CN105888971A - Active load reducing control system and method for large wind turbine blade - Google Patents

Abstract

The invention relates to an active load reducing control system and method for a large wind turbine blade. An optical fiber strain sensor measures a stress value of the root portion of the blade and transmits the stress value to a control unit; the control unit comprises a PID controller performing parameter optimization through a teaching and learning algorithm and a fuzzy controller for processing a strain force signal of the root portion of the blade; a flap swing angle is subjected to switching control through the fuzzy controller and the PID controller, and beneficial effects of fuzzy control and PID control are synthesized; meanwhile, a machine front wind field signal sensing device measures the front air speed of a wind turbine and transmits the front air speed to the control unit; a feedforward controller in the control unit calculates control quantities needed by uniform loads caused by reducing stochastic wind or turbulent wind by monitoring the change of the machine front air speed in real time; and the control unit couples the two parts of control quantities, and control over the flap swing angle is completed. The active load reducing control system and method for the large wind turbine blade effectively reduce the strain force of the root portion of the blade, the service life of the blade is prolonged, and the using cost of a wind turbine unit is reduced.

Description

A kind of large scale wind power machine blade actively load shedding control system and method

Technical field

The invention belongs to technical field of wind power generation, particularly to a kind of large scale wind power machine blade actively load shedding control System and method processed.

Background technology

Along with the high speed development of wind-power electricity generation, wind energy conversion system the most gradually towards offshore, the trend development of maximization, The core component fan blade of wind energy conversion system is just had higher requirement by this.Blower fan maximizes and means to increase Add the load of blower fan and the quality of system, made fatigue load and ultimate load sharply increase, seriously reduce In the service life of unit, too increase hardware and engineering cost and later maintenance expense simultaneously.

In order to reduce fatigue load and the ultimate load of blower fan, improve fan life, generated energy and exert oneself steady Qualitative, create a kind of large scale wind power machine blade containing active trailing edge flaps in recent years, its hardware configuration and Control method is not quite similar with traditional blade.Due to the complexity of this blade construction, set up it and count accurately Learn model be extremely difficult, and RANDOM WIND and turbulent wind very important to the loading effect of fan blade, Therefore conventional single-stage PID controls to be difficult to meet the load shedding demand of blade.

Summary of the invention

Not enough for prior art, the invention provides a kind of large scale wind power machine blade actively load shedding control system And method.

A kind of large scale wind power machine blade actively load shedding control system, described system includes signal acquisition module, control Molding block and electrical servo module；

Described signal acquisition module includes that wind field sensing equipment and optical fiber should before fibre optic strain sensor, machine Varying signal processing equipment；Described control module includes No. 5 low pass filters, No. 5 analog-digital converters, 4 tunnel controls Unit processed, PLC, 4 way weighted-voltage D/A converters and 4 road signal isolators；Described electrical servo module includes 4 tunnels Wing flap actuator drive circuit and 4 road wing flap actuator；

Described fibre optic strain sensor is arranged on pneumatic equipment blades root, and with fibre strain signal handling equipment Connect；1 road signal output of fibre strain signal handling equipment and wind field sensing equipment corresponding 4 before machine 4 road signals of road wing flap export the most corresponding No. 1 low pass filter and No. 1 analog-digital converter is sequentially connected with；With No. 1 analog-digital converter that fibre strain signal handling equipment is corresponding is respectively connecting to 4 tunnel control units, before machine No. 4 analog-digital converters that wind field sensing equipment is corresponding connect one to one to 4 tunnel control units, and every road is controlled Unit processed is driven by the most corresponding 1 way weighted-voltage D/A converter of PLC, 1 road signal isolator, 1 road wing flap actuator Galvanic electricity road and 1 road wing flap actuator are sequentially connected with；

Described fibre optic strain sensor is for gathering the adaptability to changes signal of root of blade, described fibre strain signal Processing equipment is for being converted to voltage signal by the signal of fibre optic strain sensor collection；Wind field letter before described machine Number sensing equipment is used for measuring wind speed before wind energy conversion system；Before fibre strain signal handling equipment and machine, wind field signal passes Sense equipment transmits signals to the low pass filter of correspondence respectively, and described low pass filter is used for filtering high frequency to be done Disturb signal；Described analog-digital converter is used for converting analog signals into digital signal；Described control unit includes The feedforward controller processing wind field signal before machine, uses learning aid algorithm to carry out the PID of parameter optimization The fuzzy controller that controller and the adaptability to changes signal to root of blade process, is used for reducing root of blade The control computing of stress；Described PLC is used for switching the output signal of PID controller and fuzzy controller, and Couple the control signal from feedforward controller；Described digital to analog converter is used for converting digital signals into simulation Signal；Described signal isolator is for keeping apart control system output signal and electrical servo module；Described Wing flap actuator drive circuit produces the signal of telecommunication driving wing flap actuator；Described wing flap actuator is according to wing flap Actuator drive circuit output signal regulating flap makes it produce different pivot angles.

Described feedforward controller is when wind power generating set is run, and is changed by wind speed before real-time monitoring machine, Calculate because reducing the controlled quentity controlled variable needed for RANDOM WIND or the wind-induced uneven load of turbulent flow.

The control method of above-mentioned a kind of large scale wind power machine blade actively load shedding control system, specifically includes following step Rapid:

Step 1: to fuzzy controller FC_i, feedforward controller FB_iWith PID controller PID_iInitialize, I=1,2,3,4；

Step 2: read the stress value of current wind speed and root of blade, root stress value y (k) that will obtain Stress value r (k) specified with root of blade carries out difference operation, obtains stress-deviation e (k) and deviation variation rate Ec (k), specified stress value r (k) of its Leaf be dispatched from the factory by blower fan before test and record；

Step 21: stress-deviation e (k) that step 2 obtained, deviation variation rate ec (k) are as fuzzy controller FC_iInput variable；

Step 22: select membership function to carry out obfuscation, and foundation fuzzy rule obtains the controlled quentity controlled variable of wing flap, Fuzzy controller FC is tried to achieve after anti fuzzy method_iOutput variable, this be output as wing flap control expectation angle θ 1_i；

Step 23: using stress-deviation e (k) of step 21 as PID controller PID_iInput signal, utilize Learning aid optimizing algorithm is to PID_iParameter KP_i, KI_i, KD_iCarry out online self-tuning, described PID controller PID_iOutput variable be wing flap control expectation angle θ 2_i；

Step 24: gather wind speed v (t) before the machine of wind power generating set, as independent variable, puts wing flap Dynamic angle θ_iAs dependent variable, flap angle-wind speed is fitted, sets up the feedforward control of flap angle-wind speed Device FB processed_iModel: θ_i(v)=a₀+a₁v+a₂v²+L+a_nvⁿ, use method of least square to determine each term system Number；Using wind speed as feedforward controller FB_iInput signal, then feedforward controller FB_iOutput variable be the flap The wing controls expectation angle θ 3_i；

Step 3: wing flap expectation angle control signal step 22 and step 23 obtained is sent to PLC respectively and carries out Process, PLC arranges handoff algorithms: use from Fuzzy Control when blade root stress error is more than setting value Device FC processed_iSignal θ 1_i, from PID controller PID_iControl signal θ 2_iWill not work；When blade root should Power error uses from PID controller PID less than during setting value_iControl signal θ 2_i, from fuzzy controller FC_iSignal θ 1_iWill not work；Finally, PLC couples from feedforward controller FB again_iControl signal θ3_i, and these signals are transferred to the wing flap actuator drive circuit of correspondence；

Step 4:4 wing flap actuator accepts the signal from 4 wing flap actuator drive circuits respectively, holds Row wing flap wobbling action is to reduce root stress；

Above-mentioned steps 2-4 is run repeatedly, until completing control task.

Learning aid optimizing algorithm for pid parameter comprises the following steps:

Step 1): arranging initial parameter, region of search scope is defined as X=(x¹,x²,...,x^d) ∈ [L, U], L=(L¹,L²,...,L^d) it is space lower bound, U=(U¹,U²,...,U^d) it is the upper bound, space, d is optimization problem Dimension, d dimension space i-th student is defined asStudent's scale is N, and maximum is repeatedly Generation number is maxgen；

Step 2): the teaching phase of teacher:

Step 21): calculate the adaptive value of each student, select best individuality as teacher X_teacher, calculate Individual meansigma methodsThen according to the difference of student with individual average level Practise, such as following formula:

$X_i^{new}=w1\·X_i^{old}+r_i\·(X_{teacher}-{\mathrm{TF}}_i\·mean)---\left(1\right)$

TF_i=2-gen/max gen (2)

In formula:WithValue before expression i-th student learns respectively and after study； W1=1-gen/max gen is self adaptation weight coefficient；r_iFor the random number between 0-1；TF_iBetween 1-2 Certain number, its value changes with the change of iterations；r_iAnd TF_iFor regularized learning algorithm speed；Gen with Max gen is respectively current iteration number of times and maximum iteration time；

Step 22): student updates:

IfAdaptive value ratioAdaptive value good, then useReplaceOtherwise, continue Continuous use

Step 3): mutually learn the stage between student:

Step 31): each student X_iA learning object X is randomly selected in class_j(j ≠ i), X_iLogical Cross and analyze oneself and student X_jBetween difference carry out study adjust, such as following formula:

If X_iIt is better than X_j,

$X_i^{new}=w2\·X_i^{old}+r_i\·(X_i-X_j)---\left(3\right)$

If X_jIt is better than X_i,

$X_i^{new}=w2\·X_i^{old}+r_i\·(X_j-X_i)---\left(4\right)$

In formula: w2=1-gen/max gen is self adaptation weight coefficient；r_iFor the random number between 0-1；

Step 32): student updates:

IfAdaptive value ratioAdaptive value good, then useReplaceOtherwise, continue Continuous use

Step 4): calculate the fitness value of each student according to fitness function, its formula is as follows:

$J={\∫}_0^{\mathrm{\∞}}t\left|e\left(t\right)\right|dt---\left(5\right)$

In formula, e (t) is systematic error, updates the global optimum of student according to fitness function, works as calculating Obtained by optimal value reach setting value or time algorithm reaches maximum iteration time, exit learning aid optimizing algorithm, Otherwise return step 2).

The invention have the benefit that

1., relative to traditional single-stage PID control system, native system can be had by introducing feedforward controller Effect reduces the impact produced root stress due to the randomness fluctuation of wind.2. use fuzzy control with PID controls to switch over control at setting value, both make use of fuzzy controller to need not set up and accurately count Learn model, adapt to that controlled device is non-linear and the advantage of time variation, utilize again the letter of PID controller algorithm Single, the feature of good stability.3. use learning aid algorithm speed of searching optimization fast, the feature that solving precision is high, Three parameters of line PID controller, thus effectively reduce the adaptability to changes of root of blade, extend In the service life of blade, reduce the use cost of Wind turbine.4. the present invention designs simply, application side Just, control more accurately and reliably, be especially suitable for modeling and the control of large scale wind power machine blade.

Accompanying drawing explanation

Fig. 1 is the structured flowchart of a kind of large scale wind power machine blade actively load shedding control system.

Fig. 2 is the control principle block diagram of a kind of large scale wind power machine blade actively load shedding control system.

Fig. 3 is the learning aid optimizing algorithm flow chart that PID controller parameter carries out online self-tuning.

Fig. 4 is one large scale wind power machine blade of the present invention actively load shedding control method and existing methodical contrast Result.

Detailed description of the invention

The present invention will be further described with detailed description of the invention below in conjunction with the accompanying drawings.Under it is emphasized that State bright that be merely exemplary rather than in order to limit the scope of the present invention and application thereof.

A kind of large scale wind power machine blade actively load shedding control system as shown in Figure 1, described system includes that signal is adopted Collection module, control module and electrical servo module；

Described signal acquisition module includes that wind field sensing equipment and optical fiber should before fibre optic strain sensor, machine Varying signal processing equipment；Described control module includes No. 5 low pass filters, No. 5 analog-digital converters, 4 tunnel controls Unit processed, PLC, 4 way weighted-voltage D/A converters and 4 road signal isolators；Described electrical servo module includes 4 tunnels Wing flap actuator drive circuit and 4 road wing flap actuator；

Described fibre optic strain sensor is arranged on pneumatic equipment blades root, and with fibre strain signal handling equipment Connect；1 road signal output of fibre strain signal handling equipment and wind field sensing equipment corresponding 4 before machine 4 road signals of road wing flap export the most corresponding No. 1 low pass filter and No. 1 analog-digital converter is sequentially connected with；With No. 1 analog-digital converter that fibre strain signal handling equipment is corresponding is respectively connecting to 4 tunnel control units, before machine No. 4 analog-digital converters that wind field sensing equipment is corresponding connect one to one to 4 tunnel control units, and every road is controlled Unit processed is driven by the most corresponding 1 way weighted-voltage D/A converter of PLC, 1 road signal isolator, 1 road wing flap actuator Galvanic electricity road and 1 road wing flap actuator are sequentially connected with；

Described fibre optic strain sensor is for gathering the adaptability to changes signal of root of blade, described fibre strain signal Processing equipment is for being converted to voltage signal by the signal of fibre optic strain sensor collection；Wind field letter before described machine Number sensing equipment is used for measuring wind speed before wind energy conversion system；Before fibre strain signal handling equipment and machine, wind field signal passes Sense equipment transmits signals to the low pass filter of correspondence respectively, and described low pass filter is used for filtering high frequency to be done Disturb signal；Described analog-digital converter is used for converting analog signals into digital signal；Described control unit is used for Reducing the control computing of root stress, it includes the feedforward processing wind field signal before machine Device, described feedforward controller is when wind power generating set is run, and is changed by wind speed before real-time monitoring machine, Calculate because reducing the controlled quentity controlled variable needed for RANDOM WIND or the wind-induced uneven load of turbulent flow；Use learning aid is calculated Method carries out the PID controller of parameter optimization；And the adaptability to changes signal of root of blade processed fuzzy Controller；Described PLC for switching the output signal of PID controller and fuzzy controller, and couple from The control signal of feedforward controller；Described digital to analog converter is used for converting digital signals into analogue signal；Institute State signal isolator for control system output signal and electrical servo module being kept apart；Described wing flap start Device drive circuit produces the signal of telecommunication driving wing flap actuator；Described wing flap actuator is driven according to wing flap actuator Dynamic circuit output signal regulating flap makes it produce different pivot angles.

A kind of large scale wind power machine blade actively load shedding control method as shown in Figure 2, specifically includes following steps:

Step 1: to fuzzy controller FC_i, feedforward controller FB_iWith PID controller PID_iInitialize, I=1,2,3,4.

Step 2: read the stress value of current wind speed and root of blade, root stress value y (k) that will obtain Stress value r (k) specified with root of blade carries out difference operation, obtains stress-deviation e (k) and deviation variation rate Ec (k), specified stress value r (k) of its Leaf be dispatched from the factory by blower fan before test and record；

Step 21: stress-deviation e (k) that step 2 obtained, deviation variation rate ec (k) are as fuzzy controller FC_iInput variable；

Step 22: select membership function to carry out obfuscation, and foundation fuzzy rule obtains the controlled quentity controlled variable of wing flap, Fuzzy controller FC is tried to achieve after anti fuzzy method_iOutput variable, this be output as wing flap control expectation angle θ 1_i；

Step 23: using stress-deviation e (k) of step 21 as PID controller PID_iInput signal, utilize Learning aid optimizing algorithm is to PID_iParameter KP_i, KI_i, KD_iCarry out online self-tuning, described PID controller PID_iOutput variable be wing flap control expectation angle θ 2_i；

As it is shown on figure 3, the learning aid optimizing algorithm for pid parameter comprises the following steps:

Step 1): arranging initial parameter, region of search scope is defined as X=(x¹,x²,...,x^d) ∈ [L, U], L=(L¹,L²,...,L^d) it is space lower bound, U=(U¹,U²,...,U^d) it is the upper bound, space, d is optimization problem Dimension, d dimension space i-th student is defined asStudent's scale is N, and maximum is repeatedly Generation number is max gen；

Step 2): the teaching phase of teacher:

Step 21): calculate the adaptive value of each student, select best individuality as teacher X_teacher, calculate Individual meansigma methodsThen according to the difference of student with individual average level Practise, such as following formula:

$X_i^{new}=w1\·X_i^{old}+r_i\·(X_{teacher}-{\mathrm{TF}}_i\·mean)---\left(1\right)$

TF_i=2-gen/max gen (2)

In formula:WithValue before expression i-th student learns respectively and after study； W1=1-gen/max gen is self adaptation weight coefficient；r_iFor the random number between 0-1；TF_iBetween 1-2 Certain number, its value changes with the change of iterations；r_iAnd TF_iFor regularized learning algorithm speed；Gen with Max gen is respectively current iteration number of times and maximum iteration time；

Step 22): student updates:

IfAdaptive value ratioAdaptive value good, then useReplaceOtherwise, continue Continuous use

Step 3): mutually learn the stage between student:

Step 31): each student X_iA learning object X is randomly selected in class_j(j ≠ i), X_iLogical Cross and analyze oneself and student X_jBetween difference carry out study adjust, such as following formula:

If X_iIt is better than X_j,

$X_i^{new}=w2\·X_i^{old}+r_i\·(X_i-X_j)---\left(3\right)$

If X_jIt is better than X_i,

$X_i^{new}=w2\·X_i^{old}+r_i\·(X_j-X_i)---\left(4\right)$

In formula: w2=1-gen/max gen is self adaptation weight coefficient；r_iFor the random number between 0-1；

Step 32): student updates:

IfAdaptive value ratioAdaptive value good, then useReplaceOtherwise, continue Continuous use

Step 4): calculate the fitness value of each student according to fitness function, its formula is as follows:

$J={\∫}_0^{\mathrm{\∞}}t\left|e\left(t\right)\right|dt---\left(5\right)$

In formula, e (t) is systematic error, updates the global optimum of student according to fitness function, works as calculating Obtained by optimal value reach setting value or time algorithm reaches maximum iteration time, exit learning aid optimizing algorithm, Otherwise return step 2)；

Step 24: gather wind speed v (t) before the machine of wind power generating set, as independent variable, puts wing flap Dynamic angle θ_iAs dependent variable, flap angle-wind speed is fitted, sets up the feedforward control of flap angle-wind speed Device FB processed_iModel: θ_i(v)=a₀+a₁v+a₂v²+L+a_nvⁿ, use method of least square to determine each term system Number；Using wind speed as feedforward controller FB_iInput signal, then feedforward controller FB_iOutput variable be the flap The wing controls expectation angle θ 3_i。

Step 3: wing flap expectation angle control signal step 22 and step 23 obtained is sent to PLC respectively and carries out Process, PLC arranges handoff algorithms: use from Fuzzy Control when blade root stress error is more than setting value Device FC processed_iSignal θ 1_i, from PID controller PID_iControl signal θ 2_iWill not work；When blade root should Power error uses from PID controller PID less than during setting value_iControl signal θ 2_i, from fuzzy controller FC_iSignal θ 1_iWill not work；Finally, PLC couples from feedforward controller FB again_iControl signal θ3_i, and these signals are transferred to the wing flap actuator drive circuit of correspondence.

Step 4:4 wing flap actuator accepts the signal from 4 wing flap actuator drive circuits respectively, holds Row wing flap wobbling action is to reduce root stress.

Above-mentioned steps 2-4 is run repeatedly, until completing control task.

5WM with reference to wind energy conversion system under the turbulent wind that wind regime is 11.4m/s, using the blade root moment of flexure of blade as wind Power machine load shedding target, uses one large scale wind power machine blade of the present invention actively load shedding control method and existing method Comparing result as shown in Figure 4, it is seen that use the method for the present invention to effectively reduce the adaptability to changes of root of blade.

The above, be only presently preferred embodiments of the present invention, and the present invention not makees any pro forma restriction, Any those familiar with the art in the technical scope that the invention discloses, the letter that can readily occur in Single amendment, equivalent variations, all should contain within protection scope of the present invention.Therefore, the protection of the present invention Scope should be as the criterion with scope of the claims.

Claims (4)

1. a large scale wind power machine blade actively load shedding control system, it is characterised in that described system includes Signal acquisition module, control module and electrical servo module；

Described signal acquisition module includes that wind field sensing equipment and optical fiber should before fibre optic strain sensor, machine Varying signal processing equipment；Described control module includes No. 5 low pass filters, No. 5 analog-digital converters, 4 tunnel controls Unit processed, PLC, 4 way weighted-voltage D/A converters and 4 road signal isolators；Described electrical servo module includes 4 tunnels Wing flap actuator drive circuit and 4 road wing flap actuator；

Described fibre optic strain sensor is arranged on pneumatic equipment blades root, and with fibre strain signal handling equipment Connect；1 road signal output of fibre strain signal handling equipment and wind field sensing equipment corresponding 4 before machine 4 road signals of road wing flap export the most corresponding No. 1 low pass filter and No. 1 analog-digital converter is sequentially connected with；With No. 1 analog-digital converter that fibre strain signal handling equipment is corresponding is respectively connecting to 4 tunnel control units, before machine No. 4 analog-digital converters that wind field sensing equipment is corresponding connect one to one to 4 tunnel control units, and every road is controlled Unit processed is driven by the most corresponding 1 way weighted-voltage D/A converter of PLC, 1 road signal isolator, 1 road wing flap actuator Galvanic electricity road and 1 road wing flap actuator are sequentially connected with；

Described fibre optic strain sensor is for gathering the adaptability to changes signal of root of blade, described fibre strain signal Processing equipment is for being converted to voltage signal by the signal of fibre optic strain sensor collection；Wind field letter before described machine Number sensing equipment is used for measuring wind speed before wind energy conversion system；Before fibre strain signal handling equipment and machine, wind field signal passes Sense equipment transmits signals to the low pass filter of correspondence respectively, and described low pass filter is used for filtering high frequency to be done Disturb signal；Described analog-digital converter is used for converting analog signals into digital signal；Described control unit includes The feedforward controller processing wind field signal before machine, uses learning aid algorithm to carry out the PID of parameter optimization The fuzzy controller that controller and the adaptability to changes signal to root of blade process, is used for reducing root of blade The control computing of stress；Described PLC is used for switching the output signal of PID controller and fuzzy controller, and Couple the control signal from feedforward controller；Described digital to analog converter is used for converting digital signals into simulation Signal；Described signal isolator is for keeping apart control system output signal and electrical servo module；Described Wing flap actuator drive circuit produces the signal of telecommunication driving wing flap actuator；Described wing flap actuator is according to wing flap Actuator drive circuit output signal regulating flap makes it produce different pivot angles.

The most according to claim 1, a kind of large scale wind power machine blade actively load shedding control system, its feature exists In, described feedforward controller is when wind power generating set is run, and is changed by wind speed before real-time monitoring machine, Calculate because reducing the controlled quentity controlled variable needed for RANDOM WIND or the wind-induced uneven load of turbulent flow.

3. a kind of large scale wind power machine blade actively load shedding control system described in claim 1-2 any claim Control method, it is characterised in that specifically include following steps:

Step 1: to fuzzy controller FC_i, feedforward controller FB_iWith PID controller PID_iInitialize, I=1,2,3,4；

Step 2: read the stress value of current wind speed and root of blade, root stress value y (k) that will obtain Stress value r (k) specified with root of blade carries out difference operation, obtains stress-deviation e (k) and deviation variation rate Ec (k), specified stress value r (k) of its Leaf be dispatched from the factory by blower fan before test and record；

Step 21: stress-deviation e (k) that step 2 obtained, deviation variation rate ec (k) are as fuzzy controller FC_iInput variable；

Step 22: select membership function to carry out obfuscation, and foundation fuzzy rule obtains the controlled quentity controlled variable of wing flap, Fuzzy controller FC is tried to achieve after anti fuzzy method_iOutput variable, this be output as wing flap control expectation angle θ 1_i；

Step 23: using stress-deviation e (k) of step 21 as PID controller PID_iInput signal, utilize Learning aid optimizing algorithm is to PID_iParameter KP_i, KI_i, KD_iCarry out online self-tuning, described PID controller PID_iOutput variable be wing flap control expectation angle θ 2_i；

Step 24: gather wind speed v (t) before the machine of wind power generating set, as independent variable, puts wing flap Dynamic angle θ_iAs dependent variable, flap angle-wind speed is fitted, sets up the feedforward control of flap angle-wind speed Device FB processed_iModel: θ_i(v)=a₀+a₁v+a₂v²+L+a_nvⁿ, use method of least square to determine each term system Number；Using wind speed as feedforward controller FB_iInput signal, then feedforward controller FB_iOutput variable be the flap The wing controls expectation angle θ 3_i；

Step 3: wing flap expectation angle control signal step 22 and step 23 obtained is sent to PLC respectively and carries out Process, PLC arranges handoff algorithms: use from Fuzzy Control when blade root stress error is more than setting value Device FC processed_iSignal θ 1_i, from PID controller PID_iControl signal θ 2_iWill not work；When blade root should Power error uses from PID controller PID less than during setting value_iControl signal θ 2_i, from fuzzy controller FC_iSignal θ 1_iWill not work；Finally, PLC couples from feedforward controller FB again_iControl signal θ3_i, and these signals are transferred to the wing flap actuator drive circuit of correspondence；

Step 4:4 wing flap actuator accepts the signal from 4 wing flap actuator drive circuits respectively, holds Row wing flap wobbling action is to reduce root stress；

Above-mentioned steps 2-4 is run repeatedly, until completing control task.

A kind of control method, it is characterised in that for pid parameter religion with Optimizing algorithm comprises the following steps:

Step 1): arranging initial parameter, region of search scope is defined as X=(x¹,x²,...,x^d) ∈ [L, U], L=(L¹,L²,...,L^d) it is space lower bound, U=(U¹,U²,...,U^d) it is the upper bound, space, d is optimization problem Dimension, d dimension space i-th student is defined asStudent's scale is N, and maximum is repeatedly Generation number is maxgen；

Step 2): the teaching phase of teacher:

Step 21): calculate the adaptive value of each student, select best individuality as teacher X_teacher, calculate Individual meansigma methodsThen according to the difference of student with individual average level Practise, such as following formula:

$X_i^{new}=w1\·X_i^{old}+r_i\·(X_{teacher}-{\mathrm{TF}}_i\·mean)---\left(1\right)$

TF_i=2-gen/maxgen (2)

In formula:WithValue before expression i-th student learns respectively and after study； W1=1-gen/maxgen is self adaptation weight coefficient；r_iFor the random number between 0-1；TF_iBetween 1-2 Certain number, its value changes with the change of iterations；r_iAnd TF_iFor regularized learning algorithm speed；Gen with Max gen is respectively current iteration number of times and maximum iteration time；

Step 22): student updates:

IfAdaptive value ratioAdaptive value good, then useReplaceOtherwise, continue Continuous use

Step 3): mutually learn the stage between student:

Step 31): each student X_iA learning object X is randomly selected in class_j(j ≠ i), X_iLogical Cross and analyze oneself and student X_jBetween difference carry out study adjust, such as following formula:

If X_iIt is better than X_j,

$X_i^{new}=w2\·X_i^{old}+r_i\·(X_i-X_j)---\left(3\right)$

If X_jIt is better than X_i,

$X_i^{new}=w2\·X_i^{old}+r_i\·(X_j-X_i)---\left(4\right)$

In formula: w2=1-gen/maxgen is self adaptation weight coefficient；r_iFor the random number between 0-1；

Step 32): student updates:

IfAdaptive value ratioAdaptive value good, then useReplaceOtherwise, continue Continuous use

Step 4): calculate the fitness value of each student according to fitness function, its formula is as follows:

$J={\∫}_0^{\mathrm{\∞}}t\left|e\left(t\right)\right|dt---\left(5\right)$

In formula, e (t) is systematic error, updates the global optimum of student according to fitness function, works as calculating Obtained by optimal value reach setting value or time algorithm reaches maximum iteration time, exit learning aid optimizing algorithm, Otherwise return step 2).