A novel data-driven model based parameter estimation of nonlinear systems. (4th August 2019) - Ge, Xiaobiao; Luo, Zhong; Ma, Ying; Liu, Haopeng; Zhu, Yunpeng
Abstract: In practice, it is usually difficult to estimate the characteristic parameters of a system due to the system nonlinearity and uncertainty. To address this problem, in this study, the characteristic parameters of a nonlinear system are identified by using a data driven method based on the best discretization of the Nonlinear Differential Equation (NDE) model of the system. The best discretization of a NDE model is firstly determined, and then the discretized model, known as the Nonlinear Auto Regressive with eXegenous input (NARX) model, is determined by using the Least Squares (LS) algorithm from the input and output data of system. A case study is discussed to validate the proposed system parameter identification method, where the characteristic parameters of a rotating blade-casing system are evaluated under a bandwidth rub impact in the horizontal direction with noise. The result shows that the identified model can be used to describe the characteristics of the underlying system accurately, which provides a reliable model for the dynamic analysis, control of rotating blade-casing system. Graphical abstract: Used the input and output datum of system and the functional relationship between parameters and coefficients of NARX model to confirm these key parameters.Image 1 Highlights: Four different discrete methods are compared and the best discretization of a NDE model is confirmed. The function relationship between characteristic parameters of systems and coefficients of NARX model is discussed. The error of each discrete method is analyzed theoretically. A novel identification method, referred as the DDM, is utilized to identify the parameters of a rotating blade-casing system.