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Volume 4 Issue 3
Jul.  2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2017  >  4(3): 507-511
Xinxin Fu, Yu Kang and Pengfei Li, "Sampled-data Observer Design for a Class of Stochastic Nonlinear Systems Based on the Approximate Discrete-time Models," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 507-511, July 2017. doi: 10.1109/JAS.2017.7510559
Citation: Xinxin Fu, Yu Kang and Pengfei Li, "Sampled-data Observer Design for a Class of Stochastic Nonlinear Systems Based on the Approximate Discrete-time Models," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 507-511, July 2017. doi: 10.1109/JAS.2017.7510559
shu

Sampled-data Observer Design for a Class of Stochastic Nonlinear Systems Based on the Approximate Discrete-time Models

doi: 10.1109/JAS.2017.7510559
  • 1.

    Department of Automation, University of Science and Technology of China, Hefei 230026, China

  • 2.

    State Key Laboratory of Fire Science, Department of Automation, Institute of Advanced Technology, University of Science and Technology of China, Heifei 230027, China, and also with the Key Laboratory of Technology in GeoSpatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100192, China

Funds:

the National High Technology Research and Development Program of China (863 Program) 2014AA06A503

the National Natural Science Foundation of China 61422307

the National Natural Science Foundation of China 61673361

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    Abstract
  • In this paper, we studied the approximate sampleddata observer design for a class of stochastic nonlinear systems. Euler-Maruyama approximation was investigated in this paper because it is the basis of other higher precision numerical methods, and it preserves important structures of the nonlinear systems. Also, the form of Euler-Maruyama model is simple and easy to be calculated. The results provide a reference for sampled-data observer design method for such stochastic nonlinear systems, and may be useful to many practical control applications, such as tracking control in mechanical systems. And the effectiveness of the approach is demonstrated by a simulation example.

     

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