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Nonlinear industrial process robust identification and output estimation method

It is an industrial process and non-linear technology, which is applied in computing, manufacturing computing systems, complex mathematical operations, etc., and can solve problems such as the reduction of system identification accuracy.

Active Publication Date: 2020-02-21
HARBIN INST OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to propose a method for robust identification and output estimation of nonlinear industrial processes in view of the problem in the prior art that when there are outliers in the output data, the identification accuracy of the system will be reduced

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  • Nonlinear industrial process robust identification and output estimation method
  • Nonlinear industrial process robust identification and output estimation method
  • Nonlinear industrial process robust identification and output estimation method

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specific Embodiment approach 1

[0081] Specific implementation mode one: refer to figure 1 Specifically explaining this embodiment, a nonlinear industrial process robust identification and output estimation method described in this embodiment includes the following steps:

[0082] Step 1: Select the local model of the system, and establish a robust probability model of the multi-model nonlinear system based on the Laplace distribution;

[0083] Step 2: According to the variational Bayesian framework, establish the posterior distribution of hidden variables and the iterative update formula of the parameters to be estimated;

[0084] Step 3: Set the posterior distribution of hidden variables established in step 2 and the termination condition of the iterative update formula of the parameters to be estimated. When the iteration is terminated, record the final iteration result as the estimated optimal parameter, and then obtain it through local model interpolation Model output value.

[0085] Detailed steps of...

Embodiment

[0149] (1) Select a first-order process whose transfer function is as follows:

[0150]

[0151] where K(w)=w 2 +0.6 is the system gain, τ(w)=0.5w 3 +3 is the system time constant, and the value range of scheduling variable w is w∈[1,4]. Because the system gain and time constant can vary by more than 10 times in this working range, it is difficult for a single linear model to describe the dynamic characteristics of the system. The identification experiment of this nonlinear process is now carried out by means of weighted combination of multiple local ARX models. Three operating points are chosen: w=1, w=2.25 and w=4. The scheduling variables vary as follows:

[0152] 1~100s: at the working point w=1;

[0153] 101~400s: change linearly from working point w=1 to working point w=2.25;

[0154] 401~550s: at the working point w=2.25;

[0155] 551~750s: linear change from working point w=1 to working point w=4;

[0156] 751~900s: at the working point w=...

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Abstract

The invention discloses a nonlinear industrial process robust identification and output estimation method. The invention relates to the field of industrial process modeling and model parameter identification, and aims to solve the problem that in the prior art, when an abnormal value exists in output data, the system identification precision is reduced, and the method comprises the following steps: 1, selecting a system local model, and establishing a robust probability model of a multi-model nonlinear system based on Laplace distribution; 2, according to the variational Bayesian framework, establishing an iterative updating formula of hidden variable posteriori distribution and to-be-estimated parameters; and 3, setting the posteriori distribution of the hidden variables established in the step 2 and a termination condition of a to-be-estimated parameter iteration updating formula, recording a final iteration result as an optimal parameter for estimation when iteration is terminated,and further obtaining a model output value through local model interpolation.

Description

technical field [0001] The invention relates to the field of industrial process modeling and model parameter identification, in particular to a method for robust identification and output estimation of a nonlinear industrial process. Background technique [0002] In the actual industrial process, due to the consideration of resources, costs and other factors, it is often transferred in multiple working conditions, which will lead to nonlinear characteristics of the system. Accurate modeling of these dynamic characteristics is a necessary prerequisite for subsequent state estimation and control. It is often difficult for a single linear model to describe the nonlinear characteristics of the system in a large working range. However, the weighted combination of multiple linear models can better reflect the nonlinear dynamics existing in industrial processes by virtue of the local linear structure and global nonlinear characteristics, so it is widely used. [0003] Due to the ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/18G06Q50/04
CPCG06F17/18G06Q50/04Y02P90/30
Inventor 刘新鹏杨宪强
Owner HARBIN INST OF TECH
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