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Journal of Systems Science and Complexity

, Volume 32, Issue 2, pp 453–478 | Cite as

Performance Improvement of NCSs Under Complex Network via Concurrent Paths

  • Zhan-Yu WangEmail author
  • Guo-Ping LiuEmail author
Article
  • 25 Downloads

Abstract

Different from the single paths between controllers and plants of networked control systems (NCSs), the complex network provides widespread links and brings plenty of paths from the controller side to the plant side. Benefit from this advantage, a novel data transmission dispatching strategy is proposed. When the direct path can not satisfy the condition of system demand, neither stability nor performance, some specified paths compose a concurrent-path to transmit the control signal concurrently. Firstly, the networked control systems (NCSs) are expressed by the switched systems model with the constant network-induced delay, and the network communication is described as a packet-loss process. Secondly, taking system’s exponential decay rate as its performance indicator, the relationship between control signal transmission paths and system performance is quantitatively given by considering packet losses as the compound poisson process and the alternating renewal process. Then, due to that different combination of paths induces the different statistical properties of packet losses, the approach to find an appropriate concurrent-path is proposed concerning two mutually constrained factors, the system performance demand and the utilization of network resources. After the theoretical analysis, a distributed communication platform based on peer-to-peer (P2P) network technology is designed and implemented to realize the concurrent-path transmission on internet. Finally, the practical experiment on the platform demonstrates the validity and effectiveness of proposed approach.

Keywords

Dispatching strategy networked control system P2P switched system transmission platform for networked control systems 

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References

  1. [1]
    Ding L, Yu P, Liu Z W, et al., Consensus and performance optimisation of multi-agent systems with position-only information via impulsive control, IET Control Theory and Applications, 2013, 7(1): 1–46.MathSciNetCrossRefGoogle Scholar
  2. [2]
    Zhu W, Consensus of multiagent systems with switching jointly reachable interconnection and time delays, IEEE Transactions on Systems, Man, and Cybernetics, 2012, 42(2): 348–358.Google Scholar
  3. [3]
    Guan Z H, Wu Y H, and Feng G, Consensus analysis based on impulsive systems in multiagent networks, IEEE Transactions on Circuits and Systems, 2012, 59(1): 170–178.MathSciNetCrossRefGoogle Scholar
  4. [4]
    Yoichiro M, Kentaro H, and Tomomichi H, Modified state predictive control of continuous-time systems with input delay, Proceeding on 2017 IEEE International Conference on Industrial Technology, Toronto, 2017.Google Scholar
  5. [5]
    Alessandretti A, Aguiar A P, and Jones C N, An input-to-state-stability approach to economic optimization in model predictive control, IEEE Transactions on Automatic Control, 2017, 62(12): 6081–6093.MathSciNetCrossRefzbMATHGoogle Scholar
  6. [6]
    Mohammad B S, Mostafa M, Robert S B, et al., Predictive control of a capacitorless matrix converter-based STATCOM, IEEE Journal of Emerging and Selected Topics in Power Electronics, 2017, 5(2): 796–808.CrossRefGoogle Scholar
  7. [7]
    Pedro E, Juan C A, Sebastian Z, et al., Predictive control for laser beam shaping, IEEE Latin America Transactions, 2017, 15(4): 626–631.CrossRefGoogle Scholar
  8. [8]
    Gao H, Wu B, Xu D W, et al., Pablo acunamodel predictive switching pattern control for currentsource converters with space-vector-based selective harmonic elimination, IEEE Transactions on Power Electronics, 2017, 32(8): 6558–6569.CrossRefGoogle Scholar
  9. [9]
    Tan C and Liu G P, Consensus of discrete-time linear networked multi-agent systems with communication delays, IEEE Transactions on Automatic Control, 2013, 58(11): 2962–2968.MathSciNetCrossRefzbMATHGoogle Scholar
  10. [10]
    Yang X R and Liu G P, Consensus of descriptor multi-agent systems via dynamic compensators, IET Control Theory and Applications, 2014, 8(6): 389–398.MathSciNetCrossRefGoogle Scholar
  11. [11]
    Jian S and Jie C, Less conservative stability criteria for linear systems with interval time-varying delays, International Journal of Robust & Nonlinear Control, 2015, 25(4): 475–485.MathSciNetCrossRefzbMATHGoogle Scholar
  12. [12]
    Jian S, Jie C, Liu G P, et al., Delay-range-dependent and rate-range-dependent stability criteria for linear systems with time-varying delays, Proceedings of the 48th IEEE Conference on Decision and Control, Shanghai, China, 2009, 251–256.Google Scholar
  13. [13]
    Jian S and Jie C, Controller design for networked control systems with time-varying delay via switched system approach, Proceeding of the 11th World Congress on Intelligent Control and Automation, Shenyang, China, 2015, 2705–2710.Google Scholar
  14. [14]
    Jian S and Jie C, Networked predictive control for systems with unknown or partially known delay, IET Control Theory and Applications, 2014, 8(18): 2282–2288.MathSciNetCrossRefGoogle Scholar
  15. [15]
    Yang X R and Liu G P, On wireless links for vehicle-to-infrastructure communications, IEEE Transactions on Vehicular Technology, 2010, 59(1): 269–281.CrossRefGoogle Scholar
  16. [16]
    Vehbi C G and Gerhard P H, Industrial wireless sensor networks: Challenges, design principles, and technical approaches, IEEE Transactions on Industrial Electronics, 2009, 56(10): 4258–4265.CrossRefGoogle Scholar
  17. [17]
    Tariq S, John S B, and Datta G, Network-centric systems for military operations in urban terrain: The role of UAVs, Proceeding of IEEE, 2007, 95(1): 92–107.CrossRefGoogle Scholar
  18. [18]
    Liu Y, Mohammad S H, and Yu H N, Modelling and remote control of an excavator, International Journal of Automation and Computing, 2010, 7(3): 349–358.CrossRefGoogle Scholar
  19. [19]
    Qiao Y L, Liu G P, Geng Z, et al., NCSLab: A web-based global-scale control laboratory with rith interactive features, IEEE Transactions on Industrial Electronics, 2010, 57(10): 3253–3265.CrossRefGoogle Scholar
  20. [20]
    Henrik S, Saurabh A, and Karl H J, Cyberphysical security in networked control systems, IEEE Control System Magazine, 2015, 19(1): 20–23.MathSciNetGoogle Scholar
  21. [21]
    Pang Z H and Liu G P, Design and implementation of secure networked predictive control systems under deception attacks, IEEE Transactions on Control System Techonlogy, 2012, 20(5): 1334–1342.CrossRefGoogle Scholar
  22. [22]
    Zou L, Wang Z D, and Zhou D H, Event-based control and filtering of networked systems: A survey, International Journal of Automation and Computing, 2017, 14(3): 239–253.CrossRefGoogle Scholar
  23. [23]
    Xie H B, Hu S L, and Shang C W, Event-based reliable control for linear networked control systems, Proceedings of the 36th Chinese Control Conference, Dalian, 2017, 7863–7868.Google Scholar
  24. [24]
    Lai C L and Pau-lo H, Design the remote control system with the time-delay estimator and the adaptive smith predictor, IEEE Transactions on Control Industrial Informatics, 2010, 6(1): 73–80.CrossRefGoogle Scholar
  25. [25]
    HuW S, Liu G P, David R, et al., Design and implementation of web-based control laboratory for test rigs in geographically diverse locations, IEEE Transactions on Control Industrial Electronics, 2008, 55(6): 2343–2354.CrossRefGoogle Scholar
  26. [26]
    Qiao Y L, Liu G P, Geng Z, et al., Design and realization of networked control experiments in a web-based laboratory, Proceeding of UKACC International Conference on Control, Coventry, U.K., 2010, 848–853.Google Scholar
  27. [27]
    Hu W S, Zhou H, and Deng Q J, Design of web-based 3D control laboratory, Proceeding of the 2nd International Conference on Intelligent Control and Information, Kunming, 2011, 590–594.Google Scholar
  28. [28]
    Pang Z H, Liu G P, and Qiao Y L, Web-based compilation of C-MEX S-Functions and its application in NCSLab, Proceeding of the 30th Chinese Control Conference, Yantai, 2011, 6533–6538.Google Scholar
  29. [29]
    Zermane H and Mouss H, Development of an internet and fuzzy based control system of manufacturing process, International Journal of Automation and Computing, 2017, 14(6): 706–718.CrossRefGoogle Scholar
  30. [30]
    Zhang W A, Yu L, and Feng G, Optimal linear estimation for networked systems with communication constraint, Automatica, 2011, 47(9): 1992–2000.MathSciNetCrossRefzbMATHGoogle Scholar
  31. [31]
    Lian F L, Analysis, design, modeling and control of networked control systems, Doctor’s degree dissertation, University of Michigan, Michigan, 2001.Google Scholar
  32. [32]
    Xiong J and Lam J, Stabilization of linear systems over networks with bounded packet loss, Automatica, 2007, 43(1): 80–87.MathSciNetCrossRefzbMATHGoogle Scholar
  33. [33]
    Pham-Gia T and Turkkan N, System availability in a Gamma alternating renewal process, Naval Research Logistics, 1999, 46(7): 822–844.MathSciNetCrossRefzbMATHGoogle Scholar
  34. [34]
    Jerome V, Practical JXTA, https://jxta.kenai.com/, Lulu Enterprise, Rotterdam, 2010.Google Scholar

Copyright information

© The Editorial Office of JSSC & Springer-Verlag GmbH Germany 2019

Authors and Affiliations

  1. 1.School of Mechanical and Electrical EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.School of EngineeringUniversity of South WalesPontypriddUK

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