Simple method for neural network decoupling of multi-variable system based on model reference adaptive control

An adaptive control and neural network technology, applied in the field of complex system intelligent modeling and decoupling control, can solve problems such as system structure and parameter uncertainty, modeling, interference, etc.

Inactive Publication Date: 2006-06-21
ANHUI UNIVERSITY OF TECHNOLOGY
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0002] The control of multi-variable complex process control systems faces two major difficulties: one is that it is difficult to use traditional theories and methods to model the system in real time and online because of the complexity and serious uncertainty of the system; Deterministic, it is difficult to decouple it with analytical model-based decoupling theory
Although the research papers of many scholars have conducted in-depth and systematic research and discussion on the decoupling control of mu

Method used

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  • Simple method for neural network decoupling of multi-variable system based on model reference adaptive control
  • Simple method for neural network decoupling of multi-variable system based on model reference adaptive control
  • Simple method for neural network decoupling of multi-variable system based on model reference adaptive control

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Embodiment Construction

[0030] The composition of the simulation experiment system:

[0031] In this system, the industrial computer communicates with the upper computer through Industrial Ethernet (IE), and connects with the remote I / O interface ET200M through the field bus Profibus DP (DP). The specific structure is as follows: figure 2 shown.

[0032] Graphite electrodes, short nets, scrap steel, molten steel, etc. in the main circuit of the three-phase electric arc furnace can be represented by equivalent time-varying resistance. In order to simulate the operation process of the actual system, a set of three-phase simulation experimental device is designed in the laboratory, such as image 3 shown.

[0033] System hardware configuration:

[0034] ①Distributed I / O ET200M of Siemens Company is selected for remote I / O, including analog input module (1), analog output module (1), digital input module (1), digital output module (1);

[0035] ②The AC frequency converter adopts Japanese YASKAWA US ...

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Abstract

The invention discloses a shortcut method of nerve network decoupling based on the adaptive control multiple-variable system in the complex system intelligent modeling and decoupling control technique domain, which is characterized by the following: adapting on-line identification and decoupling of nerve network; guiding the other two-phase control signal into the input end of nerve network identifier as two signal reference and identification when certain one phase of the three-phase electrode system is on-line identifying; decoupling and controlling the three-phase electrode.

Description

technical field [0001] The invention belongs to the technical field of complex system intelligent modeling and decoupling control, and in particular relates to a simple method for decoupling a neural network of a multivariable system based on model reference adaptive control. Background technique [0002] The control of multi-variable complex process control systems faces two major difficulties: one is that it is difficult to use traditional theories and methods to model the system in real time and online because of the complexity and serious uncertainty of the system; Deterministic, it is difficult to decouple it with decoupling theory based on analytical models. Although the research papers of many scholars have conducted in-depth and systematic research and discussion on the decoupling control of multivariable process control systems, these theories are all based on a premise: that is, it is necessary to write an accurate analytical model of the multivariable process syst...

Claims

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Application Information

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IPC IPC(8): G05B13/00G05B13/02G05B13/04H05B7/144H05B7/156
CPCY02P10/25
Inventor 张绍德
Owner ANHUI UNIVERSITY OF TECHNOLOGY
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