CN103792959A - Genetic algorithm optimized fuzzy PID flow control method in variable-rate spraying system - Google Patents
Genetic algorithm optimized fuzzy PID flow control method in variable-rate spraying system Download PDFInfo
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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
The method that the present invention relates to genetic algorithm optimization fuzzy control flow in variable rate spray system, belongs to automatic control technology field.
Background technology
Current domestic variable rate spray mainly adopts premix medicine formula, and equipment for plant protection and pesticide application technology are all relatively backward, and the low inferior problem of dispenser level of operation has not only caused a large amount of wastes and the environmental pollution of agricultural chemicals, has also jeopardized the people's life and health.Development " the sustainable environmental protection agricultural of low input " and " water conservation " are advocated in the agricultural research field of European and American developed countries always; equipment for plant protection and spray technique are also gone on along at the leading ranks in the world, and the effective rate of utilization of agricultural chemicals generally can reach more than 60%.Therefore, constantly improve variable and spray technology and improve the effective rate of utilization of agricultural chemicals, for economizing on resources, the sustainable development of protection of the environment and promotion agricultural has very important meaning.In traditional variable rate spray process, exist that spraying droplet is not of uniform size always, the problem such as the slow and less stable of droplet skewness, control rate.For this problem, a kind of fuzzy flow control system based on genetic algorithm is proposed, adopt genetic algorithm to come membership function, the control law of global optimization fuzzy controller, thereby obtain the optimum solution of system control, to improve the accuracy of variable rate spray flow in process control and the adaptability of enhancing system, make the control performance of control system good, fast response time, strong robustness, reached the object of saving the energy and improving agricultural chemicals utilization factor.
Summary of the invention
Technical matters to be solved by this invention is the method that genetic algorithm optimization Bang-Bang fuzzy control flow in variable rate spray system is provided for the deficiencies in the prior art.
The method of genetic algorithm optimization Bang-Bang fuzzy control flow in variable rate spray system, comprises the following steps:
The first step: gather actual diseases and pests of agronomic crop information, carry out the required actual dose of decision-making crops according to standard crops prescription map;
Second step: the deviation between calculated flow rate setting value flow and actual flow;
The 3rd step: the rate of change that calculates flow deviation in second step;
The 4th step: utilize genetic algorithm to be optimized fuzzy controller.First,, according to the feature of flow control system, determine the membership function of fuzzy control and the citation form of fuzzy rule; Then, the parameter while determining genetic algorithm computing, comprising: optimize algebraically, crossing-over rate, aberration rate, fitness function; Secondly, the random generation parameter producing, puts into fuzzy controller, utilizes fitness function to calculate its fitness; Again, according to system requirements index or optimization algebraically end loop; Finally, membership function parameter and fuzzy control rule parameter after optimizing are implanted in fuzzy controller again;
The 5th step: error and error rate are input to the fuzzy controller after genetic algorithm optimization, using the output after computing as controlled quentity controlled variable;
The 6th step: systematic error be greater than stationary value 78% time bang-bang controller output, systematic error be less than stationary value 78% time fuzzy controller output after optimizing;
The 7th step: controlled quentity controlled variable is acted on micromotor and controlled;
The 8th step: remeasure the actual flow of flow valve, enter first step circulation;
The 9th step: micromotor will no longer carry out work adjusting in the time that the deviation in second step is less than setting value.
Through emulation and experiment, the overshoot of genetic algorithm optimization fuzzy is less, and maximum is no more than 2.5%, and stable state is mistaken for ± and 0.258%, the response time is 0.86 s.The response time of fuzzy control is 3.85s, and maximum overshoot is about 8.6%, and steady-state error is ± 0.458%.The relatively performance of fuzzy control and fuzzy control after genetic algorithm optimization, for variable rate spray system, the fuzzy control performance after genetic algorithm optimization is better than fuzzy control.
Accompanying drawing explanation
Fig. 1 is flow control structure schematic diagram, and the parameter in figure all has proposition below;
The simplified structure diagram of Fig. 2 valve, the d in figure
1=5.0cm, d
2=6.5cm, d
3=5.5cm;
Fig. 3 membership function figure, in figure, a representative negative large (NB), b represent that zero (Z0), c represent honest (PB), lower same;
Fig. 4 genetic algorithm process flow diagram;
Fig. 5 adopts output variable Δ K after genetic algorithm optimization
psubordinate function
Fig. 6 adopts output variable Δ K after genetic algorithm optimization
isubordinate function
Fig. 7 is output variable Δ K after employing genetic algorithm optimization
dsubordinate function
The experimental principle figure of Fig. 8 variable rate spray flow valve control system;
The optimizing process figure of Fig. 9 objective function J;
Figure 10 is the step response curve of the Bang-Bang fuzzy control flow valve system after genetic algorithm optimization;
Figure 11 is the system step response curve of fuzzy control flow valve.
Embodiment
Below in conjunction with specific embodiment, the present invention is described in detail.
Flowrate control valve is made up of the delay link of motor, speed reduction unit, valve rotating shaft, miniature needle valve and system, as shown in Figure 1.Little needle valve is connected woven hose by an input port with a delivery outlet, and control accuracy is higher, is applicable to the control of tiny flow quantity, and its simplified structure as shown in Figure 2.
Direct current motor S221D adopts first order inertial loop matching method of approximation to measure its transport function, and assumed initial state is zero, and motor speed is ω, and the operating voltage of motor is U
r, the transport function of direct current motor is
G
1(s)=[ω(s)]/[ U
r(s)]= [K
1(s)]/(Ts+1) （1）
In formula, K
1=28.95 is the gain of direct current motor; The time constant that T=1.93 (s) is direct current motor.
Direct current motor rotating shaft is connected through the rotating shaft of speed reduction unit, and after slowing down, rotating speed is ω (s), output ω ' (s)=K
2ω (s), to ω ' (s) and ω (s) ask its transport function.
G
2(s)=[ω′(s)]/[ω(s)]= K
2 （2）
Test shows, when flowrate control valve is under certain hydraulics, flow value is only relevant with the angle of valve rotating shaft rotation, flow with
The anglec of rotation of valve becomes Г type curve.Flowrate control valve opening angle Φ (s) becomes differential relationship with the output speed ω ' of speed reduction unit,
ω ' (s)=d Φ/dt, to ω ' (s) and Φ (s) ask its transport function
G
3(s)=[Φ(s)]/[ω′(s)]=1/s （3）
In real system, electric motor starting time delay is 0.005s～0.01s, and the time delay of the time delay of speed reduction unit and valve rotating shaft is not
Exceed 0.3s, be t0=0.5 s total time delay of getting system here, and therefore the total delay factor of system is
G
4(s)=e
-t0s=e
-0.5s （4）
Work as U
r =when 12V, ω '=37.4 r/min, i.e. K after tested
1k
2=1.558, so flowrate control valve system transter is embodied as
G(s)=G
1(s)G
2(s)G
3(s)G
4(s)=(1.558e
-0.5s )/[s(1.93s+1)] （5）
Formula 5 is this variable rate spray system model.
Design of Fuzzy Controller:
Fuzzy controller has adopted triangle as membership function, and has selected Mamdani type inference method.As figure (3) institute
Show, parameter (a, b, c) has determined leg-of-mutton shape, i.e. membership function f (x).
Fuzzy control rule is as shown in table 1:
Table 1 fuzzy control rule table
U in table
11-u
33all represent the state of motor, value is 1 (negative large), 2(zero), 3(is honest) in one.
Genetic algorithms optimization based fuzzy logic controller design:
First: determine objective function
In order to improve rapidity, the stability of system responses, using rise time of system, hyperharmonic cumulative errors as index wherein, in this case reach and save the energy and the object of spraying insecticide, adopt punitive function, using overshoot as optimal control index one, its objective function (being fitness function) is suc as formula shown in (6), (7).
（6）
E in formula (t), u (t), t
ube respectively systematic error, control output and rise time; W
1, W
2, W
3, W
4represent weights.So having closely, the requirement of objective function and system contacts, can be by changing every weights W above at this
1, W
2, W
3, W
4make output meet index.At this, in order to improve response time and the precision of system, weighting is as follows: W
1=0.85, W
2=0.002, W
3=2.5, W
4the value of=200 these parameters can draw by debugging.
Second: Genetic Algorithm Model
The fundamental purpose of genetic algorithm is to leave over contemporary good gene delivery to of future generation.To small Variable Control, the good results are evident, can directly in region, carry out optimizing to variable, do not need to solve, and with Genetic algorithms optimization based fuzzy logic controller, reduced the complexity of fuzzy controller, can make controller control more accurately output.When Optimizing Fuzzy Controller, select 15 parameters in this controller as genes of individuals, as shown in table 2.
First individuality is carried out to assembly coding, form a 10*15 and determine by random value the initial value of above parameter, carry out optimizing through genetic algorithm, individual fitness is herein J
x,I, individual selection probability
P
x,i=J
x,i/ ∑ J*(note: ∑ J* is J
x, 0+ J
x, 1+ J
x, 2+ ... + J
x,m)
Utilize roulette method to carry out excellent individual selection according to the probability of formula (8).As shown in Figure 4, in figure, the parameters of genetic algorithm is as shown in table 3 for the process flow diagram of genetic algorithm.
In table 3, chromosome is all made up of the decimal system, has not only shortened code length, and is convenient to control.
As can be seen from Figure 4, genetic algorithm can be by constantly updating the information of every generation, every generation is analyzed, until find one group of optimum solution that meets current system, the condition that in figure, circulation finishes has two: first, while meeting system requirements, no matter which runs to for all can automatically jumping out circulation, output currency is as optimum solution; The second, be exactly the requirement of traditional satisfied optimization algebraically.
Genetic algorithm is parallel optimization in the process of optimizing, can improve arithmetic speed, in use to the aims of systems continuity of a function and not requirement of differentiability, and has very strong adaptability and robustness.
The method of variable rate spray system genetic algorithm optimization fuzzy control flow, comprises the following steps:
The first step: gather actual diseases and pests of agronomic crop information, carry out the required actual dose of decision-making crops according to standard crops prescription map;
Second step: the deviation between calculated flow rate setting value flow and actual flow;
The 3rd step: the rate of change of flow deviation and deviation in calculating second step;
The 4th step: utilize genetic algorithm to be optimized fuzzy controller.First,, according to the feature of flow control system, determine the membership function of fuzzy control and the citation form of fuzzy rule; Then, the parameter while determining genetic algorithm computing, comprising: optimize algebraically, crossing-over rate, aberration rate, fitness function; Secondly, the random generation parameter producing, puts into fuzzy controller, utilizes fitness function to calculate its fitness; Again, according to system requirements index or optimization algebraically end loop; Finally, membership function parameter and fuzzy control rule parameter after optimizing are implanted in fuzzy controller again; Entered the Fuzzy control system Δ K after genetic algorithm optimization
pmembership function is as Fig. 5, Δ K
imembership function is as Fig. 6, Δ K
dmembership function is as Fig. 7;
The 5th step: error and error rate are input to the fuzzy controller after genetic algorithm optimization, using the output after computing as controlled quentity controlled variable;
The 6th step: systematic error be greater than stationary value 78% time bang-bang controller output, systematic error be less than stationary value 78% time fuzzy controller output after optimizing;
The 7th step: controlled quentity controlled variable is acted on micromotor and controlled;
The 8th step: remeasure the actual flow of flow valve, enter first step circulation;
The 9th step: micromotor will no longer carry out work adjusting in the time that the deviation in second step is less than setting value.
Flow valve Control System Imitation and experiment:
Adopt Matlab software to carry out emulation and experiment to flow valve control system herein, its schematic diagram is as Fig. 8, and the membership function of fuzzy controller, flow control system transport function and GA discrete optimization in figure and fuzzy control rule are realized by s-function performance piece.
Genetic algorithm J function is in the process in 80 generations of optimization, and its result as shown in Figure 9.
Carry out emulation according to the principle of Fig. 5, under unit step signal, the result of genetic algorithm optimization as shown in figure 10.
Fuzzy PID Control Simulation result as shown in figure 11.
Added after genetic algorithm at this, can find out that from Fig. 7 and experimental result the overshoot of system is less, maximum is no more than 2.5%, and steady-state error is ± 0.258%, and the response time is 0.86s.The simulation result of fuzzy, as Fig. 8, can find out that from Fig. 8 and experimental result the overshoot of fuzzy system is 8.6%, and steady-state error is ± 0.458%, and the response time is 3.85s.With respect to fuzzy added system after genetic algorithm except having reduced overshoot, improved control accuracy, having strengthened the adaptability that has mainly also improved system robustness, in the time that genetic algebra reaches certain value, can be adapted to different environment, for variable rate spray, importance is very large, and its effect shows to use other variable control system.
Should be understood that, for those of ordinary skills, can be improved according to the above description or convert, and all these improvement and conversion all should belong to the protection domain of claims of the present invention.
Claims (6)
1. the method for the fuzzy control flow of genetic algorithm optimization in variable rate spray system, is characterized in that, comprises the following steps:
The first step: gather actual diseases and pests of agronomic crop information, carry out the required actual dose of decision-making crops according to standard crops prescription map;
Second step: the deviation between calculated flow rate setting value flow and actual flow;
The 3rd step: the rate of change that calculates flow deviation in second step;
The 4th step: utilize genetic algorithm to be optimized fuzzy controller, first, according to the feature of flow control system, determine the membership function of fuzzy control and the citation form of fuzzy rule; Then, the parameter while determining genetic algorithm computing, comprising: optimize algebraically, crossing-over rate, aberration rate, fitness function; Secondly, the random generation parameter producing, puts into fuzzy controller, utilizes fitness function to calculate its fitness; Again, according to system requirements index or optimization algebraically end loop; Finally, membership function parameter and fuzzy control rule parameter after optimizing are implanted in fuzzy controller again;
The 5th step: error and error rate are input to the fuzzy controller after genetic algorithm optimization, using the output after computing as controlled quentity controlled variable;
The 6th step: systematic error be greater than stationary value 78% time bang-bang controller output, systematic error be less than stationary value 78% time fuzzy controller output after optimizing;
The 7th step: controlled quentity controlled variable is acted on micromotor and controlled;
The 8th step: remeasure the actual flow of flow valve, enter first step circulation;
The 9th step: micromotor will no longer carry out work adjusting in the time that the deviation in second step is less than setting value.
2. the membership function and the fuzzy control rule that adopt the control of genetic algorithm optimization fuzzy according to the 4th step in claims 1, the membership function after optimization and fuzzy rule are the optimized parameters of variable rate spray system control.
3. be variable rate spray systematic error, control output and rise time according to the major parameter that in claims 1, the 4th step fitness function is chosen, wherein the overshoot of variable rate spray system and rise time are main performance index.
4. adopt expertise to determine every coefficient of PID according to the PID combination in the 5th step fuzzy control in claims 1, array mode adopts linear combination mode.
According to the 6th step systematic error in claims 1 be greater than stationary value 78% time bang-bang controller output, now variable rate spray flow valve is maximum control signal.
6.bang-bang controller improves system rapidity, and fuzzy improves system stability and accuracy, two controller mutual supplement with each other's advantages reforming system performances.
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| CN105182740A (en) * | 2014-10-27 | 2015-12-23 | 济南大学 | Raw material grinding automatic control method |
| CN105580800A (en) * | 2016-03-11 | 2016-05-18 | 台州市斯佩雷尔植保机械有限公司 | Electric sprinkling can system and control method thereof |
| CN106786590A (en) * | 2017-03-10 | 2017-05-31 | 国网江苏省电力公司常州供电公司 | A kind of grid-connected Distribution Network Harmonics detection control method |
| CN108265655A (en) * | 2018-03-23 | 2018-07-10 | 辽宁工业大学 | A kind of automatically controlled industrial cleaning device and its control method |
| CN114879505A (en) * | 2022-05-27 | 2022-08-09 | 江南大学 | Pneumatic regulating valve control method based on quantitative feedback theory |
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| CN105182740A (en) * | 2014-10-27 | 2015-12-23 | 济南大学 | Raw material grinding automatic control method |
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| CN105580800B (en) * | 2016-03-11 | 2018-03-02 | 台州市斯佩雷尔植保机械有限公司 | Electric atomizing kettle system and its control method |
| CN106786590A (en) * | 2017-03-10 | 2017-05-31 | 国网江苏省电力公司常州供电公司 | A kind of grid-connected Distribution Network Harmonics detection control method |
| CN106786590B (en) * | 2017-03-10 | 2019-06-11 | 国网江苏省电力公司常州供电公司 | A kind of grid-connected Distribution Network Harmonics detection control method |
| CN108265655A (en) * | 2018-03-23 | 2018-07-10 | 辽宁工业大学 | A kind of automatically controlled industrial cleaning device and its control method |
| CN114879505A (en) * | 2022-05-27 | 2022-08-09 | 江南大学 | Pneumatic regulating valve control method based on quantitative feedback theory |
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Application publication date: 20140514 |