CN108284836B - Vehicle longitudinal following control method - Google Patents
Vehicle longitudinal following control method Download PDFInfo
- Publication number
- CN108284836B CN108284836B CN201810070910.5A CN201810070910A CN108284836B CN 108284836 B CN108284836 B CN 108284836B CN 201810070910 A CN201810070910 A CN 201810070910A CN 108284836 B CN108284836 B CN 108284836B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- speed
- prediction
- controlled
- vehicles
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000008569 process Effects 0.000 claims abstract description 17
- 230000005540 biological transmission Effects 0.000 claims abstract description 12
- 238000005457 optimization Methods 0.000 claims description 22
- 230000008859 change Effects 0.000 claims description 16
- 238000006073 displacement reaction Methods 0.000 claims description 12
- 238000005096 rolling process Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000009471 action Effects 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 230000005484 gravity Effects 0.000 claims description 3
- 239000003638 chemical reducing agent Substances 0.000 claims description 2
- 238000005070 sampling Methods 0.000 claims description 2
- 239000000446 fuel Substances 0.000 abstract description 4
- 238000004891 communication Methods 0.000 abstract description 3
- 230000007246 mechanism Effects 0.000 abstract description 2
- 238000004088 simulation Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005312 nonlinear dynamic Methods 0.000 description 1
- BULVZWIRKLYCBC-UHFFFAOYSA-N phorate Chemical compound CCOP(=S)(OCC)SCSCC BULVZWIRKLYCBC-UHFFFAOYSA-N 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- 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
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
- B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
-
- 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
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
- B60W30/162—Speed limiting therefor
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
- Controls For Constant Speed Travelling (AREA)
Abstract
The invention discloses a longitudinal following control method for vehicles in a heavy truck fleet on a highway, which is based on a nonlinear model predictive control theory, according to the obtained current road information and considering the constraint of a physical execution mechanism, takes the control of keeping the same speed of the vehicles in the control fleet and the adjacent vehicles and the expected distance between the vehicles as a control target, optimizes the solved controlled variable and acts on the controlled vehicles; the state of the next moment of the adjacent front vehicle is predicted based on the vehicle nonlinear dynamical equation and is used as a part of a tracking control target, so that the speed deviation between the controlled vehicle and the front vehicle caused by vehicle-mounted controller delay, transmission system delay and communication delay is effectively reduced, therefore, the distance between vehicles can be controlled to be kept at a smaller value in the driving process of the vehicle queue, and the overall fuel economy of the vehicle queue is indirectly improved according to the relation between the air resistance and the distance between the vehicles in the driving process of the vehicle.
Description
Technical Field
The invention relates to a method for improving the following performance of vehicles in a highway heavy truck fleet, in particular to a longitudinal following control method of vehicles in running of the highway heavy truck fleet.
Background
Highway heavy truck transport has a significant position in the development of the world economy as an important form of transportation. In a heavy truck driving at high speed, about 53% of the fuel is consumed to overcome the air resistance. To improve fuel economy and road potential for highway heavy truck traffic, heavy truck fleet control has been increasingly proposed and is receiving much attention.
The heavy truck fleet control objective is to control all vehicles in the fleet to maintain the same speed and to control the desired inter-vehicle spacing between two adjacent vehicles. At present, a great deal of research work is already carried out on the running control of a heavy truck fleet, but still some defects exist, in most of the existing various control schemes, at each moment, a vehicle controller in a fleet takes tracking the current speed of an adjacent front vehicle and keeping an expected distance between the front vehicles as a control target, a control quantity is decided to act on the vehicle, and the speed of the vehicle is changed after a transmission system.
Therefore, the invention provides a nonlinear model prediction control scheme, the speed of the next moment of the adjacent front vehicle in the queue is predicted based on the nonlinear dynamical equation of the vehicle, the nonlinear model prediction controller takes the speed of the next moment of the adjacent front vehicle as a control target by tracking and keeping the expected distance, and the control quantity is decided according to the currently acquired road information. The nonlinear model predictive control method is an algorithm for realizing reference value tracking control based on the idea of rolling optimization, has stronger robustness, and can process the optimization problem with restriction.
Disclosure of Invention
The invention provides a longitudinal following control method for vehicles in a heavy truck fleet on a highway, which is based on a nonlinear model predictive control theory, according to the obtained current road information and considering the constraint of a physical execution mechanism, takes the control of keeping the same speed of the vehicles in the control fleet and the adjacent vehicles and the expected distance between the vehicles as a control target, optimizes the solved controlled variable and acts on the controlled vehicles; the state of the adjacent front vehicle at the next moment is predicted based on the vehicle nonlinear dynamical equation and is used as a part of a tracking control target, so that the steady-state error of the speeds of the adjacent front vehicle and the adjacent rear vehicle in the queue is effectively reduced, and the overall running performance of the queue is improved.
The invention is realized by the following technical scheme:
the method comprises the following steps: establishing a nonlinear longitudinal dynamic equation of the vehicle according to the force borne by the vehicle in the driving process and the air resistance borne by the vehicle during the driving of the vehicle queue;
step two: establishing a dynamic equation for predicting the future speed of the adjacent front vehicle by using the nonlinear longitudinal dynamic equation of the vehicle established in the first step; predicting the speed of the adjacent front vehicle at the next moment according to the current speed of the adjacent front vehicle and the current output torque of the engine or the pressure of a brake master cylinder at each moment;
step three: and establishing a nonlinear model prediction controller with constraint consideration, wherein the nonlinear model prediction controller predicts the state change of the controlled vehicle and the adjacent front vehicle within a period of time in the future by taking the current state of the controlled vehicle as a starting point according to the current speed of the controlled vehicle, the currently acquired road information and the next-time speed of the adjacent front vehicle predicted in the second step and simultaneously according to a nonlinear longitudinal kinetic equation of the vehicle established in the first step, and simultaneously solving an optimization problem to decide a control quantity to act on a vehicle system so as to ensure that the speed of the controlled vehicle and the adjacent front vehicle is consistent and track the expected distance between the vehicles.
The benefits of the invention are:
1. the invention takes the speed of the next moment of the adjacent front vehicle of the vehicle in the queue as the tracking target, effectively reduces the speed deviation between the controlled vehicle and the front vehicle caused by the delay of the vehicle-mounted controller, the delay of the transmission system and the communication delay, therefore, the distance between vehicles can be controlled to be kept at a smaller value in the driving process of the vehicle queue, and the control method provided by the invention indirectly improves the overall fuel economy of the vehicle queue according to the relationship between the air resistance and the distance between the vehicles in the driving process of the vehicle.
2. The invention effectively enhances the speed consistency of front and rear vehicles in the queue, thereby indirectly improving the overall safety of the vehicle queue and the potential capacity of a road traffic system.
Drawings
FIG. 1 is a schematic view of the air resistance experienced during the driving of a vehicle in a fleet;
FIG. 2 is a schematic view of a vehicle under force;
FIG. 3 is a block diagram of the overall control of the present invention;
FIG. 4 is a schematic view of an uphill condition;
FIG. 5 shows the change in the distance between the front and rear wheels under an uphill condition;
FIG. 6 is a comparison of speeds of a pilot vehicle and a following vehicle under an uphill condition;
FIG. 7 is a downhill operating condition;
FIG. 8 is a front-rear inter-vehicle distance change under a downhill condition;
FIG. 9 is a comparison of the speeds of a pilot vehicle and a following vehicle under a downhill condition.
Detailed Description
The technical scheme of the invention is described in detail in the following with the accompanying drawings:
a longitudinal following control method for vehicles in running of a highway heavy truck fleet comprises the following steps:
the method comprises the following steps: the longitudinal nonlinear dynamic equation of the vehicle is established according to Newton's second law, as shown in FIG. 2, and the equation is as follows:
wherein m is the vehicle mass in kg; v is the vehicle longitudinal speed in m/s; fengineIs vehicle driving force in units of N; fbrakeIs the braking force of the vehicle, and the unit is N; fgradIs the vehicle gravity component with the unit of N; frollingThe unit is N, and the unit is the ground friction force borne by the vehicle; fairdragIs the air resistance of the vehicle and has the unit of N.
Wherein, TtIs engine torque, in Nm; i.e. igIs a vehicle transmission gear ratio; i.e. i0Is the vehicle main reducer transmission ratio; etatThe transmission efficiency of the whole vehicle transmission system is improved; r is the radius of the wheel in m.
pbrakeBrake pressure generated for a brake master cylinder; k is the proportional coefficient of the braking torque and the pressure of the brake master cylinder and is determined by the internal structure of the brake master cylinder.
Fgravity＝mgsin(θ) (4)
Wherein g is gravity acceleration in m/s2(ii) a θ is road grade, in units rad.
Froll＝μmgcos(θ) (5)
Where μ represents a rolling resistance coefficient.
Wherein, CDIs the air resistance coefficient; rho is air density in kg/m3(ii) a A is the frontal area of the vehicle in m2(ii) a v is the vehicle longitudinal speed in m/s.
The air resistance experienced by the train of vehicles is shown in fig. 1, and considering that the invention is primarily concerned with the longitudinal dynamics of the travel of the truck, it is here primarily the component of the air resistance in the direction of movement of the truck during normal travel of the truck. Since the change of the inter-vehicle distance is a main factor for changing the air resistance coefficient of the rear vehicle, the air resistance coefficient with the inter-vehicle distance as a variable is introduced as shown in the following formula:
wherein, CD0、CD1、CD2And is an empirical coefficient of air resistance, diThe distance between the ith vehicle and the preceding vehicle is 1,2,3iInfinity, i.e. when calculating the air resistance experienced by the pilot vehicle, the default inter-vehicle distance is infinite, and the air resistance coefficient becomes CD(di)＝CD0。
Step two: at each time k, a kinematic equation for predicting the future speed of the adjacent preceding vehicle for a period of time Δ H may be established based on the longitudinal nonlinear dynamical equation of the vehicle established in step one, and the engine output torque T of the adjacent preceding vehicle (N-1) may be usedt(k)N-1Or brake master cylinder pressure pbrake(k)N-1And the current time vehicle speed vN-1(k) The speed v of the adjacent front vehicle after the time delta H can be calculated* N-1(k) The length of delta H can be selected according to the calculation capability of the vehicle-mounted controller, the vehicle transmission system structure and the communication system delay, and the kinematic equation is as follows:
wherein v is* N-1(k) For the velocity, v, of the adjacent preceding vehicle after Δ H duration calculated by means of the established kinematic equationN-1(k) The speed of the adjacent vehicle ahead at the current moment.
Step three: and (3) establishing a nonlinear model prediction controller, wherein the overall control block diagram of the controller is shown in fig. 3, predicting the state change of the controlled vehicle and the adjacent front vehicle within a period of time in the future by taking the current time state of the controlled vehicle and the adjacent front vehicle as a starting point according to the vehicle longitudinal nonlinear kinetic equation established in the step one, solving an optimization problem, deciding a control quantity, and acting on a vehicle system, so that the speed of the controlled vehicle and the speed of the adjacent front vehicle are consistent and the expected distance between the vehicles is tracked.
The nonlinear model predictive controller includes the steps of:
1) establishing a prediction equation:
in order to effectively process the tracking problem and consider constraint limitation in the process of solving the controlled variable, the invention adopts a nonlinear model predictive control method. Discretizing the vehicle longitudinal nonlinear dynamical equation established in the first step, and establishing a prediction equation as follows:
wherein S (k) and v (k) respectively represent the longitudinal speed of the longitudinal displacement of the controlled vehicle, and T is the sampling time.
In the process of solving the optimization problem, the invention selects the ratio of the driving force (or braking force) F (k) to the mass mAs a control quantity (wherein F (k) ═ F)engine(k)/Fbrake(k) Selecting the longitudinal speed and the longitudinal displacement x (k) ═ v (k) S (k)]As the state quantity.
2) Predicting the future dynamics of the controlled vehicle and the adjacent front vehicles:
according to the prediction equation established in the step 1), firstly, the adjacent front vehicle has the current time speed vN-1(k) And the displacement S of the current timeN-1(k) As a prediction starting point, the engine torque T is output according to the adjacent preceding vehicle at the current timet(k)N-1Or brake master cylinder pressure pbrake(k)N-1Determining the displacement change S of adjacent front vehicles in a prediction time domainN-1(k+1|k)...SN-1(k+i|k)(i＝1...N)；
Secondly, the controlled vehicle is at the current moment speed vN(k) And the current time shift SN(k) As a prediction starting point, the output u of the nonlinear model prediction controller for predicting each step in the time domainN(k+1)...uN(k + N) as an optimized independent variable to determine the controlled vehicle speed variation v in the prediction time domainN(k+1|k)...vN(k + i | k) and the variation of the displacement SN(k+1|k)...SN(k + i | k); according to the change of the displacement of the controlled vehicle and the adjacent front vehicle in the prediction time domain, the distance d between the vehicles at the current moment can be combined0(k) Calculating the inter-vehicle distance change d (k +1| k) ·. d (k + N | k) (i ═ 1.. N) in the prediction time domain, wherein N is the prediction step length of the nonlinear model prediction controller, the control time domain is equal to the prediction time domain in the invention, and the calculation of the inter-vehicle distance change in the prediction time domain can be represented by the following formula:
d(k+i|k)＝d0(k)+SN-1(k+i|k)-SN(k+i|k) (10)
3) solving an optimization problem:
the optimization problem can be expressed as:
s.t.x(k+1)＝f(x(k),u(k))
vmin≤v(k+i)≤vmax
u1 min≤u(k+i)≤u1 max(u(k)＞0)
u2 min≤u(k+i)≤u2 max(u(k)＜0)
de(k)≤d(k+i) (11)
wherein d ise(k) In order to expect the distance between vehicles, the calculation method adopts a constant time interval strategy (CTH) which is widely applied:
de(k)＝τvN(k)-L (12)
wherein τ is a fixed constant in units of s; l is the length of the vehicle body; v. ofmax,vminUpper and lower limits for speed constraints, respectively; u. of1 max,u1 minUpper and lower limits of the control amount when the control amount is driving, respectively; u. of2 max,u2 minThe upper limit and the lower limit of the control quantity when the control quantity is braking are respectively set; equation x (k +1) ═ f (x (k), u (k)) represents the prediction equation established in step 1).
In the optimization problem, the optimization function includes the prediction of the controlled vehicle speed v in the time domainN(k+1|k)…vN(k + N | k) and the next-time velocity v of the adjacent preceding vehicle* N-1(k) Accumulating the deviation and predicting the inter-vehicle distance d (k +1| k).. d (k + N | k) between the adjacent preceding vehicle and the controlled vehicle in the time domain and the expected inter-vehicle distance de(k) And accumulating the deviation, and solving an optimization algorithm to minimize the accumulated value in the prediction time domain, so that the controlled vehicle is controlled to track the adjacent front vehicle and keep the expected vehicle distance.
In the process of solving the optimization problem, the constraints of the control quantity and the state quantity need to be considered, wherein the upper limit and the lower limit v of the vehicle speed change in the optimization process can be determined according to an expressway traffic management methodmax,vmin(ii) a According to the inherent properties of the vehicle engine, the upper and lower limits of the driving force that can be achieved can be determined, and thus the upper and lower limits u of the controlled variable when the controlled variable is driving can be determined1 max,u1 min(ii) a According to the inherent property of the vehicle brake system, the upper limit and the lower limit of the brake system output brake force can be determined, so that the upper limit and the lower limit u of the control quantity when the control quantity is braking can be determined2 max,u2 min(ii) a Maintaining the inter-vehicle distance is important for vehicle fleet operation, although the objective functionHas been considered to track the inter-vehicle distance, but it is still possible that the actual inter-vehicle distance is less than the desired inter-vehicle distance, so a constraint d on the safety distance is introduced in the present inventione(k)≤d(k+i|k)。
Meanwhile, in order to overcome uncertain factors caused by model mismatch and external interference and combine the rolling optimization thought of model predictive control, each control sequence u obtained by suboptimal solution is combinedN(k+1|k)...uNFirst value u of (k + N | k)N(k +1| k) acts on the controlled vehicle and the process is repeated during the next calculation by the nonlinear model predictive controller.
Predicting controller output control u according to nonlinear modelNWhether the control action of the nonlinear model predictive controller on the vehicle is driving or braking can be determined by the positive and negative signs of the (k +1| k), and if the control action is positive, the control action is converted into driving torque T according to the formula (2) in the step onet(k+1)NActing on a controlled vehicle; if the pressure is negative, the pressure is converted into the master cylinder pressure p according to the formula (3) in the step onebrake(k+1)NActs on the controlled vehicle.
4) Control system simulation verification
In order to verify the effectiveness of the designed control method, a control system consisting of two vehicles is set up under the environment of MATLAB/SIMULINK and TRUCKSIM software combined simulation, wherein a high-precision vehicle dynamics vehicle model provided in TRUCKSIM is used as a controlled object.
Under the simulation environment, expressway simulation experiments of 300m uphill and 500m downhill are respectively carried out, the initial speeds of vehicles are respectively 80km/h and 90km/h under the uphill working condition, the vehicle speed comparison and the actual vehicle distance and expected value pair of the two vehicles under the uphill working condition are shown in fig. 5 and fig. 6, the simulation results under the downhill working condition are shown in fig. 8 and fig. 9, and as can be seen from the simulation results, under the uphill working condition and the downhill working condition, the provided nonlinear model prediction control algorithm controls the following vehicle to effectively track the front pilot vehicle, the speed deviation is basically zero, the actual vehicle distance has certain steady-state error, but the whole change is stable, and the actual vehicle distance does not exceed the expected value in the whole process, so that the control requirements of vehicle queue running are met.
Claims (5)
1. A vehicle longitudinal following control method characterized by comprising the steps of:
the method comprises the following steps: establishing a nonlinear longitudinal dynamic equation of the vehicle according to the force borne by the vehicle in the driving process and the air resistance borne by the vehicle in the driving of the vehicle queue;
step two: establishing a dynamic equation for predicting the future speed of the adjacent front vehicle by using the nonlinear longitudinal dynamic equation of the vehicle established in the first step; predicting the speed of the adjacent front vehicle at the next moment according to the current speed of the adjacent front vehicle and the current output torque of the engine or the pressure of a brake master cylinder at each moment;
step three: establishing a nonlinear model prediction controller, wherein the nonlinear model prediction controller predicts the state change of the controlled vehicle and the adjacent front vehicle within a period of time in the future by taking the current state of the controlled vehicle as a starting point and simultaneously according to the nonlinear longitudinal kinetic equation of the vehicle established in the step one according to the current speed of the controlled vehicle, the currently acquired road information and the next-time speed of the adjacent front vehicle predicted in the step two, and simultaneously solving an optimization problem to decide a control quantity and act on a vehicle system so as to ensure that the speed of the controlled vehicle is consistent with that of the adjacent front vehicle and track the expected distance between the vehicles;
the specific process of the third step comprises the following steps:
1) establishing a prediction equation:
discretizing the vehicle longitudinal nonlinear dynamical equation established in the first step, and establishing a prediction equation as follows:
wherein S (k) and v (k) respectively represent the longitudinal speed of the longitudinal displacement of the controlled vehicle, and T is sampling time; f (k) is vehicle driving force or braking force; cr is a parameter related to road surface rolling resistance coefficient μ, Cr ═ 1+ k) v (k); k is a fixed proportionality coefficient; a isFrontal area of vehicle, unit m2(ii) a Rho is air density in kg/m3；CD(di) Is the air resistance coefficient; diThe distance between the ith vehicle and the preceding vehicle is 1,2,3.. the; theta is the road grade, in units rad;
in the process of solving the optimization problem,
selecting the control quantity as the ratio of the driving force or the braking force to the mass:
wherein F (k) ═ Fengine(k)/Fbrake(k)，FengineAs a vehicle driving force, FbrakeM is vehicle braking force, m is vehicle mass, unit kg;
selecting the state quantities as the longitudinal speed and the displacement of the vehicle: x (k) ═ v (k) s (k) ];
2) predicting the future dynamics of the controlled vehicle and the adjacent front vehicles:
the prediction equation established according to the step 1) is as follows:
firstly, the speed v of the adjacent preceding vehicle at the current momentN-1(k) And the displacement S of the current timeN-1(k) As a prediction starting point, the engine torque T output from the adjacent preceding vehicle at the present time is usedt(k)N-1Or brake master cylinder pressure pbrake(k)N-1Determining the displacement change S of adjacent front vehicles in a prediction time domainN-1(k+1|k)...SN-1(k+i|k)(i＝1...N)；
Secondly, the controlled vehicle is at the current moment speed vN(k) And the current time shift SN(k) As a prediction starting point, the output u of the nonlinear model prediction controller for predicting each step in the time domainN(k+1)...uN(k + N) is used as an optimized independent variable to determine the speed change v of the controlled vehicle in the prediction time domainN(k+1|k)...vN(k + i | k) and the variation of the displacement SN(k+1|k)...SN(k+i|k)；
According to the change of the displacement of the controlled vehicle and the adjacent front vehicle in the prediction time domain, the distance d between the vehicles at the current moment can be combined0(k) Calculating the inter-vehicle distance variation in the prediction time domainD (k +1| k.. d (k + N | k) (i ═ 1.. N); the calculation of the change in the inter-vehicle distance in the prediction time domain can be represented by the following formula:
d(k+i|k)＝d0(k)+SN-1(k+i|k)-SN(k+i|k)
n is the prediction step length of the nonlinear model prediction controller, and the control time domain is equal to the prediction time domain;
3) solving an optimization problem:
the optimization problem can be expressed as:
s.t.x(k+1)＝f(x(k),u(k))
vmin≤v(k+i)≤vmax
u1 min≤u(k+i)≤u1 max(u(k)＞0)
u2 min≤u(k+i)≤u2 max(u(k)＜0)
de(k)≤d(k+i)
wherein d ise(k) To the desired inter-vehicle distance:
de(k)＝τvN(k)-L
wherein tau is a fixed constant and has the unit of s; l is the length of the vehicle body; v. ofmax,vminUpper and lower limits for speed constraints, respectively; u. of1 max,u1 minUpper and lower limits of the control amount when the control amount is driving, respectively; u. of2 max,u2 minThe upper limit and the lower limit of the control quantity when the control quantity is braking are respectively set;
equation x (k +1) ═ f (x (k), u (k)) represents the prediction equation established in step 1);
in the optimization problem, the optimization function includes the prediction of the controlled vehicle speed v in the time domainN(k+1|k)…vN(k + N | k) and the next-time velocity v of the adjacent preceding vehicle* N-1(k) Accumulating the deviation and predicting the inter-vehicle distance d (k +1| k).. d (k + N | k) and period between the adjacent preceding vehicle and the controlled vehicle in the time domainDistance d between carse(k) And accumulating the deviation, and solving an optimization algorithm to minimize the accumulated value in the prediction time domain, so that the controlled vehicle is controlled to track the adjacent front vehicle and keep the expected vehicle distance.
2. The vehicle longitudinal following control method according to claim 1, wherein the vehicle nonlinear longitudinal dynamics equation established in the first step is:
wherein m is the vehicle mass in kg; v is the vehicle longitudinal speed in m/s;
Fengineis vehicle traction force with unit of N; fbrakeIs the braking force of the vehicle, and the unit is N; fgradIs the vehicle gravity component with the unit of N; frollThe unit is N, and the unit is the ground friction force borne by the vehicle; fairdragIs the air resistance borne by the vehicle, and the unit is N;
wherein rho is air density and unit kg/m3(ii) a A is the frontal area of the vehicle in m2(ii) a v is the vehicle longitudinal speed in m/s;
CDfor the air resistance coefficient, an air resistance coefficient with the car-to-car distance as a variable is introduced:
wherein, CD0、CD1、CD2Empirical coefficient of air resistance, diI is the distance between the ith vehicle and the preceding vehicle, 1,2,3.
3. The vehicle longitudinal following control method according to claim 1, wherein the kinetic equation for predicting the future speed of the adjacent preceding vehicle established in the second step is:
wherein v is* N-1(k) For the velocity, v, of the adjacent preceding vehicle after Δ H duration calculated by means of the established kinematic equationN-1(k) The speed of the adjacent front vehicle at the current moment;
Ttis engine torque, in Nm; i.e. igIs a vehicle transmission gear ratio; i.e. i0Is the vehicle main reducer transmission ratio; etatThe transmission efficiency of the whole vehicle transmission system is improved; r is the radius of the wheel and is in m; theta is the road grade, in units rad; μ represents a rolling resistance coefficient; and K is a proportionality coefficient of the pressure of the brake master cylinder and the braking torque of the vehicle.
4. The vehicle longitudinal following control method according to claim 1, wherein the controller output control amount u is predicted based on the step three nonlinear modelNThe sign of (k +1| k) determines whether the control action of the nonlinear model predictive controller on the vehicle is driving or braking, and if positive, it is converted into driving torque Tt(k+1)NActing on a controlled vehicle; if negative, it is converted into master cylinder pressure pbrake(k+1)NActs on the controlled vehicle.
5. The vehicle longitudinal following control method according to claim 1, wherein in the third step, in order to overcome uncertainty factors caused by model mismatch and external disturbance and combine with a rolling optimization idea of model predictive control, each sub-optimization solution is used to obtain the control sequence uN(k+1|k)...uNFirst value u of (k + N | k)N(k +1| k) acts on the controlled vehicle and the process is repeated during the next calculation by the nonlinear model predictive controller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810070910.5A CN108284836B (en) | 2018-01-25 | 2018-01-25 | Vehicle longitudinal following control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810070910.5A CN108284836B (en) | 2018-01-25 | 2018-01-25 | Vehicle longitudinal following control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108284836A CN108284836A (en) | 2018-07-17 |
| CN108284836B true CN108284836B (en) | 2019-12-24 |
Family
ID=62835825
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810070910.5A Active CN108284836B (en) | 2018-01-25 | 2018-01-25 | Vehicle longitudinal following control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108284836B (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109634120B (en) * | 2018-12-26 | 2022-06-03 | 东软集团（北京）有限公司 | Vehicle control method and device |
| CN110397733B (en) * | 2019-04-12 | 2021-07-09 | 长春工业大学 | Model prediction control-based continuously variable transmission clamping force optimization method |
| CN110244721B (en) * | 2019-06-05 | 2022-04-12 | 杭州飞步科技有限公司 | Automatic driving control method, device, equipment and storage medium |
| 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 |
| CN110164124B (en) * | 2019-06-17 | 2021-07-27 | 吉林大学 | Longitudinal following control method for vehicles in running of highway heavy truck fleet |
| CN110427035B (en) * | 2019-08-13 | 2022-09-16 | 浙江吉利汽车研究院有限公司 | Target tracking method for vehicle |
| CN112519777B (en) * | 2019-08-30 | 2022-03-25 | 北京图森智途科技有限公司 | Control method of automatic driving fleet, vehicle-mounted device, vehicle and system |
| EP3790295B1 (en) * | 2019-09-09 | 2024-05-29 | Volkswagen AG | Method, computer program, and apparatus for determining a minimum inter-vehicular distance for a platoon, vehicle, traffic control entity |
| DE102019134499A1 (en) * | 2019-12-16 | 2021-06-17 | Knorr-Bremse Systeme für Nutzfahrzeuge GmbH | Method and device for determining the driving behavior of a vehicle within a platoon, as well as a vehicle with such a device |
| CN113031576B (en) * | 2019-12-24 | 2022-12-27 | 北京图森智途科技有限公司 | Vehicle control method in automatic driving fleet, vehicle-mounted device and vehicle |
| CN111391831B (en) * | 2020-02-27 | 2021-03-16 | 中南大学 | Automobile following speed control method and system based on preceding automobile speed prediction |
| CN112437412B (en) * | 2020-10-30 | 2023-04-14 | 北方工业大学 | Mixed-driving vehicle formation control method based on vehicle-road cooperation |
| CN112644504B (en) * | 2020-12-31 | 2022-02-08 | 山东交通学院 | Aerodynamic optimization-based queue vehicle driving management method |
| CN114475605B (en) * | 2022-02-16 | 2024-01-19 | 东风商用车有限公司 | Double-layer prediction control method for energy conservation of heavy truck based on internet connection information |
| CN115285120B (en) * | 2022-07-07 | 2023-08-18 | 东南大学 | Model predictive control-based vehicle following hierarchical control system and method |
| CN115273448B (en) * | 2022-07-20 | 2023-11-10 | 岚图汽车科技有限公司 | Method for reducing aerodynamic resistance of front and rear vehicles and front and rear vehicle system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012108797A (en) * | 2010-11-18 | 2012-06-07 | Toyota Motor Corp | Vehicle control device |
| DE102013007716A1 (en) * | 2013-05-04 | 2014-11-06 | Conti Temic Microelectronic Gmbh | Method for operating a vehicle |
| CN104249736A (en) * | 2014-08-25 | 2014-12-31 | 河南理工大学 | Hybrid electric vehicle energy-saving predictive control method based on platoons |
| CN106945664A (en) * | 2015-09-15 | 2017-07-14 | 丰田自动车株式会社 | The control device of vehicle and follow driving system |
| CN107089231A (en) * | 2017-03-27 | 2017-08-25 | 中国第汽车股份有限公司 | It is a kind of automatic with car drive-control system and its method |
| CN107117170A (en) * | 2017-04-28 | 2017-09-01 | 吉林大学 | A kind of real-time estimate cruise control system driven based on economy |
| CN107521496A (en) * | 2017-09-08 | 2017-12-29 | 吉林大学 | A kind of transverse and longitudinal of vehicle coordinates control track follow-up control method |
-
2018
- 2018-01-25 CN CN201810070910.5A patent/CN108284836B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012108797A (en) * | 2010-11-18 | 2012-06-07 | Toyota Motor Corp | Vehicle control device |
| DE102013007716A1 (en) * | 2013-05-04 | 2014-11-06 | Conti Temic Microelectronic Gmbh | Method for operating a vehicle |
| CN104249736A (en) * | 2014-08-25 | 2014-12-31 | 河南理工大学 | Hybrid electric vehicle energy-saving predictive control method based on platoons |
| CN106945664A (en) * | 2015-09-15 | 2017-07-14 | 丰田自动车株式会社 | The control device of vehicle and follow driving system |
| CN107089231A (en) * | 2017-03-27 | 2017-08-25 | 中国第汽车股份有限公司 | It is a kind of automatic with car drive-control system and its method |
| CN107117170A (en) * | 2017-04-28 | 2017-09-01 | 吉林大学 | A kind of real-time estimate cruise control system driven based on economy |
| CN107521496A (en) * | 2017-09-08 | 2017-12-29 | 吉林大学 | A kind of transverse and longitudinal of vehicle coordinates control track follow-up control method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108284836A (en) | 2018-07-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108284836B (en) | Vehicle longitudinal following control method | |
| Li et al. | Energy management of hybrid electric vehicle using vehicle lateral dynamic in velocity prediction | |
| CN111867911B (en) | Vehicle control method and apparatus | |
| CN108475465B (en) | Method for moving, in particular controlling or regulating a vehicle fleet | |
| Kamal et al. | Ecological vehicle control on roads with up-down slopes | |
| US11072329B2 (en) | Ground vehicle control techniques | |
| Xu et al. | Instantaneous feedback control for a fuel-prioritized vehicle cruising system on highways with a varying slope | |
| Kamal et al. | On board eco-driving system for varying road-traffic environments using model predictive control | |
| EP2440440B1 (en) | Method and module for determining of reference values for a vehicle control system | |
| JP4466717B2 (en) | Traveling locus generation method and traveling locus generation device | |
| CN102458943B (en) | Method and module for determining of velocity reference values for a vehicle control system | |
| JP4466718B2 (en) | Traveling locus generation method and traveling locus generation device | |
| KR101601889B1 (en) | Method and module for controlling a vehicle's speed based on rules and/or costs | |
| GB2546869A (en) | Vehicle curvature determination | |
| GB2547076A (en) | Vehicle acceleration determination | |
| Raslavičius et al. | Management of hybrid powertrain dynamics and energy consumption for 2WD, 4WD, and HMMWV vehicles | |
| CN103732937A (en) | Vehicle control device | |
| Luo et al. | Multi-objective decoupling algorithm for active distance control of intelligent hybrid electric vehicle | |
| CN103718220A (en) | Traffic control system, vehicle control system, traffic regulation system, and traffic control method | |
| WO2011126431A1 (en) | Method and module pertaining to cruise control | |
| Kolmanovsky et al. | Terrain and traffic optimized vehicle speed control | |
| CN116457270A (en) | Predictive energy and motion management for multi-trailer heavy vehicles | |
| CN111923896B (en) | HEV (electric vehicle) energy management method based on rolling dynamic programming | |
| CN114746316A (en) | Model-based predictive control of a vehicle taking into account time-of-arrival factors | |
| Zheng et al. | Research on control target of truck platoon based on maximizing fuel saving rate |
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 |