Sampled-data Observer Design for a Class of Stochastic Nonlinear Systems Based on the Approximate Discrete-time Models |
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| Authors: | Xinxin Fu Yu Kang Pengfei Li |
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| Affiliation: | 1.Department of Automation, University of Science and Technology of China, Hefei 230026, China2.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 |
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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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| Keywords: | Approximation model exponentially bounded sampled-data observer stochastic nonlinear |
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