Dynamic and static data hybrid driven Hammerstein nonlinear industrial system simple grey box space identification method

Abstract

The invention relates to a dynamic and static data hybrid driven Hammerstein nonlinear industrial system simple grey box space identification method, and belongs to the field of control theory and control engineering nonlinear system identification. The method comprises the following steps: S1, collecting dynamic data and static data of a system; S2, selecting and processing a Hammerstein nonlinear system model to obtain a prediction model; S3, constructing a dynamic simple model of the Hammerstein nonlinear system; S4, constructing a static simple model of the Hammerstein nonlinear system; and S5, solving system parameter fusion identification by using a hierarchical Lagrange optimal weighting method. Dynamic and static data are adopted at the same time, and based on the decomposed simplemodel, the estimation of additional intermediate parameters is avoided, the variance of the estimation model is reduced, and the model precision is improved.

Description

technical field [0001] The invention belongs to the field of control theory and control engineering nonlinear system identification, and relates to a Hammerstein nonlinear industrial system simple gray box space identification method driven by dynamic / static data hybrid. Background technique [0002] The Hammerstein nonlinear system is a modular nonlinear system consisting of static nonlinear modules and linear dynamic modules, which can effectively reflect the system characteristics of most actual input nonlinear industrial processes, and has been widely used in actual industrial processes. Describe, for example, blast furnace ironmaking systems, battery management systems, wireless power transmission systems, etc. In the past few decades, a lot of research work on the identification of Hammerstein nonlinear systems has been carried out in the industry, and a variety of mature identification methods have been proposed one after another, such as the maximum likelihood estima...

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Problems solved by technology

[0004] In view of this, the object of the present invention is to provide a novel gray-box parsimony subspace identification method driven by dynamic / static data to accurately identify the Hammerstein nonlinear system, so as to overcome the fact that the existing black-box space identification method cannot provide low-frequency information of the system , leading to the problem of low model accuracy

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