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State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements

Author

Listed:
  • Jinliang Liu
  • Jie Cao
  • Zhiang Wu
  • Qiong Qi

Abstract

This paper is concerned with the state estimation problem for the complex networked systems with randomly occurring nonlinearities and randomly missing measurements. The nonlinearities are included to describe the phenomena of nonlinear disturbances which exist in the network and may occur in a probabilistic way. Considering the fact that probabilistic data missing may occur in the process of information transmission, we introduce the randomly data missing into the sensor measurements. The aim of this paper is to design a state estimator to estimate the true states of the considered complex network through the available output measurements. By using a Lyapunov functional and some stochastic analysis techniques, sufficient criteria are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable in the mean square. Furthermore, the state estimator gain is also obtained. Finally, a numerical example is employed to illustrate the effectiveness of the proposed state estimation conditions.

Suggested Citation

  • Jinliang Liu & Jie Cao & Zhiang Wu & Qiong Qi, 2014. "State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1364-1374, July.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:7:p:1364-1374
    DOI: 10.1080/00207721.2014.880200
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    Cited by:

    1. Shi, Zhicheng & Yang, Yongqing & Chang, Qi & Xu, Xianyun, 2020. "The optimal state estimation for competitive neural network with time-varying delay using Local Search Algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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