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CN 34-1054/N

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Control scheme for networked systems based on model parameter identification

  • Anhui Sanlian University, Hefei 230033, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.07.012
  • Received Date: 06 March 2014
  • Accepted Date: 17 May 2014
  • Rev Recd Date: 17 May 2014
  • Publish Date: 30 July 2014
    Abstract

  • Abstract

    In view of the limited bandwidth issues prevalently existing in the network control system (NCS), a system control scheme based on model parameter identification was presented. The system consists of several subsystems,each subsystem including an identification module, an update module and a controller module. In the closed-loop system update cycle, NCS constructed a dynamic model of the controlled object and reconstructed its state through online identification by the identification module, the update module updated the model parameter of the controlled object by the identified parameters, and finally the controller module output control variables. By designing rational cycle time and adjusting the matching degree of the module with the controlled object, the systems requirement for network bandwidth was reduced, and network utilization was improved, and thus the model-based control scheme was achieved. Matlab simulation was conducted in combination with the networked system of DC motors and the result shows the effectiveness of the proposed method.

    Abstract

    In view of the limited bandwidth issues prevalently existing in the network control system (NCS), a system control scheme based on model parameter identification was presented. The system consists of several subsystems,each subsystem including an identification module, an update module and a controller module. In the closed-loop system update cycle, NCS constructed a dynamic model of the controlled object and reconstructed its state through online identification by the identification module, the update module updated the model parameter of the controlled object by the identified parameters, and finally the controller module output control variables. By designing rational cycle time and adjusting the matching degree of the module with the controlled object, the systems requirement for network bandwidth was reduced, and network utilization was improved, and thus the model-based control scheme was achieved. Matlab simulation was conducted in combination with the networked system of DC motors and the result shows the effectiveness of the proposed method.
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    • References(12)
  • [1]
    You K Y, Xie L H. Survey of recent progress in network control systems[J]. Acta Automation Sinica, 2013, 39(2): 101-118.
    游科友，谢立华. 网络控制系统的最新研究综述[J]. 自动化学报, 2013, 39(2): 101-118.
    [2]
    Feisheng Yang, Huaguang Zhang, Guotao Hui, Shenquan Wang. Mode-independent fuzzy fault-tolerant variable sampling stabilization of nonlinear networked systems with both time-varying and random delays[J]. Fuzzy Sets and Systems. 2012, 207(11): 45-63.
    [3]
    Liu G P, Sun J, Zhao Y B. Design, analysis and real-time implementation of networked predictive control systems[J]. Acta Automation Sinica, 2013, 39(11): 1 769-1 777.
    [4]
    Feng J, Wang S Q. Reliable fuzzy control for a class of nonlinear networked control systems with time delay[J]. Acta Automatica Sinica, 2012, 38(7): 1 091-1 099.
    [5]
    Ma H,Yuan Z K,Tang G Y, et al. Time delay dependent optimal control of networked control system[C]// 32nd Chinese Control Conference. Xian, China: IEEE Press, 2013: 6 634-6 637.
    [6]
    Wang Z W, Guo G, Luo D S. Stability analysis of NCS with quantized state feedback[J]. Control Engineering of China, 2009, 16(4): 495-497.
    王志文,郭戈,骆东松. 量化状态反馈网络化系统的稳定性分析[J]. 控制工程, 2009, 16(4): 495-497.
    [7]
    Zhang X R, Lu, Han Y D. Expectative modeling of wide-area damping networked control systems with long stochastic time delay[C]// 25th Control and Design Conference. Guiyang, China: IEEE Press, 2013: 3 103-3 108.
    [8]
    Wang Z W, Guo G. On model-based networked control systems with multi-rate input sampling[J]. International Journal of Modelling, Identification and Control, 2010, 10(1/2): 160-166.
    [9]
    Tian Z D, Gao X W, Li K. A hybrid time-delay prediction method for networked control system[J]. International Journal of Automation and Computing, 2014, 11(1): 19-24.
    [10]
    Tang X M, Ding B C. Design of networked control systems with bounded arbitrary time delays[J]. International Journal of Automation & Computing, 2012, 9(2): 182-190.
    [11]
    Zhao X L, Fei S M, Lin J X. Impulsive controller design for nonlinear networked control systems with time delay and packet dropouts[J]. Journal of Systems Engineering and Electronics, 2012, 23(3): 414-418.
    [12]
    Che W W, Wang J L, Yang G H. Quantized H∞ fault-tolerant control for networked control systems[J]. International Journal of Automation & Computing, 2012, 9(4): 352-357.
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    [1]
    You K Y, Xie L H. Survey of recent progress in network control systems[J]. Acta Automation Sinica, 2013, 39(2): 101-118.
    游科友，谢立华. 网络控制系统的最新研究综述[J]. 自动化学报, 2013, 39(2): 101-118.
    [2]
    Feisheng Yang, Huaguang Zhang, Guotao Hui, Shenquan Wang. Mode-independent fuzzy fault-tolerant variable sampling stabilization of nonlinear networked systems with both time-varying and random delays[J]. Fuzzy Sets and Systems. 2012, 207(11): 45-63.
    [3]
    Liu G P, Sun J, Zhao Y B. Design, analysis and real-time implementation of networked predictive control systems[J]. Acta Automation Sinica, 2013, 39(11): 1 769-1 777.
    [4]
    Feng J, Wang S Q. Reliable fuzzy control for a class of nonlinear networked control systems with time delay[J]. Acta Automatica Sinica, 2012, 38(7): 1 091-1 099.
    [5]
    Ma H,Yuan Z K,Tang G Y, et al. Time delay dependent optimal control of networked control system[C]// 32nd Chinese Control Conference. Xian, China: IEEE Press, 2013: 6 634-6 637.
    [6]
    Wang Z W, Guo G, Luo D S. Stability analysis of NCS with quantized state feedback[J]. Control Engineering of China, 2009, 16(4): 495-497.
    王志文,郭戈,骆东松. 量化状态反馈网络化系统的稳定性分析[J]. 控制工程, 2009, 16(4): 495-497.
    [7]
    Zhang X R, Lu, Han Y D. Expectative modeling of wide-area damping networked control systems with long stochastic time delay[C]// 25th Control and Design Conference. Guiyang, China: IEEE Press, 2013: 3 103-3 108.
    [8]
    Wang Z W, Guo G. On model-based networked control systems with multi-rate input sampling[J]. International Journal of Modelling, Identification and Control, 2010, 10(1/2): 160-166.
    [9]
    Tian Z D, Gao X W, Li K. A hybrid time-delay prediction method for networked control system[J]. International Journal of Automation and Computing, 2014, 11(1): 19-24.
    [10]
    Tang X M, Ding B C. Design of networked control systems with bounded arbitrary time delays[J]. International Journal of Automation & Computing, 2012, 9(2): 182-190.
    [11]
    Zhao X L, Fei S M, Lin J X. Impulsive controller design for nonlinear networked control systems with time delay and packet dropouts[J]. Journal of Systems Engineering and Electronics, 2012, 23(3): 414-418.
    [12]
    Che W W, Wang J L, Yang G H. Quantized H∞ fault-tolerant control for networked control systems[J]. International Journal of Automation & Computing, 2012, 9(4): 352-357.
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