4.7 Article

Design of nonsmooth Kalman filter for compound sandwich systems with backlash and dead zone

Journal

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
Volume 31, Issue 15, Pages 7072-7086

Publisher

WILEY
DOI: 10.1002/rnc.5683

Keywords

convergence analysis; nonsmooth Kalman filter; sandwich system; state estimation

Funding

  1. Hebei Higher Education Teaching Reform Project [2018GJJG477]
  2. Innovation Driven Development Special Fund Project of Guangxi [AA18118002-3]
  3. key laboratory of coal mine safety monitoring and control technology safety production [2015SYS003]
  4. Langfang City Science and Technology Support Project [2018011039]
  5. National Natural Science Foundation of China [61763006]
  6. National Science Foundation of China [61873243]
  7. Fundamental Research Funds for the Central Universities [3142018006]

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A new nonsmooth Kalman filtering method is proposed for noise suppression of compound sandwich systems with backlash and dead zone. The filter can automatically switch among different operating zones and shows better convergence and accuracy in state estimation compared to conventional methods.
In this article, a new nonsmooth Kalman filtering (KF) method is proposed for noise suppression of the compound sandwich with backlash and dead zone. Based on the characteristics of the system, a nonsmooth stochastic state-space equation is constructed. Then, nonsmooth KF is developed to adapt the nonsmooth stochastic state space model. The filter can switch automatically among different operating zones according to the operating conditions. Moreover, the convergence of the switchable compound nonsmooth KF is discussed. Subsequently, a motor-driving mechanical transmission system which can be described as a compound nonsmooth nonlinear sandwich system with backlash and dead zone is studied as an application case. Finally, the comparison between the proposed filtering scheme and conventional filter are presented. It is demonstrated that the nonsmooth KF has a better performance than the conventional one in terms of convergence and accuracy in state estimation under noisy environment.

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