Decomposition-Based Gradient Iterative Estimation for Input Nonlinear Model by Using the Kalman Filter

  • Qiuling Fei
  • Junxia MaEmail author
  • Weili Xiong
  • Jing Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)


This paper considers the joint iterative estimation of the parameters and states for Hammerstein nonlinear state space systems. By applying the model decomposition technique, the unknown parameters to be estimated are distributed into two regression identification models. Furthermore, under the framework of the gradient iterative algorithm and the hierarchical identification theory, a decomposition-based gradient iterative algorithm is proposed to estimate the unknown parameters of nonlinear system. Finally, a numerical simulation example is given to validate the effectiveness of the algorithm.


Input nonlinear model Kalman filter Model decomposition Gradient iterative 



This work was supported in part by the National Natural Science Foundation of China under Grants 61803183 and 61773182, the Natural Science Foundation of Jiangsu Province under Grants BK20180591 and BK20170198, and the Fundamental Research Funds for the Central Universities under Grant JUSRP11923.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Qiuling Fei
    • 1
  • Junxia Ma
    • 1
    Email author
  • Weili Xiong
    • 1
  • Jing Chen
    • 1
  1. 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things EngineeringJiangnan UniversityWuxiChina

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