CN113721470B - Air guide sleeve elevation angle control system based on combined double-stage fuzzy controller - Google Patents
Air guide sleeve elevation angle control system based on combined double-stage fuzzy controller Download PDFInfo
- Publication number
- CN113721470B CN113721470B CN202111057628.1A CN202111057628A CN113721470B CN 113721470 B CN113721470 B CN 113721470B CN 202111057628 A CN202111057628 A CN 202111057628A CN 113721470 B CN113721470 B CN 113721470B
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- elevation angle
- variable
- control
- alpha
- 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.)
- Active
Links
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
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- 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
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
- Y02T10/82—Elements for improving aerodynamics
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
A dome elevation angle control system based on a combined two-stage fuzzy controller comprises a sensing detection device, a control switch and a fuzzy controllerA control module FC1 and a fuzzy control module FC2. The sensing detection device comprises a vehicle speed sensor, a gradient sensor and a temperature sensor. When the control switch judges that the road is downhill according to the gradient signal and the gradient is more than or equal to 3%, the control switch is switched to the fuzzy control module FC2 to work, and the control quantity is the elevation angle control quantity alpha 2 The method comprises the steps of carrying out a first treatment on the surface of the When the control switch judges other working conditions according to the gradient signal, the control switch is switched to the fuzzy control module FC1 to work, and the control quantity is the elevation angle control quantity alpha 1 . The system can carry out fuzzy control on the elevation angle of the guide cover of the special automobile which runs at a high speed on a gentle road section based on the change of the vehicle speed, so as to achieve the purposes of reducing air resistance and oil consumption, and meanwhile, on a large-gradient long downhill road section, the elevation angle of the guide cover can be subjected to fuzzy control adjustment by the temperature change of the wheel brake, thereby realizing pneumatic resistance increase and reducing the downhill energy load of an auxiliary braking device or a service brake.
Description
Technical Field
The invention relates to the field of intelligent control, in particular to a dome elevation angle control system based on a combined double-stage fuzzy controller.
Background
The automobile air guide sleeve can improve the aerodynamic performance of an automobile, improve the production efficiency of the automobile, reduce the fuel consumption and improve the service performance. For special vehicles such as a train and a van, which travel on a highway, a large windward area, a high traveling speed, and poor flow linearity as a whole are generally provided with a guide cover.
The pod is a monocoque cover that is mounted to the roof of the tractor cab and is typically stamped from sheet steel. The guide cover has two types of fixed and adjustable guide cover elevation angles alpha as shown in figure 1. After the fixed air guide sleeve is installed on the cab ceiling, the elevation angle of the fixed air guide sleeve is invariable. The adjustable guide cover is provided with an elevation angle adjusting rod piece, and can be adapted to different cargo heights or carriage heights by adjusting the elevation angle of the guide cover so as to obtain smaller air resistance, and the adjustment of the elevation angle is generally carried out during parking.
The air guide sleeve has a great influence on the outflow field airflow of the special automobile. If the air guide cover is not arranged, the air flow can not be smoothly conveyed from the surface and the top of the cab to the top surface of the cargo box during the running of the automobile, and direct impact is formed in the area, higher than the windward side of the cab, of the front end surface of the cargo box, so that the positive pressure is high, and the air resistance is increased. Similar effects can occur when the pod is not properly elevated. Meanwhile, according to CFD numerical simulation, when no flow guide cover exists, airflow separation can occur at the upper edge of the windward side of the cab and the upper edge of the front end face of the container, vortex structure flow state occurs, partial surface pressure is reduced, and automobile air resistance is increased.
By comprehensively analyzing the existing literature and combining the driving working conditions, two points of the current application of the dome are required to be further perfected: firstly, under various higher running speeds, the elevation angle of the air guide sleeve can be correspondingly adjusted so as to be beneficial to reducing the air flow separation at the windward upper edge of the cab and the container, reducing the influence of vortex on local surface pressure and reducing air resistance. However, the existing air guide sleeve can not realize dynamic control and adjustment of the elevation angle of the air guide sleeve according to the change of the speed of the vehicle during running. Furthermore, for the special automobile which often runs on the expressway in the mountain area, if the air-operated resistance increase can be carried out on a long downhill road section by utilizing the elevation angle change of the diversion cover, the downhill energy load of other auxiliary braking devices or service brakes can be lightened to a certain extent, so that the speed-controlled running and the running safety are facilitated.
The existing fairings generally have certain optimized geometric shapes (including the angle of elevation of the fairings) to meet the requirements of lower air resistance coefficient Cd and lower air resistance of the automobile in the vicinity of typical higher running speeds, so that the automobile has better performance. Such optimized geometry (including pod elevation angle) can be obtained without the guidance of fluid physics theory and the study of pod aerodynamic drag characteristics, and with the help of test or CFD calculation means. For the air guide sleeve elevation angle, only the optimized fixed air guide sleeve elevation angle can not be well adapted to the requirement that the Cd value is still relatively low in a larger vehicle speed range, and the pneumatic index requirement and the whole vehicle drag reduction requirement can not be met correspondingly, so that the air guide sleeve elevation angle is required to be adaptively adjusted to realize reasonable control of the airflow of the external flow field.
When the automobile runs on a long downhill road section with a larger gradient, the auxiliary braking device is used for stabilizing the downhill speed, and meanwhile, the heat fading of a service brake caused by excessive use of the service brake for downhill speed control braking is avoided, so that the running safety is ensured. If the air guide sleeve with adjustable elevation angle is used for pneumatic resistance increase in the long downhill section of the expressway in the mountain area, the downhill energy load of the auxiliary braking device or the service brake for speed control braking can be reduced to a certain extent.
In order to realize reasonable control and utilization of air flow under different running conditions so as to realize pneumatic drag reduction or pneumatic drag increase of the whole vehicle, a control system based on timely adjustment of the elevation angle of the air guide sleeve is necessary to be constructed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a guide cover elevation angle control system based on a combined double-stage fuzzy controller.
In order to achieve the technical purpose, the adopted technical scheme is as follows: a dome elevation control system based on a combined double-stage fuzzy controller comprises a sensing detection device, a control switch, a fuzzy control module FC1 and a fuzzy control module FC2, wherein the control switch is respectively connected with the fuzzy control module FC1 and the fuzzy control module FC2, the sensing detection device consists of a vehicle speed sensor, a gradient sensor and a temperature sensor, the vehicle speed sensor, the gradient sensor and the temperature sensor respectively measure vehicle speed information, gradient information and rear wheel brake temperature information, the vehicle speed sensor inputs the vehicle speed information into the fuzzy control module FC1, the gradient sensor inputs the gradient information into the control switch, the temperature sensor inputs the rear wheel brake temperature information into the fuzzy control module FC2, when the control switch judges that a driving road is downhill according to the gradient information and the gradient is more than or equal to 3%, the control switch is switched to the fuzzy control module FC2 to work, the fuzzy control module FC2 compares the rear wheel brake temperature information with a given temperature according to the fuzzy control module FC2The elevation angle fuzzy control rule table obtains the corresponding flow guide sleeve elevation angle control quantity alpha 2 By the guide cover driving device, the control quantity alpha is controlled according to the elevation angle of the guide cover 2 When the control switch judges other working conditions except that the running road is downhill and the gradient is more than or equal to 3 percent according to gradient information, the control switch is switched to the fuzzy control module FC1 to work, the fuzzy control module FC1 compares the vehicle speed information with a given vehicle speed, and the corresponding guide cover elevation angle control quantity alpha is obtained according to an elevation angle fuzzy control rule table of the fuzzy control module FC1 1 By the guide cover driving device, the control quantity alpha is controlled according to the elevation angle of the guide cover 1 Elevation control is performed.
The FUZZY control module FC1 comprises a FUZZY controller FUZZZY 1 and a differentiator I, wherein the vehicle speed sensor transmits a given vehicle speed and a current vehicle speed to the FUZZY reasoning module FC1, the speed deviation obtained after the comparison of the given vehicle speed and the current vehicle speed is used as input to the FUZZY controller FUZZZY 1, the speed deviation is also transmitted to the differentiator I, and the change rate of the speed deviation obtained by the differentiator I is also input to the FUZZY controller FUZZY1.
The FUZZY inference module FC2 comprises a FUZZY controller FUZZZY 2 and a differentiator II, the temperature sensor transmits the given temperature and the temperature of the rear wheel brake to the FUZZY inference module FC2, the temperature deviation obtained after the comparison of the given temperature and the temperature of the rear wheel brake is used as input to the FUZZY controller FUZZY2, the temperature deviation is also transmitted to the differentiator II, and the temperature deviation change rate obtained by the differentiator II is also transmitted to the FUZZY controller FUZZY2.
Flow guide cover elevation angle control quantity alpha 1 The specific obtaining process is as follows:
step one, carrying out quantization processing on the speed deviation ve and the speed deviation change rate vc, wherein the basic argument of the speed deviation ve is [ a, b ]]The basic domain of the speed deviation change rate vc is [ c, d]The speed deviation quantization takes 2n+1 grades, and the fuzzy theory field X of the speed deviation fuzzy variable VE 1 Is { -n, -n+1, …,0, …, n-1, n }, the speed deviation change rate quantization takes 2m+1 grades, the speed deviation change rate fuzzy variable VC ambiguity theory domain Y 1 Is { -m, -m+1, …,0, …, m-1, m };
step two, calculating the input accurate value x of the speed deviation ve And the speed deviation change rate accurate value x vc Is a fuzzy quantization result y of (2) ve And y vc
y ve ＝2n[x ve -(a+b)/2]/(b-a)
y vc ＝2m[x vc -(c+d)/2]/(d-c)
If the calculation result is not an integer, the calculation result needs to be respectively classified into the nearest integer in the corresponding fuzzy theory domain;
third, the elevation angle control quantity alpha of the air guide sleeve 1 The basic domain of discussion is [ p, q ]]Its fuzzy variable alpha F1 Is the fuzzy theory domain Z of (1) 1 Is { -l, -l+1, … …,0, … …, l-1, l }, the scale factor is K α1 Representation, K α1 The size is as follows
K α1 ＝(q-p)/2l
Step four, according to the fuzzy quantization result y ve And y vc Domain X of the ambiguous theory 1 And fuzzy theory field Y 1 The fuzzy theory domain Z is obtained by inquiring elevation angle fuzzy control rule table based on VE and VC at the input level 1 Multiplying the output level by K α1 And (p+q)/2 is converted into the guide cover elevation angle control quantity alpha 1 。
Flow guide cover elevation angle control quantity alpha 2 The specific obtaining process is as follows:
step one, quantifying the temperature deviation te and the temperature deviation change rate tc, wherein the basic argument of the temperature deviation te is [ e, f ]]The basic theory of the temperature deviation change rate vc is [ g, h ]]The temperature deviation is quantified to obtain 2v+1 grades, and the fuzzy theory field X of the temperature deviation fuzzy variable TE 2 Is { -v, -v+1, …,0, … …, v-1, v }, the temperature deviation change rate is quantized to 2w+1 grades, and the temperature deviation change rate fuzzy variable TC is a fuzzy theory domain Y 2 Is { -w, -w+1, …,0, … …, w-1, w };
step two, calculating the input accurate value x of the temperature deviation te And the accurate value x of the temperature deviation change rate tc Is a fuzzy quantization result y of (2) te And y tc
y te ＝2v[x te -(e+f)/2]/(f-e)
y tc ＝2w[x tc -(g+h)/2]/(h-g)
If the calculation result is not an integer, the calculation result needs to be respectively classified into the nearest integer in the corresponding fuzzy theory domain;
third, the elevation angle control quantity alpha of the air guide sleeve 2 The basic domain of discussion is [ s, t ]]Its fuzzy variable alpha F2 Is the fuzzy theory domain Z of (1) 2 Is { -r, -r+1, … …,0, … …, r-1, r }, the scale factor is K α2 Representation, K α2 The size is as follows
K α2 ＝(t-s)/2r
Step four, according to the fuzzy quantization result y te And y tc Domain X of the ambiguous theory 2 And fuzzy theory field Y 2 By consulting elevation angle fuzzy control rule table based on TE and TC to obtain fuzzy theory domain Z 2 Multiplying the output level by K α2 And (s+t)/2 is converted into the guide cover elevation angle control quantity alpha 2 。
The method for establishing the elevation angle fuzzy control rule table based on VE and VC comprises the following steps:
step one, defining a speed deviation fuzzy variable VE, a speed deviation change rate fuzzy variable VC and a guide cover elevation angle fuzzy variable alpha F1 The word sets of (1) are { NB, NM, NS, O, PS, PM, PB }, 7 FUZZY sets on respective domains are correspondingly obtained, and a FUZZY control rule of the FUZZY controller FUZZZY 1 is constructed according to influence analysis of CFD simulation dome elevation angle change on an automobile external flow field and aerodynamic resistance. Velocity deviation fuzzy variable VE, velocity deviation change rate fuzzy variable VC and dome elevation fuzzy variable alpha F1 Selecting a triangular membership function or a Gaussian membership function;
step two, utilizing the determined speed deviation fuzzy variable VE, speed deviation change rate fuzzy variable VC and guide cover elevation angle fuzzy variable alpha F1 And FUZZY control rules of the FUZZY controller FUZZZY 1, determining a FUZZY matrix R, namely
Wherein VE i 、VC j 、(α F1 ) ij Is respectively defined in a speed deviation fuzzy variable VE, a speed deviation change rate fuzzy variable VC and a guide cover elevation angle fuzzy variable alpha F1 Fuzzy sets on the respective domains, i=1, 2, …,7; j=1, 2, … 7;
the membership degree of the fuzzy matrix R is
Wherein x is 1 ∈X 1 ，y 1 ∈Y 1 ，z 1 ∈Z 1 ，Respectively fuzzy sets VE i 、VC j 、(α F1 ) ij Definition in the field X 1 、Y 1 And Z 1 Membership in the list;
step three, for a certain quantization grade of the speed deviation fuzzy variable VE and the speed deviation change rate fuzzy variable VC on respective domains, a fuzzy variable assignment table is utilized, and according to the maximum membership of the grade, a certain fuzzy vector A, B on the respective domains can be respectively and correspondingly determined, if the input is A, B, the air guide cover elevation angle fuzzy variable alpha is obtained F1 The output of (2) is U, which can be obtained by fuzzy reasoning, namely
The membership degree of U is
Wherein mu A (x 1 )、μ B (y 1 )、μ R (x 1 ,y 1 ,z 1 ) Respectively a fuzzy vector A, a fuzzy vector B and a fuzzy matrixMembership of R;
and step four, calculating different quantized grades of the speed deviation FUZZY variable VE and the speed deviation change rate FUZZY variable VC in respective domains according to a FUZZY variable assignment table and a FUZZY control rule of the FUZZY controller FUZZY1 to obtain corresponding output U, and taking the grade corresponding to the largest element of membership degree in the U as an output grade. And determining an elevation angle fuzzy control rule table based on VE and VC based on the mapping relation between different input grades of the speed deviation fuzzy variable VE and the speed deviation change rate fuzzy variable VC and the output grade of U.
The elevation angle fuzzy control rule table establishment method based on TE and TC comprises the following steps:
step one, defining a temperature deviation fuzzy variable TE, a temperature deviation change rate fuzzy variable TC and a flow guide cover elevation fuzzy variable alpha F2 The word sets of the flow guide cover are { NB, NM, NS, O, PS, PM and PB }, 7 FUZZY sets on respective domains are correspondingly obtained, and a FUZZY control rule of the FUZZY controller FUZZZY 2 is constructed according to a rule that the elevation angle of the CFD simulation flow guide cover is increased along with the temperature rise of the rear wheel brake and the pneumatic resistance is correspondingly changed. Temperature deviation fuzzy variable TE, temperature deviation change rate fuzzy variable TC and flow guide cover elevation fuzzy variable alpha F2 Selecting a triangular membership function or a Gaussian membership function;
step two, utilizing the determined temperature deviation fuzzy variable TE, temperature deviation change rate fuzzy variable TC and flow guide cover elevation fuzzy variable alpha F2 The membership function of (a) and the FUZZY control rule of the FUZZY controller FUZZZY 2, determining a FUZZY matrix M, namely
Wherein TE is i 、TC j 、(α F2 ) ij Is defined in the fuzzy variable TE of temperature deviation, the fuzzy variable TC of temperature deviation change rate and the fuzzy variable alpha of air guide sleeve elevation angle respectively F2 Fuzzy sets on the respective domains, i=1, 2, …,7; j=1, 2, … 7;
the membership degree of the fuzzy matrix M is
Wherein x is 2 ∈X 2 ，y 2 ∈Y 2 ，z 2 ∈Z 2 ，Respectively fuzzy sets TE i 、TC j 、(α F2 ) ij Definition in the field X 2 、Y 2 And Z 2 Membership in the list;
step three, for a certain quantization grade of the temperature deviation fuzzy variable TE and the temperature deviation change rate fuzzy variable TC on respective domains, a fuzzy variable assignment table is utilized, and according to the maximum membership of the grade, a certain fuzzy vector C, D on the respective domains can be respectively and correspondingly determined, if the input is C, D, the air guide cover elevation fuzzy variable alpha is obtained F2 The output of (2) is E, which can be obtained by fuzzy reasoning, namely
E membership degree is
Wherein mu C (x 2 )、μ D (y 2 )、μ M (x 2 ,y 2 ,z 2 ) Membership degrees of the fuzzy vector C, the fuzzy vector D and the fuzzy matrix M respectively;
and step four, calculating different quantized grades of the temperature deviation FUZZY variable TE and the temperature deviation change rate FUZZY variable TC on respective domains according to a FUZZY variable assignment table and a FUZZY control rule of the FUZZY controller FUZZY2 to obtain corresponding output E, and taking the grade corresponding to the largest element of membership degree in the E as an output grade. And determining an elevation angle fuzzy control rule table based on TE and TC based on the mapping relation between different input grades of the temperature deviation fuzzy variable TE and the temperature deviation change rate fuzzy variable TC and the output grade of E.
For the FUZZY controller FUZZZY 1, an adjustment factor beta is introduced 1 And beta 2 Alpha in elevation angle FUZZY control rule table of FUZZY controller FUZZZY 1 F1 Is modified to make VE, VC and alpha F1 The following analytical expressions are satisfied between the levels of (2)
Wherein 1 is>β 1 >β 2 >0, symbol< >The representation takes the integer closest to the value of the intra-symbol expression and is numbered the same as the value of the expression.
For the FUZZY controller FUZZZY 2, an adjustment factor beta is introduced 3 And beta 4 Alpha in elevation angle FUZZY control rule table of FUZZY controller FUZZZY 2 F2 Is modified to make TE, TC and alpha F2 The following analytical expressions are satisfied between the levels of (2)
Wherein 1 is>β 3 >β 4 >0, symbol< >The representation takes the integer closest to the value of the intra-symbol expression and is numbered the same as the value of the expression.
The invention has the beneficial effects that:
(1) The system can not only carry out fuzzy control adjustment on the elevation angle of the guide cover of the special automobile which runs at a high speed on a gentle road section so as to achieve the purposes of reducing air resistance and oil consumption, but also can carry out fuzzy control adjustment on the elevation angle of the guide cover by using a fuzzy controller when the special automobile runs on a long downhill of a highway in a mountain area, and can reduce the downhill energy load of a service brake or other auxiliary braking devices and improve the driving safety by increasing the air resistance and dissipating part of the downhill energy.
(2) When the speed of the special automobile is between the economic speed and the highest speed limit of the expressway to the automobile model, the speed is higher, and the adjustment factor takes a high value beta 1 The speed deviation is weighted to a large degree, which is beneficial to realizing the rapid adjustment of the elevation angle alpha of the air guide sleeve 1 And the pneumatic drag reduction requirements are met more quickly. When the speed of the special automobile is between the lowest speed limit of the expressway and the economical speed, the adjusting factor takes a low value beta 2 At this time, the weighting degree of the speed deviation change rate is increased, which is beneficial to improving the elevation angle alpha of the guide cover 1 Stability of the adjustment.
(3) When the temperature deviation of the rear wheel brake of the special automobile is large, the adjusting factor takes a high value beta 3 The temperature deviation is weighted to a large degree, which is favorable for realizing rapid adjustment of the elevation angle alpha of the air guide sleeve 2 And the pneumatic resistance increasing device is more quickly suitable for the pneumatic resistance increasing requirement. When the temperature deviation of the rear wheel brake of the special automobile is not large, the adjusting factor takes a low value beta 4 At this time, the weighting degree of the speed deviation change rate is relatively increased, which is beneficial to improving the elevation angle alpha of the guide cover 2 Stability of the adjustment.
Drawings
FIG. 1 is an elevation schematic view of a pod;
FIG. 2 is a schematic block diagram of a control system of the present invention;
fig. 3 is a block diagram showing the constitution of the fuzzy inference module FC1 of the present invention;
fig. 4 is a block diagram showing the constitution of the fuzzy inference module FC2 of the present invention.
Detailed Description
A dome elevation control system based on a combined double-stage fuzzy controller comprises a sensing detection device, a control switch, a fuzzy control module FC1 and a fuzzy control module FC2, wherein the control switch is respectively connected with the fuzzy control module FC1 and the fuzzy control module FC2, the sensing detection device consists of a vehicle speed sensor, a gradient sensor and a temperature sensor, the purpose of reducing air resistance and oil consumption can be achieved by performing fuzzy control adjustment on the dome elevation of a special vehicle running at a high speed on a gentle road section, meanwhile, when the special vehicle runs on a long downhill of a highway in a mountain area, the fuzzy control adjustment can be performed on the dome elevation by using the fuzzy controller, and the heat load of a service brake and other auxiliary braking devices is lightened by increasing the air resistance and dissipating part of downhill energy, so that the running safety is improved.
1. Establishment of combined two-stage fuzzy control system
The complexity of the driving working condition and the characteristic of fuzzy control are considered, meanwhile, the complex structure of the controller and serious adverse effects on the control effect caused by exponential increase of the number of fuzzy rules along with the increase of input parameters due to the use of a single fuzzy controller are avoided, and a combined double-stage fuzzy control system aiming at the elevation angle of the air guide sleeve is constructed, and a system block diagram is shown in figure 2.
In fig. 2, the sensing device detects and processes the vehicle speed information, the gradient information and the rear wheel brake temperature information by using a vehicle speed sensor, a gradient sensor and a temperature sensor, wherein the vehicle speed sensor inputs the vehicle speed information into the fuzzy control module FC1, the gradient sensor inputs the gradient information into the control switch, and the temperature sensor inputs the rear wheel brake temperature information into the fuzzy control module FC2.
The control switch judges whether the current road on which the automobile is running is ascending or descending and the gradient according to the input road gradient information, and further determines whether the fuzzy inference module FC1 or the fuzzy inference module FC2 plays a control role. When the running road is judged to be downhill and the gradient is more than or equal to 3%, the control switch is switched to enable the fuzzy inference module FC2 to start working, and the fuzzy inference module FC1 does not control output. When the road is judged to be other various upward and downward slopes (including level roads), the control switch is switched to enable the fuzzy inference module FC1 to operate and output control quantity, and the fuzzy inference module FC2 stops working.
The input of the fuzzy inference module FC1 is a given vehicle speed and a given vehicle speed, and the output is a guide cover elevation angle control quantity alpha 1 . The fuzzy inference module FC2 inputs the given temperature and the rear wheel brake temperature and outputs the control quantity alpha of the air guide sleeve elevation angle 2 . The output control quantity of the elevation angle of the guide cover controls the elevation angle of the guide cover to be reasonably adjusted according to different working conditions through the driving device so as to be suitable for the change of airflow in an external flow field, and the pneumatic resistance increase or pneumatic resistance reduction of the special vehicle is realizedEnsuring the expected service performance of the automobile.
The block diagram of the structure of the FUZZY inference module FC1, as shown in fig. 3, includes a FUZZY controller FUZZY1 and a differentiator one. The FUZZY controller FUZZY1 is a two-dimensional FUZZY rule self-adjusting FUZZY controller. The vehicle speed sensor transmits the given vehicle speed and the vehicle speed to the FUZZY inference module FC1, the speed deviation obtained after the comparison of the given vehicle speed and the vehicle speed is used as input to the FUZZY controller FUZZY1, in addition, the speed deviation is also transmitted to the differentiator I, and the obtained speed deviation change rate is also input to the FUZZY controller FUZZY1.
The FUZZY controller FUZZZY 1 performs FUZZY, FUZZY reasoning and defuzzification on the input to obtain the control quantity alpha of the guide cover elevation angle 1 Is transmitted to the guide cover, and then the elevation angle change of the guide cover is regulated by the guide cover elevation angle driving device.
The block diagram of the structure of the FUZZY inference module FC2, as shown in fig. 4, includes a FUZZY controller FUZZY2 and a differentiator two. The FUZZY controller FUZZY2 is a two-dimensional FUZZY rule self-adjusting FUZZY controller. The sensing detection device transmits the given temperature and the rear wheel brake temperature to the FUZZY inference module FC2, the temperature deviation obtained after the comparison of the given temperature and the rear wheel brake temperature is used as input to the FUZZY controller FUZZZY 2, the temperature deviation is also transmitted to the differentiator II, and the obtained temperature deviation change rate is also transmitted to the FUZZY controller FUZZY2. Similarly, the FUZZY controller FUZZZY 2 performs fuzzification, FUZZY reasoning and defuzzification on the input to obtain the guide cover elevation angle control quantity alpha 2 Is transmitted to the guide cover, and then the elevation angle change of the guide cover is regulated by the guide cover elevation angle driving device.
2. Fuzzy controller design
1) FUZZY controller FUZZY1 design
Firstly, the basic domain of input and output of the FUZZY controller FUZZY1 needs to be quantized. The fundamental domain of the speed deviation ve is [ a, b ], and the fundamental domain of the speed deviation change rate vc is [ c, d ].
The speed deviation quantization takes 2n+1 grades, and the fuzzy theory field X of the speed deviation fuzzy variable VE 1 Is { -n, -n+1, …,0, …, n-1, n }. If it isn＝6，X 1 Then { -6, -5, …,0, …,5,6}.
Likewise, 2m+1 levels may be used for rate of change quantization of speed deviation. Velocity bias rate of change fuzzy variable VC fuzzy theory domain Y 1 Is { -m, -m+1, …,0, …, m-1, m }. If m=6, vc ambiguity theory field Y 1 Then { -6, -5, …,0, …,5,6}.
K for quantization factor of speed deviation ve K for the rate of change of the speed deviation quantization factor vc And (3) representing. K (K) ve The size is as follows
K ve ＝2n/(b-a)＝12/(b-a)
K vc The size is as follows
K vc ＝2m/(d-c)＝12/(d-c)
Input deviation accurate value x ve And the exact value x of the rate of change of the deviation vc Is a fuzzy quantization result y of (2) ve And y vc The following two formulas may be used to calculate respectively:
y ve ＝2n[x ve -(a+b)/2]/(b-a)＝12[x ve -(a+b)/2]/(b-a)
y vc ＝2m[x vc -(c+d)/2]/(d-c)＝12[x vc -(c+d)/2]/(d-c)
if the calculation result is not an integer, the calculation result needs to be respectively classified into the nearest integer in the corresponding fuzzy theory domain.
Flow guide cover elevation angle control quantity alpha 1 The basic domain of discussion is [ p, q ]]Its fuzzy variable alpha F1 Is the fuzzy theory domain Z of (1) 1 Is { -l, -l+1, … …,0, … …, l-1, l }, if l= 7,Z 1 Then { -7, -6, …,0, …,6,7}. K for scaling factor α1 Representation, K α1 The size is as follows
K α1 ＝(q-p)/2l＝(q-p)/14
The fuzzy controller queries the reasoning result obtained by the elevation angle fuzzy control rule table based on VE and VC, and needs to multiply K α1 And (p+q)/2 is converted into the guide cover elevation angle control quantity alpha 1 To control the controlled object.
If the fuzzy variable speed deviation VE, speed deviation change rate VC and air guide sleeve elevation angle alpha are used F1 The word sets of (a) are { NB, NMNS, O, PS, PM, PB, respectively, resulting in 7 fuzzy sets over the respective domains. The FUZZY control rules of the FUZZY controller FUZZZY 1 are constructed according to the analysis of the influence of the elevation change of the air guide sleeve on the external flow field and aerodynamic resistance of the automobile, which is recognized by CFD simulation, as shown in the table 1. Velocity deviation fuzzy variable VE, velocity deviation change rate fuzzy variable VC and dome elevation fuzzy variable alpha F1 The membership functions of (a) may be selected from triangular membership functions or gaussian membership functions.
Table 1 FUZZY control rules of FUZZY controller FUZZZY 1
Using the determined speed deviation fuzzy variable VE, speed deviation change rate fuzzy variable VC and guide cover elevation angle fuzzy variable alpha F1 The membership functions and fuzzy control rules of (a) can further determine a fuzzy matrix R, namely
Wherein VE i 、VC j 、(α F1 ) ij Is respectively defined in a speed deviation fuzzy variable VE, a speed deviation change rate fuzzy variable VC and a guide cover elevation angle fuzzy variable alpha F1 Fuzzy sets i=1, 2, …,7 on the respective domains; j=1, 2, … 7.
Membership function of R is
Wherein mu is A (x 1 )、μ B (y 1 )、μ R (x 1 ,y 1 ,z 1 ) Membership degrees of the blur vector a, the blur vector B, and the blur matrix R, respectively.
For a certain quantization level of the speed deviation VE and the speed deviation change rate VC in respective domains, a fuzzy variable assignment table is utilized to rootBased on the maximum membership of the classes, a fuzzy set (language variable value) A, B on the respective domains can be determined correspondingly. If the input is A, B, the elevation angle alpha of the air guide sleeve F1 The output U of (2) can be obtained by fuzzy reasoning, namely
The membership function of U is
Wherein mu A (x 1 )、μ B (y 1 )、μ R (x 1 ,y 1 ,z 1 ) Membership degrees of the blur vector a, the blur vector B, and the blur matrix R, respectively.
The defuzzification can be carried out according to the principle of maximum membership, and the grade corresponding to the maximum membership element in the fuzzy vector U is taken as the output control quantity.
Therefore, according to the fuzzy variable assignment table and the fuzzy control rule, for different quantization grades of the speed deviation fuzzy variable VE and the speed deviation change rate fuzzy variable VC in respective domains, the corresponding output U can be obtained by calculation, and the grade corresponding to the element with the largest membership degree in the U is taken as the output grade. In this way, the elevation angle fuzzy control rule table based on VE and VC is determined based on the mapping relation between different input grades of VE and VC and the output grade of U.
In the concrete actual calculation, according to the sampled accurate value x of the vehicle speed deviation ve And the accurate value x of the deviation change rate of the vehicle speed vc The elevation angle alpha of the dome can be inquired and determined by quantization processing and directly utilizing an elevation angle fuzzy control rule table based on VE and VC F1 Domain level z of (c). Domain level z multiplied by a scale factor K α1 And adding (p+q)/2 to obtain the guide hood elevation angle control quantity alpha of the FUZZY controller FUZZZY 1 1 To control the controlled object.
2) FUZZY controller FUZZY2 design
The FUZZY controller FUZZY2 is similar in design to FUZZY1. Fuzzy theory field X of temperature deviation fuzzy variable TE and temperature deviation change rate fuzzy variable TC 1 、Y 1 Are all chosen as { -6, -5, …,0, …,5,6}, and the control amount of the air guide sleeve elevation angle alpha 2 Is a fuzzy variable alpha of (2) F2 Is the fuzzy theory domain Z of (1) 2 Still { -7, -6, …,0, …,6,7}.
The FUZZY controller FUZZY2 is similar to the FUZZY controller FUZZY1 in the aspects of determination of a FUZZY matrix, FUZZY reasoning calculation, defuzzification, determination of an elevation angle FUZZY control rule table and the like.
Firstly, the basic domain of input and output of the FUZZY controller FUZZY2 needs to be quantized. The basic argument of the temperature deviation te is [ e, f ], and the basic argument of the temperature deviation change rate vc is [ g, h ].
The temperature deviation is quantized to 2v+1 grades, and the fuzzy theory domain X of the temperature deviation fuzzy variable TE 2 Is { -v, -v+1, …,0, …, v-1, v }. If v=6, x 2 Then { -6, -5, …,0, …,5,6}.
The temperature deviation change rate is quantified to be 2w+1 grades, and the temperature deviation change rate fuzzy variable TC fuzzy theory field Y 2 Is { -w, -w+1, …,0, …, w-1, w }. If w=6, the temperature deviation change rate fuzzy variable TC fuzzy theory field Y 2 Then { -6, -5, …,0, …,5,6}.
K for quantifying temperature deviation te K for quantifying the rate of change of temperature deviation tc And (3) representing. K (K) te The size is as follows
K te ＝2v/(f-e)＝12/(f-e)
K tc The size is as follows
K tc ＝2w/(h-g)＝12/(h-g)
Input accurate value x of temperature deviation te And the accurate value x of the temperature deviation change rate tc Is a fuzzy quantization result y of (2) te And y tc The following two formulas may be used to calculate respectively:
y te ＝2v[x te -(e+f)/2]/(f-e)＝12[x te -(e+f)/2]/(f-e)
y tc ＝2w[x tc -(g+h)/2]/(h-g)＝12[x tc -(g+h)/2]/(h-g)
if the calculation result is not an integer, the calculation result needs to be respectively classified into the nearest integer in the corresponding fuzzy theory domain.
Flow guide cover elevation angle control quantity alpha 2 The basic domain of discussion is [ s, t ]]Its fuzzy variable alpha F2 Is the fuzzy theory domain Z of (1) 2 Is { -r, -r+1, … …,0, … …, r-1, r }, if r= 7,Z 2 Then { -7, -6, …,0, …,6,7}. K for scaling factor α2 Representation, K α2 The size is as follows
K α2 ＝(t-s)/2r＝(t-s)/14
The fuzzy controller queries the reasoning result obtained by the elevation angle fuzzy control rule table based on TE and TC, and needs to multiply K α2 And (s+t)/2 is converted into the guide cover elevation angle control quantity alpha 2 To control the controlled object.
Fuzzy variable temperature deviation TE, temperature deviation change rate TC and air guide sleeve elevation angle alpha F2 The word sets of (a) are { NB, NM, NS, O, PS, PM, PB }, and 7 fuzzy sets on respective domains are correspondingly obtained. The elevation angle of the air guide sleeve is increased along with the temperature rise of the rear wheel brake, and the aerodynamic resistance is correspondingly increased, so that the driving safety of the expressway long downhill road section in the mountain area is improved. The FUZZY control rules of the FUZZY controller FUZZY2 are constructed as shown in the table 2.
Table 2 FUZZY control rules of FUZZY controller FUZZZY 2
Using the determined temperature deviation fuzzy variable TE, temperature deviation change rate fuzzy variable TC and flow guide cover elevation fuzzy variable alpha F2 The membership function of (a) and the FUZZY control rule of the FUZZY controller FUZZZY 2, determining a FUZZY matrix M, namely
Wherein TE is i 、TC j 、(α F2 ) ij Is defined in the fuzzy variable TE of temperature deviation, the fuzzy variable TC of temperature deviation change rate and the fuzzy variable alpha of air guide sleeve elevation angle respectively F2 Fuzzy sets on the respective domains, i=1, 2, …,7; j=1, 2, … 7.
The membership degree of the fuzzy matrix M is
Wherein x is 2 ∈X 2 ，y 2 ∈Y 2 ，z 2 ∈Z 2 ，Respectively fuzzy sets TE i 、TC j 、(α F2 ) ij Definition in the field X 2 、Y 2 And Z 2 Membership in the table.
For a certain quantization level of the temperature deviation fuzzy variable TE and the temperature deviation change rate fuzzy variable TC on respective domains, a fuzzy variable assignment table is utilized to respectively and correspondingly determine a certain fuzzy vector C, D on the respective domains according to the maximum membership of the level, and if the input is C, D, the air guide cover elevation fuzzy variable alpha is obtained F2 The output of (2) is E, which can be obtained by fuzzy reasoning, namely
E membership degree is
Wherein mu C (x 2 )、μ D (y 2 )、μ M (x 2 ,y 2 ,z 2 ) Membership degrees of the blur vector C, the blur vector D, and the blur matrix M, respectively.
The defuzzification can be carried out according to the principle of maximum membership, and the grade corresponding to the maximum membership element in the fuzzy vector U is taken as the output control quantity.
Therefore, according to the FUZZY variable assignment table and the FUZZY control rule of the FUZZY controller FUZZZY 2, for different quantization grades of the temperature deviation FUZZY variable TE and the temperature deviation change rate FUZZY variable TC on respective domains, calculating to obtain corresponding output E, and taking the grade corresponding to the element with the largest membership degree in the E as the output grade. And determining an elevation angle fuzzy control rule table based on TE and TC based on the mapping relation between different input grades of the temperature deviation fuzzy variable TE and the temperature deviation change rate fuzzy variable TC and the output grade of E.
In the concrete practical calculation, according to the temperature deviation accurate value x obtained by sampling te And the accurate value x of the temperature deviation change rate tc After quantization processing, the elevation angle fuzzy variable alpha of the dome can be inquired and determined by directly utilizing an elevation angle fuzzy control rule table based on TE and TC F2 Domain level z of (c). Domain level z multiplied by a scale factor K α2 And adding (s+t)/2 to obtain the precise control quantity alpha of the elevation angle output of the guide hood of the FUZZY controller FUZZZY 2 2 To control the controlled object.
3. Adjustment factor of fuzzy control rule
The determination of the fuzzy control rules has an important impact on the controller performance. Likewise, the adjustability of the fuzzy control rules also affects controller performance. It is necessary to weight the fuzzy rule according to the magnitude of the deviation using an adjustment factor.
For the FUZZY controller FUZZZY 1, an adjustment factor beta is introduced 1 And beta 2 For alpha in the elevation angle FUZZY control rule table of the FUZZY controller FUZZZY 1 established above F1 Is modified to cause VE, VC and alpha F1 The following analytical expressions are satisfied between the levels of (2)
When n=6, m=6
Here, 1>β 1 >β 2 >0, symbol<>The representation takes the integer closest to the value of the intra-symbol expression and is numbered the same as the value of the expression.
When the speed of the special automobile is between the economic speed and the highest speed limit of the expressway to the automobile model, the speed is higher, and the adjustment factor takes a high value beta 1 The speed deviation is weighted to a large degree, which is beneficial to realizing the rapid adjustment of the elevation angle alpha of the air guide sleeve 1 And the pneumatic drag reduction requirements are met more quickly. When the speed of the special automobile is between the lowest speed limit of the expressway and the economical speed, the adjusting factor takes a low value beta 2 At this time, the weighting degree of the speed deviation change rate is increased, which is beneficial to improving the elevation angle alpha of the guide cover 1 Stability of the adjustment.
For the FUZZY controller FUZZZY 2, an adjustment factor beta is introduced 3 And beta 4 For alpha in the elevation angle FUZZY control rule table of the FUZZY controller FUZZZY 2 established above F2 Is modified to make TE, TC and alpha F2 The following analytical expressions are satisfied between the levels of (2)
When v= 6,w =6
Here, 1>β 3 >β 4 >0, symbol<>The representation takes the integer closest to the value of the intra-symbol expression and is numbered the same as the value of the expression.
When the temperature deviation of the rear wheel brake of the special automobile is large, the adjusting factor takes a high value beta 3 The temperature deviation is weighted to a large degree, which is favorable for realizing rapid adjustment of the elevation angle alpha of the air guide sleeve 2 And the pneumatic resistance increasing device is more quickly suitable for the pneumatic resistance increasing requirement. When specially usedWhen the temperature deviation of the rear wheel brake of the automobile is not large, the adjusting factor takes a low value beta 4 At this time, the weighting degree of the speed deviation change rate is relatively increased, which is beneficial to improving the elevation angle alpha of the guide cover 2 Stability of the adjustment.
Claims (7)
1. A pod elevation angle control system based on a combined two-stage fuzzy controller is characterized in that: the intelligent air guide cover elevation angle control system comprises a sensing detection device, a control switch, a fuzzy control module FC1 and a fuzzy control module FC2, wherein the control switch is respectively connected with the fuzzy control module FC1 and the fuzzy control module FC2, the sensing detection device consists of a vehicle speed sensor, a gradient sensor and a temperature sensor, the vehicle speed sensor, the gradient sensor and the temperature sensor respectively measure vehicle speed information, gradient information and rear wheel brake temperature information, the vehicle speed sensor inputs the vehicle speed information into the fuzzy control module FC1, the gradient sensor inputs the gradient information into the control switch, the temperature sensor inputs the rear wheel brake temperature information into the fuzzy control module FC2, when the control switch judges that a running road is downhill according to the gradient information and the gradient is more than or equal to 3%, the control switch is switched to the fuzzy control module FC2 to work, the fuzzy control module FC2 compares the rear wheel brake temperature information with a given temperature, and obtains a corresponding air guide cover elevation angle control quantity alpha according to an elevation angle fuzzy control rule table of the fuzzy control module FC2 2 By the guide cover driving device, the control quantity alpha is controlled according to the elevation angle of the guide cover 2 When the control switch judges other working conditions except that the running road is downhill and the gradient is more than or equal to 3 percent according to gradient information, the control switch is switched to the fuzzy control module FC1 to work, the fuzzy control module FC1 compares the vehicle speed information with a given vehicle speed, and the corresponding guide cover elevation angle control quantity alpha is obtained according to an elevation angle fuzzy control rule table of the fuzzy control module FC1 1 By the guide cover driving device, the control quantity alpha is controlled according to the elevation angle of the guide cover 1 Elevation control is performed;
flow guide cover elevation angle control quantity alpha 1 The specific obtaining process is as follows:
step one, carrying out quantization processing on the speed deviation ve and the speed deviation change rate vc, wherein the basic argument of the speed deviation ve is [ a, b ]]Variation of speed deviationThe rate vc base domain of arguments is [ c, d]The speed deviation quantization takes 2n+1 grades, and the fuzzy theory field X of the speed deviation fuzzy variable VE 1 Is { -n, -n+1, …,0, …, n-1, n }, the speed deviation change rate quantization takes 2m+1 grades, the speed deviation change rate fuzzy variable VC ambiguity theory domain Y 1 Is { -m, -m+1, …,0, …, m-1, m };
step two, calculating the input accurate value x of the speed deviation ve And the speed deviation change rate accurate value x vc Is a fuzzy quantization result y of (2) ve And y vc
y ve ＝2n[x ve -(a+b)/2]/(b-a)
y vc ＝2m[x vc -(c+d)/2]/(d-c)
If the calculation result is not an integer, the calculation result needs to be respectively classified into the nearest integer in the corresponding fuzzy theory domain;
third, the elevation angle control quantity alpha of the air guide sleeve 1 The basic domain of discussion is [ p, q ]]Its fuzzy variable alpha F1 Is the fuzzy theory domain Z of (1) 1 Is { -l, -l+1, … …,0, … …, l-1, l }, the scale factor is K α1 Representation, K α1 The size is as follows
K α1 ＝(q-p)/2l
Step four, according to the fuzzy quantization result y ve And y vc Domain X of the ambiguous theory 1 And fuzzy theory field Y 1 The fuzzy theory domain Z is obtained by inquiring elevation angle fuzzy control rule table based on VE and VC at the input level 1 Multiplying the output level by K α1 And (p+q)/2 is converted into the guide cover elevation angle control quantity alpha 1 ；
Flow guide cover elevation angle control quantity alpha 2 The specific obtaining process is as follows:
step one, quantifying the temperature deviation te and the temperature deviation change rate tc, wherein the basic argument of the temperature deviation te is [ e, f ]]The basic theory of the temperature deviation change rate vc is [ g, h ]]The temperature deviation is quantified to obtain 2v+1 grades, and the fuzzy theory field X of the temperature deviation fuzzy variable TE 2 Is { -v, -v+1, …,0, …, v-1, v }, the temperature deviation change rate is quantized to 2w+1 grades, and the temperature deviation change rate fuzzy variable TC is a fuzzy theory domain Y 2 Is { -w, -w+1, …,0, …, w-1, w };
step two, calculating the input accurate value x of the temperature deviation te And the accurate value x of the temperature deviation change rate tc Is a fuzzy quantization result y of (2) te And y tc
y te ＝2v[x te -(e+f)/2]/(f-e)
y tc ＝2w[x tc -(g+h)/2]/(h-g)
If the calculation result is not an integer, the calculation result needs to be respectively classified into the nearest integer in the corresponding fuzzy theory domain;
third, the elevation angle control quantity alpha of the air guide sleeve 2 The basic domain of discussion is [ s, t ]]Its fuzzy variable alpha F2 Is the fuzzy theory domain Z of (1) 2 Is { -r, -r+1, … …,0, … …, r-1, r }, the scale factor is K α2 Representation, K α2 The size is as follows
K α2 ＝(t-s)/2r
Step four, according to the fuzzy quantization result y te And y tc Domain X of the ambiguous theory 2 And fuzzy theory field Y 2 By consulting elevation angle fuzzy control rule table based on TE and TC to obtain fuzzy theory domain Z 2 Multiplying the output level by K α2 And (s+t)/2 is converted into the guide cover elevation angle control quantity alpha 2 。
2. The pod elevation control system based on a combined two-stage fuzzy controller of claim 1, wherein: the FUZZY control module FC1 comprises a FUZZY controller FUZZZY 1 and a differentiator I, wherein the vehicle speed sensor transmits a given vehicle speed and a current vehicle speed to the FUZZY reasoning module FC1, the speed deviation obtained after the comparison of the given vehicle speed and the current vehicle speed is used as input to the FUZZY controller FUZZZY 1, the speed deviation is also transmitted to the differentiator I, and the change rate of the speed deviation obtained by the differentiator I is also input to the FUZZY controller FUZZY1.
3. The pod elevation control system based on a combined two-stage fuzzy controller of claim 1, wherein: the FUZZY inference module FC2 comprises a FUZZY controller FUZZZY 2 and a differentiator II, the temperature sensor transmits the given temperature and the temperature of the rear wheel brake to the FUZZY inference module FC2, the temperature deviation obtained after the comparison of the given temperature and the temperature of the rear wheel brake is used as input to the FUZZY controller FUZZY2, the temperature deviation is also transmitted to the differentiator II, and the temperature deviation change rate obtained by the differentiator II is also transmitted to the FUZZY controller FUZZY2.
4. The pod elevation control system based on a combined two-stage fuzzy controller of claim 1, wherein: the method for establishing the elevation angle fuzzy control rule table based on VE and VC comprises the following steps:
step one, defining a speed deviation fuzzy variable VE, a speed deviation change rate fuzzy variable VC and a guide cover elevation angle fuzzy variable alpha F1 The word sets of (1) are { NB, NM, NS, O, PS, PM, PB }, 7 FUZZY sets on respective domains are correspondingly obtained, and a FUZZY control rule of a FUZZY controller FUZZY1, a speed deviation FUZZY variable VE, a speed deviation change rate FUZZY variable VC and a guide cover elevation FUZZY variable alpha are constructed according to the influence analysis of CFD simulation guide cover elevation angle change on an automobile external flow field and aerodynamic resistance F1 Selecting a triangular membership function or a Gaussian membership function;
step two, utilizing the determined speed deviation fuzzy variable VE, speed deviation change rate fuzzy variable VC and guide cover elevation angle fuzzy variable alpha F1 And FUZZY control rules of the FUZZY controller FUZZZY 1, determining a FUZZY matrix R, namely
Wherein VE i 、VC j 、(α F1 ) ij Is respectively defined in a speed deviation fuzzy variable VE, a speed deviation change rate fuzzy variable VC and a guide cover elevation angle fuzzy variable alpha F1 Fuzzy sets on the respective domains, i=1, 2, …,7; j=1, 2, … 7;
the membership degree of the fuzzy matrix R is
Wherein x is 1 ∈X 1 ，y 1 ∈Y 1 ，z 1 ∈Z 1 ，Respectively fuzzy sets VE i 、VC j 、(α F1 ) ij Definition in the field X 1 、Y 1 And Z 1 Membership in the list;
step three, for a certain quantization grade of the speed deviation fuzzy variable VE and the speed deviation change rate fuzzy variable VC on respective domains, a fuzzy variable assignment table is utilized, and according to the maximum membership of the grade, a certain fuzzy vector A, B on the respective domains can be respectively and correspondingly determined, if the input is A, B, the air guide cover elevation angle fuzzy variable alpha is obtained F1 The output of (2) is U, which can be obtained by fuzzy reasoning, namely
The membership degree of U is
Wherein mu A (x 1 )、μ B (y 1 )、μ R (x 1 ,y 1 ,z 1 ) Membership degrees of the fuzzy vector A, the fuzzy vector B and the fuzzy matrix R respectively;
and step four, according to the FUZZY variable assignment table and the FUZZY control rules of the FUZZY controller FUZZY1, calculating different quantized grades of the speed deviation FUZZY variable VE and the speed deviation change rate FUZZY variable VC on respective domains to obtain corresponding output U, taking the grade corresponding to the largest element of membership degree in the U as the output grade, and determining the elevation angle FUZZY control rule table based on VE and VC based on the mapping relation between different input grades of the speed deviation FUZZY variable VE and the speed deviation change rate FUZZY variable VC and the output grade of the U.
5. The pod elevation control system based on a combined two-stage fuzzy controller of claim 1, wherein: the elevation angle fuzzy control rule table establishment method based on TE and TC comprises the following steps:
step one, defining a temperature deviation fuzzy variable TE, a temperature deviation change rate fuzzy variable TC and a flow guide cover elevation fuzzy variable alpha F2 The word sets of (a) are { NB, NM, NS, O, PS, PM, PB }, 7 FUZZY sets on respective domains are correspondingly obtained, and a FUZZY control rule of a FUZZY controller FUZZZY 2, a FUZZY variable TE of temperature deviation, a FUZZY variable TC of temperature deviation change rate and a FUZZY variable alpha of the elevation angle of the air guide cover are constructed according to the rule that the elevation angle of the CFD simulation air guide cover is increased along with the temperature rise of the rear wheel brake and the phase change of aerodynamic resistance is large F2 Selecting a triangular membership function or a Gaussian membership function;
step two, utilizing the determined temperature deviation fuzzy variable TE, temperature deviation change rate fuzzy variable TC and flow guide cover elevation fuzzy variable alpha F2 The membership function of (a) and the FUZZY control rule of the FUZZY controller FUZZZY 2, determining a FUZZY matrix M, namely
Wherein TE is i 、TC j 、(α F2 ) ij Is defined in the fuzzy variable TE of temperature deviation, the fuzzy variable TC of temperature deviation change rate and the fuzzy variable alpha of air guide sleeve elevation angle respectively F2 Fuzzy sets on the respective domains, i=1, 2, …,7; j=1, 2, … 7;
the membership degree of the fuzzy matrix M is
Wherein x is 2 ∈X 2 ，y 2 ∈Y 2 ，z 2 ∈Z 2 ，Respectively fuzzy sets TE i 、TC j 、(α F2 ) ij Definition in the field X 2 、Y 2 And Z 2 Membership in the list;
step three, for a certain quantization grade of the temperature deviation fuzzy variable TE and the temperature deviation change rate fuzzy variable TC on respective domains, a fuzzy variable assignment table is utilized, and according to the maximum membership of the grade, a certain fuzzy vector C, D on the respective domains can be respectively and correspondingly determined, if the input is C, D, the air guide cover elevation fuzzy variable alpha is obtained F2 The output of (2) is E, which can be obtained by fuzzy reasoning, namely
E membership degree is
Wherein mu C (x 2 )、μ D (y 2 )、μ M (x 2 ,y 2 ,z 2 ) Membership degrees of the fuzzy vector C, the fuzzy vector D and the fuzzy matrix M respectively;
and step four, according to the FUZZY variable assignment table and the FUZZY control rule of the FUZZY controller FUZZY2, calculating different quantized grades of the temperature deviation FUZZY variable TE and the temperature deviation change rate FUZZY variable TC on respective domains to obtain corresponding output E, taking the grade corresponding to the largest element of membership degree in the E as the output grade, and determining an elevation angle FUZZY control rule table based on TE and TC based on the mapping relation between different input grades of the temperature deviation FUZZY variable TE and the temperature deviation change rate FUZZY variable TC and the output grade of the E.
6. The pod elevation control system based on a combined two-stage fuzzy controller of claim 4, wherein: for the FUZZY controller FUZZZY 1, an adjustment factor beta is introduced 1 And beta 2 Alpha in elevation angle FUZZY control rule table of FUZZY controller FUZZZY 1 F1 Is modified to make VE, VC and alpha F1 The following analytical expressions are satisfied between the levels of (2)
Wherein 1 is>β 1 >β 2 >0, symbol<>The representation takes the integer closest to the value of the intra-symbol expression and is numbered the same as the value of the expression.
7. The pod elevation control system based on a combined two-stage fuzzy controller of claim 5, wherein: for the FUZZY controller FUZZZY 2, an adjustment factor beta is introduced 3 And beta 4 Alpha in elevation angle FUZZY control rule table of FUZZY controller FUZZZY 2 F2 Is modified to make TE, TC and alpha F2 The following analytical expressions are satisfied between the levels of (2)
Wherein 1 is>β 3 >β 4 >0, symbol<>The representation takes the integer closest to the value of the intra-symbol expression and is numbered the same as the value of the expression.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111057628.1A CN113721470B (en) | 2021-09-09 | 2021-09-09 | Air guide sleeve elevation angle control system based on combined double-stage fuzzy controller |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111057628.1A CN113721470B (en) | 2021-09-09 | 2021-09-09 | Air guide sleeve elevation angle control system based on combined double-stage fuzzy controller |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113721470A CN113721470A (en) | 2021-11-30 |
| CN113721470B true CN113721470B (en) | 2023-08-29 |
Family
ID=78683025
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111057628.1A Active CN113721470B (en) | 2021-09-09 | 2021-09-09 | Air guide sleeve elevation angle control system based on combined double-stage fuzzy controller |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113721470B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114506395A (en) * | 2022-02-09 | 2022-05-17 | 一汽解放汽车有限公司 | Air guide sleeve control method and device, computer equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5305425A (en) * | 1989-04-14 | 1994-04-19 | Omron Corporation | Method of and apparatus for evaluating membership functions or rules in fuzzy reasoning system |
| CN103542493A (en) * | 2013-09-25 | 2014-01-29 | 浙江吉利控股集团有限公司 | System and method for intelligent control over automobile air conditioner according to fuzzy control |
| CN105045090A (en) * | 2015-06-25 | 2015-11-11 | 吉林大学 | Constant speed control method and apparatus for hydraulic retarder based on fuzzy controls |
| CN105629720A (en) * | 2015-12-30 | 2016-06-01 | 常州联德电子有限公司 | Wide-band oxygen sensor temperature control method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1206485C (en) * | 2003-06-13 | 2005-06-15 | 贵州汇通华城楼宇科技有限公司 | Central air-conditioning energy conserving fuzzy controlling method and fuzzy controller thereof |
-
2021
- 2021-09-09 CN CN202111057628.1A patent/CN113721470B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5305425A (en) * | 1989-04-14 | 1994-04-19 | Omron Corporation | Method of and apparatus for evaluating membership functions or rules in fuzzy reasoning system |
| CN103542493A (en) * | 2013-09-25 | 2014-01-29 | 浙江吉利控股集团有限公司 | System and method for intelligent control over automobile air conditioner according to fuzzy control |
| CN105045090A (en) * | 2015-06-25 | 2015-11-11 | 吉林大学 | Constant speed control method and apparatus for hydraulic retarder based on fuzzy controls |
| CN105629720A (en) * | 2015-12-30 | 2016-06-01 | 常州联德电子有限公司 | Wide-band oxygen sensor temperature control method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113721470A (en) | 2021-11-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Guo et al. | ARIMA-based road gradient and vehicle velocity prediction for hybrid electric vehicle energy management | |
| Chen et al. | Integrated vehicle dynamics and control | |
| Fourrié et al. | Bluff-body drag reduction using a deflector | |
| Agathangelou et al. | Aerodynamic design considerations of a Formula 1 racing car | |
| CN113721470B (en) | Air guide sleeve elevation angle control system based on combined double-stage fuzzy controller | |
| CN109664890B (en) | Automobile rollover early warning algorithm based on SVM model | |
| CN105243430A (en) | Train energy-saving operation target speed curve optimization method | |
| CN109969220A (en) | A kind of heavy haul train cyclic brake method and system based on double air brake pipes | |
| CN109878509A (en) | Monoblock type tank car Multi-source Information Fusion rollover method for early warning based on fuzzy logic | |
| CN114987539B (en) | Automatic driving automobile personalized collision grading early warning method and system based on risk field model | |
| Huang et al. | Using 3D road geometry to optimize heavy truck fuel efficiency | |
| CN114954494A (en) | Heavy commercial vehicle load rapid estimation method | |
| Nie et al. | Eco-Co-Optimization strategy for connected and automated fuel cell hybrid vehicles in dynamic urban traffic settings | |
| CN110027537B (en) | Hydraulic retarder control method based on quality identification | |
| Jin et al. | Road friction estimation method based on fusion of machine vision and vehicle dynamics | |
| Luu et al. | Dynamics model and design for adaptive cruise control vehicles | |
| CN116533952B (en) | Semitrailer braking method, device, equipment and storage medium | |
| Zhu et al. | Research on Cooperative Adaptive Cruise Control (CACC) Based on Fuzzy PID Algorithm | |
| CN114148318B (en) | Vehicle path tracking method based on feedback linearization and LQR in ice and snow environment | |
| Fukagawa et al. | Modeling of transient aerodynamic forces based on crosswind test | |
| Watkins et al. | Wind-Tunnel Modelling off Commercial Vehicle Drag-Reducing Devices: Three Case Studies | |
| CN105216769B (en) | A kind of fuzzy spray control system of automobile drum brake and its control method | |
| Huang et al. | 3D road geometry based optimal truck fuel economy | |
| Krishnamurthy et al. | Aerodynamic effect on lift characteristics of a sedan car due to attached wing | |
| Wang et al. | Longitudinal acceleration tracking control of low speed heavy-duty vehicles |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |