CN113669443A - Control method and control system for vehicle automatic transmission - Google Patents
Control method and control system for vehicle automatic transmission Download PDFInfo
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- CN113669443A CN113669443A CN202010401870.5A CN202010401870A CN113669443A CN 113669443 A CN113669443 A CN 113669443A CN 202010401870 A CN202010401870 A CN 202010401870A CN 113669443 A CN113669443 A CN 113669443A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
- F16H61/02—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used
- F16H61/0202—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric
- F16H61/0204—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric for gearshift control, e.g. control functions for performing shifting or generation of shift signal
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H59/00—Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
- F16H59/36—Inputs being a function of speed
- F16H59/44—Inputs being a function of speed dependent on machine speed of the machine, e.g. the vehicle
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H59/00—Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
- F16H59/50—Inputs being a function of the status of the machine, e.g. position of doors or safety belts
- F16H59/52—Inputs being a function of the status of the machine, e.g. position of doors or safety belts dependent on the weight of the machine, e.g. change in weight resulting from passengers boarding a bus
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H59/00—Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
- F16H59/60—Inputs being a function of ambient conditions
- F16H59/66—Road conditions, e.g. slope, slippery
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H59/00—Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
- F16H59/60—Inputs being a function of ambient conditions
- F16H59/66—Road conditions, e.g. slope, slippery
- F16H2059/663—Road slope
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
- F16H2061/0075—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by a particular control method
- F16H2061/0081—Fuzzy logic
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
- F16H61/04—Smoothing ratio shift
- F16H2061/0459—Smoothing ratio shift using map for shift parameters, e.g. shift time, slip or pressure gradient, for performing controlled shift transition and adapting shift parameters by learning
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- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Control Of Transmission Device (AREA)
Abstract
The invention relates to a control method for a vehicle automatic transmission and a corresponding control system. The control method comprises the following steps: acquiring basic information of the vehicle (S01); acquiring gradient information of a road ahead (S02); estimating (S03) the current total mass of the vehicle in real time to obtain an estimated current total mass of the vehicle; performing a blurring process (S04); and calculating a final gear decision and outputting the gear decision (S05). According to the invention, a fuzzy processing is carried out on the basis of the gradient information of the road ahead and the current total mass of the vehicle, and a final gear decision is calculated on the basis of the result of the fuzzy processing.
Description
Technical Field
The present invention relates to a control method for a vehicle automatic transmission, in particular for automatic gear shifting, and a corresponding control system.
Background
Patent document CN103836179 discloses a GPS-based predictive shift schedule of an automatic transmission. The adopted default gear shifting rule is based on GPS positioning, and vehicle information such as an accelerator pedal signal and vehicle speed is combined.
Patent document CN104670211 discloses a system for controlling gear shifting of a vehicle. Wherein the vehicle gear shift control means is assisted by predicting the situation of the driving road.
Patent document CN107031647 discloses a predictive transmission control method using road shape recognition. A control method is disclosed in which the shape of a road ahead of a vehicle (including the gradient and curvature) is recognized by high-precision map information to thereby perform transmission predictive control.
Patent document CN 1046966504 discloses a vehicle gear shift control method and device. The neural network algorithm is used for extracting characteristic quantities of various gear shifting parameters, calculating membership values of various gear shifting modes, finally calculating to obtain the optimal gear shifting speed under the current working condition, and timely adjusting a gear shifting strategy of a transmission according to the current vehicle condition, the driving state and the change condition of the road condition in real time to enhance the driving experience of a driver.
Patent document US20100030437a1 discloses a method of adapting the gear change schedule of an automatic transmission based on global positioning system/map information. The concept of road gradient factors is provided, and the transmission gear shifting points are adjusted according to the gradient factors, so that the transmission gear shifting characteristics are improved, and the driving experience is optimized.
The technical scheme mainly adopts the front road information provided by the high-precision map to correct the gear shifting curve used by the general two-parameter gear shifting strategy so as to obtain better vehicle driving performance and economic performance improvement. However, the foregoing technical solutions generally do not consider the influence of vehicle load, and are difficult to adapt to different application scenarios.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a control method for an automatic transmission of a vehicle, which can solve the problem that the current shift control method of an automatic transmission is difficult to flexibly adapt to the change of the driving environment, and at the same time, can improve the driving comfort of the vehicle and improve the economy of the vehicle.
The invention provides a control method for a vehicle automatic transmission, which comprises the following steps:
acquiring basic information of a vehicle;
acquiring gradient information of a front road;
estimating the current total mass of the vehicle in real time to obtain the estimated current total mass of the vehicle;
carrying out fuzzy processing;
calculating a final gear decision and outputting the gear decision;
fuzzy processing is carried out on the basis of gradient information of a front road and the current total mass of the vehicle, and a final gear decision is calculated on the basis of the fuzzy processing result.
Through the comprehensive utilization of the gradient information of the front road and the current total mass of the vehicle, the gradient of the road and the dynamic vehicle weight factor can be introduced on the premise of considering the parameter information of the vehicle, and the gear shifting strategy of the transmission can be more intelligent through an intelligent fuzzy algorithm, so that better driving experience and more economic improvement are brought.
According to a preferred embodiment, the basic information of the vehicle comprises the overall vehicle service mass, the tire radius, the engine torque, the vehicle speed, the drive train efficiency.
According to a preferred embodiment, the current total vehicle mass is a real-time vehicle weight estimated by:
m is the current total mass of the vehicle,
FTis the driving force of the driving force,
Fais an air resistorThe force is applied to the inner wall of the container,
Jwis the moment of inertia of the wheel, and,
Jeis the moment of inertia of the engine and,
igis the gear ratio of the current gear of the transmission,
i0is the main reduction ratio of the speed reducer,
ηTit is the mechanical efficiency of the drive train,
v is the current vehicle speed and is,
g is a constant of gravity and is,
f is the road rolling resistance coefficient,
i is the sine of the road bank angle.
According to a preferred embodiment, the gradient information comprises one or more of the following values: the slope degree value of the slope road section, the continuous length of the slope road section and the position of the starting point of the slope road section.
According to a preferred embodiment, the slope information is checked for validity as a function of the slope value and/or the length of the slope section.
According to a preferred embodiment, the system determines that the grade value is invalid if the continuous length of the grade section is below a set length.
According to a preferred embodiment, a parameter domain is determined and a fuzzy subset is defined in the fuzzy processing.
According to a preferred embodiment, in the fuzzy processing, Sugeno direct reasoning algorithm is adopted
According to a preferred embodiment, the maximum and minimum synthesis rule is used for fuzzy relation calculation, and then the equivalent load coefficient is obtained through fuzzy solution calculation based on the gravity center method.
According to a preferred embodiment, the target gear of the vehicle is calculated by the method for a certain time in the future and the gear is shifted in advance based on the target gear of the vehicle for a certain time in the future.
According to a preferred embodiment, the advance of the early gear shift is determined by taking the current vehicle speed, the current total vehicle mass and the gradient information into account.
The invention also proposes a control system for a vehicle automatic transmission, configured to execute a method according to the invention for controlling a gear of said vehicle automatic transmission.
According to a preferred embodiment, the control system comprises an input module for acquiring parameters, a predictive rule module for fuzzifying each parameter, and a gear decision module for determining a gear.
According to a preferred embodiment, the predictive rule module comprises a parameter obfuscation sub-module, an obfuscation rule sub-module and a deblur sub-module.
Drawings
Fig. 1 is a flow chart of the overall control strategy of the method according to the invention.
Fig. 2 is a flowchart of the gradient information acquisition routine.
FIG. 3 is a flow chart of a real-time vehicle weight estimation algorithm.
FIG. 4 is a schematic of a membership function.
Fig. 5 is a schematic block diagram of a control system.
Detailed Description
The control method and the control system according to the invention will be described below by way of specific embodiments with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Fig. 1 shows a flow chart of the overall control strategy of the method according to the invention. First in step S01, basic information of the vehicle is obtained, such as, but not limited to, vehicle trim mass, tire radius, engine torque, vehicle speed, driveline efficiency, etc. The vehicle basic information can be retrieved from a memory of the vehicle or from a sensor of the vehicle, for example.
Then, in step S02, gradient information of the road ahead is acquired, which may include a gradient value, a continuous length of a gradient section, and the like. Then, proceeding to step S03, the current total mass of the vehicle is estimated in real time in step S03 to arrive at an estimated current total mass of the vehicle. The order of step S02 and step S03 may be interchanged, or may be performed in parallel.
By acquiring the basic information of the vehicle and the gradient information of the road ahead, factors affecting the vehicle resistance, such as the vehicle mass, the rolling resistance coefficient, the road gradient, and the vehicle acceleration reflecting the driver's operation intention, are taken into account. In some road scenarios, the automatic transmission controller may predict and respond in advance to changes in the road resistance ahead by obtaining such information in advance. Therefore, the problems that cyclic gear shifting is caused, the engine is far away from an economic working area and the like are avoided, and the driving performance and the economical efficiency of the vehicle are improved.
In step S04, the blurring process is performed on each parameter. In step S05, a final gear decision is calculated and output.
Fig. 2 shows a flowchart of a gradient information acquisition routine according to the present invention. It is determined in step S21 whether the gradient information of the road ahead is updated. If it is determined in step S21 that the gradient information of the road ahead is not updated, the flow ends. Whereas if it is determined in step S21 that the gradient information of the front road is updated, the gradient information of the front road, which may include a gradient value of a gradient section, a continuous length of the gradient section (i.e., a gradient length), absolute coordinates of a start point of the gradient section or an offset thereof with respect to the current vehicle position, etc., is acquired in step S22.
As an example, the receiving module at the vehicle end may obtain the gradient information from the high-precision map data stored in the cloud end, and may obtain the road gradient information within a certain range in front of the vehicle by default. The high-precision map information in the cloud is sent, for example, depending on the positioning information, such as longitude and latitude information, of the current vehicle determined by the vehicle-mounted positioning module.
Then, go to step S23, where it is determined whether the gradient information is valid. Due to the fact that the gradient value of the actual road changes gradually, for example, a gradient of 5% appears in front of a flat road, the gradient of the road acquired from the map information increases gradually, corresponding processing needs to be carried out on an information receiving end at the moment, and unnecessary gear shifting of the transmission caused by gradual change of the gradient is avoided. Therefore, the invention adopts a strategy of comprehensively considering the gradient value and the continuous length of the gradient road section, and if the continuous length of the gradient road section is lower than the set length, the system judges that the gradient value is invalid. This strategy can also be applied to the problem of unnecessary shifting of the transmission when a continuous short distance of a depressed or protruding section of road is present on the road.
If it is confirmed in step S23 that the gradient information is valid, it goes to step S24 and the map gradient information is verified. Then, it goes to step S25 and outputs gradient information.
In the following, a real-time vehicle weight estimation algorithm according to the present invention is described. Known vehicle longitudinal dynamics equations:
wherein, TengIs the output torque of the engine and is,
igis the gear ratio of the current gear of the transmission,
i0is the main reduction ratio of the speed reducer,
ηTit is the mechanical efficiency of the drive train,
r is the radius of the wheel or wheels,
CDis the coefficient of air resistance and is,
a is the frontal area of the vehicle,
v is the current vehicle speed and is,
m is the mass of the vehicle,
g is a constant of gravity and is,
f is the road rolling resistance coefficient (the effect of different rolling resistance coefficients can be ignored in current control methods, and can also be considered with existing methods),
alpha is the angle of the road slope,
Jwis the moment of inertia of the wheel, and,
Jeis engine rotation inertiaAmount of the compound (A).
From this it follows
Because the actual road grade angle α is generally small, cos α ≈ 1, sin α ≈ tan α ≈ i, i is the actual road grade,
after obtaining the gradient information of the road ahead, the current total mass of the vehicle can be estimated according to the longitudinal dynamics equation of the vehicle, namely:
wherein the rolling resistance coefficient can be calculated according to an empirical formula:
wherein k is0，k1And k2Parameters related to the tire can be obtained through calibration or self-learning.
The vehicle acceleration information dv/dt can be obtained by calculating a vehicle speed signal by real-time difference and adding low-pass filtering y (n) -y (n-1) + f (y (n) -y (n-1)). If the vehicle is equipped with an acceleration sensor, it can also be derived directly by reading the output of the acceleration sensor and cross-checking with an acceleration estimation algorithm.
After the current total mass of the vehicle is estimated, the vehicle load coefficient can be calculated as follows:
Δ＝m/m0
wherein m is0And (4) preparing the vehicle for mass.
The real-time vehicle weight estimation algorithm according to the present invention may be, for example, but not limited to, the algorithm flowchart shown in fig. 3. As shown in fig. 3, vehicle parameters are first acquired (step S11). In step S12, it is determined whether the online vehicle weight calculation condition is satisfied. For example, it may be determined that the online vehicle weight calculation condition is satisfied when there are sufficient parameters. If it is judged in step S12 that the online vehicle weight calculation condition is satisfied, it goes to step S13. In step S13, the current thrust or tractive force is calculated. Then, the vehicle acceleration at the present time is calculated or acquired and the vehicle acceleration at the next set time is calculated (S14, S15). Finally, in step S16, the current total mass of the vehicle is calculated according to the above formula, and the vehicle load factor is determined.
The shift control strategy is described below.
First, input parameters such as, but not limited to, grade information of a road ahead, a vehicle load factor, a vehicle speed signal, and an accelerator pedal opening signal are acquired. And then, carrying out fuzzification processing on the acquired parameters, wherein a parameter domain is determined and a fuzzy subset is defined. For example, the parametric universe of discourse and ambiguity subsets include:
road gradient (%):
domain of discourse: [ -20,20]
The fuzzy subset is: [ NB, NM, NS, ZO, PS, PM, PB ]
Vehicle load factor:
domain of discourse: [1,10]
The fuzzy subset is: [ PS, PM, PB ]
Vehicle speed (km/h):
the domain of discourse is: [0,120]
The fuzzy subset is: [ ZO, PS, PM, PB ]
Accelerator opening (%):
the domain of discourse is: [0,100]
The fuzzy subset is: [ ZO, PS, PM, PB ]
Equivalent load factor:
the domain of discourse is: [0,10]
The fuzzy subset is: [ ZO, PS, PM, PB ]
For the determination of the equivalent load coefficient, the fuzzy inference decision module calculates according to a fuzzy rule (for example, see the following rule table) and according to a set membership function to obtain the equivalent load coefficient.
Rule table of equivalent load coefficient based on road gradient and vehicle load
Therefore, the making of the fuzzy rule directly influences the output of the equivalent load factor, and further influences the final gear decision. The membership function is mainly formulated according to simulation and experimental data iterative optimization. The membership function formulated here is for example see fig. 4.
The fuzzy algorithm adopts a Sugeno direct reasoning algorithm, mainly utilizes a maximum and minimum synthesis rule to calculate a fuzzy relation, and obtains an equivalent load coefficient output through fuzzy solution calculation based on a gravity center method.
And inputting the equivalent load coefficient subjected to fuzzy resolving and output into a gear decision module. And in a gear decision module, correcting the target gear obtained by the current gear shifting control strategy by using the equivalent load coefficient obtained by the calculation. Because the gradient information within a certain distance in front of the current vehicle position can be acquired according to the high-precision map information, and the real-time vehicle weight estimation can be completed at the beginning of a single vehicle travel, the target gear of the vehicle within a certain time in the future can be calculated by the method. Therefore, gear shifting can be carried out in advance, and the situation that gear shifting is carried out again on a slope or cyclic gear shifting occurs on the slope can be effectively avoided.
The advance of the gear shifting can be realized by comprehensively considering the current vehicle speed v, the vehicle weight m and the gradient information (the gradient value i and the holding of the gradient road section)The continuous length l) is obtained. The specific implementation can be realized by setting a lookup table LUT and a weight factor alpha1And alpha2Calculating to obtain:
τ＝α1f1(v,m)+α2f2(i,l)
according to the control method, on the premise of considering the parameter information of the vehicle, the road gradient and the dynamic vehicle weight factor are introduced, and the gear shifting strategy of the transmission can be more intelligent through an intelligent fuzzy algorithm, so that better driving experience and more economic improvement are brought.
Fig. 5 shows a block diagram of a control system for a motor vehicle according to the invention. The control system comprises an input module for acquiring parameters, a predictive rule module 10 for fuzzifying each parameter, and a gear decision module 20 for determining gears. In addition, the control system may also include a positioning module 05 and a communication module 06. The positioning module 05 is for example a GPS module for determining the vehicle. The communication module 06, in particular, the 4G/5G communication module, is configured to obtain cloud map data 07.
The input module includes: the system comprises a gradient signal obtaining and processing submodule 01 for obtaining a gradient signal, a real-time vehicle weight estimation submodule 02, a vehicle speed obtaining submodule 03 and an accelerator opening obtaining submodule 04.
The input module forwards the input data to a predictive rules module 10 which includes a parameter obfuscation sub-module 11, an obfuscation rules sub-module 12 and a deblurring sub-module 13. The data is input to the shift decision module 20 after disambiguation. The control system for a motor vehicle can carry out the method described above and accordingly has the advantages described for the method.
Other embodiments of the present invention will readily suggest themselves to such skilled persons having the benefit of this disclosure. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
Claims (14)
1. A control method for a vehicle automatic transmission, the control method comprising the steps of:
acquiring basic information of the vehicle (S01);
acquiring gradient information of a road ahead (S02);
estimating (S03) the current total mass of the vehicle in real time to obtain an estimated current total mass of the vehicle;
performing a blurring process (S04);
calculating a final gear decision and outputting the gear decision (S05);
it is characterized in that the preparation method is characterized in that,
fuzzy processing is carried out on the basis of gradient information of a front road and the current total mass of the vehicle, and a final gear decision is calculated on the basis of the fuzzy processing result.
2. The control method of claim 1, wherein the basic information of the vehicle includes a total vehicle trim mass, a tire radius, an engine torque, a vehicle speed, a transmission system efficiency.
3. The control method according to claim 2, wherein the current total mass of the vehicle is a real-time vehicle weight estimated by:
m is the current total mass of the vehicle,
FTis the driving force of the driving force,
Fait is the air resistance that is the resistance of the air,
Jwis the moment of inertia of the wheel, and,
Jeis the moment of inertia of the engine and,
igis the gear ratio of the current gear of the transmission,
i0is the main reduction ratio of the speed reducer,
ηTit is the mechanical efficiency of the drive train,
v is the current vehicle speed and is,
g is a constant of gravity and is,
f is the road rolling resistance coefficient,
i is the sine of the road bank angle.
4. A control method according to claim 1 or 2, characterized in that the gradient information comprises one or more of the following values: the slope degree value of the slope road section, the continuous length of the slope road section and the position of the starting point of the slope road section.
5. Control method according to claim 4, characterized in that the slope information is checked for validity depending on the slope value and/or the duration of the slope section.
6. The control method according to claim 5, wherein the system determines that the gradient value is invalid if the continuous length of the gradient section is below a set length.
7. A control method according to claim 1 or 2, characterized in that the parameter domains are determined and fuzzy subsets are defined in a fuzzy process.
8. Control method according to claim 1 or 2, characterized in that in the fuzzy processing, it is Sugeno direct reasoning algorithm.
9. The control method according to claim 8, wherein the fuzzy relation is calculated by using a maximum-minimum synthesis rule, and the equivalent load coefficient is obtained by performing fuzzy solution calculation based on a gravity center method.
10. Control method according to claim 1 or 2, characterized in that the target gear of the vehicle in a certain time in the future is calculated by the method and the gear change is advanced on the basis of the target gear of the vehicle in a certain time in the future.
11. The control method according to claim 10, characterized in that the advance of the advance shift is derived by comprehensively considering the current vehicle speed, the current total mass of the vehicle, and the gradient information.
12. A control system for a vehicle automatic transmission, configured to perform the method according to any one of claims 1-11 for controlling a gear of the vehicle automatic transmission.
13. Control system according to claim 12, characterized in that it comprises an input module for obtaining parameters, a predictive rule module (10) for fuzzifying the parameters, and a gear decision module (20) for determining the gear.
14. Control system according to claim 13, characterized in that the predictive rule module (10) comprises a parameter fuzzification submodule (11), a fuzzification rule submodule (12) and a deblurring submodule (13).
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| CN108361366A (en) * | 2018-01-17 | 2018-08-03 | 北京理工大学 | A kind of automatic mechanical transmission process for gear |
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