Stabilization of NCSs by random allocation of transmission power to sensors



This study investigates networked control systems (NCSs), whose sensors communicate with remote controllers via a wireless fading channel. The sensor can choose different power levels at which it can transmit its measurement to the controller. The transmission power is selected according to a given probability distribution. The level of transmission power determines the probability of packet loss. The objective of this study is to find an appropriate transmission power probability distribution and a system controller jointly such that NCSs can be exponentially stabilized within a given energy budget. By the average dwell time technique, sufficient conditions for almost sure stability and an optimal sensor power probability distribution maximizing the stability margin are obtained. The effectiveness of the results is demonstrated by numerical simulations.



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


  1. 1

    Gaid M E M B, Cela A, Hamam Y. Optimal integrated control and scheduling of networked control systems with communication constraints: application to a car suspension system. IEEE Trans Control Syst Tech, 2006, 14: 779–787

    Article  Google Scholar 

  2. 2

    Ding S X, Zhang P, Yin S, et al. An integrated design framework of fault-tolerant wireless networked control systems for industrial automatic control applications. IEEE Trans Ind Informat, 2013, 9: 462–471

    Article  Google Scholar 

  3. 3

    Appadwedula S, Veeravalli V V, Jones D L. Energy-efficient detection in sensor networks. IEEE J Sel Areas Commun, 2005, 23: 693–702

    Article  Google Scholar 

  4. 4

    Ren Z, Cheng P, Chen J, et al. Optimal periodic sensor schedule for steady-state estimation under average transmission energy constraint. IEEE Trans Autom Control, 2013, 58: 3265–3271

    Article  Google Scholar 

  5. 5

    Shi L, Cheng P, Chen J. Sensor data scheduling for optimal state estimation with communication energy constraint. Automatica, 2011, 47: 1693–1698

    MathSciNet  Article  MATH  Google Scholar 

  6. 6

    Shi L, Xie L. Optimal sensor power scheduling for state estimation of Gauss-Markov systems over a packet-dropping network. IEEE Trans Signal Process, 2012, 60: 2701–2705

    MathSciNet  Article  Google Scholar 

  7. 7

    Han D, Cheng P, Chen J, et al. An online sensor power schedule for remote state estimation with communication energy constraint. IEEE Trans Autom Control, 2014, 59: 1942–1947

    MathSciNet  Article  Google Scholar 

  8. 8

    Li Y Z, Quevedo D E, Lau V, et al. Online sensor transmission power schedule for remote state estimation. In: Proceeding of the 52nd IEEE Conference on Decision and Control, Firenze, 2013. 10–13

    Google Scholar 

  9. 9

    Guo G, Wang L Y. Control over medium-constrained vehicular networks with fading channels and random access protocol: a networked systems approach. IEEE Trans Veh Tech, 2015, 64: 3347–3358

    Article  Google Scholar 

  10. 10

    Pappi K N, Lioumpas A S, Karagiannidis G K. θ-QAM: a parametric quadrature amplitude modulation family and its performance in AWGN and fading channels. IEEE Trans Commun, 2010, 58: 1014–1019

    Article  Google Scholar 

  11. 11

    Li Y, Quevedo D E, Lau V, et al. Optimal periodic transmission power schedules for remote estimation of ARMA processes. IEEE Trans Signal Process, 2013, 61: 6164–6174

    MathSciNet  Article  Google Scholar 

  12. 12

    Marques A G, Wang X, Giannakis G B. Minimizing transmit power for coherent communications in wireless sensor networks with finite-rate feedback. IEEE Trans Signal Process, 2008, 56: 4446–4457

    MathSciNet  Article  Google Scholar 

  13. 13

    Shiryayev A N. Probability. New York: Springer-Verlag, 1984. 100–110

    Book  Google Scholar 

  14. 14

    Elia N, Eisenbeis J N. Limitations of linear control over packet drop networks. IEEE Trans Autom Control, 2011, 56: 826–841

    MathSciNet  Article  Google Scholar 

  15. 15

    Bolzern P, Colaneri P, Nicolao G D. Almost sure stability of Markov jump linear systems with deterministic switching. IEEE Trans Autom Control, 2013, 58: 209–214

    MathSciNet  Article  Google Scholar 

  16. 16

    Dai S L, Lin H, Ge S S. Scheduling-and-control co-design for a collection of networked control systems with uncertain delays. IEEE Trans Control Syst Tech, 2010, 18: 66–78

    Article  Google Scholar 

  17. 17

    Guo Y F, Li S Y. Transmission probability condition for stabilisability of networked control systems. IET Control Theory Appl, 2008, 4: 672–682

    MathSciNet  Article  Google Scholar 

  18. 18

    Zhai G, Hou B, Yasuda K, et al. Stability analysis of switched systems with stable and unstable subsystem: an average dwell time approach. In: Proceedings of American Control Conference, Chicago, 2010. 200–204

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Ge Guo.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Guo, G. & Zhuang, Y. Stabilization of NCSs by random allocation of transmission power to sensors. Sci. China Inf. Sci. 59, 067201 (2016).

Download citation


  • networked control systems
  • power allocation
  • packet dropout
  • almost sure stability
  • transmission energy constraint


  • 网络控制系统
  • 功率分配
  • 丢包
  • 几乎必然稳定
  • 发送能量约束