CN110442016A - A kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network - Google Patents
A kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network Download PDFInfo
- Publication number
- CN110442016A CN110442016A CN201910753097.6A CN201910753097A CN110442016A CN 110442016 A CN110442016 A CN 110442016A CN 201910753097 A CN201910753097 A CN 201910753097A CN 110442016 A CN110442016 A CN 110442016A
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- neural network
- layer
- control
- fuzzy neural
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000006243 chemical reaction Methods 0.000 claims abstract description 13
- 230000006870 function Effects 0.000 claims description 7
- 238000002955 isolation Methods 0.000 claims description 6
- 238000004210 cathodic protection Methods 0.000 description 8
- 230000003044 adaptive effect Effects 0.000 description 6
- 238000011161 development Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000009415 formwork Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000005260 corrosion Methods 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 230000003260 anti-sepsis Effects 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000003767 neural control Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
- Prevention Of Electric Corrosion (AREA)
Abstract
The invention discloses a kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network, which includes: electric power main circuit and control circuit；Electric power main circuit, for carrying out power conversion to anticorrosive power；Control circuit, the intelligent control algorithm for being combined using fuzzy neural network and PID control provide required control pulse for the power conversion of the electric power main circuit.The present invention uses advanced intelligent control method, fuzzy neural network+pid control algorithm of use is the driving and size that output voltage is adjusted by voltage/current feedback, i.e. by fuzzy theory and neural network combination, pid control parameter can adaptively be adjusted, have reached non-overshooting control, to reduce error caused by external disturbance and analog circuit, the precision of electric power output voltage and electric current is improved, the intelligent Anticorrosive Power system of cathode current can be provided by devising one.
Description
Technical field
The present invention relates to anticorrosive power technical fields, more particularly relate to a kind of intelligent anti-corrosion based on fuzzy neural network
Power supply device and its control method.
Background technique
Corrosion not only causes huge economic loss, but also brings serious environmental pollution.With metallic conduit or metal
The continuous propulsion of the infrastructure projects such as building, metal erosion also increasingly sharpen, and the requirement to aseptic technic is also higher and higher.
Cathode protection technology is a kind of effective technological means in aseptic technic.Anticorrosive power is the important key measure of cathodic protection,
Cathodic protection power supply also experienced from rectifier to potentiostat, from independent operating to composition system, from electric power resource to nature
The developing stage of resource.Therefore, the cathodic protection intelligent, at low cost, the development cycle is short, stability is high, easily operated is researched and developed
Power supply has far reaching significance.
Cathodic protection power-supply system mainly has rectifier, potentiostat and other cathodic protection power supplys at present.Rectifier because
Its heavy, noise is gradually eliminated greatly；Potentiostat application is relatively more universal, but it has remote control and asks with what software technology fell behind
Topic.The cathodic protection power supply for adapting to various environment is developed in recent years both at home and abroad, such as tital generator, big energy accumulator, wind
Power generator etc., cathodic protection power supply have the following disadvantages at present towards the development of intelligent direction: modularity, general
Property it is poor, various complex environments can not be adapted to by simply modifying；It is limited to the development of power electronic technique device, hardware is anti-dry
The performance disturbed needs to be further increased；The control algolithm of power supply main control loop determines the stability of its power supply status,
How optimizing existing algorithm or studying new control algolithm is current urgent problem to be solved.
Patent one (CN102443807A) discloses a kind of anticorrosive power device, but patent one is not calculated using fuzzy theory
Method is easily trapped into local minimum point just with neural network algorithm, and fuzzy neural network has clear physical significance.Specially
Two (CN201110418288.0) of benefit disclose a kind of power circuit of cathodic protection, and patent two uses traditional control method, not
Using intelligent control method.Patent three discloses a kind of Active Power Filter-APF adaptive fuzzy sliding mode RBF neural control
Method eliminates the disturbance part in power supply, uses fuzzy neural network+sliding formwork control method, i.e., for designing filter
Using sliding formwork parameter adaptive method, shake effect is trembled when reducing sliding formwork switching.
Summary of the invention
The embodiment of the present invention provides a kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network, uses
To solve problems of the prior art.
The embodiment of the present invention provides a kind of intelligent Anticorrosive Power device based on fuzzy neural network, comprising: the main electricity of power supply
Road and control circuit；
The electric power main circuit, for carrying out power conversion to anticorrosive power；
The control circuit, the intelligent control algorithm for being combined using fuzzy neural network and PID control, is the electricity
The power conversion of source main circuit provides required control pulse.
Further, the intelligent Anticorrosive Power device provided in an embodiment of the present invention based on fuzzy neural network, further includes:
Electromagnetic isolation cabinet；The electric power main circuit and the control circuit are respectively positioned on the electromagnetic isolation cabinet inside.
Further, the electric power main circuit include: input rectifying filter circuit output end and inverter it is first defeated
Enter end electrical connection, the output end of the inverter is electrically connected with the input terminal of output rectifier and filter, the output rectification filter
The output end of wave circuit includes first lead and the second lead.
Further, the control circuit includes: voltage A/D module and electric current A/D module, the voltage A/D module
Input terminal and the input terminal of the electric current A/D module are electrically connected with the second lead of the output rectifier and filter, described
The output end of voltage A/D module and the output end of the electric current A/D module pass sequentially through single-chip microcontroller, D/A module, impulse modulation
Module, drive module are electrically connected with the second input terminal of the inverter；Wherein, the single-chip microcontroller, for running fuzzy neural
The intelligent control algorithm that network and PID control combine.
Further, the intelligent control algorithm that the fuzzy neural network and PID control combine, comprising:
Three parameters KP, KI, KD of PID are adaptively adjusted by five layers of fuzzy neural network；Wherein, it obscures for described five layers
Neural network specifically includes:
First layer is input layer, determines the number and numerical value of input variable；Fuzzy control input quantity be voltage given value with
The error and deviation variation rate of voltage measured value set the linguistic variable of deviation as E, and domain is [- 6,6], deviation variation rate
Linguistic variable is EC, and basic domain is set as [- 6,6]；
The second layer is the degree of membership layer of input variable, the blurring of input variable is realized, using triangular function to above-mentioned
Linguistic variable is blurred, and obtains E, and the fuzzy subset of EC, U are respectively P={ P i | i=1,2,3,4 }, PC=PC i | i
=1,2,3,4 }, UC={ UC k | k=1,2,3,4 }；Corresponding fuzzy language subset is equal are as follows: { ZO (0), PS (just small), PM is (just
In), PB (honest) }
Third layer is "AND" layer, and node number is number of fuzzy rules, according to voltage deviation, voltage deviation rate, and current deviation,
The relationship of current deviation rate and KP, KI, KD, establishes fuzzy inference rule；
4th layer is "or" layer, and node number is that output variable fuzziness divides number；
Layer 5 is output layer, and number of nodes is three, and the output valve after weight averaged method de-fuzzy is KP, KI, KD
Value.
The embodiment of the present invention also provides a kind of control method of intelligent Anticorrosive Power device based on fuzzy neural network, packet
It includes:
Power conversion is carried out to anticorrosive power；
The intelligent control algorithm combined using fuzzy neural network and PID control is that the power of the electric power main circuit turns
Control pulse needed for providing is provided.
Further, the intelligent control algorithm that the fuzzy neural network and PID control combine, comprising:
Three parameters KP, KI, KD of PID are adaptively adjusted by five layers of fuzzy neural network；Wherein, it obscures for described five layers
Neural network specifically includes:
First layer is input layer, determines the number and numerical value of input variable；Fuzzy control input quantity be voltage given value with
The error and deviation variation rate of voltage measured value set the linguistic variable of deviation as E, and domain is [- 6,6], deviation variation rate
Linguistic variable is EC, and basic domain is set as [- 6,6]；
The second layer is the degree of membership layer of input variable, the blurring of input variable is realized, using triangular function to above-mentioned
Linguistic variable is blurred, and obtains E, and the fuzzy subset of EC, U are respectively P={ P i | i=1,2,3,4 }, PC=PC i | i
=1,2,3,4 }, UC={ UC k | k=1,2,3,4 }；Corresponding fuzzy language subset is equal are as follows: { ZO (0), PS (just small), PM is (just
In), PB (honest) }；
Third layer is "AND" layer, and node number is number of fuzzy rules, according to voltage deviation, voltage deviation rate, and current deviation,
The relationship of current deviation rate and KP, KI, KD, establishes fuzzy inference rule；
4th layer is "or" layer, and node number is that output variable fuzziness divides number；
Layer 5 is output layer, and number of nodes is three, and the output valve after weight averaged method de-fuzzy is KP, KI, KD
Value.
The embodiment of the present invention provides a kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network, with
The prior art is compared, and its advantages are as follows:
The present invention uses advanced intelligent control method, and fuzzy neural network+pid control algorithm of use is to pass through electricity
Pressure/current feedback and the driving and size for adjusting output voltage can be adaptive that is, by fuzzy theory and neural network combination
Pid control parameter should be adjusted, non-overshooting control is had reached, to reduce error caused by external disturbance and analog circuit, is mentioned
The high precision of electric power output voltage and electric current, the intelligent Anticorrosive Power system of cathode current can be provided by devising one；It adopts
With cathode protection technology, potential compensation is carried out to metallic conduit, can be improved the stability of whole system, reliability, it is integrated and
It is open.
Detailed description of the invention
Fig. 1 is that a kind of circuit of intelligent Anticorrosive Power device based on fuzzy neural network provided in an embodiment of the present invention is former
Manage block diagram；
Fig. 2 is Fuzzy Neural-network Control structure chart provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1~2, the embodiment of the present invention provides a kind of intelligent Anticorrosive Power device based on fuzzy neural network, should
Device includes: electric power main circuit and control circuit；Electric power main circuit, for carrying out power conversion to anticorrosive power；Control circuit,
Intelligent control algorithm for being combined using fuzzy neural network and PID control is mentioned for the power conversion of the electric power main circuit
For required control pulse.
Intelligent Anticorrosive Power device provided in an embodiment of the present invention based on fuzzy neural network, further includes: electromagnetic isolation
Cabinet；Electric power main circuit and control circuit are respectively positioned on electromagnetic isolation cabinet inside.
Above-mentioned electric power main circuit includes: that the output end of input rectifying filter circuit and the first input end of inverter are electrically connected
It connects, the output end of inverter is electrically connected with the input terminal of output rectifier and filter, the output end packet of output rectifier and filter
Include first lead and the second lead.
Above-mentioned control circuit includes: voltage A/D module and electric current A/D module, the input terminal and electric current A/ of voltage A/D module
The input terminal of D-module is electrically connected with the second lead of output rectifier and filter, the output end and electric current A/ of voltage A/D module
The output end of D-module passes sequentially through the second input of single-chip microcontroller, D/A module, pulse modulation module, drive module and inverter
End electrical connection；Wherein, single-chip microcontroller, the intelligent control algorithm combined for running fuzzy neural network and PID control.
The intelligent control algorithm that above-mentioned fuzzy neural network and PID control combine, comprising:
Three parameters KP, KI, KD of PID are adaptively adjusted by five layers of fuzzy neural network；Wherein, five layers of fuzzy neural
Network specifically includes:
First layer is input layer, determines the number and numerical value of input variable；Fuzzy control input quantity be voltage given value with
The error and deviation variation rate of voltage measured value set the linguistic variable of deviation as E, and domain is [- 6,6], deviation variation rate
Linguistic variable is EC, and basic domain is set as [- 6,6]；
The second layer is the degree of membership layer of input variable, the blurring of input variable is realized, using triangular function to above-mentioned
Linguistic variable is blurred, and obtains E, and the fuzzy subset of EC, U are respectively P={ P i | i=1,2,3,4 }, PC=PC i | i
=1,2,3,4 }, UC={ UC k | k=1,2,3,4 }；Corresponding fuzzy language subset is equal are as follows: { ZO (0), PS (just small), PM is (just
In), PB (honest) }；
Third layer is "AND" layer, and node number is number of fuzzy rules, according to voltage deviation, voltage deviation rate, and current deviation,
The relationship of current deviation rate and KP, KI, KD, establishes fuzzy inference rule；
4th layer is "or" layer, and node number is that output variable fuzziness divides number；
Layer 5 is output layer, and number of nodes is three, and the output valve after weight averaged method de-fuzzy is KP, KI, KD
Value.
In turn, input signal of the output valve of five layers of fuzzy neural network as pwm chip, to obtain required
Pulse refers to the output error chi square function of system as performance since the fuzzy neural network is multilayer feed-forward structure
Mark, by gradient descent method backpropagation step by step forward, obtains KP, the optimized parameter of KI, KD.The parameter may be driven circuit and arrive
High-precision antisepsis is realized to reach the output characteristics for adjusting power supply in electric power main circuit part.
In addition, electric power main circuit part includes input rectifying filtering, power switch bridge, output rectification filter referring specifically to Fig. 1
Wave three parts, alternating current are converted to direct current through input rectifying filter circuit, are changed into high frequency square wave voltage through power switch bridge,
Most required voltage/current is converted to through exporting filter circuit afterwards.The control circuit of power supply provides required touching for main circuit
Send out pulse.The control system uses voltage, current double closed-loop control structure.It includes controlling pulse-generating circuit, driving circuit,
Feedback control circuit.
In addition, being sampled, being adopted to output voltage, the electric current of power supply by voltage, current sensor referring specifically to Fig. 2
Analog signal is become available for the digital signal of single-chip microcontroller processing through A/D converter by sample signal, and single-chip microcontroller passes through control algolithm pair
The signal carries out operation and obtains control signal, controls signal through pwm chip and generates corresponding pulse signal, through driver
The opening and closing of control main circuit later, and then control the output of power supply.Using Fuzzy Neural-network Control algorithm, simultaneously incorporating parametric can
The PID control method of control, establishes a kind of adaptive fuzzy nerve control method, and fuzzy neural network can be according to the operation shape of system
State, by the self study of neural network, weighting coefficient adjustment reaches three adjustable parameters of adaptive corrigendum PID, KP, KI,
KD handles sampled voltage/current data to be optimal control.
Based on the same inventive concept, the embodiment of the present invention also provides a kind of intelligent Anticorrosive Power based on fuzzy neural network
The control method of device, comprising:
Step 1, power conversion is carried out to anticorrosive power.
Step 2, the intelligent control algorithm combined using fuzzy neural network and PID control is the electric power main circuit
Power conversion provides required control pulse.
The intelligent control algorithm that above-mentioned fuzzy neural network and PID control combine, comprising:
Three parameters KP, KI, KD of PID are adaptively adjusted by five layers of fuzzy neural network；Wherein, it obscures for described five layers
Neural network specifically includes:
First layer is input layer, determines the number and numerical value of input variable；Fuzzy control input quantity be voltage given value with
The error and deviation variation rate of voltage measured value set the linguistic variable of deviation as E, and domain is [- 6,6], deviation variation rate
Linguistic variable is EC, and basic domain is set as [- 6,6]；
The second layer is the degree of membership layer of input variable, the blurring of input variable is realized, using triangular function to above-mentioned
Linguistic variable is blurred, and obtained fuzzy subset is E={ }, EC={ }, U={ }；
Third layer is "AND" layer, and node number is number of fuzzy rules, according to voltage deviation, voltage deviation rate, and current deviation,
The relationship of current deviation rate and KP, KI, KD, establishes fuzzy inference rule；
4th layer is "or" layer, and node number is that output variable fuzziness divides number；
Layer 5 is output layer, and number of nodes is three, and the output valve after weight averaged method de-fuzzy is KP, KI, KD
Value.
In conclusion the output of power supply has non-linear, the present invention fuzzy neural self adaptive control controllable using parameter
Pid control algorithm according to voltage close loop control design case fuzzy reasoning table, and then is obscured and is pushed away by fuzzy language fuzzy variable
Reason and de-fuzzy obtain high-precision, the anticorrosive power control system of high robust.Key be use fuzzy neural network and
The intelligent control algorithm that PID control combines has reached the control of high-precision, high stable, has met wanting for the stability of anticorrosive power
It asks.
Disclosed above is only several specific embodiments of the invention, and those skilled in the art can carry out the present invention
Various modification and variations without departing from the spirit and scope of the present invention, if these modifications and changes of the present invention belongs to the present invention
Within the scope of claim and its equivalent technologies, then the present invention is also intended to include these modifications and variations.
Claims (7)
1. a kind of intelligent Anticorrosive Power device based on fuzzy neural network characterized by comprising electric power main circuit and control
Circuit；
The electric power main circuit, for carrying out power conversion to anticorrosive power；
The control circuit, the intelligent control algorithm for being combined using fuzzy neural network and PID control, is the power supply master
The power conversion of circuit provides required control pulse.
2. the intelligent Anticorrosive Power device based on fuzzy neural network as described in claim 1, which is characterized in that further include:
Electromagnetic isolation cabinet；The electric power main circuit and the control circuit are respectively positioned on the electromagnetic isolation cabinet inside.
3. the intelligent Anticorrosive Power device based on fuzzy neural network as described in claim 1, which is characterized in that the power supply
Main circuit includes: that the output end of input rectifying filter circuit is electrically connected with the first input end of inverter, the inverter it is defeated
Outlet is electrically connected with the input terminal of output rectifier and filter, and the output end of the output rectifier and filter includes first lead
With the second lead.
4. the intelligent Anticorrosive Power device based on fuzzy neural network as claimed in claim 3, which is characterized in that the control
Circuit includes: voltage A/D module and electric current A/D module, the input terminal of the voltage A/D module and the electric current A/D module
Input terminal is electrically connected with the second lead of the output rectifier and filter, the output end of the voltage A/D module and described
The output end of electric current A/D module passes sequentially through single-chip microcontroller, D/A module, pulse modulation module, drive module and the inverter
The second input terminal electrical connection；Wherein, the single-chip microcontroller, the intelligence control combined for running fuzzy neural network and PID control
Algorithm processed.
5. the intelligent Anticorrosive Power device as described in claim 1 or 4 based on fuzzy neural network, which is characterized in that described
The intelligent control algorithm that fuzzy neural network and PID control combine, comprising:
Three parameters KP, KI, KD of PID are adaptively adjusted by five layers of fuzzy neural network；Wherein, five layers of fuzzy neural
Network specifically includes:
First layer is input layer, determines the number and numerical value of input variable；Fuzzy control input quantity is voltage given value and voltage
The error and deviation variation rate of measured value set the linguistic variable of deviation as E, and domain is [- 6,6], the language of deviation variation rate
Variable is EC, and basic domain is set as [- 6,6]；
The second layer is the degree of membership layer of input variable, the blurring of input variable is realized, using triangular function to above-mentioned language
Variable is blurred, and obtains E, and the fuzzy subset of EC, U are respectively P={ Pi | i=1,2,3,4 }, PC=PC i | and i=1,2,
3,4 }, UC={ UC k | k=1,2,3,4 }；Corresponding fuzzy language subset is equal are as follows: { ZO (0), PS (just small), PM (center), PB
(honest) }；
Third layer is "AND" layer, and node number is number of fuzzy rules, according to voltage deviation, voltage deviation rate, current deviation, electric current
The relationship of deviation ratio and KP, KI, KD, establishes fuzzy inference rule；
4th layer is "or" layer, and node number is that output variable fuzziness divides number；
Layer 5 is output layer, and number of nodes is three, and the output valve after weight averaged method de-fuzzy is the value of KP, KI, KD.
6. a kind of control method of the intelligent Anticorrosive Power device based on fuzzy neural network characterized by comprising
Power conversion is carried out to anticorrosive power；
The intelligent control algorithm combined using fuzzy neural network and PID control is mentioned for the power conversion of the electric power main circuit
For required control pulse.
7. the control method of the intelligent Anticorrosive Power device based on fuzzy neural network, feature exist as claimed in claim 6
In the intelligent control algorithm that the fuzzy neural network and PID control combine, comprising:
Three parameters KP, KI, KD of PID are adaptively adjusted by five layers of fuzzy neural network；Wherein, five layers of fuzzy neural
Network specifically includes:
First layer is input layer, determines the number and numerical value of input variable；Fuzzy control input quantity is voltage given value and voltage
The error and deviation variation rate of measured value set the linguistic variable of deviation as E, and domain is [- 6,6], the language of deviation variation rate
Variable is EC, and basic domain is set as [- 6,6]；
The second layer is the degree of membership layer of input variable, the blurring of input variable is realized, using triangular function to above-mentioned language
Variable is blurred, and obtains E, and the fuzzy subset of EC, U are respectively P={ Pi | i=1,2,3,4 }, PC=PC i | and i=1,2,
3,4 }, UC={ UC k | k=1,2,3,4 }；Corresponding fuzzy language subset is equal are as follows: { ZO (0), PS (just small), PM (center), PB
(honest) }；
Third layer is "AND" layer, and node number is number of fuzzy rules, according to voltage deviation, voltage deviation rate, current deviation, electric current
The relationship of deviation ratio and KP, KI, KD, establishes fuzzy inference rule；
4th layer is "or" layer, and node number is that output variable fuzziness divides number；
Layer 5 is output layer, and number of nodes is three, and the output valve after weight averaged method de-fuzzy is the value of KP, KI, KD.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910753097.6A CN110442016A (en) | 2019-08-15 | 2019-08-15 | A kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910753097.6A CN110442016A (en) | 2019-08-15 | 2019-08-15 | A kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110442016A true CN110442016A (en) | 2019-11-12 |
Family
ID=68435723
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910753097.6A Pending CN110442016A (en) | 2019-08-15 | 2019-08-15 | A kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110442016A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113552806A (en) * | 2020-12-14 | 2021-10-26 | 四川轻化工大学 | Method for analyzing environmental parameters of breeding house based on fuzzy control algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101813918A (en) * | 2010-04-09 | 2010-08-25 | 哈尔滨工业大学 | Control system of existing air-conditioners in communication rooms based on fuzzy neural network |
| CN102443807A (en) * | 2010-10-14 | 2012-05-09 | 黄铂 | Anti-corrosive power supply device |
| CN104052059A (en) * | 2014-06-19 | 2014-09-17 | 国家电网公司 | Active power filter control method based on fuzzy neural network PID |
| CN204287859U (en) * | 2014-12-09 | 2015-04-22 | 郭海涛 | Based on embedded ARM processor oil pipeline cathodic protection protection power source device |
| KR101842191B1 (en) * | 2017-10-26 | 2018-03-26 | 주식회사 텔다 | Power conversion appratus |
| CN109687694A (en) * | 2019-01-21 | 2019-04-26 | 温州大学 | A kind of multiple power supplies are for being electrically integrated assembly and its control method |
-
2019
- 2019-08-15 CN CN201910753097.6A patent/CN110442016A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101813918A (en) * | 2010-04-09 | 2010-08-25 | 哈尔滨工业大学 | Control system of existing air-conditioners in communication rooms based on fuzzy neural network |
| CN102443807A (en) * | 2010-10-14 | 2012-05-09 | 黄铂 | Anti-corrosive power supply device |
| CN104052059A (en) * | 2014-06-19 | 2014-09-17 | 国家电网公司 | Active power filter control method based on fuzzy neural network PID |
| CN204287859U (en) * | 2014-12-09 | 2015-04-22 | 郭海涛 | Based on embedded ARM processor oil pipeline cathodic protection protection power source device |
| KR101842191B1 (en) * | 2017-10-26 | 2018-03-26 | 주식회사 텔다 | Power conversion appratus |
| CN109687694A (en) * | 2019-01-21 | 2019-04-26 | 温州大学 | A kind of multiple power supplies are for being electrically integrated assembly and its control method |
Non-Patent Citations (2)
| Title |
|---|
| 樊波等: "基于模糊神经比例微分积分的静变电源控制", 《探测与控制学报》 * |
| 牛江川等: "静变电源的优化模糊神经网络PID控制研究", 《机械设计与制造》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113552806A (en) * | 2020-12-14 | 2021-10-26 | 四川轻化工大学 | Method for analyzing environmental parameters of breeding house based on fuzzy control algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104085265B (en) | A kind of energy regenerative suspension self adaptation off-line Neural network inverse control system and method | |
| CN108631591A (en) | A kind of control method of bidirectional DC-DC converter predicted current | |
| Wang et al. | Reduced‐order extended state observer based event‐triggered sliding mode control for DC‐DC buck converter system With parameter perturbation | |
| CN107147120A (en) | Active Power Filter-APF RBF amphineura network adaptive sliding-mode observer methods | |
| Liu et al. | Extended state observer based interval type-2 fuzzy neural network sliding mode control with its application in active power filter | |
| CN101976958A (en) | High-efficiency power regulating device based on power factor correction | |
| CN103530440A (en) | Micro-grid harmonic suppression method based on particle swarm optimization algorithm | |
| CN106712555A (en) | Common-mode voltage satisfactory decision-based FCS-MPC (Finite Control Set Model Predictive Control) method | |
| WO2023193650A1 (en) | Method for identifying both loads and mutual inductance of multi-load wireless power transfer system | |
| CN110190753A (en) | A kind of DC converter state feedback model forecast Control Algorithm | |
| CN110442016A (en) | A kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network | |
| CN115764987A (en) | Control method, new energy converter and grid-connected power system | |
| CN103311930A (en) | Sliding-mode control method for fuzzy PI parameter self-turning feedback linearization of active filter | |
| Zhang et al. | Intelligent Complementary Terminal Sliding Mode Using Multi-Loop Neural Network for Active Power Filter | |
| CN113328440A (en) | Active filtering control method for PLC circuit of electric vehicle charging station | |
| CN110518625B (en) | Grid-connected inverter direct-current component suppression method with variable learning rate BP-PID control | |
| CN104638634B (en) | Direct current micro-grid oscillation suppression method based on band-pass filter in master-slave mode | |
| Yi et al. | Adaptive backstepping sliding mode nonlinear control for buck DC/DC switched power converter | |
| CN116827110A (en) | TCM fractional order control method of Boost PFC converter | |
| CN115987086A (en) | Single-switch DC-DC converter on-line control method based on neural network | |
| Shen et al. | HOSMD and neural network based adaptive super-twisting sliding mode control for permanent magnet synchronous generators | |
| CN202617005U (en) | Power supply device for light high-strength metal material surface treatment | |
| CN114499209A (en) | DAB converter-based LADRC control method and system | |
| CN103490415B (en) | A kind of wind power system bifurcation controller based on DSP and method of work thereof | |
| CN114552739A (en) | Intelligent control method and device for hybrid energy storage system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |