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Dimension reduction recognition method for large-scale process control in process industry

A process control and process industry technology, applied in the field of parameter identification, can solve the problems of low calculation amount, low identification efficiency, identification failure, etc., and achieve the effect of reducing the amount of calculation and improving the identification accuracy.

Active Publication Date: 2020-04-17
JIANGNAN UNIV
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Problems solved by technology

[0005] In order to solve the problem of identification failure or low identification efficiency due to the excessive amount of calculation in the traditional identification algorithm in the prior art, the present invention provides a dimensionality reduction identification method for large-scale process control in the process industry. The calculation process is generally Without manual participation, the parameter identification of high-order systems can be realized with a very low calculation amount on the basis of ensuring calculation accuracy

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  • Dimension reduction recognition method for large-scale process control in process industry
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  • Dimension reduction recognition method for large-scale process control in process industry

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Embodiment Construction

[0063] like Figure 1 ~ Figure 3 As shown, the present invention is a dimensionality reduction identification method for large-scale process control in the process industry, which includes the following steps:

[0064] S1: According to the input-output relationship of the industrial process system, construct a time series model of the system:

[0065] A(z)y(t)=B(z)u(t)+v(t)

[0066] Among them: y(t) is the output of the system, u(t) is the input of the system, v(t) is the noise of the system, u(t), y(t), v(t) are subject to the mean value of zero, Gaussian distribution with variance σ, A(z) and B(z) are model polynomials respectively;

[0067] A(z), B(z) can be expressed as follows:

[0068] A(z)=1+a 1 z -1 +L+a n z -n

[0069] B(z)=b 1 z -1 +b 2 z -2 +L+b n z -n ;

[0070] The expression of the backward shift operator z is as follows:

[0071] (z -1 y(t)=y(t-1)).

[0072] S2: According to the system model, the following definitions are made:

[0073] Y(L)=[y(...

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Abstract

According to the dimension reduction recognition method for large-scale process control of the process industry, manual participation is not needed in the calculation process, and parameter recognition of a high-order system can be achieved with a very low calculation amount on the basis that the calculation precision is ensured. According to the technical scheme, in the system parameter recognition process, the to-be-identified 2n-dimensional parameters are used for constructing the Krylov subspace through the Arnoldi method, the 2n-dimensional parameters are reduced to the k-dimensional parameters, and the calculated amount of the system is reduced; then solving the parameter optimization step length through a Givens transformation method, and ensuring that the calculation method is convergent; through a preset threshold value and an iteration method, the recognition precision of system parameters is improved.

Description

technical field [0001] The invention relates to the technical field of parameter identification, in particular to a dimensionality reduction identification method for large-scale process control in the process industry. Background technique [0002] With the rapid development of the Internet of Things technology, the process industry control systems are interconnected and communicate with each other, and the scale of the control system is getting larger and larger. It is necessary to use a high-level system to describe its dynamic process. In the prior art, traditional identification algorithms such as gradient iterative method (Gradient Iterative, GI) and least squares method (Least Squares, LS) are used for parameter identification of large-scale systems. But the traditional identification algorithm has the following problems: [0003] (1) When calculating each step size, the gradient algorithm needs to calculate the eigenvalues ​​of the higher-order matrix to determine t...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 陈晶曹俊峰过榴晓
Owner JIANGNAN UNIV
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