CN109795502A - Intelligent electric automobile path trace model predictive control method - Google Patents
Intelligent electric automobile path trace model predictive control method Download PDFInfo
- Publication number
- CN109795502A CN109795502A CN201811127310.4A CN201811127310A CN109795502A CN 109795502 A CN109795502 A CN 109795502A CN 201811127310 A CN201811127310 A CN 201811127310A CN 109795502 A CN109795502 A CN 109795502A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- model
- control
- wheel
- performance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Steering Control In Accordance With Driving Conditions (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
A kind of intelligent electric automobile path trace model predictive control method, belongs to control technology field.The purpose of the present invention is that can consider that vehicle security constrains simultaneously using model predictive control method, and effectively realize the intelligent electric automobile path trace model predictive control method of the compromise optimization between vehicle route tracking performance, safety and vehicle performance.The present invention considers the intelligent electric automobile path trace model buildings of limiting condition, the sideway stability controller design based on Model Predictive Control, in carrying out control strategy derivation process, it considers the tracking performance of vehicle, vehicle safety, vehicle performance, driver comfort, save control energy, improve Full Vehicle Dynamics performance.The present invention considers the tracking performance (path follow, speed follower) of vehicle, vehicle safety (the perhaps locking that prevents from skidding rolls or whipping), vehicle performance (accelerate and braking ability), driver comfort (moment variations cannot be too big), saves control energy (saving energy under the premise of meeting performance).Improve Full Vehicle Dynamics performance.
Description
Technical field
The invention belongs to control technology fields.
Background technique
Recently as the increasingly exacerbation of environmental pollution and energy crisis, energy-saving and emission-reduction become domestic or even the world the weight
Want target.Electric car due to its high energy efficiency, low emission, low noise, can be carried out the advantages such as energy regenerating already and become current vapour
The great direction of vehicle industrial development, country have also put into effect a large amount of preferential policy and have supported business research electric car.
Electric car using In-wheel motor driving is the hot spot of recent researches, because hub motor is directly installed on by it
On wheel, space is saved, and being capable of lightweight automobile.Four-wheel wheel hub drives the driving force of each driving wheel of electric car can be with
Progress is directly independently accurately controlled, so that controlling more flexible, conveniently.Consider limiting condition intelligent electric automobile path with
Track model predictive control method as one of main control system of electric car, mainly solve in limiting condition vehicle route with
The problem of track.It specifically includes that the expected path information according to vehicle, a suitable steering wheel angle is generated, to realize
Accurate path trace.The speed control of vehicle is the another question of Trajectory Tracking Control, also referred to as the longitudinal direction of vehicle
The speed tracking control of vehicle, path following control are mainly realized in control by the driving braking control system of control vehicle
And both speed tracking controls are combined together and constitute intelligent automobile path following control.
For electric car, since its is relatively simple for structure, the available appropriate simplification of control problem, meanwhile, motor
Fast response time, the torque and revolving speed of motor are easy to obtain, this provides good base for the control system of this research
Plinth condition.The intelligent electric automobile path trace model predictive control method for considering limiting condition, mainly there is following problems:
Theoretical method is taken aim at and based on model prediction theory (MPC, Model based on pre- 1. mostly using greatly in Trajectory Tracking Control
Predictive Control) two kinds of method, however the former has ignored vehicle kinematics constraint and Dynamic Constraints, two are about
Beam not only has a significant impact track model- following control error, but also to ensuring that the stability of vehicle is of great significance, and model
Forecast Control Algorithm can account for vehicle kinematics and Dynamic Constraints during solving optimization objective function, this
Outside, the rolling optimization of MPC and feedback compensation characteristic, can be effectively reduced and even be eliminated shadow brought by closed-loop system substantive issue
It rings.
2. existing path following control is to control lateral stability, or carry out constant-speed-cruise control mostly, considers
Path follows less with the control system of speed follower comprehensively control.
3. simultaneously, vehicle is under limiting condition, influence of the vertical force variation to vehicle be can not be ignored, and previous research does not have
Consider the influence of vertical force, therefore good control cannot be carried out to the vehicle under limiting condition, accident easily occurs.
4. four wheels of four-wheel driving electric vehicle drive independently of one another, it is therefore desirable to while four wheels of control
Torque, and it is contemplated that the constraint condition, such as constraint of saturation, the security constraint of vehicle of motor etc. of vehicle itself
Deng.This is really the complex optimization control problem of a multiple target belt restraining.Common traditional algorithm has been difficult to meet the requirements.
Summary of the invention
The purpose of the present invention is that can consider that vehicle security constrains simultaneously using model predictive control method, and effectively realize
The intelligent electric automobile path trace model prediction of compromise optimization between vehicle route tracking performance, safety and vehicle performance
Control method.
The path following control model that the present invention considers includes vehicle kinematics model and kinetic model, is pre- respectively
The stable kinetic model (two degrees of freedom bicycle model) of the kinematics model and consideration Vehicular yaw taken aim at；It pre- is taken aim at according to optimal
Model determines course deviation, guarantees the stability of vehicle according to two degrees of freedom bicycle auto model, tracks given path.
(1) consider the intelligent electric automobile path trace model buildings of limiting condition:
1. XOY is earth coordinates based on the vehicle kinematics model buildings taken aim in advance, xoy is vehicle axis system
Wherein, (Xs,Ys) it is coordinate of the desired trace point B at earth coordinates XOY, (Xv,Yv) it is vehicle centroid position big
Coordinate under ground coordinate system XOY, L are preview distance,For the angle of OY and oB,For the angle of OY and ox,For expectation boat
To deviation, XdFor longitudinal direction of car displacement, YdFor vehicle lateral displacement, vxFor vehicular longitudinal velocity, vyFor vehicle side velocity,For
The course angle of vehicle.
2. the present invention considers the movement of two freedom degrees of vehicle roll and sideway, it is assumed that speed is definite value, and vehicle is simplified to
For two degrees of freedom bicycle model, vehicle centroid side drift angle β and yaw velocity γ are as state variable, the driving torque of four-wheel
With front wheel angle as inputting, obtain
Wherein, β is side slip angle, and γ is the yaw velocity of vehicle body, Fyf,FyrBefore respectively representing two degrees of freedom auto model
Wheel lateral force and rear-wheel lateral force, m represent complete vehicle quality, Lf,LrAfter the distance and mass center for respectively representing vehicle centroid to front axle arrive
The distance of axis, IzIt is vehicle around z-axis rotary inertia；MzIt is vehicle yaw moment, is expressed as
Wherein, d represents vehicle axial length；Fxi, subscript i=fl, fr, rl, rr represent the near front wheel, off-front wheel, left rear wheel and off hind wheel
Longitudinal force；
3. the driving moment of vehicle can be described as:
Tt=(Fxfl+Fxfr+Fxrl+Fxrr)·R (4)
Wherein TtFor driving moment, R is tire radius；
4. the expression formula of the side force of tire in formula (2) are as follows:
Wherein, Fyf,FyrRepresent the lateral force of front and back wheel, Cf,CrFor front-and rear-wheel steer rigidity, Ka,KbFor to magic tire model
Carry out a Lagrangian domination fitting coefficient, αf,αrFor front and rear wheel side drift angle, indicate are as follows:
Wherein, δfFor front wheel angle；
5. considering in path tracking procedure, it may occur that the operation such as steering, to guarantee that it is flat that vehicle roll is added in the safety of vehicle
Weigh equation:
Wherein, IxIt is vehicle around the rotary inertia of x-axis, dtf,dtrRepresent front and rear wheel wheelspan, Fyi, subscript i=fl, fr, rl, rr,
Represent side force of tire, Fzi, i=fl, fr, rl, rr represent tire vertical force, hRVehicle roll height is represented, ρ, which is represented, to be rolled
Angle.
6. the vertical force F in formula (7)ziDue to being influenced by longitudinal acceleration, side acceleration, inclination and pitching etc., take turns
The vertical force load simultaneous of tire are as follows:
Wherein, hcgVehicle centroid height is represented, g represents acceleration of gravity, axRepresent longitudinal acceleration, ayRepresent lateral accelerate
Degree；7. according to dynamics of vehicle:
For vehicle course angle, γ is yaw velocity.
8. the vehicle route tracking system model established is
So far, the present invention establishes a consideration vehicle kinematics and dynamic (dynamical) system model, mainly considers vertical load
Change the influence to system.
(2) the sideway stability controller design based on Model Predictive Control:
1. by side slip angle β, yaw velocity γ and roll velocityLength travel XdWith lateral displacement YdAs system
State variable, i.e.,By front wheel steering angle δfAnd four-wheel torque Txfl,Txfr,Txrl,
TxrrAs control variable, i.e. u=[δf,Txfl,Txfr,Txrl,Txrr]T；System exports y=[β, γ]T；
2. the system model of formula (13) description is carried out discretization using Euler's formula, the discrete time model of system is obtained
Are as follows:
Wherein, k is sampling instant, TsFor sampling time, matrix
3. definition prediction time domain is p, control time domain is c, p > c；Vehicle dynamic in [k+1, k+p] prediction time domain can be based on
Vehicle's current condition and prediction model obtain；I.e. at the k+p moment, vehicle-state be x (k+p)=F (x (k), u (k), u (k+1),
L,u(k+c),L,u(k+p-1))；As sampling time TsWhen greater than control time domain c, keep control input constant until predicting time domain
U (k+c-1)=u (k+c)=u (k+c+1)=L u (k+p-1)；
4. being defined on the kth moment, the optimum control input of system are as follows:
At the kth moment, the prediction output of system is
5. at the kth moment, the reference input sequence of system is
In the initial value that k-th of sampling instant, y (k) are predicted as control system, i.e. y (k | k)=y (k)；
6. adding a constraint condition to yaw velocity
Wherein, μ is coefficient of road adhesion；
(3) in carrying out control strategy derivation process, it is contemplated that the tracking performance of vehicle, vehicle safety, vehicle performance, driving
Comfort saves control energy, improves Full Vehicle Dynamics performance:
1. main optimization aim is to improve tracking performance, the vehicle performance of vehicle
Wherein, Q1,Q2,Q3,Q4,Q5It is the weighting coefficient in optimization aim；
2. motor torque means that more greatly the energy consumed from battery is bigger.In order to reduce energy consumption, control amount is put down
It just and should be as small as possible, i.e.,
Wherein, R1,R2It is the weighting coefficient in optimization aim；
2., to guarantee the comfort driven, keeping smooth steering and motor driven to reduce the change frequency of control action
Behavior, then controlling target is,
Wherein, S1,S2It is the weighting coefficient in optimization aim；
(4) to sum up, total objective function is obtained, i.e.,
Constraint:
Motor constraint of saturation:
-Temax≤Ti(k+j|k)≤Temax, i=fl, fr, rl, rr, j=1,2 ..., m-1. (23)
Security constraint:
Torque constraint:
The torque of four motors and equal to total driving torque T from driving pedalt,
Tt=Tfl(k+j|k)+Tfr(k+j|k)+Trl(k+j|k)+Trr(k+j | k), j=1,2 ..., m-1. (25).
The present invention compared with prior art the beneficial effects of the present invention are:
1. the present invention establishes vehicle non-linear dynamic model when considering limiting condition, due to vehicle vertical force under limiting condition
Load variation can not be ignored, therefore uses and consider that the non-linear electric vehicle dynamics model of vertical force variation can be with high degree
Raising vehicle route tracking performance and Full Vehicle Dynamics performance.
2. the present invention carries out pre- taking aim at theoretical and model using based on optimal during the design of path following control device
The control strategy that predictive control algorithm combines, in the case where considering vehicle kinematics constraint and Dynamic Constraints, by pre-
It takes aim at algorithm and finds sampled point on desired trajectory, calculate the front-wheel course angle of the point, vehicle is estimated by Model Predictive Control Algorithm
The variation tendency in front-wheel course, and the actual front-wheel course angle of vehicle is calculated, two kinds of algorithms, which combine, may be implemented path trace
The high-precision control of control algolithm.
3. during carrying out control strategy formulation, it is contemplated that the tracking performance of vehicle (path follow, speed follower),
Vehicle safety (preventing skid perhaps locking inclination or whipping), vehicle performance (acceleration and braking ability), driver comfort
(moment variations cannot be too big) saves control energy (energy is saved under the premise of meeting performance).Improve Full Vehicle Dynamics performance.
Detailed description of the invention
A specific embodiment of the invention is further described with reference to the accompanying drawing, these explanations of the invention will more
It is clear to understand.Wherein:
Fig. 1 is the intelligent electric automobile path trace model predictive control system structural block diagram for considering limiting condition；
Fig. 2 is expected path and vehicle location relation schematic diagram；
Fig. 3 is the two degrees of freedom bicycle model of Controller-oriented design；
Fig. 4 is Model Predictive Control basic schematic diagram；
Fig. 5 is path following control device schematic illustration；
Fig. 6 is path trace effect picture；
Fig. 7 is yaw velocity tracking effect figure.
Specific embodiment
The present invention relates to a kind of control methods for belonging to intelligent electric automobile path trace, and more specifically, the present invention relates to
And a kind of path tracking control method that can guarantee the electric car under uneven limit surface conditions.
The present invention establishes vehicle non-linear dynamic model when considering limiting condition, be then based on it is optimal it is pre- take aim at it is theoretical and
Model Predictive Control Algorithm design vehicle path following control device, the effect of Lai Shixian electric car path trace, including path
Follow, speed follows.Consider the kinetic model that limiting condition is established, can more accurately be tracked in path, simultaneously
The safety for guaranteeing vehicle prevents vehicle from rollover, whipping occurs.Model predictive control method can effectively handle multiple target complexity
Optimal Control Problem, and dominant processing constrains, and the present invention can consider vehicle security using model predictive control method simultaneously
Constraint, and effectively realize the compromise optimization between vehicle route tracking performance, safety and vehicle performance.Realize intelligent vehicle
Autonomous path following function.Cost function of the invention considers mainly to include four aspects, comprising: vehicle route tracking performance
(path follow, speed follower), vehicle performance (turning, accelerate and braking ability), (moment variations cannot be too for driver comfort
Greatly), control energy (energy is saved under the premise of meeting performance) is saved.
The present invention design the considerations of limiting condition intelligent electric automobile path trace model predictive control method it is electronic
Automobile sideway stabilizing control system can be well solved above 4 problems.The vehicle that the present invention establishes when considering limiting condition is non-
Linear kinetic model, be then based on it is optimal it is pre- take aim at theoretical and Model Predictive Control Algorithm design vehicle path following control device,
Realize the effect of electric car path trace, following including path, speed follow.Consider the power that limiting condition is established
Model is learned, can more accurately be tracked in path, while guaranteeing the safety of vehicle, prevent vehicle from rollover, whipping occurs.Mould
Type forecast Control Algorithm can effectively handle multiple target complex optimization control problem, and dominant processing constrains, and the present invention uses
Model predictive control method can consider simultaneously vehicle security constrain, and effectively realize vehicle route tracking performance, safety and
Compromise optimization between vehicle performance.Realize the autonomous path following function of intelligent vehicle
To achieve the above object, the present invention is as follows using technical solution:
Firstly, vehicle non-linear dynamic model when considering limiting condition is established, due to the dynamics of vehicle under limiting condition
Performance presents great nonlinear characteristic, and therefore, it is necessary to establish high-precision non-linear dynamic model as prediction model
Carry out controller design.Be then based on it is optimal it is pre- take aim at theoretical and Model Predictive Control Algorithm design vehicle path following control device,
Cost function considers mainly to include four aspects, comprising: vehicle route tracking performance (path follow, speed follower), vehicle
Energy (turning, acceleration and braking ability), driver comfort (moment variations cannot be too big), saving control energy and (are meeting performance
Under the premise of save energy, the effect of Lai Shixian electric car path trace, following including path, speed follow.Consider pole
The kinetic model that operating condition is established is limited, can more accurately be tracked in path, while guaranteeing the safety of vehicle, prevent vehicle
Rollover, whipping occurs.Model predictive control method can effectively handle multiple target complex optimization control problem, and dominant processing
Constraint, the present invention using model predictive control method can consider simultaneously vehicle security constrain, and effectively realize vehicle route with
Compromise optimization between track performance, safety and vehicle performance.Realize the autonomous path following function of intelligent vehicle.
For the technology contents that the present invention will be described in detail, construction features, realize purpose etc., with reference to the accompanying drawing to the present invention into
Row is explained comprehensively.
The intelligent electric automobile path trace model predictive control system structural frames of the considerations of present invention designs limiting condition
Figure is as shown in Figure 1.Firstly, given desired trajectory, the desired trajectory of intelligent electric automobile can be obtained by path planning, Huo Zhegen
It is obtained according to the lane line of detection, directly gives the transverse and longitudinal coordinate of desired trajectory herein, be given to pre- model of taking aim at and vehicle institute is calculated
The desired course angle needed, meanwhile, the course angle predicted by prediction model and actual heading angle are input to course and become
Difference function, generates course deviation, and vehicle is calculated in desired speed needed for being input to path following control device combination vehicle
Front wheel angle needed for realizing route tracking effect and car speed are input to wheel hub electric car, complete entire control process.
Modules are described in detail respectively below.
The present invention mainly studies the sideway dynamics for considering the variation of vehicle vertical load, in analysis vehicle yaw motion
When, the main movement for considering two freedom degrees of vehicle roll and sideway.Therefore, it is assumed here that speed is definite value, by vehicle
Simplifying becomes two degrees of freedom bicycle model, as shown in Figure 3.
1. considering the intelligent electric automobile path trace model buildings of limiting condition
It is inclined to calculate desired course according to the relationship of expected path and current vehicle position and pose for course deviation generator first
Difference, expected path and vehicle location relation schematic diagram are as shown in Figure 2.XOY is earth coordinates, and xoy is vehicle axis system.
Wherein, (Xs,Ys) it is coordinate of the desired trace point B at earth coordinates XOY, (Xv,Yv) it is vehicle centroid position big
Coordinate under ground coordinate system XOY, L are preview distance,For the angle of OY and oB,For the angle of OY and ox,For expectation boat
To deviation, XdFor longitudinal direction of car displacement, YdFor vehicle lateral displacement, vxFor vehicular longitudinal velocity, vyFor vehicle side velocity,
For the course angle of vehicle.
Vehicle centroid side drift angle β and yaw velocity γ makees as state variable, the driving torque and front wheel angle of four-wheel
For input, it is as shown in Figure 3 to obtain controller model.
Wherein, β is side slip angle, represents the angle between the vehicle longitudinal axis and speed direction vector, and γ is the yaw angle of vehicle body
Speed.β and γ represents two freedom degrees for simplifying two degrees of freedom vehicle dynamic model.Fyf,FyrRespectively represent two degrees of freedom vehicle
Model front-wheel lateral force and rear-wheel lateral force, m represent complete vehicle quality, Lf,LrRespectively represent vehicle centroid to front axle distance and
Distance of the mass center to rear axle, IzIt is vehicle around z-axis rotary inertia.
MzIt is vehicle yaw moment, is expressed as follows.
Wherein, d represents vehicle axial length, FxiThe longitudinal force of four wheels is represented, subscript i=fl, fr, rl, rr respectively represent left front
Wheel, off-front wheel, left rear wheel, off hind wheel.
The driving moment of vehicle can be described as:
Tt=(Fxfl+Fxfr+Fxrl+Fxrr)·R (4)
Wherein TtFor driving moment, R is tire radius.
The expression formula of side force of tire in formula (2):
Wherein, Fyf,FyrRepresent the lateral force of front and back wheel, Cf,CrFor front-and rear-wheel steer rigidity, Ka,KbFor to magic tire model
A Lagrangian domination fitting coefficient is carried out, matched curve is as shown in Figure 6.αf,αrIt is as follows for front and rear wheel side drift angle:
Wherein, δfFor front wheel angle.
Consider in path tracking procedure, it may occur that the operation such as steering, to guarantee that vehicle roll is added in the safety of vehicle
Equilibrium equation:
Wherein, IxIt is vehicle around the rotary inertia of x-axis, dtf,dtrRepresent front tread, rear track, FyiRepresent four tyre sides
Xiang Li, FziRepresent four tire vertical forces, FyiAnd FziIn subscript i=fl, fr, rl, rr respectively represent the near front wheel, off-front wheel,
Left rear wheel, off hind wheel, hRVehicle roll height is represented, ρ represents angle of heel.
Vertical force F in formula (7)ziDue to being influenced by longitudinal acceleration, side acceleration, inclination and pitching etc.,
The vertical force load simultaneous of tire are as follows:
Wherein, hcgVehicle centroid height is represented, g represents acceleration of gravity, axRepresent longitudinal acceleration, ayRepresent lateral accelerate
Degree.
According to dynamics of vehicle:
For vehicle course angle, γ is yaw velocity.
In conclusion the vehicle dynamic model established is
So far, a consideration vehicle kinematics and dynamic (dynamical) system model are established, vertical load variation pair is mainly considered
The influence of system.
2, the sideway stability controller based on Model Predictive Control
Model Predictive Control is multi-step prediction, and the open loop that basic thought can be described as in one finite time-domain of line solver is optimal
Control problem, while guaranteeing that system meets objective function, state and input constraint etc..PREDICTIVE CONTROL can be summarized simply as follows three
Step: according to the current measurement information of acquisition and the following dynamic of prediction model forecasting system；Under the conditions of guaranteeing objective function and constraining
Line solver optimization problem；First element interaction of solution is in system.Model Predictive Control be at every sampling moment repeat into
Capable, and the following dynamic starting point of forecasting system is current measured value, that is, uses the measured value of each sampling instant as prediction
Primary condition.The basic principle of Model Predictive Control is as shown in Figure 7.In current time t, measured value is obtained from controlled system
x0, according to metrical information and prediction model, forecasting system is in prediction time domain TpThe interior following dynamic behaviourOptimize open-loop performance
Target function (there are four parts for objective function in the present invention), searches out control time domain TcInterior optimal control list entries
So that the system output of prediction is with the output of desired system closer to better, i.e. in Fig. 4 hatched area minimum.
In view of the nonlinear characteristic of four-wheel wheel hub electric car, can be solved just using Model Predictive Control Algorithm such
Therefore nonlinear problem devises MPC controller herein to calculate the driving moment of front wheel angle and four wheels, thus
The yaw velocity of vehicle and side slip angle is allowed to track the desired value that upper layer is set.
By yaw velocity γ, side slip angle β and roll velocityLength travel XdWith lateral displacement YdAs being
The state variable of system, i.e.,By front wheel steering angle δfAnd four-wheel torque Txfl, Txfr,
Txrl, TxrrAs control variable, i.e. u=[δf,Txfl,Txfr,Txrl,Txrr]T；System exports y=[β, γ]T。
The system model of formula (13) description is subjected to discretization using Euler's formula, obtains the discrete time model of system
Are as follows:
Wherein, k indicates sampling instant, TsFor sampling time, matrix
Definition prediction time domain is p in the present invention, and control time domain is c, p > c.Vehicle moves in [k+1, k+p] prediction time domain
State can be obtained based on vehicle's current condition and prediction model.I.e. at the k+p moment, vehicle-state is x (k+p)=F (x (k), u
(k),u(k+1),L,u(k+c),L,u(k+p-1)).As sampling time TsWhen greater than control time domain c, keep control input constant
Until predicting time domain u (k+c-1)=u (k+c)=u (k+c+1)=L u (k+p-1).
Therefore kth moment, the optimum control input of system are defined on are as follows:
At the kth moment, the prediction output of system is
At the kth moment, the reference input sequence of system is
In the initial value that k-th of sampling instant, y (k) are predicted as control system, i.e. y (k | k)=y (k).The shape of controlled system
State variable and input, which can input to calculate according to the state variable value and system at current time, to be updated, by the of the control sequence obtained
One is used as system input action in next moment, and combines the output of subsequent time controlled system to optimize problem and ask
Solution, is achieved that the rolling optimization of control sequence repeatedly, and state at the time of to future is solved.
It for Body Control stable constraint problem, needs to add yaw velocity one constraint condition, guarantees to turn to peace
Entirely, μ is coefficient of road adhesion.
MPC control algorithm can be with effective solution multiple target multiple constraint problem, and can be denoted as having weighting square
Battle array multiple target equation, and obtain include front wheel steering angle, rear-axle steering angle multi-dimensional optimization variable.
Intelligent electric automobile path trace Model Predictive Control principle such as Fig. 4 of limiting condition is considered designed by the present invention
It is shown.During carrying out control strategy formulation, it is contemplated that the tracking performance of vehicle (path follow, speed follower), vehicle
Safety (preventing skid perhaps locking inclination or whipping), vehicle performance (acceleration and braking ability), driver comfort (power
Square variation cannot too greatly), save control energy (energy is saved under the premise of meeting performance).
In carrying out control strategy derivation process, it is contemplated that the tracking performance of vehicle, vehicle performance, is driven vehicle safety
It sails comfort, save control energy, improve Full Vehicle Dynamics performance:
(1) in order to keep vehicle stabilization and good maneuverability, it is required that expectational model on system output tracking, yaw angle speed
Spend the stability that desired value in γ and side slip angle β tracking guarantees vehicle, roll velocityDesired value guarantees vehicle in tracking
Vertical comfort level, to length travel XdWith lateral displacement YdTracking guarantee vehicle path trace performance.Therefore, main
Optimization aim is to improve tracking performance, the vehicle performance of vehicle
Q in formula1,Q2,Q3,Q4,Q5It is the weighting coefficient in optimization aim.Q1+Q2+Q3+Q4+Q5=1, Q1, Q2Represent Vehicular yaw
Stability weight, Q3, Q4Delegated path tracking effect weight, Q5Vehicle comfort weight is represented, weight coefficient is adjustable
, increase Q by adjusting1, Q2The Yaw stability of vehicle can be increased, increase Q3, Q4The path trace performance of vehicle is promoted,
Increase Q5The comfort that vehicle can be promoted can choose different weight coefficients according to different demands, for example, in order to guarantee
The tracking performance of vehicle chooses Q3, Q4It is slightly larger, Q1, Q2Secondly, Q5Numerical value is minimum, guarantees in the case where sacrificing comfort level slightly
Path trace performance and intact stability.
(2) motor torque means that more greatly the energy consumed from battery is bigger, reduces consumption energy, and control amount is put down
Square and small as far as possible, control amount is front wheel angle δ in the present inventionfWith the torque T of four wheelsfl,Tfr,Trl,Trr, therefore target letter
Number is as follows:
R in formula1,R2It is the weighting coefficient in optimization aim.R1+R2=1, R1Represent Vehicular turn motor weight, R2Represent vehicle
Difference increase R can be adjusted according to demand in driving/braking motor weight, weight coefficient1, it is that steering motor is more energy saving,
Increase R2Keep driving/braking motor more energy saving, for example under straight line operating condition, Q can be chosen2It is slightly larger, Q1It is slightly smaller, to guarantee to drive
The energy conservation of dynamic/braking motor.
(3) in order to which the change frequency for reducing control action keeps smooth steering and motor to guarantee the comfort driven
Driving behavior, then controlling target is,
S in formula1,S2It is the weighting coefficient in optimization aim.S1+S2=1, S1Represent steering motor weight, S2Represent driving/braking
Motor weight, weight coefficient can be adjusted according to demand, for example under two-track line operating condition, steering motor has control action variation,
S can be increased1Weight increases the steering motor service life so that steering motor variation is slightly smaller.
To sum up, total objective function is obtained, i.e.,
Constraint:
Motor constraint of saturation:
Turn to security constraint:
Torque constraint:
The torque of four motors and it should be equal to total driving torque T from driving pedalt,
We just establish required with constrained optimization problem in this way, and using the solution in the tool box Matlab
The fmincon function line solver optimization method of Non-Linear Programming equation, obtains control amount.
Verifying:
Realization process of the invention uses Matlab/CarSim associative simulation, writes m text in matlab according to controller model
Part chooses A grades of vehicles in carsim and carries out associative simulation.
By analogous diagram (Fig. 6) as can be seen that vehicle realizes path trace facility, and tracking effect is preferable.
As seen from Figure 7, yaw velocity tracking accuracy is higher, checks for convenience, and the amplification of black frame region is such as small
Shown in figure, control method designed by the invention, yaw-rate error be can control within 0.01rad/s.
Claims (1)
1. a kind of intelligent electric automobile path trace model predictive control method, it is characterised in that: the path following control of consideration
Model includes vehicle kinematics model and kinetic model, is that the pre- kinematics model taken aim at and consideration Vehicular yaw are stablized respectively
Kinetic model；Course deviation is determined according to optimal pre- model of taking aim at, and vehicle is guaranteed according to two degrees of freedom bicycle auto model
Stability, track given path；
(1) consider the intelligent electric automobile path trace model buildings of limiting condition:
1. XOY is earth coordinates based on the vehicle kinematics model buildings taken aim in advance, xoy is vehicle axis system
Wherein, (Xs,Ys) it is coordinate of the desired trace point B at earth coordinates XOY, (Xv,Yv) it is vehicle centroid position big
Coordinate under ground coordinate system XOY, L are preview distance,For the angle of OY and oB,For the angle of OY and ox,For expectation
Course deviation, XdFor longitudinal direction of car displacement, YdFor vehicle lateral displacement, vxFor vehicular longitudinal velocity, vyFor vehicle side velocity,For the course angle of vehicle；
2. the present invention considers the movement of two freedom degrees of vehicle roll and sideway, it is assumed that speed is definite value, and vehicle is simplified to
For two degrees of freedom bicycle model, vehicle centroid side drift angle β and yaw velocity γ are as state variable, the driving torque of four-wheel
With front wheel angle as inputting, obtain
Wherein, β is side slip angle, and γ is the yaw velocity of vehicle body, Fyf,FyrRespectively represent two degrees of freedom auto model front-wheel
Lateral force and rear-wheel lateral force, m represent complete vehicle quality, Lf,LrVehicle centroid is respectively represented to the distance and mass center of front axle to rear axle
Distance, IzIt is vehicle around z-axis rotary inertia；MzIt is vehicle yaw moment, is expressed as
Wherein, d represents vehicle axial length；FxiRepresent the longitudinal forces of four wheels, subscript i=fl, fr, rl, rr respectively represent the near front wheel,
Off-front wheel, left rear wheel, off hind wheel；
3. the driving moment of vehicle can be described as:
Tt=(Fxfl+Fxfr+Fxrl+Fxrr)·R (4)
Wherein TtFor driving moment, R is tire radius；
4. the expression formula of the side force of tire in formula (2) are as follows:
Wherein, Fyf,FyrRepresent the lateral force of front and back wheel, Cf,CrFor front-and rear-wheel steer rigidity, Ka,KbFor to magic tire model into
A row domination fitting coefficient of Lagrange, αf,αrFor front and rear wheel side drift angle, indicate are as follows:
Wherein, δfFor front wheel angle；
5. considering in path tracking procedure, it may occur that the operation such as steering, to guarantee that it is flat that vehicle roll is added in the safety of vehicle
Weigh equation:
Wherein, IxIt is vehicle around the rotary inertia of x-axis, dtf,dtrRepresent front tread, rear track, FyiRepresent four tyre sides
Xiang Li, FziRepresent four tire vertical forces, FyiAnd FziIn subscript i=fl, fr, rl, rr respectively represent the near front wheel, off-front wheel,
Left rear wheel, off hind wheel, hRVehicle roll height is represented, ρ represents angle of heel；
6. the vertical force F in formula (7)ziDue to being influenced by longitudinal acceleration, side acceleration, inclination and pitching etc., take turns
The vertical force load simultaneous of tire are as follows:
Wherein, hcgVehicle centroid height is represented, g represents acceleration of gravity, axRepresent longitudinal acceleration, ayRepresent side acceleration；
7. according to dynamics of vehicle:
For vehicle course angle, γ is yaw velocity；
8. the vehicle route tracking system model established is
So far, a consideration vehicle kinematics and dynamic (dynamical) system model are established, vertical load variation pair is mainly considered
The influence of system；
(2) the sideway stability controller design based on Model Predictive Control:
1. by side slip angle β, yaw velocity γ and roll velocityLength travel XdWith lateral displacement YdAs system
State variable, i.e.,By front wheel steering angle δfAnd four-wheel torque Txfl,Txfr,Txrl,Txrr
As control variable, i.e. u=[δf,Txfl,Txfr,Txrl,Txrr]T；System exports y=[β, γ]T；
2. the system model of formula (13) description is carried out discretization using Euler's formula, the discrete time model of system is obtained
Are as follows:
Wherein, k is sampling instant, TsFor sampling time, matrix
3. definition prediction time domain is p, control time domain is c, p > c；Vehicle dynamic in [k+1, k+p] prediction time domain can be based on
Vehicle's current condition and prediction model obtain；I.e. at the k+p moment, vehicle-state be x (k+p)=F (x (k), u (k), u (k+1),
L,u(k+c),L,u(k+p-1))；As sampling time TsWhen greater than control time domain c, keep control input constant until predicting time domain
U (k+c-1)=u (k+c)=u (k+c+1)=L u (k+p-1)；
4. being defined on the kth moment, the optimum control input of system are as follows:
At the kth moment, the prediction output of system is
5. at the kth moment, the reference input sequence of system is
In the initial value that k-th of sampling instant, y (k) are predicted as control system, i.e. y (k | k)=y (k)；
6. adding a constraint condition to yaw velocity
Wherein, μ is coefficient of road adhesion；
(3) in carrying out control strategy derivation process, it is contemplated that the tracking performance of vehicle, vehicle safety, vehicle performance, driving
Comfort saves control energy, improves Full Vehicle Dynamics performance:
1. main optimization aim is to improve tracking performance, the vehicle performance of vehicle
Wherein, Q1,Q2,Q3,Q4,Q5It is the weighting coefficient in optimization aim；
2. motor torque means that more greatly the energy consumed from battery is bigger；In order to reduce energy consumption, control amount is put down
It just and should be as small as possible, i.e.,
Wherein, R1,R2It is the weighting coefficient in optimization aim；
2., to guarantee the comfort driven, keeping smooth steering and motor driven to reduce the change frequency of control action
Behavior, then controlling target is,
Wherein, S1,S2It is the weighting coefficient in optimization aim；
(4) to sum up, total objective function is obtained, i.e.,
Constraint:
Motor constraint of saturation:
-Temax≤Ti(k+j|k)≤Temax, i=fl, fr, rl, rr, j=1,2 ..., m-1. (23)
Security constraint:
Torque constraint:
The torque of four motors and equal to total driving torque T from driving pedalt,
Tt=Tfl(k+j|k)+Tfr(k+j|k)+Trl(k+j|k)+Trr(k+j | k), j=1,2 ..., m-1. (25).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811127310.4A CN109795502B (en) | 2018-09-27 | 2018-09-27 | Intelligent electric vehicle path tracking model prediction control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811127310.4A CN109795502B (en) | 2018-09-27 | 2018-09-27 | Intelligent electric vehicle path tracking model prediction control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109795502A true CN109795502A (en) | 2019-05-24 |
| CN109795502B CN109795502B (en) | 2021-05-04 |
Family
ID=66556189
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811127310.4A Active CN109795502B (en) | 2018-09-27 | 2018-09-27 | Intelligent electric vehicle path tracking model prediction control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109795502B (en) |
Cited By (43)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110040147A (en) * | 2019-04-23 | 2019-07-23 | 吉林大学 | A kind of method of travel speed optimization under the lateral constraint of saturation of autonomous driving vehicle |
| CN110161865A (en) * | 2019-06-13 | 2019-08-23 | 吉林大学 | A kind of intelligent vehicle lane-change method for planning track based on Nonlinear Model Predictive Control |
| CN110187639A (en) * | 2019-06-27 | 2019-08-30 | 吉林大学 | A kind of trajectory planning control method based on Parameter Decision Making frame |
| CN110471428A (en) * | 2019-09-18 | 2019-11-19 | 吉林大学 | A kind of path following method of change preview distance and constraint of velocity based on model |
| CN110539752A (en) * | 2019-06-26 | 2019-12-06 | 江苏大学 | Intelligent automobile multi-prediction-range model prediction trajectory tracking control method and system |
| CN110696793A (en) * | 2019-09-19 | 2020-01-17 | 江苏理工学院 | Intelligent vehicle layered control method combining four-wheel steering and differential braking |
| CN110702121A (en) * | 2019-11-23 | 2020-01-17 | 赣南师范大学 | Optimal path fuzzy planning method for hillside orchard machine |
| CN110727271A (en) * | 2019-10-30 | 2020-01-24 | 北京科技大学 | Robot motion primitive determining method and device |
| CN110908395A (en) * | 2019-11-26 | 2020-03-24 | 西北工业大学 | Improved unmanned aerial vehicle flight path real-time planning method |
| CN110955250A (en) * | 2019-12-25 | 2020-04-03 | 湖南大学 | Vehicle reversing path tracking control method |
| CN110989625A (en) * | 2019-12-25 | 2020-04-10 | 湖南大学 | Vehicle path tracking control method |
| CN111016891A (en) * | 2019-11-06 | 2020-04-17 | 国汽(北京)智能网联汽车研究院有限公司 | Method and device for controlling vehicle driving route tracking |
| CN111158264A (en) * | 2020-01-09 | 2020-05-15 | 吉林大学 | Model prediction control rapid solving method for vehicle-mounted application |
| CN111444577A (en) * | 2020-04-02 | 2020-07-24 | 山东交通学院 | Automatic avoidance method for electric motor coach |
| CN111532283A (en) * | 2020-05-15 | 2020-08-14 | 吉林大学 | Model prediction control-based path tracking method for semi-trailer train |
| CN111547111A (en) * | 2020-05-22 | 2020-08-18 | 西南交通大学 | Autonomous guiding control method for virtual rail train |
| CN111688715A (en) * | 2020-06-24 | 2020-09-22 | 长春工业大学 | Centroid slip angle observation method of four-wheel drive electric vehicle based on fusion technology |
| CN111717282A (en) * | 2020-07-07 | 2020-09-29 | 中国人民解放军国防科技大学 | Man-machine shared driving auxiliary steering method based on model predictive control |
| CN111812974A (en) * | 2020-05-28 | 2020-10-23 | 北京理工大学 | Comprehensive control method for bilateral motor-driven tracked vehicle |
| CN111890951A (en) * | 2020-08-07 | 2020-11-06 | 吉林大学 | Intelligent electric automobile trajectory tracking and motion control method |
| CN111923908A (en) * | 2020-08-18 | 2020-11-13 | 哈尔滨理工大学 | Stability-fused intelligent automobile path tracking control method |
| CN111959500A (en) * | 2020-08-07 | 2020-11-20 | 长春工业大学 | Automobile path tracking performance improving method based on tire force distribution |
| CN112068445A (en) * | 2020-09-23 | 2020-12-11 | 北京理工大学 | Integrated control method and system for path planning and path tracking of automatic driving vehicle |
| CN112124028A (en) * | 2020-09-21 | 2020-12-25 | 华南理工大学 | Electric control air suspension system, control method and system thereof and electric control unit |
| CN112406889A (en) * | 2019-08-22 | 2021-02-26 | 初速度（苏州）科技有限公司 | Vehicle prediction control method based on kinematics and processor |
| CN112445232A (en) * | 2020-06-23 | 2021-03-05 | 国网浙江温岭市供电有限公司 | Portable somatosensory control autonomous inspection robot |
| CN112506183A (en) * | 2020-11-04 | 2021-03-16 | 中国北方车辆研究所 | Distributed driving unmanned vehicle path tracking control method and system |
| CN112622903A (en) * | 2020-10-29 | 2021-04-09 | 东北大学秦皇岛分校 | Longitudinal and transverse control method for autonomous vehicle in vehicle following driving environment |
| CN112677992A (en) * | 2020-12-31 | 2021-04-20 | 北京理工大学 | Path tracking optimization control method for distributed driving unmanned vehicle |
| CN112947073A (en) * | 2021-01-29 | 2021-06-11 | 大连理工大学 | Model prediction control-based shipboard aircraft sliding track tracking method |
| CN113126623A (en) * | 2021-04-13 | 2021-07-16 | 吉林大学 | Adaptive dynamic sliding mode automatic driving vehicle path tracking control method considering input saturation |
| CN113386781A (en) * | 2021-05-24 | 2021-09-14 | 江苏大学 | Intelligent vehicle trajectory tracking control method based on data-driven vehicle dynamics model |
| CN113495559A (en) * | 2020-03-18 | 2021-10-12 | 百度（美国）有限责任公司 | Learning-based controller for autonomous driving |
| CN113867330A (en) * | 2021-05-11 | 2021-12-31 | 吉林大学 | Control method for realizing vehicle drift under any path based on multi-degree-of-freedom prediction model |
| CN113985868A (en) * | 2021-10-09 | 2022-01-28 | 北京科技大学 | Method for realizing hierarchical path tracking control of wheeled mobile robot |
| CN114003030A (en) * | 2021-10-09 | 2022-02-01 | 北京科技大学 | Two-wheel differential motion mobile robot path tracking control method considering centroid change |
| CN114103926A (en) * | 2021-12-08 | 2022-03-01 | 北京理工大学 | Hybrid tracked vehicle speed and energy collaborative optimization method, medium and equipment |
| CN114291074A (en) * | 2021-12-02 | 2022-04-08 | 江铃汽车股份有限公司 | Vehicle rollover early warning method and device and storage medium |
| CN114379583A (en) * | 2021-12-10 | 2022-04-22 | 江苏大学 | Automatic driving vehicle trajectory tracking system and method based on neural network dynamics model |
| CN114442601A (en) * | 2020-11-06 | 2022-05-06 | 郑州宇通客车股份有限公司 | Unmanned vehicle tracking control method and device |
| CN115447603A (en) * | 2022-04-26 | 2022-12-09 | 重庆大学 | Finite time path tracking control method applied to automatic driving vehicle |
| CN115805939A (en) * | 2022-11-29 | 2023-03-17 | 长安大学 | Intelligent electric vehicle path tracking control method and device |
| CN110626340B (en) * | 2019-09-30 | 2023-07-11 | 南京航空航天大学 | Intelligent automobile path tracking control system and method based on wolf algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106184207A (en) * | 2016-07-12 | 2016-12-07 | 大连理工大学 | Four motorized wheels electric automobile adaptive cruise control system Torque distribution method |
| CN106183892A (en) * | 2016-10-09 | 2016-12-07 | 重庆理工大学 | The experimental model of electric wheel drive vehicle and driving stability control method |
| US20180119803A1 (en) * | 2016-10-28 | 2018-05-03 | Ford Global Technologies, Llc | Transmission control to avoid gain switch during shift |
| CN108437978A (en) * | 2018-05-14 | 2018-08-24 | 武汉理工大学 | Four wheel hub electricity drive vehicle running surface automatic identification and stability integrated control method |
| CN108454623A (en) * | 2018-01-22 | 2018-08-28 | 大连理工大学 | A kind of unmanned electric vehicle Trajectory Tracking Control method of four motorized wheels |
| CN108482363A (en) * | 2018-04-09 | 2018-09-04 | 吉林大学 | vehicle yaw stability prediction model control method |
-
2018
- 2018-09-27 CN CN201811127310.4A patent/CN109795502B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106184207A (en) * | 2016-07-12 | 2016-12-07 | 大连理工大学 | Four motorized wheels electric automobile adaptive cruise control system Torque distribution method |
| CN106183892A (en) * | 2016-10-09 | 2016-12-07 | 重庆理工大学 | The experimental model of electric wheel drive vehicle and driving stability control method |
| US20180119803A1 (en) * | 2016-10-28 | 2018-05-03 | Ford Global Technologies, Llc | Transmission control to avoid gain switch during shift |
| CN108454623A (en) * | 2018-01-22 | 2018-08-28 | 大连理工大学 | A kind of unmanned electric vehicle Trajectory Tracking Control method of four motorized wheels |
| CN108482363A (en) * | 2018-04-09 | 2018-09-04 | 吉林大学 | vehicle yaw stability prediction model control method |
| CN108437978A (en) * | 2018-05-14 | 2018-08-24 | 武汉理工大学 | Four wheel hub electricity drive vehicle running surface automatic identification and stability integrated control method |
Cited By (70)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110040147A (en) * | 2019-04-23 | 2019-07-23 | 吉林大学 | A kind of method of travel speed optimization under the lateral constraint of saturation of autonomous driving vehicle |
| CN110040147B (en) * | 2019-04-23 | 2020-12-04 | 吉林大学 | Method for optimizing running speed under lateral saturation constraint of automatic driving automobile |
| CN110161865A (en) * | 2019-06-13 | 2019-08-23 | 吉林大学 | A kind of intelligent vehicle lane-change method for planning track based on Nonlinear Model Predictive Control |
| CN110161865B (en) * | 2019-06-13 | 2021-04-20 | 吉林大学 | Intelligent vehicle track change trajectory planning method based on nonlinear model predictive control |
| CN110539752B (en) * | 2019-06-26 | 2020-12-18 | 江苏大学 | Intelligent automobile multi-prediction-range model prediction trajectory tracking control method and system |
| CN110539752A (en) * | 2019-06-26 | 2019-12-06 | 江苏大学 | Intelligent automobile multi-prediction-range model prediction trajectory tracking control method and system |
| CN110187639A (en) * | 2019-06-27 | 2019-08-30 | 吉林大学 | A kind of trajectory planning control method based on Parameter Decision Making frame |
| CN110187639B (en) * | 2019-06-27 | 2021-05-11 | 吉林大学 | Trajectory planning control method based on parameter decision framework |
| CN112406889B (en) * | 2019-08-22 | 2022-05-27 | 魔门塔(苏州)科技有限公司 | Vehicle prediction control method based on kinematics and processor |
| CN112406889A (en) * | 2019-08-22 | 2021-02-26 | 初速度（苏州）科技有限公司 | Vehicle prediction control method based on kinematics and processor |
| CN110471428A (en) * | 2019-09-18 | 2019-11-19 | 吉林大学 | A kind of path following method of change preview distance and constraint of velocity based on model |
| CN110471428B (en) * | 2019-09-18 | 2021-05-07 | 吉林大学 | Path tracking method based on variable pre-aiming distance and speed constraint of model |
| CN110696793A (en) * | 2019-09-19 | 2020-01-17 | 江苏理工学院 | Intelligent vehicle layered control method combining four-wheel steering and differential braking |
| CN110626340B (en) * | 2019-09-30 | 2023-07-11 | 南京航空航天大学 | Intelligent automobile path tracking control system and method based on wolf algorithm |
| CN110727271A (en) * | 2019-10-30 | 2020-01-24 | 北京科技大学 | Robot motion primitive determining method and device |
| CN111016891B (en) * | 2019-11-06 | 2022-03-22 | 国汽(北京)智能网联汽车研究院有限公司 | Method and device for controlling vehicle driving route tracking |
| CN111016891A (en) * | 2019-11-06 | 2020-04-17 | 国汽(北京)智能网联汽车研究院有限公司 | Method and device for controlling vehicle driving route tracking |
| CN110702121A (en) * | 2019-11-23 | 2020-01-17 | 赣南师范大学 | Optimal path fuzzy planning method for hillside orchard machine |
| CN110702121B (en) * | 2019-11-23 | 2023-06-23 | 赣南师范大学 | Optimal path fuzzy planning method for hillside orchard machine |
| CN110908395A (en) * | 2019-11-26 | 2020-03-24 | 西北工业大学 | Improved unmanned aerial vehicle flight path real-time planning method |
| CN110955250A (en) * | 2019-12-25 | 2020-04-03 | 湖南大学 | Vehicle reversing path tracking control method |
| CN110989625B (en) * | 2019-12-25 | 2020-11-27 | 湖南大学 | Vehicle path tracking control method |
| CN110989625A (en) * | 2019-12-25 | 2020-04-10 | 湖南大学 | Vehicle path tracking control method |
| CN110955250B (en) * | 2019-12-25 | 2021-02-23 | 湖南大学 | Vehicle reversing path tracking control method |
| CN111158264A (en) * | 2020-01-09 | 2020-05-15 | 吉林大学 | Model prediction control rapid solving method for vehicle-mounted application |
| CN113495559A (en) * | 2020-03-18 | 2021-10-12 | 百度（美国）有限责任公司 | Learning-based controller for autonomous driving |
| CN111444577A (en) * | 2020-04-02 | 2020-07-24 | 山东交通学院 | Automatic avoidance method for electric motor coach |
| CN111532283B (en) * | 2020-05-15 | 2022-03-25 | 吉林大学 | Model prediction control-based path tracking method for semi-trailer train |
| CN111532283A (en) * | 2020-05-15 | 2020-08-14 | 吉林大学 | Model prediction control-based path tracking method for semi-trailer train |
| CN111547111A (en) * | 2020-05-22 | 2020-08-18 | 西南交通大学 | Autonomous guiding control method for virtual rail train |
| CN111812974B (en) * | 2020-05-28 | 2021-07-13 | 北京理工大学 | Comprehensive control method for bilateral motor-driven tracked vehicle |
| CN111812974A (en) * | 2020-05-28 | 2020-10-23 | 北京理工大学 | Comprehensive control method for bilateral motor-driven tracked vehicle |
| CN112445232A (en) * | 2020-06-23 | 2021-03-05 | 国网浙江温岭市供电有限公司 | Portable somatosensory control autonomous inspection robot |
| CN111688715A (en) * | 2020-06-24 | 2020-09-22 | 长春工业大学 | Centroid slip angle observation method of four-wheel drive electric vehicle based on fusion technology |
| CN111717282B (en) * | 2020-07-07 | 2021-12-03 | 中国人民解放军国防科技大学 | Man-machine shared driving auxiliary steering method based on model predictive control |
| CN111717282A (en) * | 2020-07-07 | 2020-09-29 | 中国人民解放军国防科技大学 | Man-machine shared driving auxiliary steering method based on model predictive control |
| CN111959500A (en) * | 2020-08-07 | 2020-11-20 | 长春工业大学 | Automobile path tracking performance improving method based on tire force distribution |
| CN111890951A (en) * | 2020-08-07 | 2020-11-06 | 吉林大学 | Intelligent electric automobile trajectory tracking and motion control method |
| CN111959500B (en) * | 2020-08-07 | 2022-11-11 | 长春工业大学 | Automobile path tracking performance improving method based on tire force distribution |
| CN111923908A (en) * | 2020-08-18 | 2020-11-13 | 哈尔滨理工大学 | Stability-fused intelligent automobile path tracking control method |
| CN112124028B (en) * | 2020-09-21 | 2022-07-12 | 华南理工大学 | Electric control air suspension system, control method and system thereof and electric control unit |
| CN112124028A (en) * | 2020-09-21 | 2020-12-25 | 华南理工大学 | Electric control air suspension system, control method and system thereof and electric control unit |
| US11634146B2 (en) | 2020-09-23 | 2023-04-25 | Beijing Institute Of Technology | Method and system for integrated path planning and path tracking control of autonomous vehicle |
| CN112068445A (en) * | 2020-09-23 | 2020-12-11 | 北京理工大学 | Integrated control method and system for path planning and path tracking of automatic driving vehicle |
| CN112622903A (en) * | 2020-10-29 | 2021-04-09 | 东北大学秦皇岛分校 | Longitudinal and transverse control method for autonomous vehicle in vehicle following driving environment |
| CN112622903B (en) * | 2020-10-29 | 2022-03-08 | 东北大学秦皇岛分校 | Longitudinal and transverse control method for autonomous vehicle in vehicle following driving environment |
| CN112506183A (en) * | 2020-11-04 | 2021-03-16 | 中国北方车辆研究所 | Distributed driving unmanned vehicle path tracking control method and system |
| CN112506183B (en) * | 2020-11-04 | 2022-05-20 | 中国北方车辆研究所 | Distributed driving unmanned vehicle path tracking control method and system |
| CN114442601A (en) * | 2020-11-06 | 2022-05-06 | 郑州宇通客车股份有限公司 | Unmanned vehicle tracking control method and device |
| CN112677992A (en) * | 2020-12-31 | 2021-04-20 | 北京理工大学 | Path tracking optimization control method for distributed driving unmanned vehicle |
| CN112947073B (en) * | 2021-01-29 | 2023-08-29 | 大连理工大学 | Model predictive control-based carrier-based aircraft sliding track tracking method |
| CN112947073A (en) * | 2021-01-29 | 2021-06-11 | 大连理工大学 | Model prediction control-based shipboard aircraft sliding track tracking method |
| CN113126623B (en) * | 2021-04-13 | 2022-04-12 | 吉林大学 | Adaptive dynamic sliding mode automatic driving vehicle path tracking control method considering input saturation |
| CN113126623A (en) * | 2021-04-13 | 2021-07-16 | 吉林大学 | Adaptive dynamic sliding mode automatic driving vehicle path tracking control method considering input saturation |
| CN113867330A (en) * | 2021-05-11 | 2021-12-31 | 吉林大学 | Control method for realizing vehicle drift under any path based on multi-degree-of-freedom prediction model |
| CN113386781A (en) * | 2021-05-24 | 2021-09-14 | 江苏大学 | Intelligent vehicle trajectory tracking control method based on data-driven vehicle dynamics model |
| CN113386781B (en) * | 2021-05-24 | 2024-05-24 | 江苏大学 | Intelligent vehicle track tracking control method based on data-driven vehicle dynamics model |
| CN114003030B (en) * | 2021-10-09 | 2023-08-08 | 北京科技大学 | Path tracking control method of two-wheel differential mobile robot considering centroid change |
| CN113985868B (en) * | 2021-10-09 | 2023-08-08 | 北京科技大学 | Layered path tracking control implementation method for wheeled mobile robot |
| CN113985868A (en) * | 2021-10-09 | 2022-01-28 | 北京科技大学 | Method for realizing hierarchical path tracking control of wheeled mobile robot |
| CN114003030A (en) * | 2021-10-09 | 2022-02-01 | 北京科技大学 | Two-wheel differential motion mobile robot path tracking control method considering centroid change |
| CN114291074A (en) * | 2021-12-02 | 2022-04-08 | 江铃汽车股份有限公司 | Vehicle rollover early warning method and device and storage medium |
| CN114291074B (en) * | 2021-12-02 | 2023-10-17 | 江铃汽车股份有限公司 | Vehicle rollover early warning method, device and storage medium |
| CN114103926A (en) * | 2021-12-08 | 2022-03-01 | 北京理工大学 | Hybrid tracked vehicle speed and energy collaborative optimization method, medium and equipment |
| CN114379583A (en) * | 2021-12-10 | 2022-04-22 | 江苏大学 | Automatic driving vehicle trajectory tracking system and method based on neural network dynamics model |
| CN114379583B (en) * | 2021-12-10 | 2024-05-14 | 江苏大学 | Automatic driving vehicle track tracking system and method based on neural network dynamics model |
| CN115447603A (en) * | 2022-04-26 | 2022-12-09 | 重庆大学 | Finite time path tracking control method applied to automatic driving vehicle |
| CN115447603B (en) * | 2022-04-26 | 2024-05-24 | 重庆大学 | Finite time path tracking control method applied to automatic driving vehicle |
| CN115805939A (en) * | 2022-11-29 | 2023-03-17 | 长安大学 | Intelligent electric vehicle path tracking control method and device |
| CN115805939B (en) * | 2022-11-29 | 2024-06-07 | 长安大学 | Intelligent electric automobile path tracking control method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109795502B (en) | 2021-05-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109795502A (en) | Intelligent electric automobile path trace model predictive control method | |
| CN111890951B (en) | Intelligent electric automobile trajectory tracking and motion control method | |
| CN109204317B (en) | Wheel hub drive electric automobile longitudinal, transverse and vertical force integrated control optimization method | |
| US11364895B2 (en) | Yaw motion control method for four-wheel distributed vehicle | |
| CN107719372B (en) | Four-drive electric car dynamics multi objective control system based on dynamic control allocation | |
| CN109552312A (en) | Intact stability model predictive control method | |
| CN109017778A (en) | The expected path active steering control method of four motorized wheels vehicle | |
| CN103448716B (en) | Distributed electro-motive vehicle indulges-horizontal stroke-vertical force cooperative control method | |
| CN104977933A (en) | Regional path tracking control method for autonomous land vehicle | |
| CN109017760A (en) | Vehicle desired trajectory tracking, device and rolling time horizon optimization algorithm | |
| CN103057436A (en) | Yawing moment control method of individual driven electromobile based on multi-agent | |
| CN110422052B (en) | Distributed driving electric automobile stabilization and energy-saving control system | |
| CN109398361B (en) | Control method for steering stability of four-wheel independent drive vehicle | |
| Deng et al. | Torque vectoring algorithm based on mechanical elastic electric wheels with consideration of the stability and economy | |
| CN112677992B (en) | Path tracking optimization control method for distributed driving unmanned vehicle | |
| CN109017759A (en) | Expected path Vehicular yaw control method | |
| CN109017446B (en) | Expected path vehicle longitudinal speed tracking control method and device | |
| CN105644566B (en) | A kind of tracking of the electric automobile auxiliary lane-change track based on car networking | |
| CN114454871A (en) | Unmanned platform stable tracking control method for four-wheel independent drive | |
| CN113619564A (en) | Active rollover prevention control method for unmanned carrier | |
| CN109017804A (en) | Torque distribution controller is the method that each hub motor of vehicle distributes driving moment | |
| CN114408019B (en) | Distributed driving differential steering automobile longitudinal and transverse force coordination control method | |
| CN106347361A (en) | Redundant drive vehicle dynamics control distribution method | |
| Shen et al. | Stability and Maneuverability Guaranteed Torque Distribution Strategy of ddev in handling limit: a novel lstm-lmi approach | |
| CN117944660A (en) | Automatic driving automobile transverse control method suitable for complex road conditions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |