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PID (proportion, integral, derivative)-type?fuzzy logic control method based on weight rule table

A technology of fuzzy logic and control method, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problems of reducing the accuracy of fuzzy rule table, restricting the popularization and application of PID-type fuzzy logic control method, and difficulty in balancing.

Active Publication Date: 2014-05-28
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the other hand, reducing the fuzzy description variables will reduce the accuracy of the fuzzy rule table to reflect the real control characteristics
Therefore it is difficult to strike a good balance between complexity and accuracy
This greatly limits the popularization and application of PID type fuzzy logic control method

Method used

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  • PID (proportion, integral, derivative)-type?fuzzy logic control method based on weight rule table
  • PID (proportion, integral, derivative)-type?fuzzy logic control method based on weight rule table
  • PID (proportion, integral, derivative)-type?fuzzy logic control method based on weight rule table

Examples

Experimental program
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Effect test

Embodiment 1

[0091] Such as figure 1 As shown, a PID type fuzzy logic control method based on the weight rule table includes the following steps:

[0092] 1) Convert the setting signal and feedback signal through the PID signal conversion unit to obtain several conversion signals.

[0093] Among them, the setting signal is r(t), the feedback signal is y(t), the conversion signal obtained by inputting the setting signal r(t) and the feedback signal y(t) into the PID signal conversion unit is the differential signal e(t), At least one of the three types of integral signal i(t) and differential signal d(t), where differential signal e(t)=y(t)-r(t), integral signal i(t)=∑e(t )*ts, differential signal d(t)=[e(t)-e(t-1)] / ts, ts is the sampling time unit.

[0094] 2) Define a fuzzy set containing several fuzzy description variables for each conversion signal, each fuzzy set has a corresponding belonging function, and define a corresponding weight value for each fuzzy description variable.

[0...

Embodiment 2

[0108] A PID type fuzzy logic control method based on a weight rule table of an air-conditioning system is characterized in that, comprising the following steps:

[0109] 1) Convert the room temperature setting signal and room temperature feedback signal through the PID signal conversion unit to obtain several conversion signals;

[0110] Among them, the room temperature setting signal is r(t), the room temperature feedback signal is y(t), the room temperature setting signal r(t) and the room temperature feedback signal y(t) are input into the conversion signal obtained by the PID signal conversion unit There are two kinds of differential signal e(t) and differential signal d(t), where differential signal e(t)=y(t)-r(t), differential signal d(t)=[e(t)-e( t-1)] / ts, ts is the sampling time unit.

[0111] 2) Define a fuzzy set containing several fuzzy description variables for each conversion signal, each fuzzy set has a corresponding belonging function defined, and define a cor...

Embodiment 3

[0163] A PID type fuzzy logic control method based on a weight rule table of a central chilled water air conditioner comprises the following steps:

[0164] 1) Convert the water supply pressure setting signal of the water loop and the water supply pressure feedback signal of the water loop through the PID signal conversion unit to obtain several conversion signals.

[0165] Among them, the water supply pressure setting signal is r(t), the water supply pressure feedback signal is y(t), and the water supply pressure setting signal r(t) and the water supply pressure feedback signal y(t) are converted signals obtained by inputting the PID signal conversion unit It is at least one of the three types of differential signal e(t), integral signal i(t) and differential signal d(t), where differential signal e(t)=y(t)-r(t), and integral signal i(t)=∑e(t)*ts, differential signal d(t)=[e(t)-e(t-1)] / ts, ts is the sampling time unit.

[0166] 2) Define a fuzzy set containing several fuzzy ...

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Abstract

The invention discloses a PID (proportion, integral, derivative)-type?fuzzy logic control method based on a weight rule table. The method comprises the following steps: 1) a PID?signal conversion unit carries out conversion on given signals and feedback signals; 2) a fuzzy set is built and the weight value of each fuzzy description variable is defined; 3) the attribution ratio of each fuzzy description variable is determined; 4) the attribution ratio of each fuzzy description variable is multiplied by the corresponding weight value of the fuzzy description variable and adding is carried out to obtain adding signals; 5) the adding signals are outputted to a control operation unit; 6) the control operation unit outputs the signals to an execution unit for execution, and the feedback signals are acquired to the PID?signal conversion unit; 7) step 1 to step 6 are repeated until the given signals are equal to the feedback signals. According to the PID (proportion, integral, derivative)-type?fuzzy logic control method based on the weight rule table, a traditional and complicated fuzzy rule table is replaced by the simple weight rule table, such that expert experience can be presented more simply and intuitively; the entire control method is optimized in no need of a defuzzification?unit; and minimal?overshoot and?oscillation exist in the control process.

Description

technical field [0001] The invention relates to the field of fuzzy logic control, in particular to a PID type fuzzy logic control method based on a weight rule table. Background technique [0002] The PID (proportion, integral, derivative) controller has been the earliest practical controller for more than 70 years. The PID controller consists of a proportional unit (P), an integral unit (I) and a differential unit. (D) Composition, PID controller is simple and easy to understand, and does not require precise system models and other prerequisites in use, so it has become the most widely used controller. [0003] The traditional PID control method has been widely used in various control processes. However, for some complex control processes, the PID control method cannot produce good control results. For example, in the process of simultaneous temperature and humidity control using a direct expansion air conditioner, due to the high coupling between the temperature control ...

Claims

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Application Information

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IPC IPC(8): G05B13/02F24F11/00F24H9/20
Inventor 徐象国李钊邓仕明韩晓红张学军
Owner ZHEJIANG UNIV
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