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Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems
Author(s) -
Liu Siyu,
Ding Feng,
Hayat Tasawar
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4884
Subject(s) - estimator , bilinear interpolation , algorithm , sliding window protocol , iterative method , kalman filter , computer science , state (computer science) , estimation theory , noise (video) , window (computing) , mathematics , mathematical optimization , statistics , artificial intelligence , image (mathematics) , operating system , computer vision
Summary The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.