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Robust output-feedback control for stochastic nonlinear systems with modeling errors

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Abstract

In this paper, the stabilization problem of a stochastic nonlinear system with modeling errors is considered. An augmented observer is first presented to counteract the unmeasurable states as well as modeling errors. An adaptive output feedback controller is designed such that all signals in the closed-loop system are bounded in probability and the output is regulated to the origin almost surely.

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Authors and Affiliations

  1. Institute of Automation, Qufu Normal University, Qufu Shandong, 273165, China

    Zhaojing Wu

  2. School of Mathematics and Information Science, Yantai University, Yantai Shandong, 264005, China

    Zhaojing Wu & Yonghui Liu

Authors
  1. Zhaojing Wu
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  2. Yonghui Liu
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Additional information

This work was supported by the National Natural Science Foundations of China (Nos. 60974028, 10971256), the China Postdoctoral Science Foundation (No. 200904501289), the Shandong Postdoctoral Science Foundation (No. 200903042), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20103705110002), the Shandong Provincial Natural Science Foundation (Nos. ZR2009GM008, ZR2009AL014), and the Natural Science Foundation of Jiangsu Province (No. BK2009083).

Zhaojing WU received his M.S. and Ph.D. degrees from Qufu Normal University and Northeastern University, China, in 2003 and 2005, respectively. He is currently with the School of Mathematics and Information Science, Yantai University, China. Currently, he does post-doctor research at the Institute of Automation, Qufu Normal University. His research interests include nonlinear systems and stochastic control theory.

Yonghui LIU received her M.S. degree from the School of Mathematics and Information Science, Yantai University, China in 2011. Her current research interests include stochastic control and switched systems.

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Wu, Z., Liu, Y. Robust output-feedback control for stochastic nonlinear systems with modeling errors. J. Control Theory Appl. 10, 344–348 (2012). https://doi.org/10.1007/s11768-012-0068-0

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  • DOI: https://doi.org/10.1007/s11768-012-0068-0

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