CN109177744B - Lane departure auxiliary control method for hub motor driven automobile - Google Patents

Lane departure auxiliary control method for hub motor driven automobile Download PDF

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CN109177744B
CN109177744B CN201811101155.9A CN201811101155A CN109177744B CN 109177744 B CN109177744 B CN 109177744B CN 201811101155 A CN201811101155 A CN 201811101155A CN 109177744 B CN109177744 B CN 109177744B
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wheel
driving
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CN109177744A (en
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汪洪波
崔伟
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Hefei Luyang Technology Innovation Group Co.,Ltd.
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Hefei University of Technology
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    • 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
    • B60L2220/00—Electrical machine types; Structures or applications thereof
    • B60L2220/40—Electrical machine applications
    • B60L2220/42—Electrical machine applications with use of more than one motor
    • 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
    • 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

Abstract

The invention discloses an auxiliary control method for lane departure of an automobile driven by a hub motor, which comprises the following steps: according to the cross-track time boundary tau of the vehicle1、τ2And dynamic DLC boundary e1、e2Selecting one driving mode from the three driving modes for output; three driving control methods which are in one-to-one correspondence with the three driving modes are formulated, and the corresponding driving control methods are started according to the selected driving modes; the torque distribution of the vehicle is performed according to the selected driving control method. The three driving control methods are as follows: an electronic differential control method controlled only by the electronic differential controller, an active differential steering control method controlled only by the active differential steering controller, and a coordinated control method of cooperative control of the electronic differential controller and the active differential steering controller. The invention takes the lowest road adhesion utilization rate as a design target, distributes the torque of the four-hub motor and ensures that the automobile has better stability.

Description

Lane departure auxiliary control method for hub motor driven automobile
Technical Field
The invention relates to the technical field of auxiliary driving control of an automobile driven by a hub motor, in particular to an auxiliary lane departure control method of an automobile driven by a hub motor.
Background
A Lane Departure Assistance System (LDAS) is an important component of an intelligent driving technology, and a Lane departure assistance control system is an important content of driving assistance, and a Lane departure assistance is performed by taking a steering system as a bottom layer, which has certain limitations:
(1) steering input of a driver interferes with an auxiliary control system, and steering force output by the auxiliary control system causes certain discomfort to the driver;
(2) the man-machine controls the steering at the same time, and if the two are inconsistent or conflict, the operation burden of a driver is increased, and the driving safety of the vehicle is influenced.
Lane departure assistance is also achieved through a differential braking mode, so that interference between a driver and a control system can be avoided as much as possible, and the adoption of the differential braking can have certain influence on the vehicle speed.
The above studies on the lane departure assist control system have been mainly directed to the conventional internal combustion engine automobile and the electric automobile based on the conventional chassis structure having a single power source output. The hub motor driven automobile has a special structure as an electric automobile, has the advantages of independent and controllable four-wheel torque, quick motor torque response, flexible control and the like, and has incomparable advantages compared with the automobile with a chassis structure with traditional single power source output in the aspects of intelligent driving and auxiliary driving control. The lane departure auxiliary control system of the wheel hub motor driven automobile is designed by comprehensively considering decision, control and execution and actively distributing four-wheel torque while ensuring the requirement of longitudinal total driving force. The designed lane departure auxiliary control system takes the four hub motors as the actuating mechanisms instead of the steering wheel as the actuating mechanisms, so that the problem that a driver and a control system interfere with each other when the lane departure auxiliary system is designed based on steering of a traditional internal combustion engine automobile can be effectively solved. The four-wheel motor is subjected to active torque distribution on the premise of meeting the total longitudinal force requirement, so that the problem that the vehicle speed is influenced when a lane departure auxiliary control system is designed on the basis of differential braking of the traditional automobile can be effectively solved.
Disclosure of Invention
The invention provides a lane departure auxiliary control method for an automobile driven by a hub motor, which aims to adapt to more working conditions and effectively improve the operation stability of the automobile under the limit working condition.
The solution of the invention is: a lane departure auxiliary control method for an in-wheel motor driven automobile comprises the following steps:
according to the cross-track time boundary tau of the vehicle1、τ2And dynamic DLC boundary e1、e2Selecting one driving mode from the three driving modes for output; wherein the three driving modes are: a free driving mode controlled by only an electronic differential controller, an active driving mode controlled by only an active differential steering controller, and an auxiliary driving mode cooperatively controlled by the electronic differential controller and the active differential steering controller;
three driving control methods which are in one-to-one correspondence with the three driving modes are formulated, and the corresponding driving control methods are started according to the selected driving modes; the three driving control methods comprise: an electronic differential control method controlled only by the electronic differential controller, an active differential steering control method controlled only by the active differential steering controller, and a coordinated control method cooperatively controlled by the electronic differential controller and the active differential steering controller;
and performing torque distribution of the vehicle according to the selected driving control method, wherein the torque distribution control method of the lane departure assist control system adopted in the torque distribution includes:
step one, optimizing according to a torque instruction sent by the lane departure auxiliary control system to obtain target torque T of each hub motorfl、Tfr、Trl、Trr；
Wherein the moment instruction comprises the total yaw moment M of all in-wheel motors and the total longitudinal driving torque T of all in-wheel motorsqThe optimization process is as follows:
Figure GDA0002396496540000031
Tifor the target torque allocated to each motor, i ═ fl, fr, rl, rr denote the in-wheel motors of the front left, front right, rear left, and rear right wheels, respectively, Tfl、Tfr、Trl、TrrRespectively representing target torques of hub motors allocated to left front, right front, left rear and right rear wheels; fziFor vertical forces of the wheels, CiWeight coefficient, R, assigned to the torque of each wheeleffIs the radius of the wheel, mu is the road adhesion coefficient, delta is the corner of the front wheel, and c is the track; t ismaxThe peak torque of the motor is shown, and r is the effective rolling radius of the wheel;
step two, setting the target torque T of each hub motorfl、Tfr、Trl、TrrRespectively sent to corresponding hub motors of the vehicles;
wherein, every in-wheel motor adopts permanent magnet brushless motor, and the motor model characterization of design does:
Figure GDA0002396496540000032
in the formula TmiFor output of torque of the motor, TwiThe target torque of the hub motor is P, the magnetic pole pair number of the hub motor is phi, the rotor flux linkage of the hub motor is phi, the resistance of the hub motor is R, and the LmIs the mutual inductance of the hub motor, LsFor self-inductance of the in-wheel motor, s is Laplace operator, TpThe switching period of a driving circuit of the hub motor is set;
the coordination control method comprises the following steps:
step S11, according to the steering wheel angle delta input by the driverfCalculating the ideal yaw rate omegad；
Step S12, the electronic differential controller depending on the yaw rate ωdCalculating the moment Mb；
Step S13, the active differential steering controller outputs a torque K in the auxiliary driving modesMzWherein, K issAs a correlation function, 0<Ks<1，MzA yaw moment decided for the active differential steering controller;
step S14, judging the moment MbDirection and moment K ofsMzWhether the directions of (a) and (b) are the same; moment MbDirection and moment K ofsMzIf the direction of (b) is the same, step S15 is performed;
in step S15, the actual yaw rate ω of the vehicle is detectedr；
Step S16, determine whether ω isr<ωd(ii) a If yes, go to step S17, otherwise go to step S18;
step S17, the electronic differential controller and the active differential steering controller are controlled simultaneously;
in step S18, the electronic differential controller stops control.
Preferably, the coordination control method further includes:
in step S19, a desired steering wheel angle δ is calculated when there is a deviation between the vehicle and the target pathd，δdComprises the following steps:
Figure GDA0002396496540000041
wherein L is the wheel base of the automobile, i is the transmission ratio of the steering system, LsThe pre-aiming distance of the vehicle from a pre-aiming point is obtained; y islThe transverse deviation of the vehicle from the pre-aiming point is obtained;
step S110, determining deltafDirection and delta ofdWhether the directions of (a) and (b) are opposite; if not, step S18 is performed, and if the same, step S16 is performed.
As a further improvement of the above, the cross-track time boundary τ1、τ2Respectively as follows:
Figure GDA0002396496540000042
wherein, t1The minimum lane crossing time, t 'the driver reaction time and t' the actuator response time;
when crossing the track time tT＜t1When + t', defining a driving mode controlled by only the active differential steering controller; when t is1+t”＜tT＜t1At + t' + t ", defining an auxiliary driving mode belonging to the cooperative control of the electronic differential controller and the active differential steering controller; when t isT＞t1At + t' + t ", a free-driving mode belonging to the control of only the electronic differential controller is defined.
Preferably, the minimum cross-track time t1Comprises the following steps:
Figure GDA0002396496540000043
wherein v isytAs the lateral speed of the vehicle, aymaxIs the vehicle extreme lateral acceleration;
track crossing time tTComprises the following steps:
Figure GDA0002396496540000051
wherein D isLCFor track-crossing distance (take left track-crossing as an example, D)LCIndicating the distance traveled by the left front wheel while the vehicle remains in the current driving state and the left front wheel is driving from the current position to the edge of the lane line, noting: dLCEach having a different meaning from DLC, DLCRepresents the cross-track distance, and dlc (distance lane center) represents the distance from the lane centerline, the magnitude of which is denoted by e); v is the vehicle speed; omegarIs the actual yaw rate omega of the vehicler；
Figure GDA0002396496540000054
Is a yaw angle; a is the distance from the center of mass of the vehicle to the front axle; lfIs the front wheel track; y isllThe distance from the left front wheel to the left lane line; rvIs the turning radius.
Still preferably, the lane departure assist control method further includes:
one, determining DLC boundary e1And e2
Adopting three layers of feedforward networks, wherein the number of neurons in an input layer is 3, and the number is respectively a vehicle speed v, a road surface adhesion coefficient mu and a road curvature rho; the number of hidden layer neurons is 8; the number of neurons in the output layer is 2, and the two DLC boundaries e are respectively1And e2；
Wherein, let the input variable G ═ G1(n)；G2(n)；G3(n)]；G1(n) represents a vehicle speed v; g2(n) represents a road surface adhesion coefficient μ; g3(n) represents the road curvature ρ, n being the sampling time; output y of k-th layer(k)(n), (k ═ 1,2,3), and the activation functions of the hidden and output layers are:
Figure GDA0002396496540000052
the outputs of the output layers are respectively: y is1 (3)(n)＝e1，y2 (3)(n)＝e2(ii) a The output error signal of neuron j at the nth iteration is defined as: e.g. of the typej(n)＝dj(n)-yj(n) wherein,dj(n) is the target output, yj(n) is a network output;
secondly, acquiring a feature vector
Acquiring a characteristic quantity (τ e) of the vehicle at the present time,
Figure GDA0002396496540000053
i.e. tau is the reciprocal of the crossing time, and e is the distance between the center of mass of the vehicle and the center line of the lane;
thirdly, calculating a correlation function
The point corresponding to the characteristic value (τ e) is P3Points, i.e. P3(τ e), O is the origin of coordinates, and the correlation function is calculated as follows:
Figure GDA0002396496540000061
wherein X represents a domain corresponding to the free driving mode, X1Field corresponding to the driving assistance pattern, X2Field corresponding to the active driving mode, KsRepresenting a correlation function, P1Point meanings: p3The connecting line of the point and the coordinate origin point O is intersected with the boundary of the domain corresponding to the free driving mode in the extension set, and the intersection point is P1，P2Point meanings: p3The connecting line of the point and the coordinate origin point O is intersected with the boundary of the domain corresponding to the auxiliary driving mode in the extension set, and the intersection point is P2；
Wherein, KsThe more than 1 represents that the characteristic quantity is in a domain corresponding to the free driving mode, and at the moment, the driver occupies full initiative; k is more than 0sWhen the characteristic quantity is less than 1, the characteristic quantity is in a domain corresponding to the auxiliary driving mode, and a driver and the control system drive the vehicle at the same time; ksWhen the characteristic quantity is less than 0, the characteristic quantity is in a domain corresponding to the active driving mode, and the control system occupies the driving initiative.
Preferably, the coordination control method further includes:
step S111, adjusting a proportional control parameter, an integral control parameter and a differential control parameter of a PID course angle controller of the lane departure auxiliary control system according to the expected course angle and the actual course angle;
when the vehicle is to be corrected to the center line of the lane, and the following conditions are met, starting the PID course angle controller:
(1) road curvature ρ is 0;
(2) the derivative of the absolute value of the lateral offset e of the vehicle is less than or equal to zero;
(3) correlation function Ks>1；
(4) Yaw angle of vehicle at current position
Figure GDA0002396496540000062
According to the expected course angle and the actual course angle, three output signals x satisfying the following formula are designed1(n)、x2(n)、x3(n)，
Figure GDA0002396496540000071
Wherein e (n) is the expected heading angle-the actual heading angle, and n is the sampling time;
for three output signals x1(n)、x2(n)、x3(n) learning by using a neural network, and replacing the proportional control parameter, the integral control parameter and the differential control parameter after learning.
Further, in step S11, if δ is greaterfWhen it is 0, the electronic differential controller stops the control.
Preferably, the moment MbDirection and moment K ofsMzIf the direction is opposite, the electronic differential controller stops the control.
The execution layer mainly comprises a torque distribution controller and each execution motor, wherein the torque distribution controller distributes the torque of the four-hub motor by taking the lowest road adhesion utilization rate as a design target. The lane departure auxiliary control system with four-wheel active differential steering and electronic differential coordination is designed on the basis of dynamic boundary extension decision by fully utilizing the advantage that the torque of four wheels of an automobile driven by a hub motor is independently controllable, and comprises a decision layer, a control layer and an execution layer. The decision layer is mainly based on an extension theory, a dynamic extension boundary which changes along with the vehicle speed, the road curvature and the road surface attachment coefficient is designed by applying a neural network algorithm, and different driving modes are divided according to different deviation degrees of the vehicle.
At present, the research on the DLC extension boundary is mainly to set the DLC extension boundary as a fixed boundary, but the fixed DLC boundary can not be well adapted to all working conditions.
Drawings
Fig. 1 is a block diagram of the general structure of a lane departure auxiliary control system of a wheel hub motor-driven automobile.
Fig. 2 is a schematic diagram of a two-dimensional topology set.
FIG. 3 is a flow chart of the coordinated control method of the electronic differential and the active differential steering of the present invention.
FIG. 4 is a block diagram of a single neuron adaptive PID heading angle controller.
FIG. 5 is a cross-track time model.
FIG. 6 is a schematic diagram of simulation results of straight-through operating conditions.
FIG. 7 is a diagram illustrating simulation results of straight road conditions at different vehicle speeds.
FIG. 8 is a diagram illustrating a simulation result of the curve operating condition.
Fig. 9 is a schematic diagram of a simulation result of the comprehensive working conditions.
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.
The embodiment discloses an in-wheel motor drive car lane departure auxiliary control system, the control system structure includes: an upper dynamic boundary extension decision layer, a middle control layer and a bottom execution layer. The extension decision layer divides different driving modes according to different lane departure degrees on the basis of the dynamic boundary extension decision; the middle control layer adopts different control methods according to different driving modes divided by the decision layer; the control quantity decided by the control layer is distributed to each hub motor by the bottom execution layer in an active torque distribution mode, and the lane departure auxiliary function of the hub motor driven automobile is well realized by adopting the upper-layer, middle-layer and lower-layer hierarchical control strategies. The upper-layer extension decision layer improves the conventional scheme of the fixed DLC extension boundary, and a neural network algorithm is adopted to design a dynamic DLC extension boundary which changes along with the vehicle speed, the road curvature and the road adhesion coefficient so as to better adapt to more working conditions; the middle control layer is designed with an electronic differential controller and an active differential steering controller aiming at the specific structural form of the hub motor driven automobile, and provides a coordination control strategy of the electronic differential controller and the active differential steering controller for the first time, meanwhile, the invention not only considers the process when the vehicle is rectified to the center of the lane, but also considers the safety problem after the vehicle is rectified to the center line of the lane, namely, the vehicle is possibly provided with a yaw angle when the vehicle is rectified to the center line of the lane by a control system due to the deviation of the lane, at the moment, if a driver fails to react in time due to non-concentration, the yaw angle is possibly provided with a secondary deviation danger after the vehicle is rectified to the center line of the lane, therefore, a vehicle course angle controller is designed for controlling the yaw angle when the vehicle is rectified to the center line of the lane; the lower layer implementation layer performs torque distribution with the minimum tire adhesion utilization rate as an optimization target in order to improve vehicle running stability.
At present, the research on the DLC extension boundary is mainly to set the DLC extension boundary as a fixed boundary, but the fixed DLC boundary can not be well adapted to all working conditions.
The invention provides a coordination control strategy aiming at the electronic differential speed and the active differential steering of the hub motor driven automobile for the first time, which is characterized in that the coordination control problem of the electronic differential speed and the active differential steering is very important when the hub motor driven automobile is provided with a lane departure auxiliary control system due to the special structural form of the hub motor driven automobile.
At present, the research and invention aiming at the lane departure auxiliary control system only consider the process of correcting the deviation of the vehicle, but do not consider the state of the vehicle after being corrected to the center line of the lane. If a yaw angle exists when the vehicle is rectified to the center line of the lane, and the driver cannot timely respond due to lack of concentration at the moment, the vehicle can generate a secondary deviation danger.
Referring to fig. 1, it is a general structural framework of a lane departure auxiliary control system of a wheel hub motor driven vehicle, mainly including an extensible decision layer, a middle control layer, and a bottom execution layer. The decision layer is based on the cross-road time boundary tau of the vehicle1、τ2And dynamic DLC boundary e1、e2Selecting one driving mode from three driving modes and outputting the driving mode to the control layer; wherein the three driving modes are: a free driving mode controlled by only an electronic differential controller, an active driving mode controlled by only an active differential steering controller, and an auxiliary driving mode cooperatively controlled by the electronic differential controller and the active differential steering controller. The control layer makes three driving control methods which are in one-to-one correspondence with the three driving modes, and starts the corresponding driving control methods according to the driving mode selected by the decision layer; the three driving control methods comprise: electronic differential control method controlled only by the electronic differential controller, active differential steering control method controlled only by the active differential steering controller, the electronic differential controller, and the active differential rotationA coordinated control method for coordinated control to a controller. The execution layer performs torque distribution according to the driving control method selected by the control layer.
The control method of the lane departure assist control system corresponding to the lane departure assist control system includes the steps of:
according to the cross-track time boundary tau of the vehicle1、τ2And dynamic DLC boundary e1、e2Selecting one driving mode from the three driving modes to output; wherein the three driving modes are: a free driving mode controlled by only an electronic differential controller, an active driving mode controlled by only an active differential steering controller, and an auxiliary driving mode cooperatively controlled by the electronic differential controller and the active differential steering controller;
three driving control methods which are in one-to-one correspondence with the three driving modes are formulated, and the corresponding driving control methods are started according to the selected driving modes; the three driving control methods comprise: an electronic differential control method controlled only by the electronic differential controller, an active differential steering control method controlled only by the active differential steering controller, and a coordinated control method cooperatively controlled by the electronic differential controller and the active differential steering controller;
the torque distribution of the vehicle is performed according to the selected driving control method.
Next, the three major layers of the lane departure auxiliary control system of the wheel hub motor driven vehicle will be described in detail one by one.
Extension decision layer based on dynamic boundary extension decision
A neural network self-learning algorithm is adopted to design a dynamic extension boundary which changes along with the speed, the curvature of a road and the road surface adhesion coefficient based on an extension decision theory, different lane departure degrees are divided into different driving modes through the dynamic extension boundary, the lane departure degrees are divided into a free driving mode, an auxiliary driving mode and an active driving mode in sequence according to the lane departure degrees of vehicles from small to large, a driver completely occupies the driving initiative in the free driving mode, the driver and a control system drive the vehicles together in the auxiliary driving mode, and the active driving mode completely depends on the control system to drive the vehicles.
One of the important aspects of the lane departure support control system specifically includes the following.
(1) Minimum cross-track time t1And (4) calculating.
Minimum cross-track time t1The shortest time required for the vehicle to move to the edge of the lane line when the vehicle moves at the extreme lateral acceleration is calculated according to the following formula:
Figure GDA0002396496540000111
in the formula, vytAs the lateral speed of the vehicle, aymaxIs the vehicle limit lateral acceleration.
Track crossing time tTAnd (3) calculating: track crossing time tTWhich is the time required for the vehicle to travel to the edge of the lane line at the current lateral speed, is shown schematically in fig. 5. The calculation formula is as follows:
Figure GDA0002396496540000112
Figure GDA0002396496540000113
Figure GDA0002396496540000114
wherein the content of the first and second substances,
Figure GDA0002396496540000115
is a yaw angle; y islThe distance from the center of mass of the vehicle to the left lane line; a is the distance from the center of mass of the vehicle to the front axle; lfIs the front wheel track; y isllThe distance from the left front wheel to the left lane line.
(2) Determining cross-track time boundary τ1、τ2。
Determining cross-track time boundaries, i.e. determining the abscissa τ in fig. 21、τ2When crossing the track for time tT＞t1When the driver is in the state of + t' + t ", it is considered that the driver can be completely relied on to avoid the lane departure as long as the driver reacts in time. When t is1+t”＜tT＜t1+ t' + t ", it is assumed that the driver needs assistance from an assistance system to avoid lane departure. When t isT＜t1At + t ", the driver is unable to avoid the lane departure, and at this time, the driver needs to rely entirely on the assistance system to avoid the lane departure.
Figure GDA0002396496540000116
In the formula, t1For minimum lane crossing time, t' is driver reaction time, and t "is actuator response time.
(3) Determining dynamic DLC boundaries e1、e2。
Determining DLC boundaries, i.e. determining the ordinate e in FIG. 21、e2The value of (c). The invention considers the influence of the vehicle speed v, the road adhesion coefficient mu and the road curvature rho on the DLC boundary, and adopts a BP neural network to design a dynamic DLC boundary function which changes along with the vehicle speed, the road adhesion coefficient and the road curvature. DLCEach having a different meaning from DLC, DLCIndicating the cross-track distance (take the left cross-track as an example, D)LCIndicating the distance the front left wheel travels while the vehicle remains in the current driving state and the front left wheel is driven from the current position to the edge of the lane line), and dlc (distance to lane center) indicating the distance from the lane center line, the magnitude of which is denoted by e.
Three layers of feedforward networks are adopted, the number of neurons in an input layer is 3, and the number is the vehicle speed, the road surface attachment coefficient and the road curvature. Empirical formula for the number of neurons in hidden layer
Figure GDA0002396496540000121
Determining, wherein l is the number of neurons in a hidden layer, n is the number of neurons in an input layer, and m is the number of neurons in an output layer; a is an adjusting constant between 1 and 10. The number of hidden layer neurons was determined to be 8 from the above formula. The number of neurons in the output layer is 2,are respectively e in FIG. 21And e2。
Let input variable G ═ G1(n)；G2(n)；G3(n)]；G1(n) represents a vehicle speed v; g2(n) represents a road surface adhesion coefficient μ; g3(n) represents a road curvature ρ; and n is the sampling time. Output y of k-th layer(k)(n), (k ═ 1,2,3), and the activation functions of the hidden and output layers are:
Figure GDA0002396496540000122
the outputs of the output layers are respectively: y is1 (3)(n)＝e1，y2 (3)(n)＝e2
The output error signal of the neuron j at the nth iteration (i.e. inputting the nth training sample) is defined as
ej(n)＝dj(n)-yj(n)
dj(n) is the target output, yjAnd (n) is the network output, and n is the sampling time.
Defining a cost function for network training:
Figure GDA0002396496540000123
a group of target data samples related to the vehicle speed, the road adhesion coefficient, the road curvature and the DLC boundary are obtained by analyzing various different working conditions and are used for training a neural network, iterative correction is carried out on the network weighting coefficient by adopting a BP learning algorithm, searching and adjusting are carried out on the negative gradient direction of the weighting coefficient according to epsilon (n), and a momentum term which enables the search to be fast converged and has a minimum overall situation is added.
Figure GDA0002396496540000131
Wherein η is the learning rate, α is the momentum factor, ω isliThe weighting coefficients of the hidden layer and the output layer.
(4) The characteristic amount is acquired.
After the determination of the extension boundary(s),acquiring a characteristic quantity (τ e) of the vehicle at the present time,
Figure GDA0002396496540000132
namely, tau is the reciprocal of the crossing time, e is the distance between the center of mass of the vehicle and the center line of the lane, and the vehicle-road deviation can be reflected more visually.
(5) Calculating a correlation function:
the point corresponding to the characteristic value (τ e) is P3Points, i.e. P3(τ e), O is the origin of coordinates. P1Point meanings: p3The connecting line of the point and the coordinate origin point O is intersected with the boundary of the domain corresponding to the free driving mode in the extension set, and the intersection point is P1。P2Point meanings: p3The connecting line of the point and the coordinate origin point O is intersected with the boundary of the domain corresponding to the auxiliary driving mode in the extension set, and the intersection point is P2。
According to fig. 2, the correlation function calculation formula is as follows:
Figure GDA0002396496540000133
wherein X represents a domain corresponding to the free driving mode, X1Field corresponding to the driving assistance pattern, X2Field corresponding to the active driving mode, KsRepresenting the correlation function.
KsThe more than 1 represents that the characteristic quantity is in a domain corresponding to the free driving mode, and at the moment, the driver occupies full initiative; k is more than 0sWhen the characteristic quantity is less than 1, the characteristic quantity is in a domain corresponding to the auxiliary driving mode, and a driver and the control system drive the vehicle at the same time; ksWhen the characteristic quantity is less than 0, the characteristic quantity is in a domain corresponding to the active driving mode, and the control system occupies the driving initiative. Thereby passing through the correlation function KsThe magnitude of the value can determine the degree of deviation of the vehicle and the driving mode of the vehicle.
Control layer of two, middle layers
The driving modes of the vehicle are divided by an upper-layer extension decision layer, and different control schemes are selected in different driving modes. The electronic differential controller is designed aiming at the problems that the transmission mechanisms such as a transmission shaft, a speed reducer and a mechanical differential are omitted in the structure of the wheel hub motor driven automobile compared with the traditional automobile, when the automobile is in a free driving mode, a driver operates a steering wheel to steer, and in order to prevent the left wheel and the right wheel from slipping or dragging when the automobile steers, the electronic differential controller is designed. When the automobile is in an active driving mode, an active differential steering controller is designed aiming at the characteristic that the four-wheel torque of the automobile driven by the hub motor is independently controllable, and active steering is realized by carrying out active torque distribution on the four-wheel hub motor. The switching and the combined control of the electronic vehicle speed controller and the active differential steering controller are realized in different driving modes. The electronic differential control is adopted in the free driving mode, the electronic differential and active differential steering combined control is adopted in the auxiliary driving mode, and the active differential steering control is adopted in the active driving mode. In order to realize reasonable switching and combination between the electronic differential controller and the active differential steering controller, a coordinated control strategy of electronic differential and active differential steering is firstly provided. The invention not only considers the process that the vehicle deviates from the lane and is corrected to the center of the lane, but also considers the safety problem after the vehicle is corrected to the center of the lane, and designs the single-neuron self-adaptive PID course angle controller in order to prevent the occurrence of secondary deviation danger caused by the existence of a yaw angle when the vehicle is corrected to the center line of the lane. Wherein, the electronic differential controller and the active differential steering controller work coordinately, and the course angle controller works independently, as shown in fig. 1. And the torque output by the electronic differential controller and the active differential steering controller through coordination work is summed with the torque decided by the course angle controller and is sent to the torque distribution controller of the bottom execution layer.
The second important aspect of the lane departure support control system specifically includes the following.
(1) Designing an electronic differential speed controller:
the invention adopts sliding mode control to design an electronic differential controller based on ideal yaw velocity, and controls a driving motor according to a torque instruction, so that the rotating speed of wheels is followed, and the self-adaptive differential of each wheel is realized.
According to the two-degree-of-freedom reference model of the vehicle, when the driver inputs a steering wheel angle, a desired yaw rate ω can be obtaineddDesired yaw rate andthe deviation of the actual yaw velocity generates a feedback moment through sliding mode control, the moment is distributed to each hub motor, each wheel rotates along with the feedback moment, and the actual yaw velocity omega is enabled to be in a follow-up staterThe desired yaw rate is tracked and the calculation is as follows.
Differential equation of two-degree-of-freedom reference model of vehicle:
Figure GDA0002396496540000151
in the formula, k1、k2The cornering stiffness of the front wheel and the rear wheel respectively, β is a centroid cornering angle, a and b are distances from the centroid to the front and rear axes respectively, m is the mass of the whole vehicle, IzAnd the moment of inertia of the whole vehicle around the z axis.
According to the steering wheel input angle deltafThe ideal yaw rate omega can be calculated by the two-degree-of-freedom model of the vehicled，
Figure GDA0002396496540000152
Designing a sliding mode surface equation as follows: s- ωd-ωr。
Selecting an exponential approximation law:
Figure GDA0002396496540000153
the electronic differential controller of the present invention rewrites the differential equation of the two-degree-of-freedom reference model of the vehicle into a differential equation of the two-degree-of-freedom reference model by applying a moment to the vehicle to make the vehicle track the desired yaw rate
Figure GDA0002396496540000154
The above equation is combined to obtain the output of the electronic differential controller as:
Figure GDA0002396496540000155
(2) active differential steering controller design
Considering sliding mode controlThe method has stronger robustness, still adopts sliding mode control to design an active differential steering controller, and selects the yaw angle of the vehicle at the current moment
Figure GDA0002396496540000156
And a lateral offset Y at the home pointlAs two quantities for evaluating the vehicle deviating state, and a sliding-mode surface equation is designed based on the two quantities:
Figure GDA0002396496540000161
order to
Figure GDA0002396496540000162
Designing an active differential steering control sliding mode surface equation:
Figure GDA0002396496540000163
in the formula, kc2Is a constant coefficient.
Selected exponential approximation law
Figure GDA0002396496540000164
And combining an improved differential equation of a two-degree-of-freedom reference model of the vehicle to obtain the output of the active differential steering controller as follows:
Figure GDA0002396496540000165
in the formula (I), the compound is shown in the specification,
Figure GDA0002396496540000166
Figure GDA0002396496540000167
(3) electronic differential and active differential steering coordination control strategy:
when in the free-driving mode, only the electronic differential controller operates. At this time, if there is a steering wheel angle input, the electronic differential controller generates an auxiliary yaw moment to effect differentiation of the four wheels.
When in the assisted driving mode, the electronic differential controller and the active differential steering controller operate simultaneously. At this time, the active differential steering controller is partially involved in work, and the output yaw moment is: m is Mb+KsMzWhere M is the total output yaw moment, MbYaw moment, M, determined for an electronic differential controllerzYaw moment, K, determined for an active differential steering controllersIs a correlation function, at this time 0<Ks<1。
When in the driving-assist mode, if the driver inputs a steering wheel angle deltafThen the electronic differential controller will decide a moment MbMeanwhile, the active differential steering controller can also decide a moment K during auxiliary drivingsMz. Under the simultaneous action of the two moments, the vehicle can generate a yaw angular velocity omegar. The purpose of the electronic differential controller is to let the actual yaw rate ωrTracking a desired yaw rate ωd. If the vehicle is at yaw moment MbAnd KsMzActual yaw rate omega generated under combined actionr＜ωdWhen M is found by analysisbAnd KsMzIn the same direction, i.e. ωr＜ωdMeanwhile, the two can be superposed at the same time; if the vehicle is at MbAnd KsMzYaw rate omega produced under combined actionr＞ωdIt was found by analysis that the electronic differential controller was designed to let ω berTracking upper omegadWill generate an AND KsMzOpposite direction moment MbSo that omegarDecreases, which increases the risk of the vehicle deviating out of the lane. Therefore, in the assisted driving mode, when ω isr＞ωdThe electronic differential controller is deactivated. Meanwhile, when a deviation amount exists between the vehicle and the target path, a desired party can be calculated according to the yaw angle of the vehicle at the current moment and the transverse deviation at the aiming pointSteering wheel corner deltad，δdThe calculation process of (2) is as follows.
Yaw rate ω according to the current time of the vehiclerVehicle speed vxThe centroid slip angle β and the road curvature ρ are used to determine the yaw angle of the vehicle at the current location and the lateral offset of the vehicle at the pre-line of sight.
Figure GDA0002396496540000171
And integrating to obtain the yaw angle of the current position of the vehicle and the transverse deviation at the aiming point. Wherein
Figure GDA0002396496540000175
Yaw angle, Y, representing the current positionlRepresents the lateral deviation at the pre-aiming point,/sIndicating the pre-range. Aiming time:
Figure GDA0002396496540000172
knowing the lateral deviation Y at the preview pointlIn this case, if a desired steering wheel angle δ is selecteddThe vehicle will generate a lateral acceleration aySo that the lateral deviation is reduced to zero within the preview distance.
Figure GDA0002396496540000173
According to the vehicle kinematic relationship, the ideal steering wheel angle can be obtained as follows:
Figure GDA0002396496540000174
wherein L is the vehicle wheelbase and i is the steering system gear ratio.
If the driver now inputs a steering wheel angle deltafAnd deltadIf the direction is opposite, the driver performs misoperation, and the electronic differential controller stops working at the moment.
When the vehicle is in the active driving mode, the active differential steering controller is completely involved to work, once the vehicle enters the active driving mode, the electronic differential steering controller stops working immediately, and the active differential steering controller is completely involved to work. The strategy for coordinating the control of the electronic vehicle speed controller with the active differential steering controller is shown in fig. 3.
(4) Course angle controller
The invention takes the safety problem that the vehicle is rectified back to the center of the lane into consideration, and when the vehicle deviates from the target path to a certain extent, the auxiliary system can rectify the deviation of the vehicle and make the vehicle return to the center line of the lane. However, since the auxiliary system applies an additional auxiliary yaw moment to the vehicle when correcting the deviation of the vehicle, the additional auxiliary yaw moment causes a yaw angle around the z-axis of the vehicle body, and due to the yaw angle, when the vehicle is corrected back to the center line of the lane, the longitudinal axis of the vehicle body is not parallel to the center line of the lane, but a yaw angle exists, and if the driver fails to respond in time due to lack of concentration, the existence of the yaw angle may cause the vehicle to deviate twice. By analysis, the above situation appears particularly well on straight roads and not on curves, because the desired heading angle changes from moment to moment in a curve. Therefore, in order to prevent the vehicle from generating secondary deviation, the invention designs a single-neuron self-adaptive PID course angle controller under the straight-road working condition, and controls the course angle in the process that the vehicle is corrected to the center line of the lane to enable the yaw angle to be 0, thereby preventing the vehicle from generating the danger of secondary deviation.
When the vehicle is to be corrected to the center line of the lane, the vehicle is usually in a free driving mode, at this time, the driver takes full driving initiative, and in order to avoid the designed course angle controller from generating interference on the driver as much as possible and avoid the vehicle from secondary deviation, the following trigger conditions need to be set for the course angle controller: (1) road curvature ρ is 0; (2) the derivative of the absolute value of the lateral offset e is less than or equal to zero; (3) correlation function Ks>1; (4) yaw angle of vehicle at current position
Figure GDA0002396496540000181
The condition (1) ensures that the course angle controller is triggered on a straight road; the condition (2) ensures that the course angle controller is triggered when the vehicle approaches the center line of the lane and is not triggered when the vehicle is far away from the center line of the lane, and the condition (2) is mainly used for avoiding the interference to the driver as much as possible; condition (3) ensures that the controller is only triggered during the free-driving mode (i.e., near the lane centerline); condition (4) ensures that the vehicle is triggered when a yaw angle is present near the lane centerline. When the four conditions are simultaneously met, the course angle controller can trigger the starting operation.
The three parameters of the classical PID control are fixed, the control effect is deteriorated when the external environment is changed frequently, and the accurate mathematical model is required. Aiming at the defects of classical PID control, the invention combines a single neuron with classical PID control, and establishes a single neuron self-adaptive PID controller to control a course angle. Three parameters of the PID controller can be adjusted in a self-adaptive mode through adjustment of the synapse weight of the neuron to adapt to changes of the external environment.
The structure of the single-neuron adaptive PID heading angle controller is shown in FIG. 4.
In fig. 4, the inputs to the converter are the desired heading angle and the actual heading angle, and the output of the converter is:
Figure GDA0002396496540000191
where e (n) is the desired heading angle — the actual heading angle.
Synaptic weight omega of neuronj(n) are respectively:
Figure GDA0002396496540000192
adopting unsupervised Hebb learning rule, the synapse weight of the neural network is corrected as follows:
Figure GDA0002396496540000193
wherein, ηp，ηi，ηdThe learning rates of the proportional, integral and differential coefficients are respectively.
From the control law of the available single-neuron adaptive PID controller to
Figure GDA0002396496540000194
The electronic differential controller works in coordination with the active differential steering controller, and the yaw angle controller works independently, as shown in fig. 1. And the moment decided by the yaw angle controller is summed with the moments decided by the electronic differential controller and the active differential steering controller, and the summed control moment is sent to a torque distribution controller of a bottom layer actuating mechanism.
In summary, the second important aspect of the lane departure auxiliary control system provides a coordinated control method for electronic differential and active differential steering of an in-wheel motor driven vehicle. The in-wheel motor driven automobile is provided with a lane departure auxiliary control system, and the lane departure auxiliary control system is provided with: a free driving mode controlled by only an electronic differential controller, an active driving mode controlled by only an active differential steering controller, and an auxiliary driving mode cooperatively controlled by the electronic differential controller and the active differential steering controller.
The cooperative control method is applied to the assist driving mode, and includes the following steps.
Step S11, according to the steering wheel angle delta input by the driverfCalculating the ideal yaw rate omegad。
Step S12, the electronic differential controller depending on the yaw rate ωdCalculating the moment Mb。
Step S13, the active differential steering controller outputs a torque K in the auxiliary driving modesMzWherein, K issAs a correlation function, 0<Ks<1，MzA yaw moment determined for the active differential steering controller.
Step S14, judging the moment MbDirection and moment K ofsMzWhether the directions of (a) and (b) are the same; moment MbDirection and moment K ofsMzIf the direction of (b) is the same, step S15 is performed.
In step S15, the actual yaw rate ω of the vehicle is detectedr。
Step S16, determine whether ω isr<ωd(ii) a If yes, go to step S17, otherwise go to step S18.
And step S17, controlling the electronic differential controller and the active differential steering controller simultaneously.
In step S18, the electronic differential controller stops control.
Step S19, when there is a deviation between the vehicle and the target path, according to the yaw angle of the vehicle at the current position
Figure GDA0002396496540000201
And the lateral deviation Y of the vehicle from the pre-aiming pointlOutputting a desired steering wheel angle deltad(ii) a Wherein, deltadComprises the following steps:
Figure GDA0002396496540000202
wherein L is the wheel base of the automobile, i is the transmission ratio of the steering system, LsThe pre-aiming distance of the vehicle from the pre-aiming point.
Step S110, determining deltafDirection and delta ofdWhether the directions of (a) and (b) are opposite; if not, step S18 is executed.
Step S111, adjusting a proportional control parameter, an integral control parameter and a differential control parameter of a PID course angle controller of the lane departure auxiliary control system according to the expected course angle and the actual course angle;
when the vehicle is to be corrected to the center line of the lane, and the following conditions are met, starting the PID course angle controller: (1) road curvature ρ is 0; (2) derivative of absolute value of lateral offset e of vehicle
Figure GDA0002396496540000203
(3) Correlation function Ks>1; (4) yaw angle of vehicle at current position
Figure GDA0002396496540000204
Designing x according to the expected course angle and the actual course angle1(n)、x2(n)、x3(n) satisfying the formula:
Figure GDA0002396496540000211
where e (n) is the desired heading angle — the actual heading angle.
For three output signals x1(n)、x2(n)、x3(n) learning by using a neural network, and replacing the proportional control parameter, the integral control parameter and the differential control parameter after learning.
Third, the execution layer of the bottom layer
The execution layer mainly comprises a torque distribution controller and four hub motors, and the torque distribution controller receives a torque command sent by the control layer and distributes the torque command to the four hub motors.
The third important aspect of the lane departure support control system specifically includes the following.
(1) Torque distribution controller
In the torque distribution process, in order to ensure that the automobile has better stability, the relation between the load rate and the adhesive force of the tire is considered, the minimum road surface adhesion consumption rate is taken as an optimization target, and an objective function is defined as follows:
Figure GDA0002396496540000212
wherein i ═ fl, fr, rl, rr denote left front, right front, left rear, right rear wheels, respectively, FziFor vertical forces of the wheels, CiWeight coefficient, R, assigned to the torque of each wheeleffIs the wheel radius.
And optimally distributing the torque of the hub motor by adopting a quadratic programming method. When torque optimization distribution is carried out, the limitation of road surface adhesion and the maximum output torque of the motor is required on the premise of meeting an upper-layer torque instruction, and the limitation conditions are as follows:
Figure GDA0002396496540000213
where δ is the front wheel angle, TmaxFor peak motor torque, TqFor total longitudinal force demand, M is total yaw moment demand.
In this embodiment, the target torque T of each in-wheel motor is obtained by performing optimization according to the torque command sent by the lane departure auxiliary control systemfl、Tfr、Trl、Trr. The moment instruction comprises the total yaw moment M of all in-wheel motors and the total longitudinal driving torque T of all in-wheel motorsq。
(2) Wheel hub motor model
The invention adopts the permanent magnet brushless motor as the hub motor and establishes a corresponding motor model. Because the torque closed-loop control of the permanent magnet brushless motor is quick in response, a motor model is simplified into a second-order system:
Figure GDA0002396496540000221
in the formula TmiFor output of torque of the motor, TwiThe motor parameter ξ is composed of motor pole pair number P, motor resistance R, rotor flux phi and self-inductance LsMutual inductance LmDrive circuit switching period TpWhen the parameters are determined, ξ is 0.05.
And the torque distribution controller receives the torque command sent by the control layer, and sends the obtained target torque of each hub motor to each motor by taking the minimum road surface adhesion consumption rate as an optimization target.
When the lane departure auxiliary control system of the wheel hub motor-driven automobile is used for simulation, as shown in fig. 6, the working condition speed is 20m/s, the road adhesion coefficient is 0.85, the lane width is 3.5m, a straight lane is arranged, and a driver mistakenly operates and inputs a step steering wheel turning angle of 20 degrees within 20-24 s. The curve in fig. 6 shows that the maximum offset is 0.83m, no deviation from the lane occurs, the blue solid line shows that the course angle controller can effectively prevent the secondary deviation risk, and the black dotted line shows that the vehicle 34s starts to generate the secondary deviation risk when no course angle controller intervenes.
Fig. 7 shows simulation results at different vehicle speeds, except for different speeds, the other conditions are the same as those shown in fig. 6, and the three curves in fig. 7 show that the vehicle does not deviate from the lane at different vehicle speeds.
The simulation conditions are as shown in FIG. 8: under the condition of a curve, the road adhesion coefficient is 0.85, the road width is 3.5m, a driver does not operate on the curve, and the result shows that the maximum offset is 0.87m and the vehicle does not deviate from a lane.
The working conditions shown in FIG. 9 are as follows: in order to verify the effectiveness of the designed dynamic DLC extension boundary, on the road surface with the changed curvature and road surface adhesion coefficient, the vehicle is slowly accelerated to 100km/h from 0 to 50s, and a driver does not operate. As shown in fig. 9, the simulation result indicates that the vehicle did not deviate from the lane at the dynamic DLC boundary. The vehicle deviates out of the lane at the boundary of the fixed DLC as indicated by the dashed line.
In conclusion, the invention designs the lane departure auxiliary control system with four-wheel active differential steering and electronic differential coordination by fully utilizing the advantage that the four-wheel torque of the hub motor-driven automobile is independently controllable based on the dynamic boundary extension decision, and the designed lane departure auxiliary control system comprises a decision layer, a control layer and an execution layer. The decision layer is mainly based on an extension theory, a dynamic extension boundary which changes along with the vehicle speed, the road curvature and the road surface attachment coefficient is designed by applying a neural network algorithm, and different driving modes are divided according to different deviation degrees of the vehicle.
The control layer mainly comprises an electronic differential controller and an active differential steering controller. The independent control or the combined control of the electronic differential speed and the active differential steering is realized in different driving modes, and the coordinated control strategy of the electronic differential speed and the active differential steering is firstly provided. Considering the safety problem after the vehicle is corrected to the center of the lane, aiming at the problem that the vehicle has a secondary deviation danger due to the fact that a yaw angle exists when the vehicle is corrected to the center line of the lane, a single-neuron self-adaptive PID course angle controller is designed, and the triggering condition of the course angle controller is set to avoid interference on a driver as much as possible.
The execution layer mainly comprises a torque distribution controller and each execution motor, and the torque distribution controller distributes the torque of the four-hub motor by taking the lowest road adhesion utilization rate as a design target.
The lane departure auxiliary control system of the hub motor driven automobile provided by the invention fully utilizes the advantages of independent controllability and quick response of the four-wheel hub motor, realizes the lane departure auxiliary function by carrying out active torque distribution on the four wheels, and effectively solves the problem that a driver and a control system are mutually interfered when the lane departure auxiliary control system is designed on the basis of steering of the traditional automobile. Meanwhile, the problem that the vehicle speed is influenced when the traditional automobile is used for lane departure assistance based on differential braking is solved. The dynamic DLC extension boundary can better adapt to more working conditions, and meanwhile, the operation stability of the vehicle under the limit working condition can be improved. The proposed electronic differential and active differential steering coordinated control strategy can reasonably and effectively coordinate the working relation between the two controllers. The proposed yaw angle controller can effectively prevent the occurrence of a risk of a secondary deviation of the vehicle.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (8)

1. A lane departure auxiliary control method for an in-wheel motor driven automobile is characterized by comprising the following steps:
according to the cross-track time boundary tau of the vehicle1、τ2And dynamic DLC boundary e1、e2Selecting one driving mode from the three driving modes for output; wherein the three driving modes are: a free driving mode controlled by only an electronic differential controller, an active driving mode controlled by only an active differential steering controller, and an auxiliary driving mode cooperatively controlled by the electronic differential controller and the active differential steering controller;
three driving control methods which are in one-to-one correspondence with the three driving modes are formulated, and the corresponding driving control methods are started according to the selected driving modes; the three driving control methods comprise: an electronic differential control method controlled only by the electronic differential controller, an active differential steering control method controlled only by the active differential steering controller, and a coordinated control method cooperatively controlled by the electronic differential controller and the active differential steering controller;
and performing torque distribution of the vehicle according to the selected driving control method, wherein the torque distribution control method of the lane departure assist control system adopted in the torque distribution includes the steps of:
step one, optimizing according to a torque instruction sent by the lane departure auxiliary control system to obtain target torque T of each hub motorfl、Tfr、Trl、Trr；
Wherein the moment instruction comprises the total yaw moment M of all in-wheel motors and the total longitudinal driving torque T of all in-wheel motorsqThe optimization process is as follows:
Figure FDA0002396496530000011
Tifor the target torque allocated to each motor, i ═ fl, fr, rl, rr denote the in-wheel motors of the front left, front right, rear left, and rear right wheels, respectively, Tfl、Tfr、Trl、TrrRespectively representing target torques of hub motors allocated to left front, right front, left rear and right rear wheels; fziFor vertical forces of the wheels, CiWeight coefficient, R, assigned to the torque of each wheeleffIs the radius of the wheel, mu is the road adhesion coefficient, delta is the corner of the front wheel, and c is the track; t ismaxThe peak torque of the motor is shown, and r is the effective rolling radius of the wheel;
step two, setting the target torque T of each hub motorfl、Tfr、Trl、TrrRespectively sent to corresponding hub motors of the vehicles;
wherein, every in-wheel motor adopts permanent magnet brushless motor, and the motor model characterization of design does:
Figure FDA0002396496530000021
in the formula TmiFor output of torque of the motor, TwiThe target torque of the hub motor is P, the magnetic pole pair number of the hub motor is phi, the rotor flux linkage of the hub motor is phi, the resistance of the hub motor is R, and the LmIs the mutual inductance of the hub motor, LsFor self-inductance of the in-wheel motor, s is Laplace operator, TpThe switching period of a driving circuit of the hub motor is set;
the coordination control method comprises the following steps:
step S11, according to the steering wheel angle delta input by the driverfCalculating the ideal yaw rate omegad；
Step S12, the electronic differential controller depending on the yaw rate ωdCalculating the moment Mb；
Step S13, the active differential steering controller outputs a torque K in the auxiliary driving modesMzWherein, K issAs a correlation function, 0<Ks<1，MzA yaw moment decided for the active differential steering controller;
step S14, judging the moment MbDirection and moment K ofsMzWhether the directions of (a) and (b) are the same; moment MbDirection and moment K ofsMzIf the direction of (b) is the same, step S15 is performed;
in step S15, the actual yaw rate ω of the vehicle is detectedr；
Step S16, determine whether ω isr<ωd(ii) a If yes, go to step S17, otherwise go to step S18;
step S17, the electronic differential controller and the active differential steering controller are controlled simultaneously;
in step S18, the electronic differential controller stops control.
2. The lane departure auxiliary control method for an in-wheel motor driven vehicle as claimed in claim 1, wherein the cross-lane time boundary τ is1、τ2Respectively as follows:
Figure FDA0002396496530000031
wherein, t1The minimum lane crossing time, t 'the driver reaction time and t' the actuator response time;
when crossing the track time tT＜t1When + t', defining an active driving mode controlled by only an active differential steering controller; when t is1+t”＜tT＜t1At + t' + t ", defining an auxiliary driving mode belonging to the cooperative control of the electronic differential controller and the active differential steering controller; when t isT＞t1At + t' + t ", a free-driving mode belonging to the control of only the electronic differential controller is defined.
3. The in-wheel motor driven vehicle lane departure auxiliary control method as claimed in claim 1, wherein the minimum lane crossing time t is1Comprises the following steps:
Figure FDA0002396496530000032
wherein v isytAs the lateral speed of the vehicle, aymaxIs the vehicle extreme lateral acceleration;
track crossing time tTComprises the following steps:
Figure FDA0002396496530000033
wherein D isLCIs the cross-track distance; v is the vehicle speed; omegarIs the actual yaw rate omega of the vehicler；
Figure FDA0002396496530000034
Is a yaw angle; a is the distance from the center of mass of the vehicle to the front axle; lfIs the front wheel track; y isllThe distance from the left front wheel to the left lane line; rvIs the turning radius.
4. The in-wheel motor driven vehicle lane departure assist control method as claimed in claim 3, further comprising:
one, determining DLC boundary e1And e2
Adopting three layers of feedforward networks, wherein the number of neurons in an input layer is 3, and the number is respectively a vehicle speed v, a road surface adhesion coefficient mu and a road curvature rho; the number of hidden layer neurons is 8; the number of neurons in the output layer is 2, and the two DLC boundaries e are respectively1And e2；
Wherein, let the input variable G ═ G1(n)；G2(n)；G3(n)]；G1(n) represents a vehicle speed v; g2(n) represents a road surface adhesion coefficient μ; g3(n) represents the road curvature ρ, n being the sampling time; output y of k-th layer(k)(n), (k ═ 1,2,3), and the activation functions of the hidden and output layers are:
Figure FDA0002396496530000041
the outputs of the output layers are respectively: y is1 (3)(n)＝e1，y2 (3)(n)＝e2(ii) a The output error signal of neuron j at the nth iteration is defined as: e.g. of the typej(n)＝dj(n)-yj(n) wherein dj(n) is the target output, yj(n) is a network output;
secondly, acquiring a feature vector
Acquiring a characteristic quantity (τ e) of the vehicle at the present time,
Figure FDA0002396496530000042
i.e. tau is the reciprocal of the crossing time, and e is the distance between the center of mass of the vehicle and the center line of the lane;
thirdly, calculating a correlation function
The point corresponding to the characteristic value (τ e) is P3Points, i.e. P3(τ e), O is the origin of coordinates, and the correlation function is calculated as follows:
Figure FDA0002396496530000043
wherein X representsField corresponding to free driving mode, X1Field corresponding to the driving assistance pattern, X2Field corresponding to the active driving mode, KsRepresenting a correlation function, P1Point meanings: p3The connecting line of the point and the coordinate origin point O is intersected with the boundary of the domain corresponding to the free driving mode in the extension set, and the intersection point is P1，P2Point meanings: p3The connecting line of the point and the coordinate origin point O is intersected with the boundary of the domain corresponding to the auxiliary driving mode in the extension set, and the intersection point is P2；
Wherein, KsThe more than 1 represents that the characteristic quantity is in a domain corresponding to the free driving mode, and at the moment, the driver occupies full initiative; k is more than 0sWhen the characteristic quantity is less than 1, the characteristic quantity is in a domain corresponding to the auxiliary driving mode, and a driver and the control system drive the vehicle at the same time; ksWhen the characteristic quantity is less than 0, the characteristic quantity is in a domain corresponding to the active driving mode, and the control system occupies the driving initiative.
5. The in-wheel motor driven vehicle lane departure assist control method as claimed in claim 1, wherein said coordinated control method further comprises:
in step S19, a desired steering wheel angle δ is calculated when there is a deviation between the vehicle and the target pathd，δdComprises the following steps:
Figure FDA0002396496530000051
wherein L is the wheel base of the automobile, i is the transmission ratio of the steering system, LsIs the pre-aiming distance, Y, of the vehicle from the point of pre-aiminglThe transverse deviation of the vehicle from the pre-aiming point is obtained;
step S110, determining deltafDirection and delta ofdWhether the directions of (a) and (b) are opposite; if not, step S18 is performed, and if the same, step S16 is performed.
6. The in-wheel motor driven vehicle lane departure assist control method as claimed in claim 1, wherein said coordinated control method further comprises:
step S111, adjusting a proportional control parameter, an integral control parameter and a differential control parameter of a PID course angle controller of the lane departure auxiliary control system according to the expected course angle and the actual course angle;
when the vehicle is to be corrected to the center line of the lane, and the following conditions are met, starting the PID course angle controller:
(1) road curvature ρ is 0;
(2) the derivative of the absolute value of the lateral offset e of the vehicle is less than or equal to zero;
(3) correlation function Ks>1；
(4) Yaw angle of vehicle at current position
Figure FDA0002396496530000052
According to the expected course angle and the actual course angle, three output signals x satisfying the following formula are designed1(n)、x2(n)、x3(n)，
Figure FDA0002396496530000053
Wherein e (n) is the expected heading angle-the actual heading angle, and n is the sampling time;
for three output signals x1(n)、x2(n)、x3(n) learning by using a neural network, and replacing the proportional control parameter, the integral control parameter and the differential control parameter after learning.
7. The lane departure assist control method for an in-wheel motor driven vehicle according to claim 6, wherein if δ is δ 11fWhen it is 0, the electronic differential controller stops the control.
8. The lane departure auxiliary control method for an in-wheel motor driven vehicle according to claim 1, wherein the moment M isbDirection and moment K ofsMzIf the direction is opposite, the electronic differential controller stops the control.
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