CN108832853A - A kind of DC brushless motor speed regulating method based on fuzzy PI-PD control - Google Patents
A kind of DC brushless motor speed regulating method based on fuzzy PI-PD control Download PDFInfo
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- CN108832853A CN108832853A CN201810641587.2A CN201810641587A CN108832853A CN 108832853 A CN108832853 A CN 108832853A CN 201810641587 A CN201810641587 A CN 201810641587A CN 108832853 A CN108832853 A CN 108832853A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
- H02P6/34—Modelling or simulation for control purposes
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
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Abstract
A kind of DC brushless motor speed regulating method based on fuzzy PI-PD control of the present invention.Foundation and speed regulating method including model, the foundation of model include two-stage fuzzy controller, and level-one fuzzy control adjusts PI controller parameter in real time, and two pole fuzzy controls adjust the zoom factor of level-one fuzzy controller output.Speed regulating method is first to motor actual speed n and given rotating speed nrIt is compared calculating, obtains final deviation e and deviation variation rate ec, is blurred above-mentioned two deviations in fuzzy controller 1, gives fuzzy controller to carry out reasoning work the E and EC after blurring, to obtain the k after defuzzification1pAnd k1i, k is obtained using same method in fuzzy controller 22pAnd k2i, then two fuzzy controller outputs are multiplied, are as a result input in PI controller, are finally input in brshless DC motor model by PD control adjusting, achieve the purpose that control motor speed.And the present invention needs not rely on the mathematical models of controlled motor using fuzzy control, keeps it more stable in state of a control, and can effectively inhibit the nonlinear situation of controlled motor；In control process, obscure PI-PD Self-tuning System can the constantly Real-time Feedback of the variation of monitoring parameter and parameter, make being optimal of control effect.
Description
Technical field
The invention belongs to DC motor speed-regulating technical fields, and in particular to it is a kind of based on fuzzy PI-PD control direct current without
Brush motor speed regulating method.
Background technique
Brshless DC motor has been applied to mechanical and power equipment, electric car, in the fields such as robot.But it is brushless straight
Galvanic electricity motivation is a kind of non-linear operating status in speed regulation process, can not be well to direct current using traditional PID control
Motor is adjusted the speed.
Traditional control strategy, if PID control has many advantages, such as that structure simply, is easily realized, usually in the matched feelings of parameter
It can get preferable performance under condition, but in system parameter variations or load disturbance, often not can guarantee to obtain ideal
Closed-loop control performance.
Summary of the invention
In order to solve the above-mentioned technical problems, the present invention provides the methods that one kind is capable of stability contorting controlled device：It is a kind of
DC brushless motor speed regulating method based on fuzzy PI-PD control.Foundation and speed regulating method including model, the foundation packet of model
Two-stage fuzzy controller is included, level-one fuzzy control adjusts PI controller parameter in real time, and two pole fuzzy controls adjust level-one Fuzzy Control
The zoom factor of device output processed.Speed regulating method is first to motor actual speed n and given rotating speed nrIt is compared calculating, is obtained
Above-mentioned two deviations, are blurred by final deviation e and deviation variation rate ec in fuzzy controller 1, will be by fuzzy
E and EC after change give fuzzy controller to carry out reasoning work, to obtain the k after defuzzification1pAnd k1i, in Fuzzy Control
K is obtained using same method in device 2 processed2pAnd k2i, then two fuzzy controller outputs are multiplied, are as a result input to PI control
It in device processed, is finally input in brshless DC motor model by PD control adjusting, achievees the purpose that control motor speed.
A kind of DC brushless motor speed regulating method based on fuzzy PI-PD control provided by the invention, the model are built
Vertical step is as follows：
Step 1：Two fuzzy controllers are established, the input value of described two fuzzy controllers and the mould of output valve are defined
Paste subset；
Step 2：Establish the subordinating degree function of the fuzzy subset and the fuzzy control model of each fuzzy controller；
Step 3：According to the subordinating degree function of the fuzzy subset and the fuzzy control model of each fuzzy controller, answer
The fuzzy matrix table of PI control parameter is obtained with fuzzy synthetic reason；
Step 4：Anti fuzzy method is carried out to the fuzzy subset using gravity model appoach, obtains the clear amount for control.
The speed regulating method is to define described two fuzzy controllers according to two fuzzy controllers of above-mentioned model foundation
Input value and output valve fuzzy subset in, further include：Using revolving speed deviation and deviation variation rate as described two Fuzzy Controls
The input value of device processed, defines the fuzzy subset of the revolving speed deviation and deviation variation rate, and by revolving speed deviation and deviation variation rate
Fuzzy subset be mapped on domain；The output valve of two fuzzy controllers is multiplied respectively, is as a result used as conventional PI control device
Ratio, the correction value of integration control parameter, define the fuzzy subset of described two fuzzy controller output valves, and by described two
The fuzzy subset of a fuzzy controller output valve is mapped on domain.
The fuzzy matrix table of PI control parameter is calculated as follows to obtain：
kp=k2p×k1p+kp0
ki=k2i×k1i+ki0
Wherein, kp0、ki0It is the preset value of system parameter, k1p、k1iIt is the output valve of fuzzy controller 1, k2p、k2iIt is fuzzy
The output valve of controller 2, can be according to the value of the state adjust automatically PI control parameter of controlled device.
Compared with the prior art, the beneficial effects of the present invention are：The present invention is needed not rely on controlled using fuzzy control
The mathematical models of object keep it more stable in state of a control, and can effectively inhibit the non-thread of controlled device
The case where property；In control process, obscure PI-PD Self-tuning System can the constantly variation of monitoring parameter and parameter it is real-time
Feedback, makes control effect reach idealization.
Detailed description of the invention
Fig. 1 show the present invention is based on fuzzy PI-PD control DC brushless motor speed regulating method model schematic.
Fig. 2 show the present invention is based on fuzzy PI-PD control DC brushless motor speed regulating method specific workflow figure.
Specific embodiment
Below in conjunction with specific embodiment the present invention is described in detail.It should be noted that skill described in following embodiments
The combination of art feature or technical characteristic is not construed as isolated, they can be combined with each other to reaching better
Technical effect.
The present invention provides a kind of DC brushless motor speed regulating methods based on fuzzy PI-PD control.Building including model
Vertical and speed regulating method, the foundation of model include two-stage fuzzy controller, and level-one fuzzy control adjusts PI controller parameter in real time, and two
Pole fuzzy control adjusts the zoom factor of level-one fuzzy controller output.Speed regulating method be first to motor actual speed n with to
Determine revolving speed nrIt is compared calculating, obtains final deviation e and deviation variation rate ec, by above-mentioned two in fuzzy controller 1
Deviation is blurred, and gives fuzzy controller to carry out reasoning work the E and EC after blurring, to obtain Xie Mo
K after gelatinization1pAnd k1i, k is obtained using same method in fuzzy controller 22pAnd k2i, then by two fuzzy controllers
Output is multiplied, and is as a result input in PI controller, is finally input to brshless DC motor model by the result that PD control is adjusted
In, achieve the purpose that control motor speed.
The present invention is based on fuzzy PI-PD control DC brushless motor speed regulating method model schematic as shown in Figure 1, its
Specific modeling procedure is as follows：
Step 1：Two fuzzy controllers are established, the input value of described two fuzzy controllers and the mould of output valve are defined
Paste subset；
Step 2：Establish the subordinating degree function of the fuzzy subset and the fuzzy control model of each fuzzy controller；
Step 3：According to the subordinating degree function of the fuzzy subset and the fuzzy control model of each fuzzy controller, answer
The fuzzy matrix table of PI control parameter is obtained with fuzzy synthetic reason；
Step 4：Anti fuzzy method is carried out to the fuzzy subset using gravity model appoach, obtains the clear amount for control.
Be illustrated in figure 2 the present invention is based on fuzzy PI-PD control DC brushless motor speed regulating method specific workflow
Figure, is described in detail workflow below with reference to the step of model foundation：
Step 1：Two fuzzy controllers are established, the input value of described two fuzzy controllers and the mould of output valve are defined
Paste subset；
Using revolving speed deviation and deviation variation rate as the input value of described two fuzzy controllers, the revolving speed deviation is defined
With the fuzzy subset { NB, NM, NS, ZO, PS, PM, PB } of deviation variation rate, the fuzzy son of input and output linguistic variable is respectively represented
Collection：In negative big, negative, bear it is small, zero, it is just small, center, honest, and the fuzzy subset of revolving speed deviation and deviation variation rate is mapped to
On domain [- 6,6]；
The output valve of two fuzzy controllers is multiplied respectively, as a result the ratio as conventional PI control device, integration control
The correction value of parameter defines the fuzzy subset { NB, NM, NS, ZO, PS, PM, PB } of described two fuzzy controller output valves, point
The fuzzy subset of input and output linguistic variable is not represented：In negative big, negative, bear it is small, zero, it is just small, center, honest, and by described two
The fuzzy subset of each and every one fuzzy controller output valve is mapped on domain [- 10,10].
Step 2：Establish the subordinating degree function of the fuzzy subset and the fuzzy control model of each fuzzy controller；
Step 3：According to the subordinating degree function of the fuzzy subset and the fuzzy control model of each fuzzy controller, answer
The fuzzy matrix table of PI control parameter is obtained with fuzzy synthetic reason；
The fuzzy matrix table of PI control parameter is calculated as follows to obtain：
kp=k2p×k1p+kp0
ki=k2i×k1i+ki0
Wherein, kp0、ki0It is the preset value of system parameter, k1p、k1iIt is the output valve of fuzzy controller 1, k2p、k2iIt is fuzzy
The output valve of controller 2, can be according to the value of the state adjust automatically PI control parameter of controlled device.
The quality of Control platform depends primarily on the reasonability of selection of control parameter.Rule of thumb, from the response speed of system
Degree, stability, overshoot, stable state accuracy, pid control parameter kp、kiAnd kdEffect etc. consider, in controlled process
Under corresponding different deviation e and deviation variation rate ec variation, PI controller parameter kp, kiSelf-adjusting to meet following Adjustment principle；
(1) when error E is larger, to eliminate error as early as possible, response speed, k are improved1p、k2pIt takes large values, k1i、k2iIt takes smaller
Value or zero；When error E is smaller, to continue to eliminate error, and prevents that overshoot is excessive and generate oscillation, k1p、k2pValue will reduce, k1i、
k2iIt gets the small value, in error E very little, to eliminate static difference, avoiding system from generating oscillation near setting value keeps system stable as early as possible,
k1p、k2pValue continues to reduce k1i、k2iIt is worth constant or slightly takes greatly a bit.
(2) as E and EC jack per line, controlled volume is to given value direction change is deviateed, and should tighten control effect, subtracts error court
Small direction change should take smaller k1i、k2i；As E and when EC contrary sign, controlled volume is to close to given value direction change, therefore in error
When E is larger, lesser k is taken1p、k2pValue or zero is to accelerate the dynamic process controlled.
(3) EC is bigger, k1p、k2pValue is smaller, k1i、k2iValue is bigger.
Step 4：Anti fuzzy method is carried out to the fuzzy subset using gravity model appoach, obtains the clear amount for control.
Anti fuzzy method is exactly to convert the linguistic variable of output to accurate numerical value, and fuzzy controller of the invention is using weight
Heart method is to fuzzy subset's anti fuzzy method.It is power system to control action fuzzy subset's subordinating degree function with the point on control action domain
Number, which is weighted and averaged, acquires anti fuzzy method result.The result of anti fuzzy method is input in brshless DC motor model, is reached
Control the purpose of electric machine speed regulation.
A kind of DC brushless motor speed regulating method based on fuzzy PI-PD control of the present invention, due to using fuzzy control not
Need to rely on the mathematical models of controlled device, so keeping motor more stable in state of a control, and can be effectively
Inhibit the nonlinear situation of controlled motor；In control process, the Self-tuning System for obscuring PI-PD can constantly monitoring parameter
The Real-time Feedback of variation and parameter makes being optimal of effect for controlling motor.
Although having been presented for some embodiments of the present invention herein, it will be appreciated by those of skill in the art that
Without departing from the spirit of the invention, the embodiments herein can be changed.Above-described embodiment is only exemplary, no
It should be using the embodiments herein as the restriction of interest field of the present invention.
Claims (6)
1. a kind of DC brushless motor speed regulating method based on fuzzy PI-PD control, which is characterized in that building including Controlling model
Vertical and motor speed regulating method, the foundation of Controlling model include two-stage fuzzy controller, and level-one fuzzy control adjusts PI control in real time
Device parameter, two pole fuzzy controls adjust the zoom factor of level-one fuzzy controller output.Motor speed regulating method is first to motor
Actual speed n and given rotating speed nrIt is compared calculating, final deviation e and deviation variation rate ec is obtained, in fuzzy controller 1
It is middle to be blurred above-mentioned two deviations, give fuzzy controller to carry out reasoning work the E and EC after blurring,
To obtain the k after defuzzification1pAnd k1i, k is obtained using same method in fuzzy controller 22pAnd k2i, then by two
A fuzzy controller output is multiplied, and is as a result input in PI controller, is finally input to brushless dc by PD control adjusting
In machine model, achieve the purpose that control motor speed.
2. a kind of DC brushless motor speed regulating method based on fuzzy PI-PD control according to claim 1, feature exist
In：The modeling procedure of the Controlling model is：
Step1：Two fuzzy controllers are established, the input value of described two fuzzy controllers and the fuzzy son of output valve are defined
Collection；
Step2：Establish the subordinating degree function of the fuzzy subset and the fuzzy control model of each fuzzy controller；
Step3：According to the subordinating degree function of the fuzzy subset and the fuzzy control model of each fuzzy controller, using fuzzy
Compositional rule of inference obtains the fuzzy matrix table of PI control parameter；
Step4：Anti fuzzy method is carried out to the fuzzy subset using gravity model appoach, obtains the clear amount for control.
3. a kind of DC brushless motor speed regulating method based on fuzzy PI-PD control according to claim 2, feature exist
In：The motor speed regulating method be on the basis of established Controlling model, to the fuzzy rules of two fuzzy controllers into
Row definition.
4. a kind of DC brushless motor speed regulating method based on fuzzy PI-PD control according to claim 3, feature exist
In：Described establishes two fuzzy controllers, using revolving speed deviation and deviation variation rate as the defeated of described two fuzzy controllers
Enter value, defines the fuzzy subset { NB, NM, NS, ZO, PS, PM, PB } of the revolving speed deviation and deviation variation rate, respectively represent defeated
Enter the fuzzy subset of output language variable：In negative big, negative, bear it is small, zero, it is just small, center, honest, and by revolving speed deviation and deviation
The fuzzy subset of change rate is mapped on domain [- 6,6]；The output valve of two fuzzy controllers is multiplied respectively, as a result conduct
Ratio, the correction value of integration control parameter of conventional PI control device, define the fuzzy son of described two fuzzy controller output valves
Collect { NB, NM, NS, ZO, PS, PM, PB }, the fuzzy subset of representative input and output linguistic variable, and will be described with above-mentioned subset
The fuzzy subset of two fuzzy controller output valves is mapped on domain [- 10,10].
5. a kind of DC brushless motor speed regulating method based on fuzzy PI-PD control according to claim 3, feature exist
In：The fuzzification process is according to the subordinating degree function for establishing the fuzzy subset and the fuzzy control of each fuzzy controller
Model obtains the fuzzy matrix table of PI control parameter using fuzzy synthetic reason；The fuzzy matrix table of PI control parameter is as the following formula
It is calculated：
kp=k2p×k1p+kp0
ki=k2i×k1i+ki0
Wherein, kp0、ki0It is the preset value of system parameter, k1p、k1iIt is the output valve of fuzzy controller 1, k2p、k2iIt is fuzzy control
The output valve of device 2, can be according to the value of the state adjust automatically PI control parameter of controlled motor.
6. a kind of DC brushless motor speed regulating method based on fuzzy PI-PD control according to claim 2, feature exist
In：Described carries out anti fuzzy method to the fuzzy subset using gravity model appoach, exactly converts the linguistic variable of output to accurately
Numerical value, control action fuzzy subset's subordinating degree function is weighted and averaged as weight coefficient using the point on control action domain and is asked
Anti fuzzy method is obtained as a result, the result of anti fuzzy method is input in brshless DC motor model, reaches the mesh of control electric machine speed regulation
's.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109884883A (en) * | 2019-03-15 | 2019-06-14 | 长春工业大学 | A kind of configurable brshless DC motor speed regulation fuzzy controller |
| CN111381492A (en) * | 2020-03-24 | 2020-07-07 | 湖南盛鼎科技发展有限责任公司 | Brushless direct current motor control method based on interval two-type fuzzy integral PID |
| CN112859616A (en) * | 2021-01-27 | 2021-05-28 | 顺德职业技术学院 | Sensor sampling interval fuzzy controller |
| CN113156809A (en) * | 2021-04-22 | 2021-07-23 | 广东第二师范学院 | Motor rotating speed control method based on differential regulation improved PD algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6119446A (en) * | 1997-11-10 | 2000-09-19 | Samsung Aerospace Industries, Ltd. | Method for controlling gas turbine using fuzzy logic |
| US20030153990A1 (en) * | 2002-01-22 | 2003-08-14 | Igor Boiko | Method and apparatus for tuning a PID controller |
| JP2013003845A (en) * | 2011-06-16 | 2013-01-07 | Institute Of National Colleges Of Technology Japan | Built-in intelligence controller, control system, control program, recording medium, and control method |
| CN103760766A (en) * | 2014-01-08 | 2014-04-30 | 江苏新绿能科技有限公司 | Method and system for fuzzy control over genetic algorithm optimization of shielded gate portal crane controller |
| CN106054616A (en) * | 2016-07-27 | 2016-10-26 | 昆明理工大学 | Titanium tape reel continuous pickling loop sleeve height control method for fuzzy logic optimizing PID controller parameters |
-
2018
- 2018-06-20 CN CN201810641587.2A patent/CN108832853B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6119446A (en) * | 1997-11-10 | 2000-09-19 | Samsung Aerospace Industries, Ltd. | Method for controlling gas turbine using fuzzy logic |
| US20030153990A1 (en) * | 2002-01-22 | 2003-08-14 | Igor Boiko | Method and apparatus for tuning a PID controller |
| JP2013003845A (en) * | 2011-06-16 | 2013-01-07 | Institute Of National Colleges Of Technology Japan | Built-in intelligence controller, control system, control program, recording medium, and control method |
| CN103760766A (en) * | 2014-01-08 | 2014-04-30 | 江苏新绿能科技有限公司 | Method and system for fuzzy control over genetic algorithm optimization of shielded gate portal crane controller |
| CN106054616A (en) * | 2016-07-27 | 2016-10-26 | 昆明理工大学 | Titanium tape reel continuous pickling loop sleeve height control method for fuzzy logic optimizing PID controller parameters |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109884883A (en) * | 2019-03-15 | 2019-06-14 | 长春工业大学 | A kind of configurable brshless DC motor speed regulation fuzzy controller |
| CN111381492A (en) * | 2020-03-24 | 2020-07-07 | 湖南盛鼎科技发展有限责任公司 | Brushless direct current motor control method based on interval two-type fuzzy integral PID |
| CN112859616A (en) * | 2021-01-27 | 2021-05-28 | 顺德职业技术学院 | Sensor sampling interval fuzzy controller |
| CN113156809A (en) * | 2021-04-22 | 2021-07-23 | 广东第二师范学院 | Motor rotating speed control method based on differential regulation improved PD algorithm |
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