Genetic algorithm optimized fuzzy PID flow control method in variable-rate spraying system

A spray system and genetic algorithm technology, applied in the field of genetic algorithm optimization fuzzy PID control flow, can solve the problems of different spray droplet sizes, poor stability, uneven droplet distribution, etc.

Inactive Publication Date: 2014-05-14
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the traditional variable spraying process, there have always been problems such as different spray droplet sizes, uneven droplet distribution, slow control speed and poor stability.

Method used

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  • Genetic algorithm optimized fuzzy PID flow control method in variable-rate spraying system
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  • Genetic algorithm optimized fuzzy PID flow control method in variable-rate spraying system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0028] The flow control valve is composed of a motor, a reducer, a valve shaft, a small needle valve and a delay link of the system, such as figure 1 shown. The small needle valve is connected to the infusion tube through an input port and an output port, which has high control precision and is suitable for the control of small flow. Its simplified structure is as follows: figure 2 shown.

[0029] DC motor S221D uses the first-order inertia link fitting approximation method to measure its transfer function, assuming that the initial state is zero, the motor speed is ω, and the operating voltage of the motor is U r , the transfer function of the DC motor is

[0030] G 1 (s)=[ω(s)] / [U r (s)] = [K 1 (s)] / (Ts+1) (1)

[0031] In the formula, K 1 =28.95 is the gain of the DC motor; T=1.93(s) is the time constant of the DC motor.

[0032] The shaft of the DC motor is connected through the shaft of the reducer, the speed after deceleration is ω(s), and the output ω′(s)=K 2 ω...

Embodiment 2

[0045] Fuzzy controller design:

[0046] The fuzzy controller adopts the triangle as the membership function, and chooses the Mamdani type reasoning method. As shown in figure (3)

[0047] Shown, the parameters (a, b, c) determine the shape of the triangle, that is, the membership function f(x).

[0048] The fuzzy control rules are shown in Table 1:

[0049] Table 1 Fuzzy control rule table

[0050]

[0051] u in the table 11 -u 33 Both represent the state of the motor, and the value is one of 1 (negative large), 2 (zero), and 3 (positive large).

Embodiment 3

[0053] Genetic algorithm optimization fuzzy controller design:

[0054] First: determine the objective function

[0055] In order to improve the quickness and stability of the system response, the rise time, overshoot and cumulative error of the system are used as the indicators. Here, in order to achieve the purpose of saving energy and spraying pesticides, the penalty function is used, and the overshoot is used as the optimal control. One of the indicators, then its objective function (that is, the fitness function) is shown in formulas (6) and (7).

[0056] (6)

[0057] (7)

[0058] where e(t), u(t), t u are system error, control output and rise time respectively; W 1 , W 2 , W 3 , W 4 Indicates the weight. Therefore, the objective function is closely related to the requirements of the system, here we can change the weight W 1 , W 2 , W 3 , W 4 To make the output meet the target. Here, in order to improve the response time and precision of the system, t...

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Abstract

The invention discloses a genetic algorithm optimized fuzzy PID flow control method in a variable-rate spraying system and belongs to the technical field of automatic control. The method includes the following steps that: actual crop pest and disease damage information is collected, such that required actual drug dosage can be determined; deviation between set flow and actual flow is calculated; flow deviation and the change rate of the deviation are calculated; and optimization is performed on a fuzzy controller is through using a genetic algorithm, wherein the optimization includes the steps of determining the optimized algebra, crossover rate, mutation rate and fitness function of the computation of the genetic algorithm, and optimizing the fuzzy controller according to system index requirements; errors and the change rate of the errors are inputted to the fuzzy PID controller optimized by the genetic algorithm, and output after the computation is adopted as control quantity of the variable-rate spraying system; and when the system has large errors, bang-bang control is adopted, and when the system has small errors, optimized fuzzy PID control is adopted. With the genetic algorithm optimized fuzzy PID flow control method of the invention adopted, a precise control method can be provided for a precise agriculture drug application system, and agricultural agents can be effectively saved.

Description

technical field [0001] The invention relates to a method for optimizing fuzzy PID control flow by a genetic algorithm in a variable spray system, and belongs to the technical field of automatic control. Background technique [0002] At present, domestic variable spraying mainly adopts pre-mixed pesticides. The plant protection machinery and pesticide application technology are relatively backward, and the low level of pesticide application operations has not only caused a lot of waste of pesticides and environmental pollution, but also endangered people's lives and health. The field of agricultural research in developed countries in Europe and the United States has always advocated the development of "low-input sustainable green agriculture" and "protection of water resources". Plant protection machinery and spraying technology have also been at the forefront of the world, and the effective utilization rate of pesticides can generally reach 60%. % above. Therefore, it is of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D7/06G05B13/02A01M7/00
Inventor 宋乐鹏
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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