System controller structure of neural net and system identification structure

A technology of neural network control and system controller, applied in the direction of adaptive control, general control system, control/regulation system, etc.

Inactive Publication Date: 2009-07-29
李华嵩
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0010] Aiming at the shortcomings of the current artificial neural network controller, the present invention aims to provide a simple, economical and practical neural network structure and its system identification and system control calculation method, which can improve the output of the neural network controller or system identification and data acquisition. Accuracy against changes in the use environment, temperature drift of system hardware, aging of control system components or components over time, errors between the system description function and design requirements caused by errors in electronic components themselves, offsetting the accumulation caused by too many intermediate links error

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  • System controller structure of neural net and system identification structure
  • System controller structure of neural net and system identification structure
  • System controller structure of neural net and system identification structure

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example 1

[0071] Such as figure 2 , for example, there is a field-level analog output unit whose function is to perform analog output according to the received control output command, which is usually the output value sent by the upper control bus, such as sending 0-20mA, 4-20mA, 0-5V Such conventional analog output values.

[0072] Due to specific component errors, performance aging, temperature drift and other issues, the electrical parameters of the hardware have changed, and the error between the output signal and the expected value of the control exceeds the requirements. It is required to design a software identification structure. When necessary, through a button or key, start the neural network verification program, and use software to optimize and change control parameters and improve control accuracy.

[0073] For example, if the hardware of a current analog output channel outputs 0-20mA current according to the input command code 0000-1111, its structure is as follows: im...

example 2

[0089] For many control systems, due to the many intermediate links involved, the error is difficult to control. After the completion of the system, the accuracy of the entire system needs to be debugged. figure 1 The advanced artificial neural network structure can realize the automatic adjustment of system precision, saving a lot of manpower, material resources and time.

[0090] For example, assume that Figure 7 The centrifugal pump constant pressure control system is a simple single-loop control system. The pressure sensor PT installed on the outlet pipeline of the centrifugal pump converts the outlet pressure of the centrifugal pump into a voltage signal, which is amplified by the amplifier and output to the PC industrial control computer. After the PC compares the pressure signal with the pressure given value, it needs to set the network weight according to the According to the adjustment law, the excitation signal of frequency conversion speed regulation is output, an...

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Abstract

The invention relates to a controller structure in a neural network system and a structure thereof used for automatically distinguishing and automatically improving the control precision of the system; wherein, the controller structure of the neural network system comprises a neural network control structure, a basis function structure linearly independent in the system control, and an analysis and iterative training structure for corresponding weight and weight value; according to samples within a coverable control range, the weight value of the neural network basis function is adjusted, and the neural network correction treatment on the expected output value is carried out in practical control so as to improve the output control precision. The neural network system distinguishing structure is characterized in that the structure comprises a neural network distinguishing structure, a basis function structure linearly independent in the system distinguishing and an analysis and iterative training structure for corresponding weight and weight value; according to samples within a coverable control range, the weight value of the neural network basis function is adjusted, and the description function of the control system is re-built by the basis function and the weight value of the neural network.

Description

technical field [0001] The invention relates to an automatic control system, in particular to a controller structure of a neural network system and a structure for automatic identification and automatic improvement of system control precision. Background technique [0002] In reality, time-varying is a basic characteristic of control products and systems. For example, changes in the operating environment of the control system, temperature drift of the system hardware, and aging of control system components or components over time will cause changes in the description function between the system input and output. The extent of this change cannot be accurately predicted during system design or product production, and the error of the electronic components itself aggravates the design error of the system, resulting in the inability of the system or product to achieve high design control accuracy in specific use. For example, adding a feedback link in the control sometimes fails...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/02
Inventor 李华嵩
Owner 李华嵩
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