An improved fuzzy Kalman filter for state estimation of non-linear systems

  • Authors:

  • Zhijie Zhou;Changhua Hu;Maoyin Chen;Huafeng He;Bangcheng Zhang

  • Affiliations:

  • High-Tech Institute of Xi'an, Xi'an, PR China,Department of Automation, Tsinghua University, Beijing 100084, PR China;High-Tech Institute of Xi'an, Xi'an, PR China;Department of Automation, Tsinghua University, Beijing 100084, PR China;High-Tech Institute of Xi'an, Xi'an, PR China;School of Mechatronic Engineering, Changchun University of Technology, Changchun, Jilin 130012, PR China

  • Venue:
  • International Journal of Systems Science
  • Year:
  • 2010

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Abstract

The extended fuzzy Kalman filter (EFKF) of non-linear systems which can deal with fuzzy uncertainty effectively has been developed recently. But it seems to be inapplicable to the cases where the states change abruptly or there exist model mismatches in non-linear systems. Therefore, based on the EFKF, a new concept of the improved fuzzy Kalman filter (IFKF) is proposed in this article. Due to the introduction of the extension orthogonality principle given as a criterion to design the new algorithm, the IFKF can track the abrupt changes of the states and has definite robustness against the model mismatches. Finally, computer simulations with a MIMO non-linear model are presented, which illustrate that the proposed IFKF has the strong tracking ability and robustness against the model mismatches.