Dyanmic-static mixed nerve network modeling-based anti-interference control method for random system

A hybrid neural network, static neural network technology, applied in adaptive control, general control system, control/regulation system, etc.

Active Publication Date: 2013-01-09
BEIHANG UNIV
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide an anti-interference compound control method based on dynamic and static mixed neural network modeling in a random distribution control system for the lack of complex industrial process modeling and the influence of interference, which is used to improve the complex industrial process. control accuracy

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  • Dyanmic-static mixed nerve network modeling-based anti-interference control method for random system
  • Dyanmic-static mixed nerve network modeling-based anti-interference control method for random system
  • Dyanmic-static mixed nerve network modeling-based anti-interference control method for random system

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Embodiment Construction

[0033] The technical problem to be solved by the present invention is to provide a data-based neural network modeling scheme and a disturbance observer-based anti-disturbance control method to improve the control accuracy in view of the deficiency and disturbance of the random distribution control system modeling of complex industrial processes .

[0034] The technical solution of the present invention is: firstly, establish a static neural network approximation model for the batch output of the random distribution control system; secondly, perform a dynamic neural network approximation on the weight of the static neural network model to form a weight dynamic system; The bounded disturbance design observer in the system is estimated and fed-forward compensation; finally, based on the linear matrix inequality method, the observer gain and controller gain are designed for the compound control system, so that the weight dynamic system is stable and meets certain anti-interference pe...

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Abstract

The invention discloses a dyanmic-static mixed nerve network modeling-based anti-interference control method for a random system, and relates to random distributed control system batch output static nerve network modeling, dynamic nerve network modeling of output weight and composite anti-interface control based on an observer. The method comprises the following steps of: firstly, building a static nerve network approaching model for batch output of a random distributed control system; forming a weight dynamic system to dynamic nerve network approach of s static nerve network model; then, designing the observer aiming at bounded interface in the system to evaluate and perform feedforward compensation; and finally, designing gain of the observer and gain of a controller on the composite control system based on a linear matrix non-equality method, so that the system is stable and meets certain anti-interference performance.

Description

technical field [0001] The invention relates to an anti-interference composite control method based on dynamic and static mixed neural network modeling in a random distribution control system, which can be used for batch output processes such as particle processing, papermaking, ore grinding, and combustion, and process monitoring and control based on image information. Background technique [0002] With the rapid development of modern industry, industrial processes are becoming more and more complex, and more and more information needs to be monitored. In particle processing, papermaking, ore grinding, and chemical processes, people are concerned about the statistical information of batch output information, such as the uniformity of processed particles, the uniformity of paper, etc., which is attributed to a random distribution control system. The research on the combustion process found that an important index to measure the efficiency of the combustion process is the dis...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/00
Inventor 郭雷张玉民
Owner BEIHANG UNIV
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