Wentao Liu, Junxia Ma, Weili Xiong*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 873-892, 2023, DOI:10.32604/cmes.2022.020565
Abstract This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space
models in the presence of disturbances. A bilinear state observer is designed for deriving identification algorithms
to estimate the state variables using the input-output data. Based on the bilinear state observer, a novel gradient
iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous
mixed p-norm cost function. The gain at each iterative step adapts to the data quality so that the algorithm has
good robustness to the noise disturbance. Furthermore, to More >
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