Robust-nonsmooth Kalman Filtering for Stochastic Sandwich Systems with Dead-zone

Abstract

In this paper, a robust-nonsmooth Kalman filtering approach for stochastic sandwich systems with dead-zone is proposed, which can guarantee the variance of filtering error to be upper bounded. In this approach, the stochastic sandwich system with dead-zone is described by a stochastic nonsmooth state-space function. Then, in order to approximate the nonsmooth sandwich system within a bounded region around the equilibrium point, a linearization approach based on nonsmooth optimization is proposed. For handling the model uncertainty caused by linearization and modeling, the robust-nonsmooth Kalman filtering method is proposed for state estimation of the stochastic sandwich system with dead-zones with model uncertainty. Finally, both simulation and experimental examples are presented for evaluating the performance of the proposed filtering scheme.

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Authors

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Correspondence to Yonghong Tan or Ruili Dong.

Additional information

Recommended by Associate Editor Guangdeng Zong under the direction of Editor Hamid Reza Karimi.

This work was supported in part by the National Natural Science Foundation of China under Grant 61971120 and Grant 61671303, and in part by Shanghai Pujiang Program under Grant 18PJ1400100, and in part by the project of the Science and Technology Commission of Shanghai under Grant 18070503000, and in part by the Project of Fundamental Research Funds for the Central Universities.

Baoan Li is a Ph.D. candidate of Mathematics and Science College in Shanghai Normal University. Currently, He is also an associate professor at the School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China. His research interests are in the area of robust control and modeling and control of nonlinear systems.

Yonghong Tan received his Ph.D. degree in Electrical Engineering from the University of Ghent, Ghent, Belgium, in 1996. He is currently a Professor at the College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China. His research interests include modeling and control of nonlinear systems, mechatronics, intelligent control, and signal processing.

Lei Zhou is a Ph.D. candidate of Mathematics and Science College in Shanghai Normal University. Presently, He is also a lecturer at the Shanghai University of Engineering and Science, China. His research interests are in the area of robust filtering and control of nonlinear systems.

Ruili Dong received her Ph.D. degree from Shanghai Jiaotong University, Shanghai, China, in 2009. Presently, she is a professor at the College of Information Science and Technology, Donghua University, Shanghai, China. Dr. Dong was an owner of the Fellowship awarded by the K.C. Wong Education Foundation and German Academic Exchange Service (DAAD) in 2014 and she was awarded the Dresden Junior Fellow by the Dresden Fellowship Program in 2015. She has also been selected into the Shanghai Pujiang Talent Program in 2018 and the Shanghai Songjiang Science and Technology Leading Talent Program in 2020, respectively. Her research interests include identification, signal processing and control of mechatronics and nonlinear systems.

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Li, B., Tan, Y., Zhou, L. et al. Robust-nonsmooth Kalman Filtering for Stochastic Sandwich Systems with Dead-zone. Int. J. Control Autom. Syst. (2020). https://doi.org/10.1007/s12555-019-1027-z

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Keywords

  • Dead-zone
  • Kalman filter
  • random noise
  • robustness
  • sandwich systems