CN112668095A - Design method of active front wheel steering controller - Google Patents

Design method of active front wheel steering controller Download PDF

Info

Publication number
CN112668095A
CN112668095A CN202011526982.XA CN202011526982A CN112668095A CN 112668095 A CN112668095 A CN 112668095A CN 202011526982 A CN202011526982 A CN 202011526982A CN 112668095 A CN112668095 A CN 112668095A
Authority
CN
China
Prior art keywords
vehicle
front wheel
yaw rate
slip angle
wheel steering
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.)
Granted
Application number
CN202011526982.XA
Other languages
Chinese (zh)
Other versions
CN112668095B (en
Inventor
魏振亚
汪洪波
张先锋
崔国良
丁雨康
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui Kasip Intelligent Technology Co ltd
Original Assignee
Anhui Kasip Intelligent Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Anhui Kasip Intelligent Technology Co ltd filed Critical Anhui Kasip Intelligent Technology Co ltd
Priority to CN202011526982.XA priority Critical patent/CN112668095B/en
Publication of CN112668095A publication Critical patent/CN112668095A/en
Application granted granted Critical
Publication of CN112668095B publication Critical patent/CN112668095B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Steering Control In Accordance With Driving Conditions (AREA)

Abstract

The invention relates to the field of vehicle active safety control, in particular to a design method of an active front wheel steering controller. The method comprises the following steps: s1: determining a basic motion equation and a state space equation of a vehicle model; s2: obtaining the expected yaw rate omega according to the vehicle modeldAnd desired slip angle betad(ii) a S3: for the maximum expected yaw rate ωd maxAnd a maximum desired slip angle betad maxCorrecting; s4: setting a combined deviation e of a yaw rate and a centroid slip angle of a vehicle1And an integral term is introduced into the switching function to obtain a sliding mode switching function s1(ii) a S5: obtaining a first derivative function of a sliding mode switching function
Figure DDA0002851103090000011
S6: meterCalculating a switching control angle u of a vehicleswAnd equivalent control angle ueqThe final control steering angle of the active front wheel steering controller is deltaAFS＝usw+ueq. The controller designed based on the method can improve the response speed of the vehicle steering system, can adapt to the stability state of the vehicle, and can stabilize the vehicle under the conditions of larger interference and poorer stability.

Description

Design method of active front wheel steering controller
Technical Field
The invention relates to the field of vehicle active safety control, in particular to a design method of an active front wheel steering controller.
Background
With the continuous improvement of living standard of people, the automobile keeping quantity of residents in China continuously rises, and meanwhile, the incidence rate of traffic accidents in China also increases year by year. Related researches find that the loss of the transverse stability of the automobile is a great cause of frequent traffic accidents; the driver often has difficulty in accurately judging whether the vehicle is in a dangerous unstable state according to the driving experience of the driver. If the vehicle can discover the dangerous state in time and give an early warning to the driver, or can carry out active transverse stable control after judging the dangerous state, most traffic accidents can be avoided. This is one of the latest research directions in the technical field of active safety control of vehicles, and is an important basis for automatic driving technology of vehicles, and at present, a great amount of technical achievements have been obtained in this field by various automobile manufacturers and research institutions.
The method effectively judges the transverse stable state of the vehicle to avoid the vehicle from being out of control, and has important significance for ensuring the safety of a driver. However, in the prior art, there still exist a plurality of problems in identifying the lateral stable state of the vehicle in the running process. On one hand, the existing classification method based on the transverse stability data set is difficult to determine the initial clustering center, the processing difficulty of the data is high, and the calculation burden is caused to the system; meanwhile, the processing result cannot accurately reflect the real-time running state of the automobile, and the accurate judgment on the stability of the automobile is influenced. On the other hand, the traditional transverse stability determination method does not fully consider parameters influencing the transverse stability of the vehicle, only analyzes certain characteristic quantities representing the transverse stability of the vehicle to obtain a transverse stability criterion, the data base is relatively weak, and the selected characteristic quantities cannot fully reflect the transverse stable state of the vehicle. Paper [ vehicle driving stability determination method [ J ] liufei, lauluo, danluhua, et al, university of south china academic press (natural science edition), 2014,42(11) ] based on a phase plane method discloses a vehicle driving stability determination method, which establishes a centroid side drift angle-centroid side drift angle velocity phase diagram according to a phase plane analysis method, adopts an improved five-feature-value diamond method to divide a vehicle transverse stability area, and draws a vehicle driving all-condition phase plane diagram based on a Matlab programming environment. This solution has the drawbacks mentioned above.
In addition, the acquisition and processing of characteristic parameters both increase the hardware cost and the operating cost of the vehicle; therefore, the collection of the running characteristic information of the vehicle should also consider the contribution degree of the running characteristic information to the accurate determination of the lateral stability of the vehicle, so as to determine the most necessary and effective running characteristic data of the vehicle, which is also one of the problems to be solved.
On the basis of solving the problem of identifying the lateral stability of the vehicle, a more effective vehicle control method needs to be selected, the instability state of the lateral stability of the vehicle is regulated and controlled in a targeted manner, and a regulation strategy is executed in a targeted manner according to different types of the stability state of the vehicle, so that a better regulation effect is obtained. Obviously, there is a certain difficulty in achieving the above-mentioned objectives through the simple coordination of the existing active front-wheel steering control (AFS) and direct yaw moment control (DYC), and therefore there is a need to design a more efficient AFS and DYC combined control method based on the vehicle lateral stability recognition result.
Disclosure of Invention
In order to overcome the problems in the prior art, the controller designed based on the method can improve the response speed of a vehicle steering system, can adapt to the stability state of a vehicle, and stabilizes the vehicle under the conditions of high interference and poor stability.
The technical scheme provided by the invention is as follows:
a design method of an active front wheel steering controller comprises the following steps:
s1: based on a 2-degree-of-freedom linear vehicle model, assuming that the front wheel steering angle is small and the vehicle speed is constant, determining a basic motion equation and a state space equation of the vehicle model;
s2: obtaining the expected yaw rate omega according to the vehicle modeldAnd desired slip angle betad；
S3: considering the influence of the road surface adhesion coefficient mu on the maximum expected yaw rate omegad maxAnd a maximum desired slip angle betad maxCorrecting;
s4: setting a combined deviation e of a yaw rate and a centroid slip angle of a vehicle1And an integral term is introduced into the switching function to obtain a sliding mode switching function s1；
S5: solving a first derivative of the integral sliding mode switching function to obtain a first derivative function of the sliding mode switching function
Figure BDA0002851103070000021
Simplifying a first derivative function;
s6: bringing a two-degree-of-freedom kinetic equation into a first derivative function of the sliding mode switching function
Figure BDA0002851103070000022
In the method, under the condition of ensuring that the sliding mode arrival condition is met, the switching control corner u of the vehicle is calculatedswAnd equivalent control angle ueqThe final control steering angle of the active front wheel steering controller is deltaAFS＝usw+ueq。
Further, in step S1, the basic equation of motion of the vehicle model is:
Figure BDA0002851103070000023
in the above formula: beta is a slip angle;
Figure BDA0002851103070000039
for yaw rate, IzRotating inertia around a z-axis for the automobile; ω is the yaw rate of the vehicle,
Figure BDA00028511030700000310
yaw angular acceleration; k is a radical of1Is the total cornering stiffness of the front tyre; k is a radical of2Is the total cornering stiffness of the rear tire; u is the longitudinal speed of the automobile; deltafIs a front wheel corner; a is the distance from the center of mass to the front axis; b is the distance from the center of mass to the rear axle; m is the mass of the automobile;
the state space equation is:
Figure BDA0002851103070000031
in the equation, the ratio of the total of the components,
Figure BDA0002851103070000032
Figure BDA0002851103070000033
C＝[1 1]，
in the above-mentioned matrix, the matrix is,
Figure BDA0002851103070000034
Figure BDA0002851103070000035
Figure BDA0002851103070000036
further, in step S2, the desired yaw rate ω is obtained based on the vehicle modeldAnd desired slip angle betadRespectively shown as the following formula:
Figure BDA0002851103070000037
wherein ,
Figure BDA0002851103070000038
in the above formula, L is the vehicle wheel base; v is the vehicle lateral velocity; u is the longitudinal speed of the automobile; deltafIs a front wheel corner; a is the distance from the center of mass to the front axis; b is the distance from the center of mass to the rear axle; m is the mass of the automobile; k is a radical of1Is the total cornering stiffness of the front tyre; k is a radical of2The total cornering stiffness of the rear tire.
Further, in step S3, the maximum desired yaw rate ω is set in consideration of the influence of the road surface friction coefficientd maxAnd a maximum desired slip angle betad maxThe modified calculation formula is as follows:
Figure BDA0002851103070000041
in the above formula, mu is the friction coefficient of the road surface, u is the longitudinal speed of the automobile, and a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; m is the mass of the automobile; and L is the vehicle wheel base.
Further, in step S5, the vehicle yaw rate and the centroid-side deviation angle combined deviation e1The calculation formula of (a) is as follows:
e1＝(ωd-ω)+η(βd-β)；
in the above formula, ω is yaw rate; omegadA desired yaw rate; beta is adA desired slip angle; beta is a slip angle; eta is a system adjusting parameter, and eta is a constant with a positive value and represents the proportion of the mass center slip angle and the yaw rate participating in the control.
Further, the integral sliding mode switching function is:
Figure BDA0002851103070000042
in the above formula, e1The joint deviation of the yaw angular velocity and the centroid slip angle of the vehicle is obtained; t is time; c is a sliding mode control adjusting parameter; the sliding mode switching function is then:
Figure BDA0002851103070000043
in the above formula, ω is yaw rate; omegadA desired yaw rate; beta is adA desired slip angle; beta is a slip angle; t is time; and c is a sliding mode control adjusting parameter.
Further, in step S5, the first derivative function of the sliding mode switching function is:
Figure BDA0002851103070000044
wherein, to simplify the expression, assume that there is a variable Ω, let
Ω＝c[(ωd-ω)+η(βd-β)]，
The first derivative function transforms to:
Figure BDA0002851103070000045
further, in step S6, the system switching control coefficient uswThe calculation process of (2) is as follows:
in two-degree-of-freedom kinetic equations
Figure BDA0002851103070000046
Into a first derivative function
Figure BDA0002851103070000047
In (1), obtaining:
Figure BDA0002851103070000048
let the above formula be 0, then the equivalent control corner u can be solvedeq：
Figure BDA0002851103070000051
Further, the system switches the control corner uswThe following were used:
Figure BDA0002851103070000052
in the above formula, η is a constant whose value is positive, and represents the proportion of the centroid yaw angle and the yaw rate participating in the control.
Further, the control steering angle δ of the active front wheel steering controllerAFSCalculated from the following formula:
δAFS＝ueq+usw；
in the above formula, uswControlling the angle of rotation u for system switchingeqIs an equivalent control angle.
The active front wheel steering controller designed based on the method has the following beneficial effects:
1. the active front wheel steering control in the invention can be based on the existing steer-by-wire system of the vehicle, does not need to design other hardware structures independently, is convenient to realize and has lower cost.
2. By designing an equivalent control corner term, the response speed of a vehicle steering system can be improved, and buffeting of sliding mode control is reduced; by designing a switching control corner term, errors can be eliminated, and integral saturation is suppressed; the combination of the equivalent control corner term and the switching control corner term can improve the robustness of control.
3. In the design process, the control of the yaw rate and the yaw angle is simultaneously considered, and the proportion of the mass center yaw angle and the yaw rate participating in the control is adjusted through distribution coefficients, so that the control accuracy of the yaw rate and the yaw angle can be improved.
4. The vehicle runs in a complex environment and is subjected to more interference from the environment, integral equivalent sliding mode control based on the sliding mode idea has insensitivity to the interference, and the vehicle can be stabilized under the conditions of larger interference and poorer stability.
Drawings
Fig. 1 is a flowchart of a vehicle lateral stability determination and control process in embodiment 1;
FIG. 2 is a time-dependent curve of the steering wheel angle of the vehicle in the CarSim vehicle simulation software of the embodiment 1;
FIG. 3 is a decision diagram obtained after clustering the vehicle lateral stability data sets in this embodiment 1;
FIG. 4 is a data point distribution diagram of the vehicle lateral stability classification in the embodiment 1;
fig. 5 is a schematic structural diagram of the extensive neural network in this embodiment 1;
fig. 6 is a design flowchart of the active front wheel steering controller in the embodiment 1;
fig. 7 is a design flowchart of the direct yaw moment controller in the present embodiment 1;
FIG. 8 is a time-dependent curve of the vehicle front wheel steering angle input value during the Carsim/Simulink combined simulation test of embodiment 1;
FIG. 9 is a comparison curve of the expected value and the actual value of the vehicle yaw rate over time in the combined simulation test of this embodiment 1;
FIG. 10 is a comparison curve of the expected value and the actual value of the vehicle slip angle with time in the combined simulation test of the embodiment 1;
fig. 11 is a flowchart of a vehicle lateral stability determination and control process using the reduced attribute data set in the embodiment 2;
fig. 12 is a variation curve of the training error rate of the extended neural network in the embodiment 2 with the increase of the training period;
fig. 13 is a comparison curve showing the output class and actual class of the extended neural network of this example 2 increasing with the test data.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, the present embodiment provides a vehicle lateral stability determination and control method including the following processes:
first, data set acquisition
When this embodiment considers each item data of real car collection vehicle lateral stability data set, there is great potential safety hazard, and when the vehicle meets emergency, probably causes the injury to navigating mate's personal safety. Therefore, the present embodiment adopts the vehicle simulation software CarSim to collect the characteristic attribute value representing the lateral stability of the vehicle. CarSim, as a parameterized vehicle dynamics software, has the advantages of simple operation, high simulation speed, time saving, high reliability and the like. The present embodiment may design typical vehicle driving conditions in CarSim to obtain lateral stability data of the vehicle.
In order to comprehensively evaluate the state of the vehicle transverse stability, the longitudinal speed u and the steering wheel turning angle delta are selected by comprehensively considering each characteristic attribute representing the vehicle transverse stabilitysLateral velocity v, centroid slip angle beta, roll angle psi, roll angle velocity omegaψYaw angular velocity ω and lateral acceleration ayFront wheel lateral load transfer rate LTR1Rear wheel lateral load transfer rate LTR2And 10 parameters are taken as characteristic attributes.
Wherein the front wheel transverse load transfer rate LTR1And rear wheel lateral load transfer rate LTR2Respectively calculated by the following formula:
LTRx＝(FZLx-FZRx)/(FZLx+FZRx),x＝1,2
in the above formula, FZL1Indicating the vertical load of the left front wheel, FZL2Indicating the vertical load of the left rear wheel, FZR1Showing the vertical load of the front right and front wheels, FZR2Indicating the vertical load of the right rear wheel.
In the embodiment, the vehicle lateral stability test of the ISO standard is referred to, and the condition that the vehicle lateral stability is gradually deteriorated is further designed, and the steering wheel angle change curve after a large number of debugging operations is shown in fig. 2. 10 simulation conditions that the longitudinal speed of the vehicle is 30 km/h-120 km/h and the step length is 10km/h are set. 10 attribute values representing the stability of the vehicle were collected as one data point every 0.05 second, and the simulation time at each longitudinal speed was 10 seconds, resulting in a vehicle lateral attribute data set. The attribute data set contains 2000 data points, each of which contains 10 attribute values.
Two, lateral stability clustering
According to the obtained attribute data set, the present embodiment clusters the vehicle lateral stability by using a "fast search and density peak algorithm (CFSDPF)" to find the number of categories of the vehicle lateral stability. CFSDPF is a new density-based clustering algorithm that can automatically derive the number of classes from a collected data set without determining the initial clustering center. The core idea of CFSDPF is that the local density at the center of a class is higher than the local density of its nearest neighbors. The algorithm determines the clustering center by utilizing the decision graph, can quickly find the density peak of the data set with any shape, and effectively distributes non-central sample points. The basis of clustering is that the density of the cluster center is greater than the density of its surrounding neighbors, and for each data point, the CFSFDP first calculates its local density and the distance between it and other data points with local density.
The clustering process of the "fast search and density peak algorithm" is as follows:
assume the lateral stability data set is:
Figure BDA0002851103070000071
in the above formula, xiAs data point, data point xiAnd data point xjIn betweenThe distance is expressed as:
dij＝dist(xi,xj)；
the local density of the data points is calculated using the following formula:
Figure BDA0002851103070000072
in the above formula, dcIs an empirically determined truncation distance, d in this examplec＝0.0029。ρiDenotes those in S with xiIs less than dcA point of (a);
wherein, the function χ (x) in the calculation formula of the local density is:
Figure BDA0002851103070000073
when x isiWhen the local density of S is maximum, the data point in S and x are calculatediMaximum distance theta ofiThe calculation formula is as follows:
θi＝maxj(dij)；
in the above equation, max is a function of the calculated maximum;
when x isiIs not the maximum, the ratio x in S is calculatediLocal dense data points of (2) and (x)iMinimum distance theta ofiThe calculation formula is as follows:
Figure BDA0002851103070000081
in the above formula, min is a function for calculating the minimum value;
after CFSDPF clustering, each point in the data set can use rhoi and θiTo indicate. From rhoi and θiThe two-dimensional graph drawn as abscissa and ordinate is called a decision graph, and the category center and the number of categories can be determined from the decision graph.
And after the transverse attribute data set is normalized, a classification result is obtained by adopting a CFSDPF algorithm. The cutoff distance dc is set to 0.0029.
In this example, the decision graph containing all data points is shown in fig. 3, and it can be intuitively seen from the decision graph that ρ and θ values of three data points are large and are category centers, and the CFSDPF algorithm can accurately identify the three category centers.
As shown in table 1, the present embodiment defines these three categories as "absolutely stable", "near stable", and "almost unstable", respectively.
Table 1: lateral stability classification results
Figure BDA0002851103070000082
Fig. 4 shows 10 lines consisting of data points, each representing the vehicle stability variation for 10 longitudinal speeds. The driving situation of the vehicle with an initial longitudinal speed of 30-120km/h is shown from top to bottom. The data point at the beginning of each line indicates that the vehicle is in an "absolutely steady" state, which means that the vehicle is in an absolutely steady state, since the vehicle is travelling in a straight line during this time. As the steering wheel turns, the lateral stability of the vehicle gradually becomes "almost destabilized", i.e., the vehicle is almost destabilized, but the vehicle can still be controlled. The point at the end of the curve indicates that the vehicle is in a "near steady" condition when the longitudinal speed is 30km/h, indicating that the vehicle has the ability to remain steady while traveling at a slow speed, even if the steering wheel is rotated by a large angle. The faster the longitudinal speed, the earlier the point of instability will appear at the same steering wheel angle, indicating that longitudinal speed has a significant effect on lateral stability. The remaining black dots are halo dots, representing data points at the edges of the category.
Third, lateral stability recognition
The embodiment adopts an Extension Neural Network (ENN) to identify the lateral stability of the vehicle based on the clustered lateral attribute data set of the vehicle.
ENN was developed on the basis of the theory of extensibility, which assumes that the name of a thing is N, the attribute of the thing is h, the value of the attribute h is g, and the property of the thing can be described by the variable R ═ N, h, g.
For the vehicle lateral stability identification in the present embodiment, N denotes a vehicle lateral stability, h denotes an attribute representing the vehicle lateral stability, and g denotes an attribute value.
Assuming n attributes are present, then:
Figure BDA0002851103070000091
in the above formula, gjRepresenting the range of variation of each attribute, anThe left end point of the range of variation, bnThe right end of the range of variation is indicated.
Suppose there is NdFor data points, the variable R can be expressed as:
Figure BDA0002851103070000092
in the above formula, i is 1 … Nd，NiIs the ith data point, gij＝<aij,bij>Is attribute hiThe range of variation of (a) is called the classical domain, aijThe left end point of the range of variation, bijThe right end of the range of variation is indicated.
ENN is the product of the combination of the theory of extension and neural networks. It uses the topographies to measure the similarity between the data and the class centers. The schematic structure of ENN is shown in FIG. 5, where a is the input layer and b is the output layer. Neurons of the input layer receive attributes of an input data point, and the output layer is used to represent a category of the input data point. There are two connection weights between the input and output neurons.
Figure BDA0002851103070000093
A lower bound representing a connection weight between the jth input neuron and the kth output neuron,
Figure BDA0002851103070000094
representing an upper bound for the connection weight.
The ENN-based lateral stability recognition process comprises two parts, namely a training phase and a recognition phase. Wherein, the steps of the training process are as follows:
(1) the lateral stability dataset is defined as:
Figure BDA0002851103070000095
in the above formula, NdThe total number of data is; in this example Nd＝3000。
The ith data point can be written as:
Figure BDA0002851103070000096
in the above formula, the type of the ith data point is p, and the data point has n attributes; in the embodiment, n is 10;
(2) determining the classical domain of each lateral stability class as an initial weight according to the following formula:
Figure BDA0002851103070000101
in the above formula, min is a function for calculating the minimum value, and max is a function for calculating the maximum value; k 1 … Nc，NcThe total number of categories; in this example Nc＝3。
(3) Calculating the initial class center of each lateral stability class according to the following calculation formula:
Zk＝{zk1,zk2,…,zkn}，
wherein ,
Figure BDA0002851103070000102
(4) reading the ith lateral stability data point and the class p to which this data point belongs according to the lateral stability data set of the following formula:
Figure BDA0002851103070000103
(5) calculating data points according to the formula of calculation of extension distance in the theory of extension
Figure BDA0002851103070000104
The distance from the kth class is calculated as follows:
Figure BDA0002851103070000105
(6) finding data points
Figure BDA0002851103070000106
The class o with the smallest distance to the kth class, such that the following holds:
EDio＝min{EDik}；
in the above formula, min is a function for calculating the minimum value, EDikRepresenting data points
Figure BDA0002851103070000107
Distance from the kth class;
if o is equal to p, operating the step (7), otherwise, re-operating the step (6);
(7) updating the p-th category center and the o-th category center, wherein the updating formula is as follows:
Figure BDA0002851103070000108
updating the p-th category weight and the o-th category weight respectively, wherein the updating formulas of the p-th category weight and the o-th category weight are as follows:
Figure BDA0002851103070000109
Figure BDA0002851103070000111
in the above formula, η is the learning rate, and is determined by the priori knowledge, and in this embodiment, η is 0.01;
(8) repeating the steps (3) to (6), if all data in a certain period are trained, completing one training period, inputting the first data again and carrying out training in the next period;
(9) if the specified training period or error rate is reached, the training process of the ENN is ended.
Wherein, the training error rate is defined as follows:
Figure BDA0002851103070000112
in the above formula, E is the training error rate, NMFor error data of all training periods, NPAll data for all training periods.
In this example, the specified training period is 100 and the specified training error rate E ≦ 1x10-6。
In this example, the purpose of the training phase is to get the final weight of the updated lateral stability class. The training phase is completed and the recognition phase is entered.
Wherein, the steps of the identification process are as follows:
(1) reading the final weight value of the training stage;
(2) the initial class center of the data set is calculated using the following formula:
Zk＝{zk1,zk2,…,zkn}，
wherein ,
Figure BDA0002851103070000113
(3) reading data points collected in real time for testing to obtain a lateral stability data set:
Xt＝{xt1,xt2,...,xtn}，
and calculating the distance between the data point for testing and each category according to the following calculation formula:
Figure BDA0002851103070000114
(4) determining the category of the data points according to the calculation result of the step, and judging whether the following conditions are met:
Figure BDA0002851103070000115
in the above formula, min is a function for calculating the minimum value,
when the distance between the data point and the o-th class meets the formula, the data point is considered to belong to the o-th class;
(5) sequentially identifying all data points, and if all the test data points are identified, finishing the identification process; if the identification of all the data points is not completed, the step (3) is carried out to continue the identification until the identification of all the data points is completed.
Fourth, coordinate control
The coordinated control process for the lateral stability of the vehicle in this example is performed by a coordinated controller consisting of an Active Front Steering (AFS) controller and a direct yaw moment (DYC) controller. In this embodiment, the AFS controller and the DYC controller are respectively designed, and the proportion of the AFS controller and the DYC controller participating in the control is adjusted by an adaptive adjustment coefficient, so as to form the coordinated controller based on the AFS and the DYC in this embodiment.
1. In this example, the AFS controller is designed based on the integral equivalent sliding mode control method, as shown in fig. 6, the design process is specifically as follows:
the design of the AFS controller is based on a 2-degree-of-freedom linear vehicle model and assumes that the front wheel steering angle is small and the vehicle speed is constant.
The basic equation of motion for this model is as follows:
Figure BDA0002851103070000121
in the above formula: beta is a slip angle;
Figure BDA0002851103070000128
for yaw rate, IzRotating inertia around a z-axis for the automobile; ω is the yaw rate of the vehicle,
Figure BDA0002851103070000122
yaw angular acceleration; k is a radical of1Is the total cornering stiffness of the front tyre; k is a radical of2Is the total cornering stiffness of the rear tire; u is the longitudinal speed of the automobile; deltafIs a front wheel corner; a is the distance from the center of mass to the front axis; b is the distance from the center of mass to the rear axle; m is the mass of the vehicle;
the above equation is written in the form of a state space equation as follows:
Figure BDA0002851103070000123
wherein ,
Figure BDA0002851103070000124
C＝[1 1]，
Figure BDA0002851103070000125
Figure BDA0002851103070000126
Figure BDA0002851103070000127
obtaining the expected yaw rate omega through the modeldAnd a desired slip angle betadRespectively as follows:
Figure BDA0002851103070000131
Figure BDA0002851103070000132
wherein ,
Figure BDA0002851103070000133
in the above formula, L is the vehicle wheel base; v is the vehicle lateral velocity; u is the longitudinal speed of the automobile; deltafIs a front wheel corner; a is the distance from the center of mass to the front axis; b is the distance from the center of mass to the rear axle; m is the mass of the vehicle; k is a radical of1Is the total cornering stiffness of the front tyre; k is a radical of2The total cornering stiffness of the rear tire.
In practical situations, the desired values of vehicle yaw angle and yaw rate are limited by the road surface coefficient of friction. Therefore, in the present embodiment, the maximum desired yaw rate ω is set in consideration of the road surface adhesion coefficientd maxAnd a maximum desired slip angle betad maxExpressed as:
Figure BDA0002851103070000134
in the above formula, mu is the friction coefficient of the road surface, u is the longitudinal speed of the automobile, and a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; m is the mass of the automobile; and L is the vehicle wheel base.
And the joint deviation of the vehicle yaw velocity and the centroid slip angle is:
e1＝(ωd-ω)+η(βd-β)；
in the above formula, ω is yaw rate; omegadA desired yaw rate; beta is adA desired slip angle; beta is a slip angle; etaAnd the eta is a constant with a positive value and represents the proportion of the mass center slip angle and the yaw rate participating in the control.
An integral term is introduced into the switching function, and the integral sliding mode switching function is set as follows:
Figure BDA0002851103070000135
in the above formula, e1The joint deviation of the yaw angular velocity and the centroid slip angle of the vehicle is obtained; t is time; c is a sliding mode control adjusting parameter;
further, the sliding mode switching function is:
Figure BDA0002851103070000136
in the above formula, ω is yaw rate; omegadA desired yaw rate; beta is adA desired slip angle; beta is a slip angle; t is time; and c is a sliding mode control adjusting parameter.
And solving a first derivative of the sliding mode switching function to obtain:
Figure BDA0002851103070000141
order to
Ω＝c[(ωd-ω)+η(βd-β)]；
Then
Figure BDA0002851103070000142
In two-degree-of-freedom kinetic equations
Figure BDA0002851103070000143
Brought into
Figure BDA0002851103070000144
In (1), obtaining:
Figure BDA0002851103070000145
let the above formula be 0 to solve the equivalent control corner ueq：
Figure BDA0002851103070000146
In order to ensure that the sliding mode arrival condition is satisfied, a system switching control corner u is designedswThe following were used:
Figure BDA0002851103070000147
in the above formula, η is a constant whose value is positive, and represents the proportion of the centroid yaw angle and the yaw rate participating in the control.
The control corner of the AFS controller is as follows:
δAFS＝ueq+usw。
2. the DYC controller in this example is designed based on a fast nonsingular terminal sliding mode (NFTSM) control method; the NFTSM control has fast finite time convergence and strong robustness.
As shown in fig. 7, the specific process of designing the DYC controller is as follows:
the basic equation of motion of the vehicle model is:
Figure BDA0002851103070000148
Figure BDA0002851103070000149
in the above formula, ω is the yaw rate, u is the longitudinal speed of the vehicle,
Figure BDA00028511030700001410
is a sideDeflection angular velocity, FyFor lateral bias force, IzFor the moment of inertia of the vehicle about the z-axis,
Figure BDA00028511030700001411
for yaw angular acceleration, MDYCIs an additional yaw moment.
Differentiating the first expression and substituting the second expression to obtain:
Figure BDA00028511030700001412
wherein ,
Figure BDA00028511030700001413
namely:
Figure BDA00028511030700001414
is the actual slip angle beta and the desired slip angle betadA difference of (d);
gβthe unmodeled part of the vehicle is an unknown bounded function with the range:
|gβ|≤k′1
in the above formula, k'1Is gβThe boundary of (2);
slip angle beta and additional yaw moment MDYCThe dynamics of the second-order system are met, so the following second-order system is set:
Figure BDA0002851103070000151
in the above formula, x1，x2Representing the state of a second order system.
According to a fast nonsingular terminal sliding mode (NFTSM) control theory, setting the sliding mode surface of the NFTSM as follows:
Figure BDA0002851103070000152
in the above formula, α, γ1、ξ、γ2All are control parameters of a fast nonsingular terminal sliding mode.
Thus, the additional yaw moment is:
Figure BDA0002851103070000153
in the above formula, IzFor the moment of inertia of the vehicle about the z-axis, alpha, gamma1、ξ、γ2、k′1、k′2All are control parameters of a fast nonsingular terminal sliding mode.
In the control parameters of the fast nonsingular terminal sliding mode, alpha is more than 0, xi is more than 0, and gamma is1＞γ2And 1 < gamma1＜2，，k′1＞0，k′2＞0。
The vehicle generates an additional yaw moment through differential braking of different wheels, and which wheel is braked can be judged according to the front wheel turning angle and the direction of the yaw moment. In this example, the braking force distribution strategy is shown in Table 2, where the braking force is represented by FTAnd l is the vehicle track.
TABLE 2 braking force distribution strategy
Figure BDA0002851103070000154
Figure BDA0002851103070000161
3. The coordinated controller of the present embodiment, in which the additional front wheel steering angle δ of the coordinated controller is provided, is obtained based on the active front wheel steering (AFS) controller and the direct yaw moment (DYC) controller designed as described above* AFSAnd an additional yaw moment M* DYCThe calculation formula is as follows:
Figure BDA0002851103070000162
in the above equation, λ is an adaptive adjustment coefficient. The value of lambda is determined by the lateral stability state of the three types of vehicles obtained in the clustering link.
In this embodiment, the coordination control strategy of the coordination controller is as follows.
(1) When the vehicle is in the 'absolute stability' category, the coordination controller is not activated, so that the interference to the driver is avoided;
(2) when the vehicle is in the "near steady" category, the coordinated controller is activated to control the vehicle with the coordinated controllers for AFS and DYC, respectively, and the value of the adaptive adjustment coefficient is determined by:
Figure BDA0002851103070000163
in the above formula, dist1Distance between vehicle attribute and "absolutely stable" category center point; dist2Distance between vehicle attribute and "near instability" category center point;
(3) when the vehicle is in the "almost destabilized" category, the DYC controller is activated alone and the AFS controller is turned off, with λ ═ 0.
In this embodiment, a Carsim/Simulink joint simulation is further used to test the coordination control effect of the coordination controller, in the test process, the designed vehicle runs on a dry road at a constant speed of 80km/h, the friction coefficient μ of the road surface is 0.85, and the front wheel angle input by the driver is as shown in fig. 8; fig. 9 is a comparison curve of the expected value and the actual value of the yaw rate in the model, and fig. 10 is a comparison curve of the expected value and the actual value of the yaw angle. As can be seen from fig. 9 and 10, the yaw rate and the yaw angle of the simulated vehicle can always track the desired values well under the control of the coordinated controller. It can therefore be concluded that: the coordinated controller provided in the example can minimize the deviation of the vehicle yaw rate from the expected value, and simultaneously keep the value of the slip angle close to the expected value, thereby achieving good control effect of the vehicle lateral stability.
Example 2
Fig. 11 shows a flowchart of a vehicle lateral stability determination and control method in the present embodiment, which differs from embodiment 1 in that: in order to reduce the burden of data acquisition and processing, the embodiment appropriately reduces the acquired 10 attribute data, determines the attribute having the greatest influence on the lateral stability of the vehicle, and discards the attribute data having less influence on the lateral stability of the vehicle from the lateral stability data set.
In the embodiment, the attribute reduction is carried out by adopting a neighborhood rough set algorithm; the attribute reduction process is as follows:
the non-null-theoretic domain on the real-number set is defined as:
U＝{x1,x2,…,xn}；
in the above formula, xiA data point representing the lateral stability of the vehicle;
defining A as an attribute set; d is a vehicle lateral stability category, namely a decision attribute; c is a lateral stability characteristic quantity, i.e., a conditional attribute.
Then there are:
a ═ C ═ D and-
Figure BDA0002851103070000171
Wherein, the neighborhood decision system is:
Ndt＝<U,A,D>，
for any xiE.g. U, define
σB(xi)＝{xj|xj∈U,ΔB(xi,xj)≤σ}；
In the above formula,. DELTA.BRepresenting a distance function; σ is xiThe neighborhood radius of (d);
let M denote the neighborhood relationship in U, then < U, M > constitutes the neighborhood space, and the lower approximation and the upper approximation of X in the neighborhood space < U, M > are respectively expressed as:
MX＝{xi|σ(xi)∈X,xi∈U}；
Figure BDA0002851103070000172
in the above formula, the first and second carbon atoms are,
Figure BDA0002851103070000173
also called X belongs to the neighborhood space<U,M>The positive field of (a) above (b),
defining the negative domain of X in the neighborhood space < U, M > is:
Figure BDA0002851103070000181
similarly, for a neighborhood decision system Ndt ═ U, a, D >, if the decision attribute D divides the domain of discourse U into M categories of lateral stability, that is:
(X1,X2,…,XM)(M＝3)；
then, for any conditional attribute
Figure BDA0002851103070000189
Defining the lower approximation and the upper approximation of D with respect to B in the decision system as:
Figure BDA0002851103070000182
Figure BDA0002851103070000183
Figure BDA0002851103070000184
it was further deduced that the dependency of D on B is:
Figure BDA0002851103070000185
for a decision system<U,A,D>，
Figure BDA0002851103070000186
To say that. If phi isB-b(D)<φB(D) The attribute B is important for the decision attribute set D and cannot be reduced. On the contrary, if phiB-b(D)＝φB(D) After the attribute B is deleted from the system, the dependency degree of D on B is unchanged, and the attribute B is redundant to the decision attribute set D and can be reduced.
A conditional attribute B is a reduction of the attribute set A when B satisfies the following two conditions:
Figure BDA0002851103070000187
the present embodiment adopts the above method to reduce the 10 characteristic attributes of the lateral stability of the vehicle in embodiment 1. The attribute reduction results are shown in table 3,
table 3: reduction results of feature attributes
Figure BDA0002851103070000188
According to the reduction result:
front wheel load transfer rate LTR1Rear wheel load transfer rate LTR2Mass center slip angle beta, longitudinal speed u, yaw angular speed omega and steering wheel angle deltasThe lateral inclination angle psi, the influence of the total 7 attributes on the lateral stability of the vehicle is the largest; the embodiment uses the three attributes to judge the transverse stable state of the vehicle, and meanwhile, the three attributes are abandoned, and the workload of data acquisition is reduced.
In this example, the attribute data set is reduced to include 2000 7-dimensional data points, each dimension representing an attribute of lateral stability of the vehicle. Thus, the extensive neural network consists of 7 input neurons and 3 output neurons.
In this exampleIn the training and recognition process of the extensive neural network, the learning rate eta is set to be 0.01 and the iteration times is set to be 100, and each data point is sequentially input into the ENN model. The algorithm calculates the distance EDikAnd deciding whether to update the category center and the category weight. Part of the data is used for training the ENN, and the rest of the data points are used for testing the recognition effect of the trained ENN. The training error is generated by the loop iteration of the ENN algorithm, and fig. 12 shows the variation of the training error rate of ENN with the increase of the training period. The result shows that the training speed of the neural network is very fast, and the error rate can be reduced to 0.001 by only four iterations. The error rate of 50 iterations is almost zero and convergence can be achieved. Fig. 13 shows the results of comparing the output categories of data for testing with their actual categories. The circled portions in the figure are misclassified points. The curve comparison in the graph can find that the output category of the extension neural network is almost completely consistent with the actual category, which reflects that the vehicle lateral stability identification method provided by the embodiment still has extremely high identification accuracy after attribute reduction.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A design method of an active front wheel steering controller is characterized by comprising the following steps:
s1: based on a 2-degree-of-freedom linear vehicle model, assuming that the front wheel steering angle is small and the vehicle speed is constant, determining a basic motion equation and a state space equation of the vehicle model;
s2: obtaining the expected yaw rate omega according to the vehicle modeldAnd desired slip angle betad；
S3: considering the influence of the road surface adhesion coefficient mu on the maximum expected yaw rate omegadmaxAnd a maximum desired slip angle betadmaxCorrecting;
s4: setting a combined deviation e of a yaw rate and a centroid slip angle of a vehicle1And is combined withAn integral term is introduced into the switching function to obtain a sliding mode switching function s1；
S5: solving a first derivative of the integral sliding mode switching function to obtain a first derivative function of the sliding mode switching function
Figure FDA0002851103060000011
Simplifying a first derivative function;
s6: bringing a two-degree-of-freedom kinetic equation into a first derivative function of the sliding mode switching function
Figure FDA0002851103060000012
In the method, under the condition of ensuring that the sliding mode arrival condition is met, the switching control corner u of the vehicle is calculatedswAnd equivalent control angle ueqThe final control steering angle of the active front wheel steering controller is deltaAFS＝usw+ueq。
2. The design method of an active front wheel steering controller according to claim 1, characterized in that: in step S1, the basic equation of motion of the vehicle model is:
Figure FDA0002851103060000013
in the above formula: beta is a slip angle;
Figure FDA0002851103060000014
for yaw rate, IzRotating inertia around a z-axis for the automobile; ω is the yaw rate of the vehicle,
Figure FDA0002851103060000015
yaw angular acceleration; k is a radical of1Is the total cornering stiffness of the front tyre; k is a radical of2Is the total cornering stiffness of the rear tire; u is the longitudinal speed of the automobile; deltafIs a front wheel corner; a is the distance from the center of mass to the front axis; b is the distance from the center of mass to the rear axle; m is the mass of the automobile;
the state space equation is:
Figure FDA0002851103060000021
in the equation, the ratio of the total of the components,
Figure FDA0002851103060000022
Figure FDA0002851103060000023
C＝[1 1]，
in the above-mentioned matrix, the matrix is,
Figure FDA0002851103060000024
3. the design method of the active front wheel steering controller according to claim 2, characterized in that: in step S2, the desired yaw rate ω obtained based on the vehicle modeldAnd desired slip angle betadRespectively shown as the following formula:
Figure FDA0002851103060000025
wherein ,
Figure FDA0002851103060000026
in the above formula, L is the vehicle wheel base; v is the vehicle lateral velocity; u is the longitudinal speed of the automobile; deltafIs a front wheel corner; a is the distance from the center of mass to the front axis; b is the distance from the center of mass to the rear axle; m is the mass of the automobile; k is a radical of1For total cornering stiffness of front tyreDegree; k is a radical of2The total cornering stiffness of the rear tire.
4. The design method of the active front wheel steering controller according to claim 3, characterized in that: in step S3, the maximum desired yaw rate ω is determined in consideration of the influence of the road surface friction coefficientdmaxAnd a maximum desired slip angle betadmaxThe modified calculation formula is as follows:
Figure FDA0002851103060000031
in the above formula, mu is the friction coefficient of the road surface, u is the longitudinal speed of the automobile, and a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; m is the mass of the automobile; and L is the vehicle wheel base.
5. The design method of the active front wheel steering controller according to claim 4, characterized in that: in the step S5, the vehicle yaw rate and the centroid slip angle combined deviation e1The calculation formula of (a) is as follows:
e1＝(ωd-ω)+η(βd-β)；
in the above formula, ω is yaw rate; omegadA desired yaw rate; beta is adA desired slip angle; beta is a slip angle; eta is a system adjusting parameter, and eta is a constant with a positive value and represents the proportion of the mass center slip angle and the yaw rate participating in the control.
6. The design method of an active front wheel steering controller according to claim 5, characterized in that: the integral sliding mode switching function is as follows:
Figure FDA0002851103060000032
in the above formula, e1The joint deviation of the yaw angular velocity and the centroid slip angle of the vehicle is obtained; t is time; c is a sliding mode control adjusting parameter;
the sliding mode switching function is then:
Figure FDA0002851103060000033
in the above formula, ω is yaw rate; omegadA desired yaw rate; beta is adA desired slip angle; beta is a slip angle; t is time; and c is a sliding mode control adjusting parameter.
7. The method for designing an active front wheel steering controller according to claim 6, wherein in step S5, the first derivative function of the sliding mode switching function is:
Figure FDA0002851103060000034
wherein, to simplify the expression, assume that there is a variable Ω, let
Ω＝c[(ωd-ω)+η(βd-β)]，
The first derivative function transforms to:
Figure FDA0002851103060000041
8. the design method of an active front wheel steering controller according to claim 7, characterized in that: in step S6, the system switching control coefficient uswThe calculation process of (2) is as follows:
in two-degree-of-freedom kinetic equations
Figure FDA0002851103060000042
Into a first derivative function
Figure FDA0002851103060000043
In (1), obtaining:
Figure FDA0002851103060000044
let the above formula be 0, then the equivalent control corner u can be solvedeq：
Figure FDA0002851103060000045
9. The design method of an active front wheel steering controller according to claim 8, characterized in that: the system switches over the control corner uswThe following were used:
Figure FDA0002851103060000046
in the above formula, η is a constant whose value is positive, and represents the proportion of the centroid yaw angle and the yaw rate participating in the control.
10. The design method of an active front wheel steering controller according to claim 9, characterized in that: control steering angle delta of the active front wheel steering controllerAFSCalculated from the following formula:
δAFS＝ueq+usw；
in the above formula, uswControlling the angle of rotation u for system switchingeqIs an equivalent control angle.
CN202011526982.XA 2020-12-22 2020-12-22 Design method of active front wheel steering controller Active CN112668095B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011526982.XA CN112668095B (en) 2020-12-22 2020-12-22 Design method of active front wheel steering controller

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011526982.XA CN112668095B (en) 2020-12-22 2020-12-22 Design method of active front wheel steering controller

Publications (2)

Publication Number Publication Date
CN112668095A true CN112668095A (en) 2021-04-16
CN112668095B CN112668095B (en) 2023-10-27

Family

ID=75407516

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011526982.XA Active CN112668095B (en) 2020-12-22 2020-12-22 Design method of active front wheel steering controller

Country Status (1)

Country Link
CN (1) CN112668095B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113353061A (en) * 2021-07-14 2021-09-07 广东工业大学 Four-motor-driven FSAE racing car electronic differential algorithm based on sliding mode control

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130144476A1 (en) * 2010-06-03 2013-06-06 Mira Ltd. Yaw Motion Control of a Vehicle
CN104176054A (en) * 2014-08-18 2014-12-03 大连理工大学 Automobile active anti-collision automatic lane change control system and operating method thereof
CN104443022A (en) * 2014-11-11 2015-03-25 深圳职业技术学院 Four-wheeled independently-driven electric automobile stability control method and system
CN106828464A (en) * 2017-01-06 2017-06-13 合肥工业大学 A kind of vehicle body stable control method and system based on coefficient of road adhesion estimation
CN107292048A (en) * 2017-07-05 2017-10-24 合肥工业大学 One kind is based on veDYNA tracks keeping method and system
CN108646756A (en) * 2018-07-05 2018-10-12 合肥工业大学 Intelligent automobile crosswise joint method and system based on piecewise affine fuzzy sliding mode
CN110239621A (en) * 2019-06-17 2019-09-17 北京理工大学 A kind of distributed electric automobile Yaw stability control method and system
CN110605973A (en) * 2019-09-18 2019-12-24 北京理工大学 Control method for operation stability of multi-axis distributed electrically-driven vehicle based on layered structure
WO2020187259A1 (en) * 2019-03-18 2020-09-24 长城汽车股份有限公司 Safety monitoring method and system for autonomous vehicle, and motion control system
CN111731267A (en) * 2020-06-02 2020-10-02 南京航空航天大学 Distributed electric vehicle stability control system and method equipped with non-inflatable elastic wheels

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130144476A1 (en) * 2010-06-03 2013-06-06 Mira Ltd. Yaw Motion Control of a Vehicle
CN104176054A (en) * 2014-08-18 2014-12-03 大连理工大学 Automobile active anti-collision automatic lane change control system and operating method thereof
CN104443022A (en) * 2014-11-11 2015-03-25 深圳职业技术学院 Four-wheeled independently-driven electric automobile stability control method and system
CN106828464A (en) * 2017-01-06 2017-06-13 合肥工业大学 A kind of vehicle body stable control method and system based on coefficient of road adhesion estimation
CN107292048A (en) * 2017-07-05 2017-10-24 合肥工业大学 One kind is based on veDYNA tracks keeping method and system
CN108646756A (en) * 2018-07-05 2018-10-12 合肥工业大学 Intelligent automobile crosswise joint method and system based on piecewise affine fuzzy sliding mode
WO2020187259A1 (en) * 2019-03-18 2020-09-24 长城汽车股份有限公司 Safety monitoring method and system for autonomous vehicle, and motion control system
CN110239621A (en) * 2019-06-17 2019-09-17 北京理工大学 A kind of distributed electric automobile Yaw stability control method and system
CN110605973A (en) * 2019-09-18 2019-12-24 北京理工大学 Control method for operation stability of multi-axis distributed electrically-driven vehicle based on layered structure
CN111731267A (en) * 2020-06-02 2020-10-02 南京航空航天大学 Distributed electric vehicle stability control system and method equipped with non-inflatable elastic wheels

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
XU LIU等: "Vehicle stability control based on RBF adaptive terminal sliding mode controller", 《2017 IEEE MIT UNDERGRADUATE RESEARCH TECHNOLOGY CONFERENCE (URTC)》 *
王进;郭景华;: "分布式电动车辆横向稳定性模糊滑模控制", 厦门大学学报(自然科学版), no. 02 *
郭海文;罗玉涛;: "基于自适应滑模的车辆稳定性控制策略", 机械设计与制造工程, no. 09 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113353061A (en) * 2021-07-14 2021-09-07 广东工业大学 Four-motor-driven FSAE racing car electronic differential algorithm based on sliding mode control

Also Published As

Publication number Publication date
CN112668095B (en) 2023-10-27

Similar Documents

Publication Publication Date Title
O'Brien et al. Vehicle lateral control for automated highway systems
Wei et al. Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance
Mohajer et al. Enhancing passenger comfort in autonomous vehicles through vehicle handling analysis and optimization
CN112141101B (en) Method and system for pre-aiming safety path based on CNN and LSTM
CN114379583A (en) Automatic driving vehicle trajectory tracking system and method based on neural network dynamics model
Chen et al. Longitudinal-lateral-cooperative estimation algorithm for vehicle dynamics states based on adaptive-square-root-cubature-Kalman-filter and similarity-principle
CN112699924A (en) Method for identifying lateral stability of vehicle
Cai et al. A robust fuzzy PD controller for automatic steering control of autonomous vehicles
Zhang et al. Human-centered torque vectoring control for distributed drive electric vehicle considering driving characteristics
Liu et al. Vehicle state estimation based on unscented Kalman filtering and a genetic-particle swarm algorithm
CN107878453B (en) A kind of automobile emergency collision avoidance integral type control method for hiding dynamic barrier
CN112550299B (en) Vehicle lateral stability determination and control method
CN116560223A (en) Intelligent vehicle dynamics model, ILQR control algorithm and track tracking controller based on physical information neural network
CN113341994B (en) Intelligent automobile path tracking control method based on optimal control of piecewise affine system
CN116834754A (en) Transverse and longitudinal cooperative control method for self-adaptive speed regulation of automatic driving vehicle
CN112668095A (en) Design method of active front wheel steering controller
CN112590769A (en) Coordination control method for lateral stability of vehicle
Shahabi et al. Wheel slip ratio regulation for investigating the vehicle's dynamic behavior during braking and steering input
CN116198522B (en) Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions
CN111994085A (en) Estimation method for vehicle driving stability area under complex road condition
CN112560351A (en) Vehicle transverse stable state judgment method with higher accuracy
CN112590737B (en) Design method of direct yaw moment controller
CN116185012A (en) Self-adaptive obstacle avoidance method for automatic driving vehicle
Sun et al. Trajectory Tracking Control Design for 4WS Vehicle Based on Particle Swarm Optimization and Phase Plane Analysis
Fényes Application of data-driven methods for improving the peformances of lateral vehicle control systems

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