CN109017446B - Expected path vehicle longitudinal speed tracking control method and device - Google Patents

Expected path vehicle longitudinal speed tracking control method and device Download PDF

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CN109017446B
CN109017446B CN201810857600.8A CN201810857600A CN109017446B CN 109017446 B CN109017446 B CN 109017446B CN 201810857600 A CN201810857600 A CN 201810857600A CN 109017446 B CN109017446 B CN 109017446B
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torque
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葛平淑
张江燕
赵秀春
张涛
宋鹏
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Dalian Minzu University
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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
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/32—Control or regulation of multiple-unit electrically-propelled vehicles
    • B60L15/38—Control or regulation of multiple-unit electrically-propelled vehicles with automatic control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/10—Path keeping
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/423—Torque
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/08—Electric propulsion units
    • B60W2710/083—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
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Abstract

The expected path vehicle longitudinal speed tracking control method and device belong to the field of unmanned vehicle control, and in order to solve the problem of expected path vehicle longitudinal speed tracking control, a torque distribution controller calculates and distributes total driving torque to driving torque of each hub motor, the output torque of the hub motors acts on wheels, and longitudinal speed is controlled to track an expected track, wherein tire utilization rate is used as an optimization function, a torque distribution algorithm is designed according to a pseudo-inverse method to distribute the total torque, the effect is that tires are in a stable range and do not exceed an adhesion limit, the distribution requirement precision is higher, and the tracking of the longitudinal speed is more accurate.

Description

Expected path vehicle longitudinal speed tracking control method and device
Technical Field
The invention belongs to the field of unmanned vehicle control, and particularly relates to a four-wheel independent drive unmanned electric vehicle trajectory tracking control working method.
Background
Electromotion and intellectualization are the development direction of the automobile industry at present, and become the research hotspots of domestic and foreign scholars, scientific research institutions and enterprises. The electric automobile not only can reduce the consumption of non-renewable resources by human beings and improve the environmental problem, but also can bring NVH quality which is difficult to reach by the traditional fuel oil vehicle. The four-hub motor independent drive is a unique drive form of the electric automobile, and the driving torque and the rotating speed of each wheel can be independently and accurately controlled due to the fact that a power system is directly integrated on the wheels, and the structure lays a foundation for the realization of an advanced control algorithm. The unmanned technology is an advanced stage of vehicle intellectualization, is a key technology for realizing 'zero death' of traffic accidents, and the trajectory tracking is a basic requirement for realizing the autonomous driving of intelligent vehicles.
The trajectory tracking control is a key technology for realizing accurate motion control of the unmanned vehicle and is a primary condition for realizing intellectualization and practicability of the unmanned vehicle. The motion control of the vehicle can be divided into three types: longitudinal motion control, transverse motion control and longitudinal and transverse motion control. The longitudinal motion control means to maintain the vehicle speed within the target vehicle speed range quickly and with high accuracy. The transverse motion control is to control the transverse swing motion and the steering motion of the vehicle, and aims to ensure that the vehicle can keep transverse stability and stably track an expected track under different working conditions, so that the vehicle can realize the functions of lane keeping or autonomous overtaking, obstacle avoidance and the like. At present, most unmanned vehicle trajectory tracking algorithms only carry out simple decoupling on longitudinal motion and transverse motion, the vehicle speed is assumed to be a certain value, but the vehicle is a highly nonlinear and strongly coupled system, and if the interrelation between the longitudinal motion and the transverse motion is not considered, the control precision and the vehicle stability cannot be ensured. And particularly, when the vehicle runs under a high-speed working condition and a low-attachment working condition, the instability condition is more easily caused. On the other hand, most of the existing control algorithms relate to kinematic control, that is, the lateral stability and the longitudinal motion control of the vehicle are not taken into consideration, if the dynamic constraint is not considered, the driving insecurity of the vehicle under the working conditions of high speed and low adhesion road surface is increased, and the control precision is reduced. Therefore, when designing a track following control strategy of the FWID unmanned electric vehicle, an algorithm which needs to fully consider the correlation between longitudinal and transverse motions and the driving stability is particularly important.
Disclosure of Invention
In order to solve the problem of the expected path vehicle longitudinal speed tracking control, the invention provides the following technical scheme: the expected path vehicle longitudinal speed tracking control method is characterized in that errors and error change rates of ideal longitudinal speed and actual longitudinal speed of a vehicle are used as controller input, the controller outputs electronic throttle opening, and a hub motor torque Map corresponding to the electronic throttle opening which is compiled in advance is searched for so as to output total driving torque of the vehicle; and calculating and distributing total driving torque to the driving torque of each hub motor by a torque distribution controller, wherein the output torque of the hub motors acts on the wheels, and the longitudinal speed is controlled to track an expected track, wherein the tire utilization rate is used as an optimization function, and a torque distribution algorithm is designed according to a pseudo-inverse method to distribute the total torque.
An expected path vehicle longitudinal velocity tracking control apparatus, said control apparatus having stored thereon a plurality of instructions adapted to be loaded and executed by a processor to:
the error of the ideal longitudinal speed and the actual longitudinal speed of the vehicle and the error change rate are used as the input of a controller;
the controller outputs the opening of an electronic throttle valve, and searches a hub motor torque Map corresponding to the opening of the electronic throttle valve which is compiled in advance so as to output the total driving torque of the vehicle;
calculating and distributing the total driving torque to the driving torque of each hub motor by a torque distribution controller; the output torque of the hub motor acts on the wheel and controls the longitudinal speed to track the expected track, wherein the tire utilization rate is used as an optimization function, and a torque distribution algorithm is designed according to a pseudo-inverse method to distribute the total torque.
Compared with the prior art, the invention has the following beneficial effects: the fuzzy controller inputs ideal longitudinal speed and actual longitudinal speed, establishes the correlation between the opening of the electronic throttle valve and the Map of the torque Map of the hub motor, obtains the total driving torque of the vehicle, ensures the following performance of longitudinal vehicle speed, and provides accurate total torque for distributing torque to each wheel. The tire longitudinal velocity tracking method takes the tire utilization rate as an optimization function, designs the total moment distribution of the moment distribution algorithm based on the pseudo-inverse method, ensures that the tire is in a stable range and does not exceed the adhesion limit, and has higher required distribution precision, so that the longitudinal velocity tracking is more accurate.
Drawings
FIG. 1 is a two-degree-of-freedom vehicle dynamics model
FIG. 2 is a three-degree-of-freedom vehicle dynamics model
FIG. 3 is a fuzzy adaptive PI longitudinal speed controller
FIG. 4 is a membership function of the longitudinal velocity error e and the error rate of change ec: (a) membership function of longitudinal speed error e; (b) membership function of longitudinal speed error change rate ec;
FIG. 5 is a graph of the longitudinal velocity controller parameter Δ kpAnd Δ kiMembership function of (d): (a) parameter Δ kpInput/output switchIs (b) a parameter Δ kiThe input-output relationship of (1);
fig. 6 is a block diagram schematically illustrating the structure of the tracking system.
Detailed Description
The track tracking control strategy of the unmanned vehicle is researched by taking a Four-Wheel independent drive electric vehicle (FWID-EV) as an object, and not only can the track be accurately tracked, but also the requirements of high speed and low-attachment working condition driving stability can be met.
In order to improve the stability and the accuracy of track tracking of a vehicle on a high-speed and low-attachment road surface, the invention provides a track tracking algorithm of a four-wheel independent drive unmanned electric vehicle. In view of the research content of the track tracking algorithm of the prior unmanned vehicle, the stability control and the longitudinal speed control of the vehicle are not considered, and the track tracking algorithm is not suitable for four-wheel independent drive electric vehicles. The invention provides a layered trajectory tracking control strategy for a four-wheel independent drive unmanned electric vehicle.
The track tracking strategy designed by the invention is divided into three layers, the upper layer establishes a rolling time domain optimization algorithm of front wheel active steering, and when an optimization function is designed, the track tracking precision is taken as the most basic target; secondly, in order to improve riding comfort, the control quantity constraint is added into an optimization problem. In order to enable the yaw rate to represent the stability of the vehicle, the centroid sideslip angle constraint is added in the optimization solution. The middle-layer controller takes the tracking of the expected yaw angular velocity as a control target, and an equivalent control item is designed by utilizing a three-degree-of-freedom vehicle model on the basis of equivalent sliding film control during algorithm design; and a hyperbolic tangent function is used for replacing a discontinuous symbol function to design a switching robust control item, so that the buffeting phenomenon is effectively reduced. The lower-layer controller considers the influence of speed change on the track tracking precision, improves the stability and robustness of longitudinal vehicle speed control, takes speed error and the change rate thereof as the input of a fuzzy controller, and sets PI controller parameters on line through fuzzy reasoning, thereby ensuring the following performance of longitudinal vehicle speed. The tire utilization rate is used as an optimization function, and a moment distribution algorithm is designed based on a pseudo-inverse method.
1 upper layer controller for realizing active steering control according to expected track
1.1 building a lateral dynamics model of a vehicle
Two-degree-of-freedom linear bicycle models are commonly used to describe vehicle lateral and yaw motion. The following assumptions were made in modeling: assuming that the vehicle is traveling on a flat surface, the vertical motion of the vehicle and the suspension motion are not considered, and assuming that the vehicle is rigid; front-to-back and left-to-right load transfer of the vehicle is not considered; the longitudinal and transverse coupling relation of the tire force is not considered, and only the pure cornering tire characteristic is considered; while ignoring the longitudinal and lateral aerodynamics. Based on the above assumptions, a two-degree-of-freedom vehicle dynamics model is built, as shown in fig. 1.
According to the two-degree-of-freedom vehicle dynamics model shown in fig. 1, in order to reduce the influence of strong coupling parameters, improve the flexibility of the system, ignore the longitudinal dynamics of the vehicle, and only consider the transverse motion and the transverse pendulum motion of the vehicle, the two-degree-of-freedom vehicle transverse dynamics equation can be derived as follows:
Figure GDA0002630856810000041
in the formula: m is the mass v of the automobilexLongitudinal speed, beta as mass center slip angle, gamma as yaw angular speed, IzFor the moment of inertia, l, of the body about the Z-axisfIs the distance of the center of mass to the front axis,/rIs the distance of the center of mass to the rear axis, FxfIs the longitudinal force of the front wheel FxrIs the longitudinal force of the rear wheel, FyfIs the lateral force of the front wheel FyrIs the rear wheel lateral force.
The front and rear wheel cornering powers can be calculated by:
Figure GDA0002630856810000042
in the formula: cfIs front wheel cornering stiffness, CrIs the rear wheel side yaw stiffness, alphafIs the front wheel side slip angle, alpharIs a rear wheel side slip angle.
According to the small angle assumption, the front and rear wheel side slip angles can be simplified as follows:
Figure GDA0002630856810000051
in the formula:fis the corner of the front wheel.
Therefore, a two-degree-of-freedom vehicle lateral dynamics model can be obtained as follows:
Figure GDA0002630856810000052
in the formula: v. ofyIs the transverse speed,
Figure GDA0002630856810000053
Is the yaw angle.
Selecting the lateral position y (k) and the yaw angle at the time k
Figure GDA0002630856810000054
The mass center slip angle beta (k) and the yaw rate gamma (k) are state quantities x (k), and the front wheel steering angle at the moment k is selectedf(k) Selecting the transverse position y (k) at the time k as an output quantity for controlling the quantity u (k), and writing the kinetic model into a discretization state space equation in the form of:
Figure GDA0002630856810000055
in the formula:
Figure GDA0002630856810000056
Tsis a sampling period, tau is an integral variable, A is a system matrix, B is an input matrix, and
Figure GDA0002630856810000061
1.2 design trajectory tracking active steering controller based on rolling time domain optimization algorithm
The rolling time domain optimization algorithm consists of a prediction model, rolling optimization, feedback correction and the like.
The prediction time domain is P, the control time domain is M, and M is less than or equal to P. At the current time k, assuming that the control quantity is a constant value outside the control time domain, i.e., u (k + M-1) ═ u (k + M) · u (k + P-1), a prediction model at the time k is determined according to a vehicle lateral dynamics model as follows:
Figure GDA0002630856810000062
defining the prediction output vector Y (k +1| k) and the control input vector U (k) as:
Figure GDA0002630856810000063
in the formula: y (k + P) is the transverse position of the P-th step in the prediction time domain at the time k, and u (k + M-1) is the control quantity of the M-th step in the control time domain at the time k.
The above prediction model can be simplified as follows:
Y(k+1)＝Sxx(k)+SuU(k) (9)
in the formula:
in the formula:
Figure GDA0002630856810000071
defining a desired sequence of lateral positions Ydes(k + i) is:
Figure GDA0002630856810000072
in the formula: y isdes(k + P) predicts the desired lateral position of the P-th step in the time domain for time k.
In order to enable the unmanned vehicle to quickly track the expected track and plan a reasonable front wheel corner, the following two control targets are selected: firstly, the error between the actual track and the expected track of the vehicle is reduced; secondly, in order to avoid generating excessive lateral acceleration and ensure the running smoothness of the vehicle, the control quantity is required to be as small as possible. Thus, a rolling optimization problem is established:
Figure GDA0002630856810000073
in the formula: j is a rolling optimization objective function,y、uAre weight coefficients.
The weight coefficients can be defined as a diagonal matrix:
Figure GDA0002630856810000074
in the formula:yPpredicting the weight coefficient of the P step in the time domain for the k time,uMAnd controlling the weight coefficient of the Mth step of the time domain for the k moment.
The front wheel steering angle cannot exceed the limit steering angle due to the limitation of the vehicle steering structure, and at the same time, the increment of the control amount needs to be limited in consideration of the response speed of the mechanical structure and the riding comfort, and therefore, the constraint conditions are set as follows:
Figure GDA0002630856810000075
in the formula: Δ u (k + i) ═ u (k + i +1) -u (k + i) representing the increment of the controlled variable, i ═ 0,1, …, M-1; u (k + i) is the control quantity of the ith step of the control time domain at the time k; u. ofmaxA right extreme position of a vehicle front wheel corner; u. ofminThe left extreme position of the vehicle front wheel steering angle.
The yaw angular velocity can directly reflect the stability of the vehicle, and in order to control the centroid slip angle beta within a small range, the constraint on the centroid slip angle is added in the constraint condition:
βmin≤β(k+i)≤βmax(14)
in the formula: beta (k + i) is the centroid slip angle of the ith step of the prediction time domain at the moment k, and betaminAnd betamaxRespectively, the centroid slip angle minimum and maximum.
In summary, the trajectory tracking active steering controller based on the rolling time domain optimization algorithm can be converted into the following optimization problem:
Figure GDA0002630856810000081
the constraint conditions are as follows:
Δumin≤Δu(k+i)≤Δumax
umin≤u(k+i)≤umax
βmin≤β(k+i)≤βmax
the optimization problem can be converted into a quadratic programming problem, and the QP problem with inequality constraints can be directly solved by an active set solution. Controlling the solved time k to input a vector U (k) ═ u (k) u (k +1) … u (k + M-1)]TAnd the obtained front wheel steering angle realizes the active steering control of the vehicle, and the process is repeated to finish the track tracking control process.
2 medium layer controller, designing yaw moment controller to track ideal yaw rate
2.1 building three-degree-of-freedom vehicle dynamics model
In order to research the transverse motion of the vehicle, vehicle dynamics modeling which needs to be established can describe a vehicle dynamics system as accurately as possible and reduce the calculation amount. For this reason, it is assumed that the vehicle is traveling on a flat road surface, without considering the vertical movement of the vehicle and the suspension movement, and that the vehicle is rigid; the longitudinal and transverse coupling relation of the tire force is not considered, and only the pure cornering tire characteristic is considered; regardless of front-back and left-right load transfer of the vehicle, regardless of longitudinal and transverse aerodynamics, a three-degree-of-freedom vehicle dynamics model considering only longitudinal, transverse and transverse swinging motions of the vehicle is established, as shown in fig. 2.
According to Newton's second law, the stress of the three-freedom-degree vehicle is analyzed in the directions of the x axis, the y axis and the direction around the z axis, and the three-freedom-degree vehicle dynamics model is obtained as follows:
Figure GDA0002630856810000091
in the formula: m is the mass v of the automobilexIs the longitudinal velocity, vyTransverse velocity, gamma yaw angular velocity,fIs a front wheel corner, IzFor car body winding ZMoment of inertia of the shaft,/fIs the distance of the center of mass to the front axis,/rIs the distance of the center of mass to the rear axis,/wIs the wheel spacing, MxIs a yaw moment; fx1、Fx2、Fx3And Fx4The longitudinal forces of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel are respectively; fy1、Fy2、Fy3And Fy4Left front wheel, right front wheel, left rear wheel, and right rear wheel lateral forces, respectively.
2.2 based on equivalent synovial membrane control theory build horizontal swing moment controller
The desired yaw rate of the vehicle may be calculated by:
Figure GDA0002630856810000092
in the formula: gamma raydTo a desired yaw rate, gamma0Is ideal horizontal angular velocity, gammamaxSgn () is a sign function, which is the maximum value of the yaw rate.
The ideal yaw angular velocity can be calculated by the following formula:
Figure GDA0002630856810000093
considering the constraints of the grip that can be provided by the ground, the maximum value of the yaw rate can be determined by the following equation:
Figure GDA0002630856810000094
in the formula: g is the gravity acceleration and mu is the road surface adhesion coefficient.
Let s be gamma-gammadGet it
Figure GDA0002630856810000101
Then
Figure GDA0002630856810000102
The equivalent control items are designed as follows:
Figure GDA0002630856810000103
in order to reduce the buffeting phenomenon in the control process, a continuous function is adopted to replace a symbolic function, a hyperbolic tangent function is adopted to design a switching robust control item, and the hyperbolic tangent function is as follows:
Figure GDA0002630856810000104
in the formula: and if the value is more than 0, the change speed of the inflection point of the function is determined by the value.
To ensure
Figure GDA0002630856810000107
And if so, taking the switching control item as:
Figure GDA0002630856810000105
wherein: d is greater than 0.
Deducing a cross torque controller based on the equivalent sliding film as follows:
Figure GDA0002630856810000106
3 a lower layer controller for distributing the driving torque obtained by the longitudinal speed controller to each in-wheel motor
3.1 design of longitudinal velocity controller based on fuzzy adaptive PI algorithm
The longitudinal speed control not only relates to the driving safety and riding comfort of the unmanned vehicle, but also plays an important role in the trajectory tracking precision. Speed fluctuations during normal driving may cause instability in the desired trajectory tracking, and therefore, it is necessary to control the longitudinal speed.
The error of the ideal longitudinal speed and the actual longitudinal speed and the error change rate are used as controller input, the fuzzy PI controller outputs the opening of the electronic throttle valve, and then the total driving torque of the vehicle is output by searching a Map which is compiled in advance of the opening of the electronic throttle valve and the torque Map of the in-wheel motor. The total driving torque is calculated through a torque distribution controller, the driving torque of each hub motor is calculated, the output torque of the hub motors acts on wheels, stable running of the vehicle and control over longitudinal speed are achieved, tire utilization rate is used as an optimization function, and a torque distribution algorithm is designed according to a pseudo-inverse method to distribute the total torque.
The design of the longitudinal speed controller based on the fuzzy adaptive PI algorithm is shown in fig. 3.
The basic universe of longitudinal velocity error e is [ -2,2], and 3 fuzzy subsets [ negative (substituted by N), zero (substituted by Z), positive (substituted by P) ] are defined on the fuzzy universe [ -1,1] thereof; the basic universe of argument of the longitudinal speed error rate of change ec is [ -3, 3], and 3 fuzzy subsets [ negative (substituted by N), zero (substituted by Z), positive (substituted by P) ] are defined on the fuzzy universe [ -1,1 ]. e. ec is shown in fig. 4.
Controller parameter Δ kpHas a basic discourse field of [ -3,3 [)]In its domain of ambiguity [ -1,1 [ ]]3 fuzzy subsets are defined above [ negative (by N), zero (by Z), positive (by P)](ii) a Controller parameter Δ kiHas a basic discourse field of [ -0.1,0.1 [ -0.1 [ ]]In its domain of ambiguity [ -1,1 [ ]]3 fuzzy subsets are defined above [ negative (by N), zero (by Z), positive (by P)]。Δkp、ΔkiThe membership function of (a) is shown in fig. 4.
Controller scaling factor kpThe setting principle is as follows: when the response increases (i.e., e is P), Δ kpFor P, i.e. increasing the proportionality coefficient kp(ii) a When overshooting (i.e., e is N), Δ kpFor N, i.e. reducing the proportionality coefficient kp(ii) a When e is Z, the discussion is divided into three cases: when ec is N, overshoot becomes larger and larger, and Δ kpIs N, when ec is Z, Δ kpThe error can be reduced for P, when ec is P, the positive error is larger and larger, and delta kpIs N.
Controller scaling factor kiThe setting principle is as follows: determined by integral separation, i.e. Δ k when e is around ZiIs P, otherwise Δ kiIs N.
Δ k established based on the above analysisp、ΔkiThe fuzzy rule table is obtained as follows:
TABLE 1 Δ kpFuzzy rule table
Figure GDA0002630856810000121
TABLE 2 Δ kiFuzzy rule table
Figure GDA0002630856810000122
Fuzzy controller input-output relation as shown in fig. 5, when e increases, the error between the actual longitudinal speed and the ideal longitudinal speed increases, and the scaling factor k needs to be increasedp，ΔkpThe output range is 0 to 2. Conversely, when overshoot occurs, i.e., when e ranges from-1 to 0, the scaling factor k needs to be reducedpThen Δ kpThe output range is-2 to 0. When the error e is around Z, Δ kiIs P, otherwise Δ kiIs N. As can be seen from fig. 5, the input-output relationship meets the setting requirement of the PI parameter.
3.2 Torque distribution controller design
In order to realize the stability control of the vehicle, the total driving torque of the vehicle obtained by controlling the longitudinal vehicle speed and the transverse swing torque needs to be reasonably distributed to all in-wheel motors. A large number of online optimization algorithms proposed by the past scholars are large in calculation amount and poor in real-time performance. To solve this problem, a torque distribution controller is proposed. The wheel longitudinal force of the vehicle can be expressed as:
FX＝[Fx1Fx2Fx3Fx4]T(25)
in the formula: fXAs longitudinal force vector of wheel, Fx1、Fx2、Fx3And Fx4The left front wheel, the right front wheel, the left rear wheel and the right rear wheel are respectively longitudinal force.
Let FTThe longitudinal force vectors of the left and right wheels of the vehicle are
Figure GDA0002630856810000131
In the formula:
Figure GDA0002630856810000132
the ratio of the actual adhesion force of the wheels to the limit adhesion force provided by the road surface is defined as the tire utilization rate, and in order to improve the vehicle stability, the sum of the tire utilization rates of each wheel is taken as a research object, and the sum of the tire utilization rates is required to be as small as possible, so that the tire can be ensured to be in a stable range without exceeding the adhesion limit as much as possible.
Figure GDA0002630856810000133
In the formula: etaiAdhesion ratio of tire for i-th wheel, FxiLongitudinal force of i-th wheel, FyiIs the lateral force of the ith wheel, FziFor the vertical load of the ith wheel, i ═ 1,2,3,4 represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively.
In studying longitudinal torque distribution, neglecting wheel side forces, tire utilization calculations can be simplified as:
Figure GDA0002630856810000134
in order to improve the safe driving capacity of the vehicle on a low-attachment road surface, the sum of the tire utilization rates is used as an optimization target, and the total driving torque of the vehicle is solved, namely:
Figure GDA0002630856810000135
in the formula: mu is road surface adhesion coefficient, weighting matrix
Figure GDA0002630856810000136
The following optimization problems are established:
Figure GDA0002630856810000141
to solve this problem, the Hamiltonian is constructed as follows:
Figure GDA0002630856810000142
in the formula: xi is in the middle of R4Is a lagrange multiplier.
For F in Hamilton functionxAnd xi to make the partial derivative equal to zero, then there are:
Figure GDA0002630856810000143
from the above formula, one can obtain:
Figure GDA0002630856810000144
namely:
Figure GDA0002630856810000145
the wheel longitudinal force of the vehicle can be written as:
Figure GDA0002630856810000146
the relationship between wheel driving force and wheel longitudinal force can be written as:
Figure GDA0002630856810000147
in the formula: r is the effective rolling radius of the wheel, TiI is a driving torque of the ith wheel, and 1,2,3, and 4 represent a front left wheel, a front right wheel, a rear left wheel, and a rear right wheel, respectively.
Thus, the drive torque distribution for each wheel may be expressed as:
Figure GDA0002630856810000151
in the formula: delta T1、ΔT2The total driving torque of the left and right wheels respectively.
When yaw moment controller is not operating, Δ T1，ΔT2Should equal the total drive torque TdIs one half, i.e.
Figure GDA0002630856810000152
When the transverse swing torque controller works, transverse swing torque is applied to the left and right wheels, and the total driving torque delta T of the left and right wheels1、ΔT2The relationship of (1) is:
Figure GDA0002630856810000153
in the formula: mxIs a yaw moment lwIs the wheel spacing.
ΔT1、ΔT2Can be calculated by the following formula:
Figure GDA0002630856810000154
Figure GDA0002630856810000155
the drive torque ultimately distributed to the in-wheel motor is then:
Figure GDA0002630856810000156
the preferred embodiment of the present invention has the following advantages:
1. the invention designs a layered track tracking control strategy of a four-wheel independent drive unmanned electric vehicle considering the lateral stability of the vehicle, an expected track is tracked through an upper-layer controller, and a middle-layer controller tracks an expected yaw angular velocity by using a front wheel corner planned by the upper-layer controller, so that the stability of the vehicle in track tracking is realized. The lower-layer controller designs a vehicle longitudinal speed controller based on fuzzy PI control, and ensures the stability of the vehicle for tracking the expected longitudinal speed. The lower-layer controller utilizes a pseudo-inverse method to solve the established torque distribution controller, and the algorithm is simple and effective, short in solving time and good in real-time performance.
2. According to the invention, vehicle dynamics constraint is added into the upper layer controller, so that the model accuracy and the vehicle driving safety can be improved. The upper-layer controller improves the track tracking precision by considering the state changes of the vehicle and the reference track at the future moment. And the designed upper controller has good robustness on vehicle speed, road adhesion conditions and reference tracks.
3. The invention establishes a shear moment controller based on quasi-slip film control, and utilizes a hyperbolic tangent function to replace a symbolic function, thereby effectively reducing the buffeting phenomenon of the quasi-slip film control.
The above description is only for the purpose of creating a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the technical scope of the present invention.

Claims (1)

1. An expected path vehicle longitudinal speed tracking control method, characterized by: the method comprises the following steps that errors of ideal longitudinal speed and actual longitudinal speed of a vehicle and error change rate are used as controller input, the controller outputs the opening of an electronic throttle valve, and a hub motor torque Map corresponding to the opening of the electronic throttle valve which is compiled in advance is searched for so as to output the total driving torque of the vehicle; calculating and distributing total driving torque to the driving torque of each hub motor by a torque distribution controller, wherein the output torque of the hub motors acts on wheels, and longitudinal speed is controlled to track an expected track, wherein the tire utilization rate is used as an optimization function, and a torque distribution algorithm is designed according to a pseudo-inverse method to distribute the total torque;
the moment distribution algorithm is as follows:
the wheel longitudinal force of the vehicle is expressed as:
FX＝[Fx1Fx2Fx3Fx4]T
in the formula: fXAs longitudinal force vector of wheel, Fx1、Fx2、Fx3And Fx4The longitudinal forces of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel are respectively;
let FTThe longitudinal force vectors of the left and right wheels of the vehicle are
Figure FDA0002630856800000011
In the formula:
Figure FDA0002630856800000012
defining the ratio of the actual adhesion force of the wheel to the limit adhesion force provided by the road surface as the utilization rate of the tire;
Figure FDA0002630856800000013
in the formula: etaiAdhesion ratio of tire for i-th wheel, FxiLongitudinal force of i-th wheel, FyiIs the lateral force of the ith wheel, FziThe vertical load of the ith wheel is represented by i ═ 1,2,3 and 4 respectively representing the left front wheel, the right front wheel, the left rear wheel and the right rear wheel;
neglecting the wheel lateral force, the tire utilization calculation is simplified as:
Figure FDA0002630856800000021
the total drive torque of the vehicle is solved with the sum of the tire utilization as an optimization target:
Figure FDA0002630856800000022
in the formula: mu is road surface adhesion coefficient, weighting matrix
Figure FDA0002630856800000023
The following optimization problems are established:
Figure FDA0002630856800000024
s.t. SFX＝FT
the Hamiltonian was constructed as follows:
Figure FDA0002630856800000025
in the formula: xi is in the middle of R4Is a lagrange multiplier;
for F in Hamilton functionxAnd xi to make the partial derivative equal to zero, then there are:
Figure FDA0002630856800000026
Figure FDA0002630856800000027
is obtained by the formula:
WTFX＝-2(ξS)T
Figure FDA0002630856800000028
namely:
Figure FDA0002630856800000029
the wheel longitudinal force of the vehicle is then:
Figure FDA0002630856800000031
the relationship between the wheel driving force and the wheel longitudinal force is:
Figure FDA0002630856800000032
in the formula: r is the effective rolling radius of the wheel, TiThe drive torque of the ith wheel is 1,2,3 and 4, which respectively represent a left front wheel, a right front wheel, a left rear wheel and a right rear wheel;
the drive torque distribution of each wheel is expressed as:
Figure FDA0002630856800000033
in the formula: delta T1、ΔT2The total driving torque of the left wheel and the right wheel is respectively;
when yaw moment controller is not operating, Δ T1，ΔT2Should equal the total drive torque TdIs one half, i.e.
Figure FDA0002630856800000034
When the transverse swing torque controller works, transverse swing torque is applied to the left and right wheels, and the total driving torque delta T of the left and right wheels1、ΔT2The relationship of (1) is:
Figure FDA0002630856800000035
in the formula: mxIs a yaw moment lwIs the wheel spacing;
ΔT1、ΔT2can be calculated by the following formula:
Figure FDA0002630856800000041
Figure FDA0002630856800000042
the drive torque ultimately distributed to the in-wheel motor is then:
Figure FDA0002630856800000043
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