CN106527124B - Electromagnetic type damper control method based on non-linear neural fuzzy controller - Google Patents
Electromagnetic type damper control method based on non-linear neural fuzzy controller Download PDFInfo
- Publication number
- CN106527124B CN106527124B CN201611071224.7A CN201611071224A CN106527124B CN 106527124 B CN106527124 B CN 106527124B CN 201611071224 A CN201611071224 A CN 201611071224A CN 106527124 B CN106527124 B CN 106527124B
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- electromagnetic type
- degree function
- variable
- damping force
- 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.)
- Expired - Fee Related
Links
- 230000001537 neural effect Effects 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000013016 damping Methods 0.000 claims abstract description 41
- 230000001133 acceleration Effects 0.000 claims abstract description 33
- 238000013528 artificial neural network Methods 0.000 claims abstract description 12
- 230000006870 function Effects 0.000 claims description 54
- 238000012545 processing Methods 0.000 claims description 8
- 230000005484 gravity Effects 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- 230000033228 biological regulation Effects 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 238000005192 partition Methods 0.000 claims description 3
- 238000003786 synthesis reaction Methods 0.000 claims description 3
- 239000000725 suspension Substances 0.000 abstract description 11
- 238000005457 optimization Methods 0.000 abstract description 2
- 230000001629 suppression Effects 0.000 abstract description 2
- 238000012795 verification Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 5
- 239000000306 component Substances 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000009795 derivation Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 239000006096 absorbing agent Substances 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 230000009466 transformation Effects 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
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/0285—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks and fuzzy logic
Landscapes
- Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Vehicle Body Suspensions (AREA)
- Feedback Control In General (AREA)
Abstract
The electromagnetic type damper control method based on non-linear neural fuzzy controller that this application discloses a kind of, the following steps are included: by the nonlinear neural network method in Intelligent Control Theory to the input fuzzy variable of electromagnetic type damper, that is the vertical vibration acceleration and vertical vibrating velocity of vibration reduction platform, fuzzy variable is exported, i.e. the subordinating degree function of damping force coefficient carries out depth optimization.Interior verification electromagnetic type damper, which is controlled, finally by this carries out real-time control.The present invention solves the problems, such as the nonlinear characteristic of electromagnetic type damper complexity, and while taking into account riding comfort and operational stability, the driving performance that can be proposed effectiveness in vibration suppression, improve motor vehicle extends the service life of motor vehicle.On its semi-active suspension system that can be advantageously applied to various motor vehicles.
Description
Technical field
The application belongs to the control field of Vehicle Semi-active Suspension System, is related to a kind of Vehicle damper technology, specifically
It is a kind of electromagnetic type damper control method based on non-linear neural fuzzy controller.
Background technique
Currently, the suspension system of automobile is broadly divided into passive type suspension system and semi-active suspension system.Wherein, passive type
Suspension system is due to its suspension rate and damping force size cannot be with the variations of the driving conditions such as automobile driving speed, pavement behavior
Automatic adjustment, ride comfort, road friendliness etc. are comprehensive to be difficult to improve.And the suspension spring of semi-active suspension system is rigid
Degree and damping force can automatically adjust as needed, therefore can preferably meet the requirement of running car.Electromagnetic type subtracts
Vibration device is a kind of damper for realizing semi-active suspension system, is most important core component in vehicle suspension, is reducing vehicle
It vertically and horizontally vibrates and vehicle is made to keep having very important effect in terms of good maneuverability and stability.Cause
This, the research of the solenoid valve damper of automobile will become one of the Main way of each system research of automobile.
Currently, intelligent control is widely used in the control of battery valve type damper.Fuzzy system is good at stating structure
Property, ambiguity knowledge, extraction to knowledge and expression are particularly convenient.But fuzzy control does not have self study and adaptive energy
Power, so the self-adaptive controlled of design and implementation fuzzy system is wanted to be formed with certain difficulty.ANN Control then has parallel meter
Calculate, distributed information storage, the advantages that zmodem and self-learning capability are strong, however it and be bad to state ambiguity, structure
Property language, therefore neural network is in learning training, generally requires one random initial weight of setting, this leads to e-learning
Time lengthens significantly, easily e-learning is made to fall into local extremum.Electromagnetic type damper mainly have complicated hysteresis and
Nonlinear characteristic, because it is not easy to establish accurate mathematical model, so conventional control has been extremely difficult to good control effect.Cause
This, fuzzy system and ANN Control are combined by consideration, using neural metwork training fuzzy rule, establish fuzzy neural
System controls electromagnetic type damper.
Summary of the invention
In view of this, the application is directed to the hysteresis and nonlinear problem of electromagnetic type damper, a kind of base is provided
In the electromagnetic type damper control method of non-linear neural fuzzy controller, this method extracts fuzzy control and neural network
Respective advantage, design it is a kind of neural network is initialized using fuzzy system, same neural network is available fuzzy
Reasoning, to greatly improve the learning efficiency of neural network.
In order to solve the above-mentioned technical problem, this application discloses a kind of solenoid valves based on non-linear neural fuzzy controller
Formula damper control method, comprising the following steps:
(1) damper experiment test platform is established, using data acquisition device to force snesor and acceleration transducer
Signal is acquired, wherein the data of acquisition include: initial damping force and shaking platform vertical vibration acceleration, are generated non-thread
The input signal of nerve fuzzy controller；
(2) using fuzzy control to damping force described in step (1) and shaking platform vertical vibration acceleration input letter
Number carry out Fuzzy processing, formed input fuzzy variable, be then sent to fuzzy controller, obtain output fuzzy variable；
(3) fuzzy rule base of electromagnetic type damper non-linear neural fuzzy controller is established；Utilize institute in (1)
The subordinating degree function for damping force and shaking platform vertical vibration acceleration information the input fuzzy variable set stated, and combine output
Fuzzy variable is trained subordinating degree function center, width and weight, using First-order Gradient optimizing algorithm come to each parameter into
Row is adjusted；Fuzzy rule is automatically generated using Neural Network Self-learning ability；
(4) ambiguity solution is carried out using output fuzzy variable of the center of gravity defuzzification method to non-linear neural fuzzy controller
Change processing obtains the damping force output of fuzzy controller, by the damping force of obtained fuzzy controller divided by initial damping force, note
For threshold value h, judges whether electromagnetic type damper is faulty according to the size of threshold value h, need to overhaul, reach to electromagnetic type
The purpose of damper real-time online regulation.
Further, damper experiment test platform is established in step (1), using data acquisition device to force snesor
It is acquired with the signal of acceleration transducer specifically: data collection system is built using LABVIEW software, is acquired by NI
The signal acquisition of force snesor and acceleration transducer into LABVIE, is saved as txt file, as non-linear neural mould by card
The input signal of fuzzy controllers；Wherein, the data of acquisition include: initial damping force and shaking platform vertical vibration acceleration.
Further, input signal described in step (1) is carried out at blurring using fuzzy control in step (2)
Reason forms input fuzzy variable, is then sent to fuzzy controller, obtains output fuzzy variable specifically: pass according to power
The damping force and shaking platform vertical vibration acceleration information that sensor and acceleration transducer collect, determine domain；It will resistance
Buddhist nun's power and the blurring of shaking platform vertical vibration acceleration information, determine the grade of blurring, that is, the domain being blurred；Pass through opinion
Domain transformation transforms to damping force and shaking platform vertical vibration acceleration information in the domain of blurring；Definitional language variable,
Specially { NB NS ZO PS PB }, wherein the value of NB NS ZO PS PB determines that selection is subordinate to according to the subordinating degree function of definition
The big fuzzy set of category degree is as output.
Further, the fuzzy control for establishing electromagnetic type damper non-linear neural fuzzy controller in step (3)
Rule base；The subordinating degree function of the input fuzzy variable set of data described in (1) is utilized, and combines output fuzzy variable, it is right
Subordinating degree function center, width and weight are trained, and each parameter is adjusted using First-order Gradient optimizing algorithm；It is i.e. sharp
Fuzzy rule is automatically generated with Neural Network Self-learning ability specifically:
The input fuzzy variable set formed according to (2) blurring is appropriate for fuzzy subset's selection into subordinating degree function
Subordinating degree function, membership function use Gauss member function, expression formula are as follows:
In formula, aiFor each component of input vector, i=1,2 ..., n, j=1,2 ..., mi, n is the dimension of input quantity, mi
For aiFuzzy partition number, cijIndicate the center of subordinating degree function, σijIndicate the width of subordinating degree function；
It is tested by matlab, using error back propagation algorithm respectively to the weight ω of subordinating degree functionij, center cij, width
σijDerivation is calculatedThen it is adjusted in subordinating degree function using First-order Gradient optimizing algorithm
Heart cij, subordinating degree function width csij, weight ωij。
Further, the output using center of gravity defuzzification method to non-linear neural fuzzy controller in step (4)
Fuzzy variable carries out defuzzification processing, obtains the damping force output of fuzzy controller, exists in real time to electromagnetic type damper
Line regulation specifically:
R1: if x is A1, y is B1, then z is C1；
Also R2: if x is A2, y is B2, then z is C2；
……
Also Rn: if x is An, y is Bn, then z is Cn；
Wherein, R1, R2 ... Rn respectively represent regular 1, regular n, x, y and z represent system mode and control to rule 2 ...
The linguistic variable of amount, x and y are input quantities, and z is control amount；Ai, Bi, Ci, i=1,2,3 ..., n respectively represent linguistic variable x,
Y, z add up then composition rule library in the upper linguistic variable value of its domain X, Y, Z, these rules；It is obscured according to above-mentioned rule
Reasoning then obtains the fuzzy value z of output quantity are as follows:
In formula: ∧ representative takes small；For the subordinating degree function of output quantity z,For the degree of membership of input quantity x
Function,For the subordinating degree function of input quantity y；Controller is by all inference conclusion C of synthesis1',C'2,…,C'nIt calculates
Final output fuzzy value, the as damping force of fuzzy controller, i.e.,
In formula: ∨ representative takes big, C1',C'2,…,C'nRespectively represent the subordinating degree function of each output component；Fuzzy set
C' is calculated by following formula:
In formula, Z0For the center-of-gravity value of fuzzy set C', μC'(zi) be i-th of control amount subordinating degree function, ziIt is i-th
Control amount.
Compared with prior art, the application can be obtained including following technical effect:
1) present invention by nonlinear neural network method to the input of electromagnetic type damper, output fuzzy variable it is each
A subordinating degree function carries out depth optimization, and carries out real-time control by verification electromagnetic type damper in the control.
2) the very good solution of the present invention nonlinear problem of MR fluid shock absorber, is taking into account riding comfort and operation
While stability, the driving performance that can be proposed effectiveness in vibration suppression, improve motor vehicle extends the service life of motor vehicle.
Certainly, any product for implementing the application must be not necessarily required to reach all the above technical effect simultaneously.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please are not constituted an undue limitation on the present application for explaining the application.In the accompanying drawings:
Fig. 1 is the theory of constitution schematic block diagram that the application realizes fuzzy control.
Specific embodiment
Presently filed embodiment is described in detail below in conjunction with accompanying drawings and embodiments, how the application is applied whereby
Technological means solves technical problem and reaches the realization process of technical effect to fully understand and implement.
The electromagnetic type damper control method based on non-linear neural fuzzy controller that the invention also discloses a kind of, this
Invention realizes that the theory of constitution of fuzzy control is as shown in Figure 1, comprising the following steps:
(1) damper experiment test platform is established, using data acquisition device to the force snesor being mounted on damper
It is acquired with the signal of acceleration transducer, generates the input signal of non-linear neural fuzzy controller；Wherein, the number of acquisition
According to including: initial damping force and shaking platform vertical vibration acceleration；
Specifically: data collection system is built using LABVIEW software, by NI capture card by force snesor and acceleration
The signal acquisition of sensor saves as txt file into LABVIE, the input signal as non-linear neural fuzzy controller.
(2) defeated to damping force described in step (1), vertical vibration acceleration and vertical vibrating velocity using fuzzy control
Enter signal and carry out Fuzzy processing, forms input fuzzy variable, be then sent to non-linear neural fuzzy controller, obtain
Export fuzzy variable；
Specifically: the damping force collected according to force snesor and acceleration transducer and vertical vibration accelerate degree
According to determining domain；The damping force and shaking platform vertical vibration acceleration that force snesor and acceleration transducer are collected
Data obfuscation determines the grade of blurring, that is, the domain being blurred；It is converted by domain by force snesor and acceleration sensing
The damping force and vertical vibration acceleration information that device collects transform in the domain of blurring；Definitional language variable, such as { NB
NS ZO PS PB }, wherein the value of NB NS ZO PS PB is according to the decision of the subordinating degree function of definition, the mould for selecting degree of membership big
Paste collection is as output.
(3) fuzzy rule base of electromagnetic type damper non-linear neural fuzzy controller is established；Utilize institute in (1)
The subordinating degree function of the damping force, shaking platform vertical vibration acceleration and vertical vibrating velocity input fuzzy variable set stated,
And combine output fuzzy variable, subordinating degree function center, width and weight are trained, using First-order Gradient optimizing algorithm come
Each parameter is adjusted.Fuzzy rule is automatically generated using Neural Network Self-learning ability, specifically:
The input fuzzy variable set formed according to (2) blurring is appropriate for fuzzy subset's selection into subordinating degree function
Subordinating degree function and extremely important and crucial one work.In controller design of the present invention, membership function is used
Gauss member function, expression formula are as follows:
In formula, aiFor each component of input vector, i=1,2 ..., n, j=1,2 ..., mi, n is the dimension of input quantity, mi
For aiFuzzy partition number, cijIndicate the center of subordinating degree function, σijIndicate the width of subordinating degree function；
It is tested by matlab, using error back propagation algorithm respectively to the center c of subordinating degree functionij, width csij, weight
ωijDerivation is calculatedThen subordinating degree function is adjusted using First-order Gradient optimizing algorithm
Center cij, subordinating degree function width csij, weight ωij。
(4) ambiguity solution is carried out using output fuzzy variable of the center of gravity defuzzification method to non-linear neural fuzzy controller
Change processing obtains the damping force output of fuzzy controller, regulates and controls to electromagnetic type damper real-time online；It is fuzzy by what is obtained
The damping force of controller is denoted as threshold value h divided by initial damping force, judges that electromagnetic type damper is according to the size of threshold value h
It is no faulty, it needs to overhaul, achievees the purpose that regulate and control electromagnetic type damper real-time online.
The threshold value h's of different vehicle is of different sizes, and the size of threshold value h is specifically determined according to different vehicle, such as: general vehicle
Threshold value h selection 70%, when threshold value h is less than 70%, then judges that electromagnetic type damper is faulty, need to handle；When
When the damping force of the fuzzy controller arrived is greater than or equal to 70% initial damping force, then judge that electromagnetic type damper is normal,
It can continue to run.
The operation of step 4) specifically:
Fuzzy controller needs to carry out ambiguity solution operation to the fuzzy quantity of output, obtains fuzzy control after fuzzy reasoning
The damping force of device exports, and center of gravity defuzzification method process is as follows:
R1: if x is A1, y is B1, then z is C1；
Also R2: if x is A2, y is B2, then z is C2；
……
Also Rn: if x is An, y is Bn, then z is Cn；
Wherein, x, y and z represent the linguistic variable of system mode and control amount, and x and y are input quantities, and z is control amount；Ai,
Bi, Ci (i=1,2,3 ..., n) respectively represent linguistic variable x, and y, z add in the upper linguistic variable value of its domain X, Y, Z, these rules
Get up then composition rule library.Fuzzy reasoning is carried out according to above-mentioned rule, then the fuzzy value z of output quantity can be obtained are as follows:
In formula: ∧ representative takes small；For the subordinating degree function of output quantity z,For the degree of membership letter of input quantity x
Number,For the subordinating degree function of input quantity y；Controller is by all inference conclusion C of synthesis1',C'2,…,C'nIt calculates most
Whole output fuzzy value, the as damping force of fuzzy controller, i.e.,
In formula: ∨ representative takes big, C1',C'2,…,C'nRespectively represent the subordinating degree function of each output component；Fuzzy set
C is C1',C'2,…,C'nThese vector set, i.e. C1',C'2,…,C'nIt is the matrix of n × 1, then C, is the matrix of n × n.Mould
Pasting set C' is calculated by following formula:
In formula, Z0For the center-of-gravity value of fuzzy set C', μC'(zi) be i-th of control amount subordinating degree function, ziIt is i-th
Control amount.
The present invention is by utilizing the adaptive learning function of neural network and the inferential capability of fuzzy system, according to sensor
The shaking platform vertical vibration acceleration and damping force data measured adjusts the control rule and degree of membership of fuzzy controller in real time
Function, output control signal, realizes the control to electromagnetic type damper after drive amplification.
Above description has shown and described several preferred embodiments of invention, but as previously described, it should be understood that invention is not
It is confined to form disclosed herein, should not be regarded as an exclusion of other examples, and can be used for various other combinations, modification
And environment, and can be carried out within that scope of the inventive concept describe herein by the above teachings or related fields of technology or knowledge
Change.And changes and modifications made by those skilled in the art do not depart from the spirit and scope of invention, then it all should be in the appended power of invention
In the protection scope that benefit requires.
Claims (5)
1. a kind of electromagnetic type damper control method based on non-linear neural fuzzy controller, which is characterized in that including with
Lower step:
(1) damper experiment test platform is established, using data acquisition device to the signal of force snesor and acceleration transducer
It is acquired, wherein the data of acquisition include: initial damping force and shaking platform vertical vibration acceleration, generate non-linear mind
Input signal through fuzzy controller；
(2) using fuzzy control to damping force described in step (1) and shaking platform vertical vibration acceleration input signal into
Row Fuzzy processing forms input fuzzy variable, is then sent to fuzzy controller, obtains output fuzzy variable；
(3) fuzzy rule base of electromagnetic type damper non-linear neural fuzzy controller is established；It utilizes described in (1)
The subordinating degree function of damping force and shaking platform vertical vibration acceleration information input fuzzy variable set, and combine output fuzzy
Variable is trained subordinating degree function center, width and weight, is adjusted using First-order Gradient optimizing algorithm to each parameter
Section；Fuzzy rule is automatically generated using Neural Network Self-learning ability；
(4) it is carried out at defuzzification using output fuzzy variable of the center of gravity defuzzification method to non-linear neural fuzzy controller
Reason obtains the damping force output of fuzzy controller, by the damping force of obtained fuzzy controller divided by initial damping force, is denoted as threshold
Value h judges whether electromagnetic type damper is faulty according to the size of threshold value h, needs to overhaul, reaches to electromagnetic type vibration damping
The purpose of device real-time online regulation.
2. the electromagnetic type damper control method according to claim 1 based on non-linear neural fuzzy controller,
It is characterized in that, establishes damper experiment test platform in the step (1), to force snesor and added using data acquisition device
The signal of velocity sensor is acquired specifically:
Data collection system is built using LABVIEW software, by NI capture card by the letter of force snesor and acceleration transducer
It number collects in LABVIE, saves as txt file, the input signal as non-linear neural fuzzy controller；Wherein, acquisition
Data include: initial damping force and shaking platform vertical vibration acceleration.
3. the electromagnetic type damper control method according to claim 1 based on non-linear neural fuzzy controller,
It is characterized in that, Fuzzy processing is carried out to input signal described in step (1) using fuzzy control in the step (2),
Input fuzzy variable is formed, fuzzy controller is then sent to, obtains output fuzzy variable specifically: according to force snesor
The damping force and shaking platform vertical vibration acceleration information collected with acceleration transducer, determines domain；By damping force
It is blurred with shaking platform vertical vibration acceleration information, determines the grade of blurring, that is, the domain being blurred；Become by domain
Damping force of changing commanders and shaking platform vertical vibration acceleration information transform in the domain of blurring；Definitional language variable, specifically
For { NB NS ZO PS PB }, wherein the value of NB NS ZO PS PB is determined according to the subordinating degree function of definition, selects degree of membership
Big fuzzy set is as output.
4. the electromagnetic type damper control method according to claim 1 based on non-linear neural fuzzy controller,
It is characterized in that, the fuzzy control rule for establishing electromagnetic type damper non-linear neural fuzzy controller in the step (3)
Library；The subordinating degree function of the input fuzzy variable set of data described in (1) is utilized, and combines output fuzzy variable, to being subordinate to
Degree function center, width and weight are trained, and each parameter is adjusted using First-order Gradient optimizing algorithm；Utilize mind
Fuzzy rule is automatically generated through network self-learning capability specifically:
The input fuzzy variable set formed according to (2) blurring selects person in servitude appropriate into subordinating degree function, for fuzzy subset
Category degree function, membership function use Gauss member function, expression formula are as follows:
In formula, aiFor each component of input vector, i=1,2 ..., n, j=1,2 ..., mi, n is the dimension of input quantity, miFor ai
Fuzzy partition number, cijIndicate the center of subordinating degree function, σijIndicate the width of subordinating degree function；
It is tested by matlab, using error back propagation algorithm respectively to the weight ω of subordinating degree functionij, center cij, width csijIt asks
It leads, is calculatedThen the center of subordinating degree function is adjusted using First-order Gradient optimizing algorithm
cij, subordinating degree function width csij, weight ωij。
5. the electromagnetic type damper control method according to claim 1 based on non-linear neural fuzzy controller,
It is characterized in that, it is fuzzy using output of the center of gravity defuzzification method to non-linear neural fuzzy controller in the step (4)
Variable carries out defuzzification processing, the damping force output of fuzzy controller is obtained, to electromagnetic type damper real-time online tune
Control specifically:
R1: if x is A1, y is B1, then z is C1；
Also R2: if x is A2, y is B2, then z is C2；
……
Also Rn: if x is An, y is Bn, then z is Cn；
Wherein, R1, R2 ... Rn respectively represent rule 1, rule 2 ... regular n, x, y and z represent system mode and control amount
Linguistic variable, x and y are input quantities, and z is control amount；Ai, Bi, Ci, i=1,2,3 ..., n respectively represent linguistic variable x, and y, z exist
The upper linguistic variable value of its domain X, Y, Z, these rules add up then composition rule library；Fuzzy reasoning is carried out according to above-mentioned rule,
Then obtain the fuzzy value z of output quantity are as follows:
In formula: ∧ representative takes small；For the subordinating degree function of output quantity z,For the subordinating degree function of input quantity x,For the subordinating degree function of input quantity y；Controller is by all inference conclusion C ' of synthesis1,C'2,…,C'nIt calculates final
Output fuzzy value, the as damping force of fuzzy controller, i.e.,
In formula: ∨ representative takes big, C '1,C'2,…,C'nRespectively represent the subordinating degree function of each output component；Fuzzy set C' is
It is calculated by following formula:
In formula, Z0For the center-of-gravity value of fuzzy set C', μC'(zi) be i-th of control amount subordinating degree function, ziIt is controlled for i-th
Amount.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611071224.7A CN106527124B (en) | 2016-11-29 | 2016-11-29 | Electromagnetic type damper control method based on non-linear neural fuzzy controller |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611071224.7A CN106527124B (en) | 2016-11-29 | 2016-11-29 | Electromagnetic type damper control method based on non-linear neural fuzzy controller |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106527124A CN106527124A (en) | 2017-03-22 |
| CN106527124B true CN106527124B (en) | 2019-08-16 |
Family
ID=58353707
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611071224.7A Expired - Fee Related CN106527124B (en) | 2016-11-29 | 2016-11-29 | Electromagnetic type damper control method based on non-linear neural fuzzy controller |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106527124B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110281272A (en) * | 2019-05-22 | 2019-09-27 | 中国南方电网有限责任公司超高压输电公司昆明局 | A kind of shock mitigation system for mechanical arm tail end detection sensor |
| CN111474965B (en) * | 2020-04-02 | 2021-10-26 | 中国水利水电科学研究院 | Fuzzy neural network-based method for predicting and controlling water level of series water delivery channel |
| CN111812968B (en) * | 2020-06-24 | 2022-04-22 | 合肥工业大学 | Fuzzy neural network PID controller-based valve position cascade control method |
| CN113238487B (en) * | 2021-06-03 | 2022-08-09 | 中国第一汽车股份有限公司 | Fuzzy control method and control system for adjustable damping shock absorber |
| CN113783479B (en) * | 2021-11-11 | 2022-02-08 | 常州市佳博机械制造有限公司 | Brushless direct current motor fuzzy PID control method based on neural network matrix |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001001736A (en) * | 1999-04-20 | 2001-01-09 | Toyota Motor Corp | Damping coefficient control system |
| CN101417596B (en) * | 2008-11-18 | 2011-01-19 | 江苏大学 | Control method of automobile air suspension |
| CN102189909A (en) * | 2010-03-11 | 2011-09-21 | 蒋丰璘 | Filtering control strategy for skyhook damping frequencies of semi-active suspension of vehicle |
| CN102616245A (en) * | 2012-03-23 | 2012-08-01 | 株洲南车时代电气股份有限公司 | Half-automatic shock absorber control device of train bogie, and system as well as control method thereof |
| CN105676694A (en) * | 2016-01-16 | 2016-06-15 | 渤海大学 | Intelligent sampling data output feedback control method for automobile suspension system |
-
2016
- 2016-11-29 CN CN201611071224.7A patent/CN106527124B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001001736A (en) * | 1999-04-20 | 2001-01-09 | Toyota Motor Corp | Damping coefficient control system |
| CN101417596B (en) * | 2008-11-18 | 2011-01-19 | 江苏大学 | Control method of automobile air suspension |
| CN102189909A (en) * | 2010-03-11 | 2011-09-21 | 蒋丰璘 | Filtering control strategy for skyhook damping frequencies of semi-active suspension of vehicle |
| CN102616245A (en) * | 2012-03-23 | 2012-08-01 | 株洲南车时代电气股份有限公司 | Half-automatic shock absorber control device of train bogie, and system as well as control method thereof |
| CN105676694A (en) * | 2016-01-16 | 2016-06-15 | 渤海大学 | Intelligent sampling data output feedback control method for automobile suspension system |
Non-Patent Citations (2)
| Title |
|---|
| 基于智能控制的汽车悬架减振性能研究;刘波;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20161015(第10期);正文第23-55页 |
| 基于遗传算法的油气悬架非线性特征参数的无损评测;刘杰 等;《中国机械工程》;20070630;第18卷(第10期);第1161-1164页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106527124A (en) | 2017-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106527124B (en) | Electromagnetic type damper control method based on non-linear neural fuzzy controller | |
| Canale et al. | Semi-active suspension control using “fast” model-predictive techniques | |
| US20040153227A1 (en) | Fuzzy controller with a reduced number of sensors | |
| Soleymani et al. | Adaptive fuzzy controller for vehicle active suspension system based on traffic conditions | |
| CN108733955B (en) | Intelligent electric automobile longitudinal motion control system and method | |
| Ren et al. | State observer-based sliding mode control for semi-active hydro-pneumatic suspension | |
| CN110286586A (en) | A kind of MR damper hybrid modeling method | |
| Zin et al. | Robust LPV–∞ control for active suspensions with performance adaptation in view of global chassis control | |
| CN101916113A (en) | Automotive body gesture decoupling control method based on active suspension evaluation indicator | |
| Samadi et al. | Active suspension system control using adaptive neuro fuzzy (anfis) controller | |
| CN108345218A (en) | Vehicle active suspension PID controller design method based on teaching optimization algorithm | |
| Chen et al. | Modelling and analysis of automobile vibration system based on fuzzy theory under different road excitation information | |
| Zareh et al. | Semi-active vibration control of an eleven degrees of freedom suspension system using neuro inverse model of magnetorheological dampers | |
| Talib et al. | Development of fuzzy logic controller by particle swarm optimization algorithm for semi-active suspension system using magneto-rheological damper | |
| CN103116273A (en) | Engineering transport vehicle comprehensive driving performance control system and method | |
| Zhou | Research and simulation on new active suspension control system | |
| CN113022247B (en) | Adaptive fixed time event trigger fuzzy control method for active vehicle suspension system | |
| CN108608822B (en) | AGV suspension system elasticity adjusting method and system | |
| Kalaivani et al. | Vibration control of vehicle active suspension system using novel fuzzy logic controller | |
| Liu et al. | Experimental modeling of magneto-rheological damper and PID neural network controller design | |
| Chiang et al. | Optimized sensorless antivibration control for semiactive suspensions with cosimulation analysis | |
| Van Tan | Two‐Layer Parallel Fuzzy Logic Controller Design for Semiactive Suspension System with a Full Car Model | |
| Yuvapriya et al. | Vibration control and ride comfort analysis of a Full car with a driver model using big-bang big-crunch optimized FLC | |
| yin Tang et al. | Research on suspension system based on genetic algorithm and neural network control | |
| Khan et al. | Neuro-fuzzy wavelets based network for full car active suspension system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190816 |