Wiener nonlinear system identification method based on parameter separation

Abstract

The invention discloses a Wiener nonlinear system identification method based on parameter separation. The Wiener nonlinear system identification method comprises the following steps of: A, convertinga to-be-identified thermal power plant control system into a Wiener nonlinear system, and combining input quantities of the to-be-identified thermal power plant control system; B, analyzing the Wiener nonlinear system, wherein the Wiener nonlinear system comprises a system linear dynamic part structure, a system nonlinear static part structure, a dynamic interference type and measurement noise, determining na, nb and nc, setting an initial value, and repeatedly collecting input data u(k) and y(k) until k is greater than or equal to na+n and k is greater than or equal to nb+n; C, separating atime-invariant parameter and a time-variant parameter of the Wiener nonlinear system; D, identifying the Wiener nonlinear system; E, and returning to the step A when the identified model does not meetthe requirements, re-adjusting the structure and the initial value of the model, and re-identifying the system until the system model meeting the requirements is obtained. According to the Wiener nonlinear system identification method, the defects in the prior art can be overcome, and the precision and convergence rate of Wiener nonlinear system identification are improved.

Description

technical field [0001] The invention relates to the technical field of industrial control, in particular to a parameter separation-based Wiener nonlinear system identification method. Background technique [0002] System identification technology is a branch of the control field. Its purpose is to use the input and output data of the system to identify the system parameter model and lay the foundation for system optimization and system control. System models are generally divided into linear models and nonlinear models, among which nonlinear models exist widely, and the identification of nonlinear models has attracted the attention of many engineers and scholars. The Wiener nonlinear system is the most typical nonlinear system, and its structure is composed of a linear dynamic part and a nonlinear static part connected in series. Therefore, Wiener nonlinear systems belong to the category of dynamic systems. The Wiener nonlinear system can describe most of the industrial sy...

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Problems solved by technology

However, this method is not suitable for situations where dynamic disturbances exist. Dynamic disturbances will be superimposed on the system output, reducing the identification accuracy of the system identification method, and even failing to identify the real parameters, which reduces the applicability of the method.

Scholars D.Wang, F.Ding, etc. in the literature "Least squares based and gradient based iterative identification for Wiener nonlinear systems", (simplified translation: the application of the least squares and gradient iterative algorithm in the identification of Wiener nonlinear systems, publ

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