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 improve the performance of… More >

Shenkun Zhao, Chao Jiang^*, Zhe Zhang, Xiangyun Long

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1151-1173, 2020, DOI:10.32604/cmes.2020.08867

Abstract In the Prognostics and Health Management (PHM), remaining useful life (RUL) is very important and utilized to ensure the reliability and safety of the operation of complex mechanical systems. Recently, unscented Kalman filtering (UKF) has been applied widely in the RUL estimation. For a degradation system, the relationship between its monitored measurements and its degradation states is assumed to be nonlinear in the conventional UKF. However, in some special degradation systems, their monitored measurements have a linear relation with their degradation states. For these special problems, it may bring estimation errors to use the UKF method directly. Besides, many uncertain… More >