CN102720634A - Variable universe fuzzy electric pitch control method for optimizing parameters - Google Patents

Variable universe fuzzy electric pitch control method for optimizing parameters Download PDF

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CN102720634A
CN102720634A CN2012102343721A CN201210234372A CN102720634A CN 102720634 A CN102720634 A CN 102720634A CN 2012102343721 A CN2012102343721 A CN 2012102343721A CN 201210234372 A CN201210234372 A CN 201210234372A CN 102720634 A CN102720634 A CN 102720634A
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control
domain
fuzzy
contraction
expansion factor
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董海鹰
魏占宏
李宏伟
李晓青
王国华
李帅兵
马博
李晓楠
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Lanzhou Jiaotong University
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Abstract

The invention discloses a variable universe fuzzy electric pitch control method for optimizing parameters, and relates to three-closed-loop (namely a current loop, a velocity loop and a position loop) control established by an electric pitch system. Through analyzing the structure of contraction-expansion factors of the variable universe, the parameters thereof can be optimized by genetic algorithm, the intelligent optimization of the parameters can be realized, and a reasonable mechanism for determining the contraction-expansion factors can be established. According to the invention, the optimized variable universe fuzzy control algorithm is applied to the velocity and position control for the electric pitch system, and the appropriate target function is established according to control objectives of the velocity loop and the position loop, so that the optimal control performance of a variable universe fuzzy controller can be realized. The torque pulsation is efficiently inhibited, the dynamic and steady performances of the system are improved, and finally the quick and accurate tracing of the pitch angle position is realized, therefore, an efficient and practical control method is provided for the wind power pitch system.

Description

A kind of change domain of parameters optimization blurs the electric variable pitch controlling method
Technical field
The present invention relates to electric variable pitch controlling method technical field, particularly, relate to a kind of fuzzy electric variable pitch controlling method of change domain of parameters optimization.
Background technique
Nonlinear system for the serious big inertia that lags behind of the such high-order strong coupling of wind-powered electricity generation unit electric variable propeller system; Though conventional controller technology is very ripe; In industrial process control, obtained being widely used; But satisfy the requirement that the wind-powered electricity generation unit responds wind speed fast, the control effect of these controllers is undesirable on the contrary.Electric variable propeller system based on fuzzy control; Fuzzy controller can carry out some through the simple scale conversion of formula and adjust; Knot model to control ring need not further investigation; Can obtain certain control effect, through interval division also can further improve controlling performance to domain, this is applied in fuzzy control the advantage place of wind-powered electricity generation electric variable propeller system just.But controller still has certain blindness in the parameter tuning process; And the key that becomes the domain fuzzy control is to confirm contraction-expansion factor; Make domain change in the reasonable scope; And current mainly rely on empirical value or repeatedly the debugging realize that make that controller parameter possibly not be an optimum, controlling performance is not the best.
Summary of the invention
To the problem that exists in the existing technology, the present invention proposes a kind of change domain fuzzy control method that in the electric variable pitch control system, utilizes the genetic algorithm optimization contraction-expansion factor.The change domain FUZZY ALGORITHMS FOR CONTROL of optimizing is applied to set up suitable objective function according to the control target of speed ring and position ring in the speed and positioning control of electric variable propeller system, realizes becoming the optimal control performance of domain fuzzy controller.Suppress torque pulsation effectively, improved the dynamic performance and the steady-state behaviour of system, and finally realized quick, the accurately tracking in propeller pitch angle position.For wind power pitch-controlled system provides a kind of effective, practical controlling method.
For this reason; Adopt following technological scheme: a kind of change domain of parameters optimization blurs the electric variable pitch controlling method, and it relates to three closed loop controls that electric variable propeller system is set up, i.e. electric current loop, speed ring and position ring; Through analyzing the structure that becomes the domain contraction-expansion factor; Utilize its parameter of genetic algorithm optimization, realize the intelligent optimizing of parameter, set up the rational mechanism of confirming contraction-expansion factor; Specific as follows:
A, the change domain FUZZY ALGORITHMS FOR CONTROL of genetic algorithm optimization is applied in the speed and positioning control of said electric variable propeller system; The input of said position and speed controller is all error
Figure 928850DEST_PATH_IMAGE001
and error change amount
Figure 118523DEST_PATH_IMAGE002
; Output is all
Figure 18345DEST_PATH_IMAGE003
; Confirm the basic domain of the input of said speed and position controller, output language variable respectively; The basic domain of input language variable is respectively
Figure 20674DEST_PATH_IMAGE004
; And the basic domain of output language variable is
Figure 322343DEST_PATH_IMAGE005
, and the structure that can set up contraction-expansion factor is:
Figure 682917DEST_PATH_IMAGE006
(1)
B, set up the fuzzy control rule table of said electric variable pitch speed and positioning control, in the single output of double input fuzzy controller, based on becoming domain thought, design becomes the domain Fuzzy control system;
C, the parameter list of said contraction-expansion factor is shown as the chromosome in hereditary space or individual, encodes, obtain initial population; The parameter of said contraction-expansion factor is composed to the fuzzy inference system that is designed; Use the fuzzy control of said change domain to control, set up suitable objective function, write the fitness function that calculates ideal adaptation degree value according to this objective function according to the control target of said speed ring and position ring; Calculate each individual fitness value through this fitness function, offer heredity OperatorCarry out selection operation, interlace operation and mutation operation; Obtain new colony, after the evolution of setting iterations, algorithmic statement is in best chromosome; It promptly is the optimal parameter of said contraction-expansion factor, thereby realizes that electric variable propeller system becomes the optimal control performance of domain fuzzy controller.
Further:
Said contraction-expansion factor parameter adopts real coding, and chromosome length is 6.
Add the Error Absolute Value time integral in the said speed ring objective function and improve its transition dynamic performance.
Take penalty factor to avoid over control in the said position ring objective function.
The present invention is through analyzing the structure that becomes the domain contraction-expansion factor; Utilize its parameter of genetic algorithm optimization; Realized the intelligent optimizing of parameter; Set up the rational mechanism of definite contraction-expansion factor, efficiently solved the problem that change domain fuzzy control method contraction-expansion factor leans on experience and debugs repeatedly to confirm, improved control accuracy.The change domain FUZZY ALGORITHMS FOR CONTROL of optimizing is applied to set up suitable objective function according to speed and control of position target in the speed and positioning control of electric variable propeller system, realizes becoming the optimal control performance of domain fuzzy controller.Suppress torque pulsation effectively, improved the dynamic performance and the steady-state behaviour of system, and finally realized quick, the accurately tracking in propeller pitch angle position.For wind power pitch-controlled system provides a kind of effective, practical controlling method.
Description of drawings
Fig. 1 is an electric variable propeller system control principle structural drawing;
Fig. 2 is the speed ring of genetic algorithm optimization of the present invention and the control block diagram of position ring;
Fig. 3 is the change domain fuzzy control flow chart of genetic algorithm optimization of the present invention.
Embodiment
Below in conjunction with accompanying drawing the preferred embodiments of the present invention are described, should be appreciated that preferred embodiment described herein only is used for explanation and explains the present invention, and be not used in qualification the present invention.
When wind speed when the above cut-out wind speed of rated wind speed is following; Pitch-controlled system begins to become oar; Given drive motor rotating speed
Figure 70036DEST_PATH_IMAGE007
and propeller pitch angle
Figure 377520DEST_PATH_IMAGE008
; According to actual measured value Calculation Speed error
Figure 268116DEST_PATH_IMAGE009
, propeller pitch angle error
Figure 65171DEST_PATH_IMAGE010
and speed error variable quantity
Figure 674007DEST_PATH_IMAGE011
, propeller pitch angle error variable quantity
Figure 847499DEST_PATH_IMAGE012
; It is become the input of domain fuzzy controller as electric variable propeller system position and speed; See and attach Fig. 1 and 2; According to formula 1; Set up the structure of contraction-expansion factor, total 6 treats excellent parameter.
Set up the fuzzy control rule table of said electric variable pitch speed and positioning control, in the single output of double input fuzzy controller,, design said change domain Fuzzy control system based on becoming domain thought.Wherein,
Figure 763819DEST_PATH_IMAGE014
and
Figure 859951DEST_PATH_IMAGE015
is respectively two input variables of said fuzzy controller;
Figure 571555DEST_PATH_IMAGE016
and is respectively the pairing contraction-expansion factor of said input variable;
Figure 513284DEST_PATH_IMAGE018
then is the contraction-expansion factor of said output variable, be the controller function that said fuzzy controller approached.The computing of its basic underlying variables of Fuzzy control system depends on time step
Figure 346427DEST_PATH_IMAGE020
; Be designated as
Figure 331701DEST_PATH_IMAGE021
respectively; Therefore the available time function representes to become domain, and note is made
Figure 641460DEST_PATH_IMAGE022
.Along with the carrying out of optimizing; Input output fuzzy subset domain is also corresponding to change; Membership function
Figure 712184DEST_PATH_IMAGE023
takes place to change accordingly; Note be
Figure 195949DEST_PATH_IMAGE024
, this makes fuzzy control rule become one group of DP
Figure 35729DEST_PATH_IMAGE025
:
Figure 516389DEST_PATH_IMAGE026
(2)
When k=0, formula 2 is the fuzzy control initial control rules.Along with the variation of k, control function then changes along with domain and correspondingly distortion takes place, and can be designated as:
Figure 808830DEST_PATH_IMAGE027
(3)
Wherein,
Figure 665928DEST_PATH_IMAGE028
is the membership function number of input language variable (error);
Figure 297898DEST_PATH_IMAGE029
; is the membership function number of input language variable (error rate),
Figure 994775DEST_PATH_IMAGE031
; The occurrence of
Figure 389984DEST_PATH_IMAGE032
,
Figure 938777DEST_PATH_IMAGE033
is confirmed by real system ruuning situation.
Can know by becoming domain theory; The monotonicity that
Figure 964502DEST_PATH_IMAGE034
monotonicity has guaranteed
Figure 231535DEST_PATH_IMAGE035
, thus the validity of control function
Figure 430436DEST_PATH_IMAGE036
guaranteed.Become the domain The Design of Fuzzy Logic Controller and weakened dependence the control domain-specialist knowledge; Variation tendency according to fuzzy control rule; Correspondingly basic domain is stretched along with the variation of controller input language variable; Through to basic domain self adaption adjustment, realize the improvement of electric variable propeller system dynamic performance, improve the stable state accuracy of control.
6 parameter lists of said contraction-expansion factor are shown as the chromosome or the individuality in hereditary space; Utilize genetic algorithm that 6 parameters of contraction-expansion factor are optimized; Carry out real coding, then chromosome length .Real coding needn't carry out numerical value conversion, can be directly carries out the genetic algorithm operation on the phenotype solving.Obtain initial population; 6 parameters of said contraction-expansion factor are composed to the change domain fuzzy inference system that is designed; Use the fuzzy control of said change domain to control; Set up suitable objective function
Figure 30361DEST_PATH_IMAGE038
according to the control target of said speed ring and position ring; Add the Error Absolute Value time integral in the said speed ring objective function and improve its transition dynamic performance, take penalty factor to avoid over control in the said position ring objective function.Adopt following objective function to guarantee to have better dynamic and output controlled quentity controlled variable within limits:
Figure 784691DEST_PATH_IMAGE039
(4)
It wherein is
Figure 521703DEST_PATH_IMAGE040
controlled quentity controlled variable;
Figure 45088DEST_PATH_IMAGE041
is the rise time, and
Figure 474932DEST_PATH_IMAGE042
is the weights of objective speed function.
The difference
Figure 71370DEST_PATH_IMAGE045
of given angle that is input as propeller pitch angle of position ring
Figure 887197DEST_PATH_IMAGE043
and actual output angle
Figure 427899DEST_PATH_IMAGE044
and error change , can obtain than the reflex angle degree through fuzzy reasoning.Its structure is seen accompanying drawing 2; Adopt 6 parameter values of the said contraction-expansion factor of genetic algorithm optimization
Figure 135458DEST_PATH_IMAGE047
; Adopt penalty factor to avoid position ring output overshoot, objective function is following:
Figure 151956DEST_PATH_IMAGE048
(5)
Wherein is controlled quentity controlled variable;
Figure 156001DEST_PATH_IMAGE050
is the rise time, and
Figure 372219DEST_PATH_IMAGE051
is the weights of Place object function.
Write the fitness function that calculates ideal adaptation degree value according to said objective function, fitness function is taken as:
Figure 254724DEST_PATH_IMAGE052
(6)
Passing through type 6 fitness functions calculate each individual fitness value; Offer genetic operator (promptly order offers and selects operator, crossover operator and mutation operator) and carry out selection operation, interlace operation and mutation operation; Obtain new colony, after the evolution of setting iterations, algorithmic statement is in best chromosome; It promptly is the optimal parameter of said contraction-expansion factor, thereby realizes that electric variable propeller system becomes the optimal control performance of domain fuzzy controller.
Behind contraction-expansion factor employing genetic algorithm optimization; Can directly confirm its optimum value; Efficiently solve and become the problem that domain fuzzy control method contraction-expansion factor leans on experience and debugging repeatedly to confirm, make convenient in application and the actual effect of change domain fuzzy control on electric variable propeller system.

Claims (4)

1. the change domain of a parameters optimization blurs the electric variable pitch controlling method; It relates to three closed loop controls that electric variable propeller system is set up; Be electric current loop, speed ring and position ring, it is characterized in that:, utilize its parameter of genetic algorithm optimization through analyzing the structure that becomes the domain contraction-expansion factor; Realize the intelligent optimizing of parameter, set up the rational mechanism of confirming contraction-expansion factor; Specific as follows:
A, the change domain FUZZY ALGORITHMS FOR CONTROL of genetic algorithm optimization is applied in the speed and positioning control of said electric variable propeller system; The input of said position and speed controller is all error
Figure 2012102343721100001DEST_PATH_IMAGE001
and error change amount
Figure 437270DEST_PATH_IMAGE002
; Output is all ; Confirm the basic domain of the input of said speed and position controller, output language variable respectively; The basic domain of input language variable is respectively ; And the basic domain of output language variable is
Figure 2012102343721100001DEST_PATH_IMAGE005
, and the structure that can set up the contraction-expansion factor of said input, output language variable is:
Figure 2012102343721100001DEST_PATH_IMAGE007
(1)
B, set up the fuzzy control rule table of said electric variable pitch speed and positioning control, in the single output of double input fuzzy controller, based on becoming domain thought, design becomes the domain Fuzzy control system;
C, the parameter list of contraction-expansion factor is shown as the chromosome in hereditary space or individual, encodes, obtain initial population; The parameter of said contraction-expansion factor is composed to the fuzzy inference system that is designed, used the fuzzy control of said change domain to control, set up suitable objective function according to the control target of said speed ring and position ring; Write the fitness function that calculates ideal adaptation degree value according to this objective function; Calculate each individual fitness value through this fitness function, offer genetic operator and carry out selection operation, interlace operation and mutation operation, obtain new colony; After the evolution of setting iterations; Algorithmic statement is in best chromosome, and it promptly is the optimal parameter of said contraction-expansion factor, thereby realizes that electric variable propeller system becomes the optimal control performance of domain fuzzy controller.
2. according to the fuzzy electric variable pitch controlling method of change domain of right 1 described a kind of parameters optimization, it is characterized in that: said contraction-expansion factor parameter adopts real coding, and chromosome length is 6.
3. according to the fuzzy electric variable pitch controlling method of change domain of right 1 described a kind of parameters optimization, it is characterized in that: add the Error Absolute Value time integral in the said speed ring objective function and improve its transition dynamic performance.
4. according to the fuzzy electric variable pitch controlling method of change domain of right 1 described a kind of parameters optimization, it is characterized in that: take penalty factor to avoid over control in the said position ring objective function.
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CN105652667A (en) * 2016-03-31 2016-06-08 西南石油大学 High-precision path tracking control method for uncertain-model double-joint mechanical arms
CN105888970A (en) * 2016-05-16 2016-08-24 扬州大学 Self-adaptive internal mold vibration control method of intelligent fan blade based on grey information optimization
CN106150899A (en) * 2015-04-21 2016-11-23 兰州交通大学 A kind of front end speed governing type Wind turbines power optimization control method
CN107762730A (en) * 2017-08-23 2018-03-06 华北电力大学 A kind of large-scale change oar turbine control system and control method with trailing edge flaps
CN109209768A (en) * 2018-08-31 2019-01-15 重庆邮电大学 A kind of constant output control method of large scale wind power machine
CN110543101A (en) * 2019-10-09 2019-12-06 福建工程学院 method and device for optimizing fan fuzzy controller based on genetic algorithm
CN113759754A (en) * 2021-09-14 2021-12-07 兰州交通大学 Train active suspension system control method in strong wind environment

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Cited By (13)

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CN106150899A (en) * 2015-04-21 2016-11-23 兰州交通大学 A kind of front end speed governing type Wind turbines power optimization control method
CN106150899B (en) * 2015-04-21 2019-04-16 兰州交通大学 A kind of front end speed governing type Wind turbines power optimization control method
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CN105652667A (en) * 2016-03-31 2016-06-08 西南石油大学 High-precision path tracking control method for uncertain-model double-joint mechanical arms
CN105888970A (en) * 2016-05-16 2016-08-24 扬州大学 Self-adaptive internal mold vibration control method of intelligent fan blade based on grey information optimization
CN105888970B (en) * 2016-05-16 2018-09-14 扬州大学 The adaptive inner mould vibration control method that intelligent blower blade is optimized based on grey information
CN107762730A (en) * 2017-08-23 2018-03-06 华北电力大学 A kind of large-scale change oar turbine control system and control method with trailing edge flaps
CN107762730B (en) * 2017-08-23 2019-06-18 华北电力大学 A kind of large-scale variable pitch turbine control system and control method with trailing edge flaps
CN109209768A (en) * 2018-08-31 2019-01-15 重庆邮电大学 A kind of constant output control method of large scale wind power machine
CN110543101A (en) * 2019-10-09 2019-12-06 福建工程学院 method and device for optimizing fan fuzzy controller based on genetic algorithm
CN113759754A (en) * 2021-09-14 2021-12-07 兰州交通大学 Train active suspension system control method in strong wind environment
CN113759754B (en) * 2021-09-14 2024-04-02 兰州交通大学 Control method for train active suspension system in strong wind environment

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