E-mail:bo.yang@sjtu.edu.cn

华长春  燕山大学电气工程学院教授, 长江学者特聘教授.主要研究方向网络化控制系统的分析与综合, 基于数据驱动的故障诊断和容错控制, 网络化遥操作系统的控制.E-mail:cch@ysu.edu.cn

吕玲  上海交通大学自动化系博士研究生.主要研究方向为无线传感器, 执行器网络中可靠传输, 融合估计, 协调控制及在工业网络的应用.E-mail:sjtulvling@sjtu.edu.cn

朱善迎  上海交通大学自动化系副教授.主要研究方向为多机器人系统协调控制, 无线网络的分布式估计和优化及在工业网络的应用.E-mail:shyzhu@sjtu.edu.cn

通讯作者:
陈彩莲上海交通大学自动化系教授, 国家优秀青年科学基金获得者, 教育部青年长江学者.主要研究方向为无线传感器网络和工业应用, 计算智能, 分布式状态感知.本文通信作者.E-mail:cailianchen@sjtu.edu.cn

计量
- 文章访问数: 3634
- HTML全文浏览量: 1452
- PDF下载量: 2229
- 被引次数: 0
出版历程
- 收稿日期: 2018-07-12
- 录用日期: 2018-10-21
- 刊出日期: 2019-01-20

Towards the Integration of Sensing, Transmission and Control for Industrial Network Systems: Challenges and Recent Developments

GUAN Xin-Ping^{1,2
,},
CHEN Cai-Lian^{1,2
, ,},
YANG Bo^{1,2
,},
HUA Chang-Chun^3
,,
LYU Ling^{1,2
,},
ZHU Shan-Ying^{1,2
,}

1.
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240
2.
Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240
3.
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004

Funds:

Natural Science Foundation of Shanghai Municipality of China 18ZR1419900

National Natural Science Foundation of China 61603251

National Natural Science Foundation of China 61573245

National Natural Science Foundation of China 61731012

National Natural Science Foundation of China 61633017

National Natural Science Foundation of China 61622307

National Natural Science Foundation of China 61521063

More Information

Author Bio:
IEEE/CAA Fellow, Chair Professor of Shanghai Jiao Tong University, Director of the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, China. He is a winner of the National Science Fund for Distinguished Young Scholars, "Changjiang Scholar" by the Ministry of Education of China. He received the Ph. D. degree in electrical engineering from the Harbin Institute of Technology, in 1999. His current research interest covers the design, control and optimization in industrial network systems, wireless networking and applications in smart factory

Professor in the Department of Automation, Shanghai Jiao Tong University. His research interest covers game theoretical analysis and optimization of energy networks and wireless networks

Professor at the School of Electrical Engineering, Yanshan University. He is a "Changjiang Scholar" by the Ministry of Education of China. His research interest covers analysis and synthesis of networked control systems, data-driven fault diagnosis and fault-tolerant control, and networked teleoperation control

Ph. D. candidate in the Department of Automation, Shanghai Jiao Tong University. Her research interest covers reliable transmission, fusion estimation, coordination control in wireless sensor and actuator networks, and their applications in industrial networks

Associate professor in the Department of Automation, Shanghai Jiao Tong University. His research interest covers coordination control of mobile robots and distributed estimation and optimization in wireless networks, and their applications in industrial network

Corresponding author: CHEN Cai-Lian Professor in the Department of Automation, Shanghai Jiao Tong University. She is a winner of the National Outstanding Youth Science Foundation, and Changjiang Young Scholar of Ministry of Education. Her research interest covers wireless sensor networks and industrial applications, computational intelligence, and distributed situation awareness. Corresponding author of this paper

摘要

摘要: 工业网络系统是融合工业控制和信息通信的多维动态系统，具有维度高、动态性强、工业通信协议和网络配置嵌入等特性，如何在网络环境下实现信息感知分布性、控制适应性、整体协调性，已成为工业网络系统研究的新挑战.本文简述了工业网络系统的内涵和主要特征，分析了感知-传输-控制一体化面临的挑战和关键问题；综述了分布式状态感知、适变传输、协同控制等关键技术的研究进展；对工业网络系统的未来研究方向和潜在应用前景进行了总结和展望.
- 工业网络系统 /
- 分布式估计 /
- 适变传输 /
- 协同控制 /
- 联合设计
Abstract: Industrial network system represents a kind of multi-dimensional dynamic systems with the integration of control, information and communication. They have the following three characteristics:high dimension, strong dynamics, and deep embededness of communication protocols and network configuration. How to realize distributed sensing, control adaptability and system coordination has become a new challenge in the network environment for industrial systems. This paper is concerned with the connotation and characteristics of industrial network systems, and the challenges and key issues aiming at the integration of sensing, transmission and control. We then give a brief overview of recent developments of distributed sensing, adaptive transmission and cooperative control in industrial control systems. The future research directions and potential applications of industrial network systems are also discussed at the end of this paper.
- Industrial network systems /
- distributed estimation /
- adaptive transmission /
- cooperative control /
- co-design
注释:

1) 本文责任编委刘允刚

HTML全文

图 1 工业网络系统感知-传输-控制一体化框架

Fig. 1 Integration framework of sensing, transmission and control for industrial network systems

下载: 全尺寸图片幻灯片

图 2 终端异构性模型示意图^[25]

Fig. 2 A schematic view of node heterogeneity^[25]

下载: 全尺寸图片幻灯片

图 3 网络环境下复杂系统协同控制

Fig. 3 A schematic view of cooperative control of complex systems in network environments

下载: 全尺寸图片幻灯片

图 4 主从遥操作系统

Fig. 4 A master-slave teleoperation system

下载: 全尺寸图片幻灯片

图 5 工业网路系统的分层架构

Fig. 5 A hierarchical architecture for industrial network systems

下载: 全尺寸图片幻灯片

图 6 工业网络系统在热轧流程中的应用

Fig. 6 Application of industrial network systems to hot rolling process

下载: 全尺寸图片幻灯片

参考文献(71)

[1]	Chen C L, Zhu S Y, Guan X P, Shen X M. Wireless Sensor Networks:Distributed Consensus Estimation. Berlin, Germany:Springer, 2014.
[2]	Murray R M. Control in an Information Rich World:Report of the Panel on Future Directions in Control, Dynamics, and Systems. Philadelphia, PA, USA:SIAM, 2003.
[3]	Stenumgaard P, Chilo J, Ferrer-Coll J, Angskog P. Challenges and conditions for wireless machine-to-machine communications in industrial environments. IEEE Communications Magazine, 2013, 51(6):187-192 doi: 10.1109/MCOM.2013.6525614
[4]	Kay S M. Fundamentals of Statistical Signal Processing:Estimation Theory. Upper Saddle River, NJ, USA:Prentice Hall, 1993.
[5]	Ribeiro A, Schizas I D, Roumeliotis S I, Giannakis G B. Kalman filtering in wireless sensor networks. IEEE Control Systems Magazine, 2010, 30(2):66-86 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_3fcdaf4feb31b2e2e1160401297e05cc
[6]	Xiao L, Boyd S, Lall S. A scheme for robust distributed sensor fusion based on average consensus. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN). Boise, ID, USA: IEEE, 2005. 63-70
[7]	Schizas I D, Ribeiro A, Giannakis G B. Consensus in Ad Hoc WSNs with noisy links, Part Ⅰ:distributed estimation of deterministic signals. IEEE Transactions on Signal Processing, 2008, 56(1):350-364 https://www.mendeley.com/catalogue/consensus-ad-hoc-wsns-noisy-links-part-i-distributed-estimation-deterministic-signals/
[8]	Speranzon A, Fischione C, Johansson K H, Sangiovanni-Vincentelli A. A distributed minimum variance estimator for sensor networks. IEEE Journal on Selected Areas in Communications, 2008, 26(4):609-621 doi: 10.1109/JSAC.2008.080504
[9]	Barbarossa S, Sardellitti S, Di Lorenzo P. Distributed detection and estimation in wireless sensor networks. Academic Press Library in Signal Processing: Communications and Radar Signal Processing. New York, USA: Elsevier, 2014. 329-408
[10]	Cattivelli F S, Sayed A H. Diffusion LMS strategies for distributed estimation. IEEE Transactions on Signal Processing, 2010, 58(3):1035-1048 doi: 10.1109/TSP.2009.2033729
[11]	Zhao X C, Tu S Y, Sayed A H. Diffusion adaptation over networks under imperfect information exchange and non-stationary data. IEEE Transactions on Signal Processing, 2012, 60(7):3460-3475 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0226585347/
[12]	Zhang Q, Zhang J F. Distributed parameter estimation over unreliable networks with Markovian switching topologies. IEEE Transactions on Automatic Control, 2012, 57(10):2545-2560 https://www.mendeley.com/catalogue/distributed-parameter-estimation-unreliable-networks-markovian-switching-topologies/
[13]	张强.不确定环境下多自主体系统的分布式估计与控制.中国科学:数学, 2013, 43(6):529-540 http://d.old.wanfangdata.com.cn/Thesis/Y2166224 Zhang Qiang. Distributed estimation and control of multi-agent systems in uncertain environment. Scientia Sinica Mathematica, 2013, 43(6):529-540 http://d.old.wanfangdata.com.cn/Thesis/Y2166224
[14]	Fu M Y, Xie L H. The sector bound approach to quantized feedback control. IEEE Transactions on Automatic Control, 2005, 50(11):1698-1711 doi: 10.1109/TAC.2005.858689
[15]	Xiao J J, Cui S G, Luo Z Q, Goldsmith A J. Power scheduling of universal decentralized estimation in sensor networks. IEEE Transactions on Signal Processing, 2006, 54(2):413-422 https://www.mendeley.com/catalogue/power-scheduling-universal-decentralized-estimation-sensor-networks/
[16]	Xie S L, Li H R. Distributed LMS estimation over networks with quantised communications. International Journal of Control, 2013, 86(3):478-492 https://www.ingentaconnect.com/content/tandf/tcon/2013/00000086/00000003/art00010
[17]	El Chamie M, Liu J, Başar T. Design and analysis of distributed averaging with quantized communication. IEEE Transactions on Automatic Control, 2016, 61(12):3870-3884 doi: 10.1109/TAC.2016.2530939
[18]	Liu S, Li T, Xie L H, Fu M Y, Zhang J F. Continuous-time and sampled-data-based average consensus with logarithmic quantizers. Automatica, 2013, 49(11):3329-3336 https://www.mendeley.com/catalogue/continuoustime-sampleddatabased-average-consensus-logarithmic-quantizers/
[19]	Li T, Fu M Y, Xie L H, Zhang J F. Distributed consensus with limited communication data rate. IEEE Transactions on Automatic Control, 2011, 56(2):279-292 https://ieeexplore.ieee.org/document/5482198/
[20]	Zhu S Y, Soh Y C, Xie L H. Distributed parameter estimation with quantized communication via running average. IEEE Transactions on Signal Processing, 2015, 63(17):4634-4646 https://ieeexplore.ieee.org/document/7116612
[21]	Zhu S Y, Liu S, Soh Y C, Xie L H. Performance analysis of averaging based distributed estimation algorithm with additive quantization model. Automatica, 2017, 80:95-101 https://dl.acm.org/citation.cfm?id=3085494
[22]	Kar S, Moura J M F, Ramanan K. Distributed parameter estimation in sensor networks:nonlinear observation models and imperfect communication. IEEE Transactions on Information Theory, 2012, 58(6):3575-3605 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_0809.0009
[23]	Zhu S Y, Chen C L, Ma X L, Yang B, Guan X P. Consensus based estimation over relay assisted sensor networks for situation monitoring. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(2):278-291 doi: 10.1109/JSTSP.2014.2375851
[24]	Zhu S Y, Chen C L, Li W S, Yang B, Guan X P. Distributed optimal consensus filter for target tracking in heterogeneous sensor networks. IEEE Transactions on Cybernetics, 2013, 43(6):1963-1976 https://www.mendeley.com/catalogue/distributed-optimal-consensus-filter-target-tracking-heterogeneous-sensor-networks/
[25]	Zhu S Y, Soh Y C, Xie L H. Distributed inference for relay-assisted sensor networks with intermittent measurements over fading channels. IEEE Transactions on Signal Processing, 2016, 64(3):742-756 https://ieeexplore.ieee.org/document/7296697
[26]	Ali S, Fakoorian A, Taheri H. Optimum Reed-Solomon erasure coding in fault tolerant sensor networks. In: Proceedings of the 4th International Symposium on Wireless Communication Systems. Trondheim, Norway: IEEE, 2007. 6-10 https://www.mendeley.com/catalogue/optimum-reedsolomon-erasure-coding-fault-tolerant-sensor-networks/
[27]	Marina M K, Das S R. Ad hoc on-demand multipath distance vector routing. Wireless Communications and Mobile Computing, 2006, 6(7):969-988 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ023629265/
[28]	Villaverde B C, Rea S, Pesch D. InRout:a QoS aware route selection algorithm for industrial wireless sensor networks. Ad Hoc Networks, 2012, 10(3):458-478 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0226199838/
[29]	Heo J, Hong J M, Cho Y. EARQ:energy aware routing for real-time and reliable communication in wireless industrial sensor networks. IEEE Transactions on Industrial Informatics, 2009, 5(1):3-11 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0234349976/
[30]	Liu Y H, Zhu Y M, Ni L, Xue G T. A reliability-oriented transmission service in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 2011, 22(12):2100-2107 https://ieeexplore.ieee.org/document/5740874
[31]	Shi L, Epstein M, Nurray R M. Kalman filtering over a packet-dropping network:a probabilistic perspective. IEEE Transactions on Automatic Control, 2010, 55(3):594-604 doi: 10.1109-TAC.2009.2039236/
[32]	You K Y, Fu M Y, Xie L H. Mean square stability for Kalman filtering with Markovian packet losses. Automatica, 2011, 47(12):2647-2657 https://www.sciencedirect.com/science/article/pii/S0005109811004559
[33]	Quevedo D E, Ahlen A, Johansson K H. State estimation over sensor networks with correlated wireless fading channels. IEEE Transactions on Automatic Control, 2013, 58(3):581-593 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_1308.1725
[34]	Xia M, Gupta V, Antsaklis P J. Networked state estimation over a shared communication medium. IEEE Transactions on Automatic Control, 2017, 62(4):1729-1741
[35]	Mamduhi M H, Molin A, Tolić D, Hirche S. Error-dependent data scheduling in resource-aware multi-loop networked control systems. Automatica, 2017, 81:209-216
[36]	Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan M I, Sastry S S. Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control, 2004, 49(9):1453-1464 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_1005.2442
[37]	Kluge S, Reif K, Brokate M. Stochastic stability of the extended Kalman filter with intermittent observations. IEEE Transactions on Automatic Control, 2010, 55(2):514-518 https://ieeexplore.ieee.org/document/5378505
[38]	游科友, 谢立华.网络控制系统的最新研究综述.自动化学报, 2013, 39(2):101-118 http://www.aas.net.cn/CN/abstract/abstract17806.shtml You Ke-You, Xie Li-Hua. Survey of recent progress in networked control systems. Acta Automatica Sinica, 2013, 39(2):101-118 http://www.aas.net.cn/CN/abstract/abstract17806.shtml
[39]	Cao X H, Cheng P, Chen J M, Ge S S, Cheng Y, Sun Y X. Cognitive radio based state estimation in cyber-physical systems. IEEE Journal on Selected Areas in Communications, 2014, 32(3):489-502 doi: 10.1109/JSAC.2014.1403002
[40]	Calvo-Fullana M, Antón-Haro C, Matamoros J, Ribeiro A. Random access communication for wireless control systems with energy harvesting sensors. arXiv: 1801.10141, 2018. https://arxiv.org/abs/1801.10141
[41]	Leong A, Quevedo D E. Kalman filtering with relays over wireless fading channels. IEEE Transactions on Automatic Control, 2016, 61(6):1643-1648 doi: 10.1109/TAC.2015.2478129
[42]	Cheng P, Qi Y F, Xin K F, Chen J M, Xie L H. Energy-efficient data forwarding for state estimation in multi-hop wireless sensor networks. IEEE Transactions on Automatic Control, 2016, 61(5):1322-1327 https://ieeexplore.ieee.org/document/7172467
[43]	Cao X H, Cheng P, Chen J M, Sun Y X. An online optimization approach for control and communication codesign in networked cyber-physical systems. IEEE Transactions on Industrial Informatics, 2013, 9(1):439-450 doi: 10.1109/TII.2012.2216537
[44]	Demirel B, Zou Z H, Soldati P, Johansson M. Modular design of jointly optimal controllers and forwarding policies for wireless control. IEEE Transactions on Automatic Control, 2014, 59(12):3252-3265 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_1204.3100
[45]	Gatsis K, Pajic M, Ribeiro A, Pappas G J. Opportunistic control over shared wireless channels. IEEE Transactions on Automatic Control, 2015, 60(12):3140-3155 doi: 10.1109/TAC.2015.2416922
[46]	Knorn S, Dey S. Optimal energy allocation for linear control with packet loss under energy harvesting constraints. Automatica, 2017, 77:259-267 https://www.sciencedirect.com/science/article/pii/S0005109816304794
[47]	Schenato L, Sinopoli B, Franceschetti M, Poolla K, Sastry S S. Foundations of control and estimation over lossy networks. Proceedings of the IEEE, 2007, 95(1):163-187 https://ieeexplore.ieee.org/document/4118476
[48]	Lyu L, Chen C L, Hua C Q, Guan X P. State estimation oriented reliability enhancement with cooperative transmission in industrial CPSs. In: Proceedings of the 2016 IEEE Global Communications Conference. Washington, DC, USA: IEEE, 2016. 1-6 https://ieeexplore.ieee.org/document/7842295
[49]	Tan K T, Peng X Y, So P L, Chu Y C, Chen M Z Q. Centralized control for parallel operation of distributed generation inverters in microgrids. IEEE Transactions on Smart Grid, 2012, 3(4):1977-1987 https://ieeexplore.ieee.org/document/6268310
[50]	Bakule L. Decentralized control:an overview. Annual Reviews in Control, 2008, 32(1):87-98 doi: 10.1016/j.arcontrol.2008.03.004
[51]	Hua C C, Zhang L L, Guan X P. Robust Control for Nonlinear Time-Delay Systems. Singapore, Singapore:Springer, 2018.
[52]	Hua C C, Guan X P, Shi P. Robust backstepping control for a class of time delayed systems. IEEE Transactions on Automatic Control, 2005, 50(6):894-899 https://ieeexplore.ieee.org/document/1440580
[53]	Hua C C, Feng G, Guan X P. Robust controller design of a class of nonlinear time delay systems via backstepping method. Automatica, 2008, 44:567-573 doi: 10.1016/j.automatica.2007.06.008
[54]	Šiljak D D. Decentralized Control of Complex Systems. Boston, USA:Academic Press, 1991.
[55]	Mahmoud M S, Bingulac S. Robust design of stabilizing controllers for interconnected time-delay systems. Automatica, 1998, 34(6):795-800 https://dl.acm.org/citation.cfm?id=292993
[56]	Xie S L, Xie L H. Stabilization of a class of uncertain large-scale stochastic systems with time delays. Automatica, 2000, 36(1):161-167 https://www.sciencedirect.com/science/article/pii/S0005109899001478
[57]	Yan X G, Spurgeon S K, Edwards C. Decentralised stabilisation for nonlinear time delay interconnected systems using static output feedback. Automatica, 2013, 49(2):633-641 https://dl.acm.org/citation.cfm?id=2425508
[58]	Hua C C, Guan X P. Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks. IEEE Transactions on Neural Networks, 2008, 19(4):673-688 https://www.ncbi.nlm.nih.gov/pubmed/18390312
[59]	Zhou J. Decentralized adaptive control for large-scale time-delay systems with dead-zone input. Automatica, 2008, 44(7):1790-1799
[60]	Niemeyer G, Slotine J J E. Stable adaptive teleoperation. IEEE Journal of Oceanic Engineering, 1991, 16(1):152-162 doi: 10.1109/48.64895
[61]	Nuño E, Ortega R, Barabanov N, Basañez L. A globally stable PD controller for bilateral teleoperators. IEEE Transactions on Robotics, 2008, 24(3):753-758 doi: 10.1109/TRO.2008.921565
[62]	Hua C C, Liu X P. Delay-dependent stability criteria of teleoperation systems with asymmetric time-varying delays. IEEE Transactions on Robotics, 2010, 26(5):925-932 https://ieeexplore.ieee.org/document/5510175
[63]	Tian D P, Yashiro D, Ohnishi K. Wireless haptic communication under varying delay by switching-channel bilateral control with energy monitor. IEEE/ASME Transactions on Mechatronics, 2012, 17(3):488-498 https://ieeexplore.ieee.org/document/6162986
[64]	Yan J, Wan Y, Luo X Y, Chen C L, Hua C C, Guan X P. Formation control of teleoperating cyber-physical system with time delay and actuator saturation. IEEE Transactions on Control Systems Technology, 2018, 26(4):1458-1467 https://ieeexplore.ieee.org/document/7947157
[65]	Lyu L, Chen C L, Yan J, Lin F L, Hua C Q, Guan X P. State estimation oriented wireless transmission for ubiquitous monitoring in industrial cyber-physical systems. IEEE Transactions on Emerging Topics in Computing, 2016, DOI: 10.1109/tetc.2016.2573719
[66]	Lyu L, Chen C L, Zhu S Y, Guan X P. 5G enabled codesign of energy-efficient transmission and estimation for industrial IoT systems. IEEE Transactions on Industrial Informatics, 2018, 14(6):2690-2704 https://ieeexplore.ieee.org/document/8272467
[67]	Lyu L, Chen C L, Hua C Q, Zhu S Y, Guan X P. Co-design of stabilisation and transmission scheduling for wireless control systems. IET Control Theory and Applications, 2017, 11(11):1767-1778 https://ieeexplore.ieee.org/document/7972804
[68]	Zhu S Y, Chen C L, Guan X P. Sensor deployment for distributed estimation in heterogeneous wireless sensor networks. Ad Hoc & Sensor Wireless Networks, 2012, 16(4):297-322
[69]	Xue L, Guan X P, Liu Z X, Yang B. TREE:routing strategy with guarantee of QoS for industrial wireless sensor networks. International Journal of Communication Systems, 2014, 27(3):459-481 http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb200512013
[70]	Barnwal R P, Bharti S, Misra S, Obaidat M S. UCGNet:wireless sensor network-based active aquifer contamination monitoring and control system for underground coal gasification. International Journal of Communication Systems, 2017, 30(1):e2852 doi: 10.1002/dac.2852#citedBy
[71]	Chen C L, Yan J, Lu N, Wang Y Y, Yang X, Guan X P. Ubiquitous monitoring for industrial cyber-physical systems over relay-assisted wireless sensor networks. IEEE Transactions on Emerging Topics in Computing, 2015, 3(3):352-362

施引文献

资源附件(0)

访问统计

图(6)

计量

文章访问数: 3634
HTML全文浏览量: 1452
PDF下载量: 2229
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

工业网络系统的感知-传输-控制一体化:挑战和进展

doi: 10.16383/j.aas.c180484

通讯作者:
陈彩莲上海交通大学自动化系教授, 国家优秀青年科学基金获得者, 教育部青年长江学者.主要研究方向为无线传感器网络和工业应用, 计算智能, 分布式状态感知.本文通信作者.E-mail:cailianchen@sjtu.edu.cn

计量

Towards the Integration of Sensing, Transmission and Control for Industrial Network Systems: Challenges and Recent Developments

计量

目录

留言板

工业网络系统的感知-传输-控制一体化:挑战和进展

doi: 10.16383/j.aas.c180484

通讯作者: 陈彩莲 上海交通大学自动化系教授, 国家优秀青年科学基金获得者, 教育部青年长江学者.主要研究方向为无线传感器网络和工业应用, 计算智能, 分布式状态感知.本文通信作者.E-mail:cailianchen@sjtu.edu.cn

计量

出版历程

Towards the Integration of Sensing, Transmission and Control for Industrial Network Systems: Challenges and Recent Developments

计量

出版历程

目录

通讯作者:
陈彩莲上海交通大学自动化系教授, 国家优秀青年科学基金获得者, 教育部青年长江学者.主要研究方向为无线传感器网络和工业应用, 计算智能, 分布式状态感知.本文通信作者.E-mail:cailianchen@sjtu.edu.cn