|Extended Gradient-based Iterative Algorithm for Bilinear State-space Systems with Moving Average Noises by Using the Filtering Technique
Siyu Liu, Yanliang Zhang*, Ling Xu, Feng Ding*, Ahmed Alsaedi, and Tasawar Hayat
International Journal of Control, Automation, and Systems, vol. 19, no. 4, pp.1597-1606, 2021
Abstract : This paper develops a filtering-based iterative algorithm for the combined parameter and state estimation problems of bilinear state-space systems, taking account of the moving average noise. In order to deal with the correlated noise and unknown states in the parameter estimation, a filter is chosen to filter the input-output data disturbed by colored noise and a Kalman state observer (KSO) is designed to estimate the states by minimizing the trace of the error covariance matrix. Then, a KSO extended gradient-based iterative (KSO-EGI) algorithm and a filtering based KSO-EGI algorithm are presented to estimate the unknown states and unknown parameters jointly by the iterative estimation idea. The simulation results demonstrate the effectiveness of the proposed algorithms.
Bilinear system, data filtering, iterative search, parameter estimation, state estimation
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