CN114683871B - Driving anti-skid control method for sliding steering electric driving unmanned vehicle - Google Patents
Driving anti-skid control method for sliding steering electric driving unmanned vehicle Download PDFInfo
- Publication number
- CN114683871B CN114683871B CN202111308603.4A CN202111308603A CN114683871B CN 114683871 B CN114683871 B CN 114683871B CN 202111308603 A CN202111308603 A CN 202111308603A CN 114683871 B CN114683871 B CN 114683871B
- Authority
- CN
- China
- Prior art keywords
- slip
- driving
- skid
- driving wheel
- longitudinal
- 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
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000004364 calculation method Methods 0.000 claims description 25
- 230000000007 visual effect Effects 0.000 claims description 10
- 238000005096 rolling process Methods 0.000 claims description 9
- 239000010426 asphalt Substances 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 5
- 230000004927 fusion Effects 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- 101100243401 Caenorhabditis elegans pept-3 gene Proteins 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 2
- 238000011217 control strategy Methods 0.000 abstract 1
- 238000013461 design Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000002265 prevention Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 239000000853 adhesive Substances 0.000 description 1
- 230000001070 adhesive effect Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
- B60L15/2036—Electric differentials, e.g. for supporting steering vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2220/00—Electrical machine types; Structures or applications thereof
- B60L2220/40—Electrical machine applications
- B60L2220/44—Wheel Hub motors, i.e. integrated in the wheel hub
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/42—Drive Train control parameters related to electric machines
- B60L2240/423—Torque
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/60—Navigation input
- B60L2240/62—Vehicle position
- B60L2240/622—Vehicle position by satellite navigation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention provides a sliding steering electric driving unmanned vehicle driving anti-skid control method, which comprises the steps of obtaining the actual longitudinal speed of an unmanned platform according to a longitudinal speed signal of an inertial navigation and GPS integrated navigation system; calculating the current slip rate of each driving wheel according to the current wheel speed of each driving motor and the current longitudinal speed of the unmanned platform; according to the set slip rate limit value, designing a slip judgment logic to judge whether each driving wheel slips; and designing a slip rate PID control strategy according to the current slip rate and the optimal slip rate to obtain a driving motor driving anti-slip torque control quantity, wherein the control torque and the torque control quantity calculated by the longitudinal vehicle speed PID controller are used as a torque input command of the driving motor to drive and control the vehicle. By adopting the driving anti-skid control method, the instant skid of the driving wheel can be restrained rapidly, effectively and smoothly, and the passing capability of the sliding steering wheeled vehicle on the road surface with low attachment rate such as ice and snow, desert and the like is improved.
Description
Technical Field
The invention designs a driving anti-skid control method, and belongs to the technical field of distributed driving longitudinal control of automatic driving or unmanned vehicles.
Background
The skid steering electrically driven unmanned ground vehicle has been attracting attention in military applications due to its simple mechanical structure, low operating and maintenance costs, ability to steer with zero radius (in-situ steering), outstanding off-road capability, etc. Because the sliding steering mode is adopted, the abrasion of the tire is unavoidable, the special chassis structure of the sliding steering mode leads to the fact that individual tires are easy to slightly contact with the road surface or to be completely suspended in the moving process of the vehicle, when the driving force of the vehicle is suddenly increased or the attachment coefficient of the road surface is suddenly reduced, the wheels slip, the wheel slip rate is rapidly increased, the tires enter a nonlinear region, and the longitudinal driving force of the tires is rapidly reduced; meanwhile, as the wheel slip rate increases, the lateral performance of the tire is poor, the steering performance and stability are also poor, the tire slips under external force disturbance, the platform is unstable, and the running track is affected. Therefore, the driving anti-skid control of the wheels is necessary, and the driving anti-skid control of the unmanned ground vehicle mainly considers the starting acceleration and the low-speed running of the vehicle, and the main aim is to fully utilize the adhesive force of each driving wheel, so that the vehicle obtains the traction force as large as possible and the acceleration performance of the vehicle is improved; when the wheels are suspended, the rotating speed is controlled, so that the energy consumption is reduced; the off-road driving capability is improved on the muddy road surface of the field sandy soil, and the situation that the vehicle is trapped and slipped seriously and cannot be driven is reduced.
Aiming at the design of a distributed driving anti-skid strategy of a sliding steering electric driving unmanned ground vehicle, a fuzzy control method is proposed to improve the steady state performance and control precision of an anti-skid control system, but the stability of the fuzzy control method is difficult to prove, the design of membership functions, the formulation of fuzzy rules and the like are not universal, and the fuzzy control method is difficult to directly use on an actual unmanned ground vehicle. The engineering utility of other complex control algorithms proposed by some studies is questionable.
Disclosure of Invention
In view of the above, the invention provides a driving anti-skid control method for a sliding steering electric driving unmanned vehicle, which is small in calculated amount, convenient to calculate in real time, high in robustness and high in adaptability, and can realize anti-skid of different pavements; by adopting the method for anti-skid control, the safety and smoothness of vehicle running can be improved, the accuracy of unmanned vehicle longitudinal speed control can be improved, and the possibility of vehicle instability during high-speed running can be reduced.
The technical scheme of the invention is as follows: the anti-skid control method for the driving of the sliding steering electric driving unmanned vehicle comprises the following steps:
step one: acquiring the actual longitudinal speed v of the unmanned vehicle y ；
Step two: calculation ofCurrent slip rate lambda of each driving wheel i I=1, …, N is the total number of drive wheels of the drone;
step three: judging whether each driving wheel slips or not: setting the maximum slip limit value as the slip lambda of the driving wheel i i If the slip ratio is greater than the maximum slip ratio limit value, the driving wheel is considered to slip;
step four: longitudinal vehicle speed control with slip ratio PID controller is adopted for unmanned vehicles:
when the step three judges that the driving wheel slips, taking the optimal slip rate of the driving wheel as an expected slip rate, obtaining a driving anti-slip torque control quantity through a slip rate PID controller according to the error of the current slip rate and the expected slip rate of the driving wheel, and subtracting the driving anti-slip torque control quantity from the torque control quantity obtained by a longitudinal vehicle speed PID controller to serve as an input torque control quantity of a driving motor of the corresponding driving wheel.
In the first step, a combined navigation system of vehicle inertial navigation and GPS is adopted, and data of the GPS and the inertial navigation are subjected to data fusion to estimate the longitudinal speed of the unmanned vehicle:
firstly, judging whether two groups of longitudinal vehicle speed data provided by the integrated navigation system have differential signals or not:
if the differential signal exists, longitudinal vehicle speed data with the differential signal is taken as an actual longitudinal vehicle speed v y ；
If the differential signal is not available, further judging whether the integrated navigation system has a GPS signal;
if the GPS signal exists, the longitudinal speed data obtained by integrating the inertial navigation longitudinal acceleration is corrected by using the GPS vehicle longitudinal speed signal, and the corrected longitudinal speed is taken as the actual longitudinal speed v y ；
If no GPS signal exists, longitudinal vehicle speed data provided by pure inertial navigation in the integrated navigation system is obtained as the actual longitudinal vehicle speed v y 。
As a preferred mode of the invention, if no GPS signal is available, longitudinal vehicle speed data provided by pure inertial navigation in the integrated navigation system is obtained:
for the running condition of short distance/short time, longitudinal vehicle speed data provided by pure inertial navigation is adopted as the actual longitudinal vehicle speed v y ；
For long distance/long time driving conditions, the following method is adopted to estimate the actual longitudinal vehicle speed v y ：
v y ＝1.4826v ymedian
Wherein:n median is the median of all wheel speeds of the driving wheels, v ymedian The linear speed obtained by converting the median of the wheel speed of the driving wheel is r which is the rolling radius of the driving wheel.
As a preferred mode of the present invention, in the second step, the slip ratio is calculated according to the formula:
slip rate lambda of driving wheel i in vehicle driving state i The calculation formula of (2) is as follows:
slip rate lambda of driving wheel i in vehicle braking state i The calculation formula of (2) is as follows:
wherein: r is the rolling radius of the driving wheel; omega i Is the current angular velocity of the drive wheel i.
As a preferable mode of the present invention, the maximum slip ratio limit value of each driving wheel is:
λ imax ＝2λ iopt
wherein: lambda (lambda) imax For maximum slip limit value of driving wheel i, lambda iopt For optimum slip rate lambda of drive wheel i iopt 。
As a preferred mode of the present invention, the optimum slip rate λ iopt The estimation steps of (a) are:
estimating optimal slip rates of different road surfaces based on visual recognition, wherein the road surfaces are classified and recognized by a visual system formed by combining a monocular camera and an infrared camera during the visual recognition, and the monocular camera is used in the daytime and the infrared camera is used at night;
the tire-road surface characteristic curves of the tires of the unmanned vehicles on four typical road surfaces, namely the relation between the slip rate and the longitudinal attachment coefficient of the tires, can be obtained through tests; the four typical road surfaces are a good road surface, a wet asphalt road surface, a snow covered road surface and an ice road surface respectively; optimum slip ratio of good road surface is lambda opt1 Optimum slip ratio of wet asphalt pavement is lambda opt2 The optimal slip rate of the snow covered pavement is lambda opt3 Optimal slip ratio of ice road surface is lambda opt4 ；
The weighted average calculation formula for calculating the optimal slip ratio is:
wherein: x is x 1 ，χ 2 ，χ 3 ，χ 4 The similarity degree between the current road surface and four typical road surfaces is respectively set;
then, four typical road surfaces are matched according to the image of the current road surface acquired by the vision system, and the similarity degree χ is determined according to the similarity with each typical road surface 1 ，χ 2 ，χ 3 ，x 4 And weighting the value of (2) to the optimum slip ratio obtained therebyAs the optimum slip ratio for each drive wheel.
In a preferred embodiment of the present invention, in the fourth step, the slip ratio PID controller uses the optimum slip ratio as the reference slip ratio λ ref The actual slip rate lambda and the reference slip rate lambda of the wheel calculated in the step two are calculated ref Deviation Δλ=λ - λ ref The input quantity is obtained by proportional integration of the input quantity as the input quantity of the slip ratio PID controllerThe output of the slip ratio PID controller is the instant driving anti-slip torque control quantity T λ ：
T λ ＝K pλ Δλ+K Iλ ∫Δλdt
Wherein: k (K) pλ Is a proportionality constant, K Iλ Is an integral constant.
The beneficial effects are that:
(1) The driving anti-skid control method provided by the invention is convenient for engineering realization, does not need additional hardware cost, has high practical value, and can be widely applied to various schemes of longitudinal control of the multi-wheel independent electric driving unmanned vehicle.
(2) The driving anti-skid control method provided by the invention has a great effect on improving the stability of the whole vehicle under the limit working condition, can automatically reduce the driving moment when the tire slips, slows down the slip degree, and fully utilizes the driving wheel with good adhesion to realize self-adaptive moment debugging. By adopting the driving anti-skid method, the instant slip of the driving wheel can be quickly, effectively and smoothly restrained, and the passing capability of the sliding steering wheeled vehicle on the road surface with low attachment rate such as ice and snow, desert and the like is improved.
(3) The driving anti-skid control method provided by the invention is suitable for driving anti-skid control of the multi-axis independent electric driving unmanned vehicles such as 4x4, 6x6, 8x8 and the like which are subjected to sliding steering; the method has strong applicability to the driving anti-skid control of the multi-axis independent electric driving unmanned vehicles such as 4x4, 6x6, 8x8 and the like with steering mechanisms.
Drawings
FIG. 1 is a schematic illustration of closed loop control of longitudinal speed of an unmanned vehicle with drive antiskid control;
FIG. 2 is a flow chart of actual longitudinal vehicle speed acquisition proposed by the present invention;
FIG. 3 is a graph of four exemplary road surface characteristics;
FIG. 4 is a schematic illustration of closed loop control of longitudinal speed of an unmanned vehicle with drive slip control based on visual identification of optimal slip rate estimation;
FIG. 5 is a block diagram of a dual closed loop cascade based drive anti-skid control method;
FIG. 6 is a collection of motor speed data;
FIG. 7 is a graph of the control test of the driving anti-slip algorithm under a certain trench-crossing condition.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples.
The embodiment provides a 6x6 electric drive unmanned vehicle drive anti-skid control method, which is suitable for the electric drive unmanned vehicle with independent driving of each wheel, has small calculation amount of a drive anti-skid algorithm, is convenient for real-time calculation, has stronger robustness, has stronger adaptability and can realize the anti-skid of different pavements.
The 6x6 electrically driven drone has six wheels and each wheel is driven independently, i.e. it has six drive wheels.
The longitudinal speed closed-loop control flow of the unmanned vehicle adopting the driving anti-skid control method is shown in figure 1, and the driving anti-skid control method comprises the following specific steps:
step one: and estimating longitudinal speed of the unmanned vehicle by using multiple sensors and multiple data source information fusion:
the vehicle speed estimation method based on kinematics/dynamics alone or by only utilizing wheel speed information (wheel speed information), INS information and GPS information is difficult to meet the estimation precision requirement in the vehicle running process, and in order to make up the defects of a single method or a single information source, the scheme provides a vehicle speed estimation method based on multi-method and multi-information fusion.
As shown in fig. 2, firstly, two sets of longitudinal vehicle speed data provided by a vehicle inertial navigation (without an odometer) +gps integrated navigation system are acquired, and whether the provided longitudinal vehicle speed data has a differential signal is judged:
if there is a differential signal, longitudinal vehicle speed data v of the differential signal is obtained DIGY As the actual longitudinal vehicle speed v y ；
If the differential signal is not available, further judging whether the vehicle inertial navigation (without an odometer) +GPS integrated navigation system has a GPS signal (whether the GPS is unlocked);
if there is GPS signal, correcting the longitudinal vehicle speed data obtained by integrating the inertial navigation longitudinal acceleration by using the GPS vehicle longitudinal speed signal (namely, data fusion of the GPS and the inertial navigation data), and taking the corrected longitudinal vehicle speed as the actual longitudinal vehicle speed v y ；
If the GPS signal is not available, longitudinal vehicle speed data provided by pure inertial navigation in a vehicle inertial navigation (without an odometer) +GPS integrated navigation system is obtained, and according to the inertial navigation precision:
for short distance/short time special working condition (such as straight running, etc.), longitudinal speed data of pure inertial navigation is adopted as actual longitudinal speed v y ；
For long distance/long time general working condition running, the following method is adopted to estimate the actual longitudinal vehicle speed v y ：
The wheel speeds (namely the rotation speeds of the driving wheels) of six driving wheels in the 6x6 electrically-driven unmanned vehicle are obtained through a chassis motor controller, and then the actual longitudinal vehicle speed v is estimated through the following method y ：
101: median n for obtaining wheel speeds of six driving wheels median Wherein the drive wheel speed unit is revolutions per minute (rpm);
102: current actual longitudinal vehicle speed v y Is 1.4826 (absolute median difference) times the median n of the wheel speed of the driving wheel median Converted linear velocity v ymedian The method comprises the following steps:
v y ＝1.4826v ymedian
wherein v is ymedian The calculation formula of (2) is as follows:
v ymedian ＝rω median
wherein: r is the rolling radius of the driving wheel (here, the rolling radius of each driving wheel is considered to be the same without considering the fine difference in the rolling radius of each driving wheel caused by the difference of tire pressures); omega median For the median of the angular speeds of the six driving wheels, the calculation formula is:
the rotation speed median of the driving wheel is adoptedNumber n median The reason for (a) is that the median wheel speed is more reliable in determining whether there is wheel slip or not than the average wheel speed.
Step two: according to the estimated current actual longitudinal speed v of the vehicle in step one y And the current wheel speed w of each driving wheel i Calculating the current slip rate lambda of each driving wheel i ；
Wheel slip is defined as the relative error between the wheel speed and the absolute speed of the vehicle body.
Slip ratio λ of drive wheel i (i=1, 2,3,4,5, 6) in vehicle-driving state i The calculation formula of (2) is as follows:
slip rate λ of drive wheel i (i=1, 2,3,4,5, 6) in the vehicle braking state i The calculation formula of (2) is as follows:
wherein: omega i I=1, 2,3,4,5,6 for the current angular speed of the drive wheel i; the current rotating speed data of the driving motor controller can be fed back and obtained, and the calculation formula is as follows:n i the current rotating speed fed back by the rotating speed sensor is the driving motor of the driving wheel i, and the unit is the rotation per minute (rpm);
v yi taking the projection value of the absolute speed of the mass center of the vehicle at the position of the driving wheel i into consideration, taking the research object of the scheme into consideration that the electric driven unmanned vehicle is in sliding steering 6x6, namely the unmanned vehicle has no steering mechanism, and considering v yi ＝v y 。
Step three: judging whether each driving wheel slips or not
Setting a slip ratio limit value, and marking the driving wheel exceeding the limit value as a slip wheel, specifically:
the slip ratio control range with ideal slip ratio control (slip ratio control) effect is 10% -30%, and the maximum value of slip ratio allowed by each driving wheel (namely, the maximum slip ratio limit value) is set as follows:
λ imax ＝2λ iopt
wherein: lambda (lambda) imax A maximum slip limit value for the drive wheel i, (i=1, 2,3,4,5, 6); lambda (lambda) iopt For optimum slip rate lambda of drive wheel i iopt 。
The decision logic for whether the driving wheel i (i=1, 2,3,4,5, 6) is slipping is:
f(λ i >λ imax )
{slip_flag i ＝1；}
else
{slip_flag i ＝0；}
wherein the slice_flag i The slip determination flag bit for the driving wheel i (i=1, 2,3,4,5, 6) is considered to slip when the flag bit is 1, and is considered not to slip when the flag bit is 0. I.e. when the slip rate lambda of the driving wheel i i If the slip ratio is greater than the maximum slip ratio limit value, the driving wheel is considered to slip, otherwise, the driving wheel is not considered to slip.
The optimum slip rate lambda iopt The estimation method of (1) comprises the following steps:
optimum slip rate lambda iopt The method can be set according to an empirical value, or find out better through an evaluation index, or autonomously regulate according to the road surface classification of the autonomous sensing unit, or look up a table to obtain better slip rate.
The method adopts the estimation of the optimal slip rate of different roads based on visual identification, namely the acquisition of the optimal slip rate is a method for classifying and identifying the roads based on a visual system formed by combining a monocular camera and an infrared camera, wherein the monocular camera is used for daytime and the infrared camera is used for night. The optimal slip rate estimation steps are:
301: optimal slip rate estimation based on empirical formula:
the tire-road characteristic curve of the tire of the vehicle under investigation on various road surfaces, i.e. the slip ratio-longitudinal adhesion coefficient relationship of the tire, can be obtained by experiments. It is assumed that the tire-road characteristic curves of the known tire on four typical roads are shown as four solid lines in fig. 3. Four typical road surfaces are respectively a good road surface (dry asphalt road surface), a wet asphalt road surface, a snow covered road surface and an ice road surface; the optimal slip rates corresponding to the four typical pavements are respectively as follows:
λ opt1 ＝0.174，λ opt2 ＝0.207，λ opt3 ＝0.167，λ opt4 ＝0.076
wherein lambda is opt1 Optimal slip ratio for good road surface (dry asphalt road surface), lambda opt2 Is the optimal slip rate lambda of wet asphalt pavement opt3 Is the optimal slip rate lambda of the snow covered pavement opt4 Is the optimal slip rate of the frozen road surface.
If the similarity degree χ between the current road surface and four typical road surfaces can be obtained 1 ，χ 2 ，χ 3 ，χ 4 The optimal slip rate of the current road surface can be estimated through weighted average of four typical road surface optimal slip rates; the weighted average calculation formula for calculating the optimal slip ratio is:
302: degree of similarity x 1 ，x 2 ，x 3 ，x 4 Acquisition of (a)
Matching four typical road surfaces according to the image of the current road surface acquired by the vision system, and determining the similarity degree χ according to the similarity with each typical road surface 1 ，χ 2 ，χ 3 ，χ 4 Is a value of (2). The method comprises the steps of collecting current road surface images by using a vehicle-mounted monocular camera or an infrared camera, carrying out color feature identification, photo brightness feature analysis, characteristic matching, polarization level monitoring and the like on the road surface through algorithms such as a neural network and the like, and finally obtaining probability values p of matching similarity between the road surface on the road surface and four typical road surfaces respectively 1 ，p 2 ，p 3 ，p 4 Will probability value p 1 ，p 2 ，p 3 ，p 4 As similarity χ 1 ，χ 2 ，χ 3 ，χ 4 Is a value of (2). The method comprises the following steps:
3021. extracting a moving target from continuous sequence image data provided by a vehicle-mounted monocular camera or an infrared camera by using a Gaussian mixture model for monitoring the moving target, and then removing the moving target to obtain a background picture of an image;
3022. converting a background picture into an HIS space, wherein HIS refers to the tone, saturation and intensity of the picture; selecting N samples (N is more than or equal to 2) of four road surface states of good road surface, accumulated water, snow and ice on a background picture, determining a threshold Y1 of brightness, and pre-judging the road surface accumulated water condition when the brightness of an acquired real-time image is less than the threshold Y1; when the brightness of the real-time image is larger than a threshold value Y1, the road surface condition is prejudged;
collecting training sets of various types, randomly extracting 200 images (240 images in total) of four typical road surface states respectively acquired by a vehicle-mounted monocular camera or an infrared camera, wherein 100 images are taken as training samples, and 100 test samples are taken; selecting a characteristic vector composed of hue H, brightness I of a picture, energy, entropy and variance in a gray texture co-occurrence matrix of the picture; the samples are then trained with an SVM class learner, the selected kernel function being a gaussian radial basis function kernel RBF,
wherein: k (x, x) i ) Representing a kernel function, x is any point on the sample image in the high-dimensional feature space, x i The center point of the kernel function designated on the high-dimensional space image is the sigma of the width parameter of the set kernel function; obtaining a classification template based on a kernel function, and finally identifying road images through the classification template to respectively obtain probability values p of matching similarity between the current road surface and four typical road surfaces 1 ，p 2 ，p 3 ，p 4 As χ 1 ，χ 2 ，x 3 ，x 4 Is a value of (2).
Similarity χ 1 ，χ 2 ，χ 3 ，χ 4 The value of (2) is substituted into the weighted average calculation formula for calculating the optimal slip ratio to obtain the weighted average of the optimal slip ratioWeighting the optimum slip ratio to the average +.>As the optimum slip ratio for each drive wheel.
Step four: longitudinal speed following control of unmanned vehicle based on PID controller
In the actual application of driving anti-skid, the output torque regulation of the hub motor is influenced by the output of a longitudinal speed PID controller and a slip rate PID controller, and the difference between the two values is directly used as a torque control command of the driving motor in the scheme. A schematic block diagram of the closed-loop control of the longitudinal speed of the unmanned vehicle with the slip ratio PID controller based on the visual recognition of the optimal slip ratio estimation is shown in fig. 4.
Firstly, judging whether a driving wheel enters a slip state based on driving wheel slip judgment logic in the third step, and if the driving wheel does not slip, sending a driving motor torque command T by a longitudinal vehicle speed PID controller i Directly transmitting the torque control command to a motor controller to execute the torque control command; if the driving wheel is detected to be in a slipping state, a' double-closed-loop cascade driving anti-slip control algorithm is started, the inner loop aims at tracking the optimal slip rate, the outer loop aims at adjusting the longitudinal vehicle speed, and double-target adjustment is performed to realize anti-slip torque control, as shown in fig. 5.
That is, the inner ring takes whether the driving wheel slips as a switching condition, and the optimal slip rate lambda iopt As the desired slip ratio, a slip ratio PID controller is designed based on an error between the actual slip ratio and the desired slip ratio (i.e., a slip ratio PID controller is designed for preventing slip of the drive wheels, the slip ratio PID controller being designed for controlling the actual slip ratio around the desired slip ratio), a drive slip prevention torque control amount is obtained, and a torque control amount obtained by the longitudinal vehicle speed PID controller is subtracted from the drive slip prevention torque control amountThe amount is used as an input torque command for driving the motor. The method comprises the following steps:
in the unmanned vehicle longitudinal vehicle speed following control based on the longitudinal vehicle speed PID controller, proportional Integral (PI) control is used to control the actual longitudinal vehicle speed around the desired longitudinal vehicle speed with the unmanned vehicle longitudinal vehicle speed as a controlled amount. The expected longitudinal vehicle speed issued by the upper control system is taken as the reference longitudinal vehicle speed v yref The actual longitudinal speed v of the vehicle obtained in the step one y Deviation Deltav from the desired longitudinal vehicle speed y ＝v y -v yref As the input quantity of the longitudinal speed PID controller, the proportional integral of the input quantity is used for obtaining the output of the longitudinal speed PID controller:
wherein:is proportional constant (L)>Is an integral constant.
Calculating the longitudinal vehicle speed PID controller output T of the driving wheel i (i=1, 2,3,4,5, 6) according to the above formula i The output torque of the driving wheel corresponding to the hub motor is then sequentially adjusted so as to control the longitudinal vehicle speed of the driving wheel to be near the desired longitudinal vehicle speed.
When a certain driving wheel slips, the slip rate PID controller is started to obtain the optimal slip rate lambda of the road surface in the step three opt As reference slip rate lambda ref And (3) the deviation delta lambda=lambda-lambda between the actual slip rate lambda of the wheel detected in the step (II) and the optimal slip rate ref As an input quantity of the slip ratio PID controller, the input quantity is proportional-integrated to obtain an output of the slip ratio PID controller (i.e., the drive slip-preventing torque control quantity T) λ )：
T λ ＝K pλ Δλ+K Iλ ∫Δλdt
Wherein: k (K) pλ Is a proportionality constant, K Iλ Is an integral constant.
Thus, after the slip ratio PID controller is started, the longitudinal vehicle speed torque control amount T of the driving wheel calculated by the longitudinal vehicle speed PID controller is calculated i Subtracting the drive slip torque control amount T of the drive wheel λ The method comprises the steps of carrying out a first treatment on the surface of the And taking the difference value of the two as an input torque command of a driving motor corresponding to the driving wheel.
The effect of the drive slip strategy described above for a certain trench crossing can be analysed by the motor data and actual experimental observations collected in figures 6 and 7.
It can be seen from fig. 6 and 7 that the rotation speeds of the motor #3 and the motor #4 of the second shaft are larger than those of the motors of the other shafts, the motor is in a slipping state, and the actual torque response and the torque command curve can be used for achieving the purpose of reducing the slip trend of the torque adjustment after the intervention of the driving anti-slip control algorithm. At the same time, the slip phase is also seen to last more than 2s from the test results, because the actual state torque drops to approximately 0, but because the reverse electric braking torque is not enabled, the two motors continue to rotate due to inertia when the shaft is idling. On the one hand, the algorithm parameters need to be further optimized and debugged, and on the other hand, under the condition that the actual torque is close to 0, the short-time idle state of the motor can be regarded as a safe slip state.
Example 2:
on the basis of the above embodiment 1, considering that the divisor of which the denominator is close to 0 needs to be avoided when the actual calculation of the current slip ratio engineering is performed, the calculation formula of the slip ratio is modified to be
Slip ratio λ of drive wheel i (i=1, 2,3,4,5, 6) in vehicle-driving state i The calculation formula of (2) is as follows:
slip ratio lambda in the braking state of the vehicle i The calculation formula of (2) is as follows:
considering that the longitudinal speed tracking control driving state and the braking state of the unmanned vehicle are frequently switched, if the vehicle speed calculated according to the wheel speed is larger than the actual vehicle speed, a first formula is used, and if the vehicle speed is smaller than the actual vehicle speed, a second formula is used. The calculation formula of slip ratio can be summarized as:
in addition, the longitudinal vehicle speed is considered to be very low v y Wheel speed n at a speed of less than or equal to 0.1m/s i The measurement error of (2) is large, so that the driving skid prevention is not considered when the longitudinal vehicle speed is low.
The foregoing is only one embodiment of the present invention and is not intended to limit the invention, but any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the claims of the instant invention.
Claims (8)
1. The anti-skid control method for the driving of the sliding steering electric driven unmanned vehicle is characterized by comprising the following steps of:
step one: acquiring the actual longitudinal speed v of the unmanned vehicle y ；
Step two: calculating the current slip rate lambda of each driving wheel i I=1, …, N is the total number of drive wheels of the drone;
step three: judging whether each driving wheel slips or not: setting the maximum slip limit value as the slip lambda of the driving wheel i i If the slip ratio is greater than the maximum slip ratio limit value, the driving wheel is considered to slip;
step four: longitudinal vehicle speed control with slip ratio PID controller is adopted for unmanned vehicles:
when the step three judges that the driving wheel slips, taking the optimal slip rate of the driving wheel as an expected slip rate, obtaining a driving anti-slip torque control quantity through a slip rate PID controller according to the error of the current slip rate and the expected slip rate of the driving wheel, and subtracting the driving anti-slip torque control quantity from the torque control quantity obtained by a longitudinal vehicle speed PID controller to be used as an input torque control quantity of a driving motor of the corresponding driving wheel;
in the first step, a combined navigation system of vehicle inertial navigation and GPS is adopted, and data of the GPS and the inertial navigation are subjected to data fusion to estimate the longitudinal speed of the unmanned vehicle:
firstly, judging whether two groups of longitudinal vehicle speed data provided by the integrated navigation system have differential signals or not:
if the differential signal exists, longitudinal vehicle speed data with the differential signal is taken as an actual longitudinal vehicle speed v y ；
If the differential signal is not available, further judging whether the integrated navigation system has a GPS signal;
if the GPS signal exists, the longitudinal speed data obtained by integrating the inertial navigation longitudinal acceleration is corrected by using the GPS vehicle longitudinal speed signal, and the corrected longitudinal speed is taken as the actual longitudinal speed v y ；
If no GPS signal exists, longitudinal vehicle speed data provided by pure inertial navigation in the integrated navigation system is obtained as the actual longitudinal vehicle speed v y 。
2. The skid control method for a skid steered electrically driven unmanned vehicle as defined in claim 1, wherein if there is no GPS signal, longitudinal vehicle speed data provided by pure inertial navigation in the integrated navigation system is obtained:
for the running condition of short distance/short time, longitudinal vehicle speed data provided by pure inertial navigation is adopted as the actual longitudinal vehicle speed v y ；
For long distance/long time driving conditions, the following method is adopted to estimate the actual longitudinal vehicle speed v y ：
v y ＝1.4826v ymedian
Wherein:n median in the wheel speeds of all driving wheelsNumber of bits, v ymedian The linear speed obtained by converting the median of the wheel speed of the driving wheel is r which is the rolling radius of the driving wheel.
3. The skid control method for a skid steer electrically driven unmanned vehicle according to claim 1 or 2, wherein in the second step, the calculation formula of the skid rate is as follows:
slip rate lambda of driving wheel i in vehicle driving state i The calculation formula of (2) is as follows:
slip rate lambda of driving wheel i in vehicle braking state i The calculation formula of (2) is as follows:
wherein: r is the rolling radius of the driving wheel; omega i Is the current angular velocity of the drive wheel i.
4. The skid control method for skid steer electrically driven unmanned vehicle drive according to claim 1, wherein the maximum skid rate limit value of each drive wheel is:
λ imax ＝2λ iopt
wherein: lambda (lambda) imax For maximum slip limit value of driving wheel i, lambda iopt For optimum slip rate lambda of drive wheel i iopt 。
5. The skid control method for a skid steer electrically driven unmanned vehicle according to claim 4, wherein the optimal skid rate λ iopt The estimation steps of (a) are:
estimating optimal slip rates of different road surfaces based on visual recognition, wherein the road surfaces are classified and recognized by a visual system formed by combining a monocular camera and an infrared camera during the visual recognition, and the monocular camera is used in the daytime and the infrared camera is used at night;
the tire-road surface characteristic curves of the tires of the unmanned vehicles on four typical road surfaces, namely the relation between the slip rate and the longitudinal attachment coefficient of the tires, can be obtained through tests; the four typical road surfaces are a good road surface, a wet asphalt road surface, a snow covered road surface and an ice road surface respectively; optimum slip ratio of good road surface is lambda opt1 Optimum slip ratio of wet asphalt pavement is lambda opt2 The optimal slip rate of the snow covered pavement is lambda opt3 Optimal slip ratio of ice road surface is lambda opt4 ；
The weighted average calculation formula for calculating the optimal slip ratio is:
wherein: x-shaped articles 1 ，χ 2 ，χ 3 ，χ 4 The similarity degree between the current road surface and four typical road surfaces is respectively set;
then, four typical road surfaces are matched according to the image of the current road surface acquired by the vision system, and the similarity degree χ is determined according to the similarity with each typical road surface 1 ，χ 2 ，χ 3 ，χ 4 And weighting the value of (2) to the optimum slip ratio obtained therebyAs the optimum slip ratio for each drive wheel.
6. The skid control method for a skid steer electrically driven unmanned vehicle according to claim 1, wherein in the fourth step, the skid ratio PID controller uses the optimal skid ratio as the reference skid ratio λ ref The actual slip rate lambda and the reference slip rate lambda of the wheel calculated in the step two are calculated ref Deviation Δλ=λ - λ ref As the input quantity of the slip ratio PID controller, the output of the slip ratio PID controller obtained after proportional integration of the input quantity is the instant driving anti-slip torque control quantity T λ ：
T λ ＝K pλ Δλ+K Iλ ∫Δλdt
Wherein: k (K) pλ Is a proportionality constant, K Iλ Is an integral constant.
7. The skid control method for a skid steer electrically driven unmanned vehicle according to claim 1 or 2, wherein in the second step, the calculation formula of the skid rate is as follows:
slip rate lambda of driving wheel i in vehicle driving state i The calculation formula of (2) is as follows:
slip rate lambda of driving wheel i in vehicle braking state i The calculation formula of (2) is as follows:
wherein: i=1, …, N is the total number of drive wheels of the drone; r is the rolling radius of the driving wheel; omega i Is the current angular velocity of the drive wheel i.
8. The skid control method for a skid steer electrically driven unmanned vehicle according to claim 1 or 2, wherein in the second step, the skid rate λ of the driving wheel i is calculated i Is calculated according to the formula:
wherein: r is the rolling radius of the driving wheel; omega i Is the current angular velocity of the drive wheel i.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111308603.4A CN114683871B (en) | 2021-11-05 | 2021-11-05 | Driving anti-skid control method for sliding steering electric driving unmanned vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111308603.4A CN114683871B (en) | 2021-11-05 | 2021-11-05 | Driving anti-skid control method for sliding steering electric driving unmanned vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114683871A CN114683871A (en) | 2022-07-01 |
| CN114683871B true CN114683871B (en) | 2024-02-06 |
Family
ID=82135536
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111308603.4A Active CN114683871B (en) | 2021-11-05 | 2021-11-05 | Driving anti-skid control method for sliding steering electric driving unmanned vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114683871B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118514535B (en) * | 2024-07-19 | 2024-09-20 | 博格华纳汽车零部件（武汉）有限公司 | Anti-skid control method, system and storage medium for driving electric automobile |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004351945A (en) * | 2003-05-26 | 2004-12-16 | Tochigi Fuji Ind Co Ltd | Differential control device |
| CN101549689A (en) * | 2009-04-30 | 2009-10-07 | 清华大学 | A control method for driving torque of three-axis-driven hybrid vehicle |
| CN101858980A (en) * | 2010-05-18 | 2010-10-13 | 东南大学 | Intelligent hypercompact combination navigation method of vehicle-mounted GPS software-based receiver |
| CN102602547A (en) * | 2012-01-10 | 2012-07-25 | 大连理工大学 | Wheeled lunar vehicle driving control method based on slip ratio adjustment |
| CN107402012A (en) * | 2016-05-20 | 2017-11-28 | 北京自动化控制设备研究所 | A kind of Combinated navigation method of vehicle |
| CN108731667A (en) * | 2017-04-14 | 2018-11-02 | 百度在线网络技术（北京）有限公司 | The method and apparatus of speed and pose for determining automatic driving vehicle |
| CN109895754A (en) * | 2019-03-05 | 2019-06-18 | 中南大学 | A kind of antislip of train control method and its control device based on optimal slip rate |
| CN111688707A (en) * | 2020-05-26 | 2020-09-22 | 同济大学 | Vision and dynamics fused road adhesion coefficient estimation method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102114782B (en) * | 2010-11-29 | 2012-11-21 | 中国科学院深圳先进技术研究院 | Slip rate detection method and system for electric vehicle |
-
2021
- 2021-11-05 CN CN202111308603.4A patent/CN114683871B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004351945A (en) * | 2003-05-26 | 2004-12-16 | Tochigi Fuji Ind Co Ltd | Differential control device |
| CN101549689A (en) * | 2009-04-30 | 2009-10-07 | 清华大学 | A control method for driving torque of three-axis-driven hybrid vehicle |
| CN101858980A (en) * | 2010-05-18 | 2010-10-13 | 东南大学 | Intelligent hypercompact combination navigation method of vehicle-mounted GPS software-based receiver |
| CN102602547A (en) * | 2012-01-10 | 2012-07-25 | 大连理工大学 | Wheeled lunar vehicle driving control method based on slip ratio adjustment |
| CN107402012A (en) * | 2016-05-20 | 2017-11-28 | 北京自动化控制设备研究所 | A kind of Combinated navigation method of vehicle |
| CN108731667A (en) * | 2017-04-14 | 2018-11-02 | 百度在线网络技术（北京）有限公司 | The method and apparatus of speed and pose for determining automatic driving vehicle |
| CN109895754A (en) * | 2019-03-05 | 2019-06-18 | 中南大学 | A kind of antislip of train control method and its control device based on optimal slip rate |
| CN111688707A (en) * | 2020-05-26 | 2020-09-22 | 同济大学 | Vision and dynamics fused road adhesion coefficient estimation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114683871A (en) | 2022-07-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gräber et al. | A hybrid approach to side-slip angle estimation with recurrent neural networks and kinematic vehicle models | |
| WO2021103797A1 (en) | Method for adaptive estimation of road surface adhesion coefficient for vehicle with complex excitation conditions taken into consideration | |
| US6662097B2 (en) | System for computing a road surface frictional coefficient | |
| Lin et al. | Neural-network hybrid control for antilock braking systems | |
| CN109910897B (en) | Safe distance estimation method based on front road surface peak value adhesion coefficient | |
| US20050230172A1 (en) | Method for controlling a shiftable clutch in the drive train of a 4-wheel drive motor vehicle | |
| JP5339121B2 (en) | Slip rate estimation device and method, and slip rate control device and method | |
| US20030074123A1 (en) | Vehicle motion control system | |
| WO2018007079A1 (en) | Improvements in vehicle speed control | |
| CN114683871B (en) | Driving anti-skid control method for sliding steering electric driving unmanned vehicle | |
| AU2015273564B2 (en) | Vehicle control system and method | |
| CN112793577B (en) | Agricultural machinery four-wheel drive control method and system and agricultural machinery | |
| US20230174154A1 (en) | Differential traction drive and steering axis coordination system and method | |
| JP3873588B2 (en) | Vehicle autopilot control device | |
| Leng et al. | Tire-road peak adhesion coefficient estimation method based on fusion of vehicle dynamics and machine vision | |
| US6176336B1 (en) | Vehicle steering control | |
| Lu et al. | From vehicle stability control to intelligent personal minder: Real-time vehicle handling limit warning and driver style characterization | |
| JP2010500950A (en) | Improved anti-skid device for vehicle drive wheels and method for implementing the same | |
| CN108137092A (en) | Electric power-assisted steering apparatus | |
| Jin et al. | Road friction estimation method based on fusion of machine vision and vehicle dynamics | |
| CN102514560B (en) | Method for acquiring longitudinal running speed information of vehicle in anti-skid control system | |
| CN111267949B (en) | Slip steering control system for vehicle | |
| TWM515505U (en) | Bike deadlock prevention braking system | |
| TWI541163B (en) | Bicycle anti - lock braking system and method thereof | |
| Reina | Methods for wheel slip and sinkage estimation in mobile robots |
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 |