CN106476806B - Cooperating type self-adaption cruise system algorithm based on traffic information - Google Patents

Cooperating type self-adaption cruise system algorithm based on traffic information Download PDF

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CN106476806B
CN106476806B CN201610945654.0A CN201610945654A CN106476806B CN 106476806 B CN106476806 B CN 106476806B CN 201610945654 A CN201610945654 A CN 201610945654A CN 106476806 B CN106476806 B CN 106476806B
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vehicle
acceleration
output
prediction
speed
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CN201610945654.0A
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CN106476806A (en
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夏维
孙涛
李道飞
戴旭彬
顾立伟
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上海理工大学
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT 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
    • B60W30/00Purposes 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, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/14Adaptive cruise control
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT 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
    • B60W40/00Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/107Longitudinal acceleration
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001Details of the control system
    • B60W2050/0019Control system elements or transfer functions
    • B60W2050/0028Mathematical models, e.g. for simulation
    • B60W2050/0031Mathematical model of the vehicle
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT 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
    • B60W2520/00Input parameters relating to overall vehicle dynamics
    • B60W2520/10Longitudinal speed
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT 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
    • B60W2520/00Input parameters relating to overall vehicle dynamics
    • B60W2520/10Longitudinal speed
    • B60W2520/105Longitudinal acceleration
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT 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
    • B60W2554/00Input parameters relating to objects
    • B60W2554/80Spatial relation or speed relative to objects
    • B60W2554/801Lateral distance
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT 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
    • B60W2554/00Input parameters relating to objects
    • B60W2554/80Spatial relation or speed relative to objects
    • B60W2554/804Relative longitudinal speed

Abstract

The present invention relates to a kind of cooperating type self-adaption cruise system algorithm based on traffic information, using traffic information as available condition, using Model Predictive Control riding comfort, fuel economy, the limitation of vehicle self-ability is added as target to be optimized in safety and follow the bus, is translated into corresponding optimization aim and system restriction respectively, by establishing cost function and minimizing, obtain optimal solution sequence and first value is taken to be applied to system to realize optimization aim.Under the scene that inventive algorithm has signal lamp and front to wait fleet to CACC system in the operating condition of city, it is adjusted more initiatively from vehicle to realize from vehicle speed by handling more interference, reduce unnecessary speed maintenance or big deceleration, multiple-objection optimization is realized with this to improve fuel economy and riding comfort, reduce and reaches an optimal speed because following certain radical type drivers and adjust so as to cause also with radical, capable of reaching according to the environment of surrounding from the driving behavior of vehicle.

Description

Cooperating type self-adaption cruise system algorithm based on traffic information
Technical field
The present invention relates to a kind of transportation network technology, in particular to a kind of cooperating type adaptive cruise based on traffic information System algorithm.
Background technique
The rapid advances of wireless communication technique make in-vehicle wireless communication become research direction important in the field, respectively Big colleges and universities and enterprise also extremely attract attention.National Highway Traffic safety management bureau (NHTSA) strength promotes DSRC (dedicated short distance From communication Dedicated Short Range Communication) technology is applied to vehicle and vehicle or vehicle and landbased communication equipment Between communication, the driving safety of vehicle is improved with this.Vehicle-mounted DSRC relates to many novel mobile communication standards, should Technology is integrated into the pith of basic common structure in the U.S., support intelligent transportation system truck traffic (V2V) and Vehicle and infrastructure-based communication (V2I).Pilot and demonstration projects have also been made in many cities in China simultaneously, to the traffic key skill of intelligence Art has carried out different degrees of exploitation and application.
In addition CACC (cooperating type self-adaption cruise system Cooperative Adaptive Cruise Control) can be with It is considered the notional extension of ACC (adaptive cruise control system Adaptive Cruise Control).CACC exists In the technical foundation of ACC, measured in addition to having used radar or laser radar with leading vehicle distance and front truck acceleration, can be with These data are transmitted by in-vehicle wireless communication technology, realize the automatic control of vehicle in the longitudinal direction.CACC system ratio ACC system further reduces the delay of response front vehicles, improves the stability of fleet's traveling.
Summary of the invention
The present invention be directed to current ACC can not track front signal light and traffic conditions information, thus only according to preceding garage To adjust the problem of maintaining from the big deceleration and unnecessary speed that vehicle speed may cause, propose a kind of based on friendship The cooperating type self-adaption cruise system algorithm of communication breath uses model of the present invention using traffic information as available condition PREDICTIVE CONTROL (MPC, Model Predictive Control) is riding comfort, fuel economy, safety and follow the bus The limitation of vehicle self-ability is added as target to be optimized in four performances, is translated into corresponding optimization aim respectively And system restriction, by establishing cost function and minimizing, obtain optimal solution sequence and first value is taken to be applied to system to realize Optimization aim.There are signal lamp and front to wait under the scene of fleet in the operating condition of city to CACC system based on MPC algorithm, passes through The more interference of processing reduce unnecessary speed maintenance or big deceleration from vehicle speed to realize to adjust from vehicle more initiatively, Multiple-objection optimization is realized with this to improve fuel economy and riding comfort.
The technical solution of the present invention is as follows: a kind of cooperating type self-adaption cruise system algorithm based on traffic information, specific to wrap Include following steps:
1) according to car-following model, determine output equation: system is controlled using upper and lower level, and upper layer is received according to sensor institute Distance, speed, acceleration data calculate expectation acceleration adesAnd lower layer's actuator is passed to, lower layer's control is inverse vertical by vehicle Accelerator open degree and brake pressure are controlled to kinetic model to realize desired acceleration ades
Three rank discrete state equations models of follow the bus system:
X (t+1)=Ax (t)+Buu(t)+Bww(t)
Wherein:
W (t)=[ap(t),ds(t)]T, x (t)=[Δ d (t), Δ dsl(t),Δds(t),vp(t),vh(t),ah(t)]T,
In formula: from vehicle and leading vehicle distance Δ d (t), from vehicle and signal lamp distance, delta dsl(t), front motorcade length Δ ds (t), preceding vehicle speed vp(t), from vehicle speed vh(t), from vehicle acceleration ah(t) it is used as state variable x (t), and front truck acceleration ap(t) and front motorcade length ds(t) system disturbance w (t), T are used assFor the systematic sampling time, taking 0.001s, u (t) is lower layer The input a of controldes;τ is time constant;
It is derived from vehicle and leading vehicle distance Δ d (t), from vehicle and front truck relative velocity vrel(t), from vehicle speed vh(t), from vehicle plus Speed ah(t) it is used as output variable y (t), obtains output equation:
Y (t)=Cx (t)
Wherein:
2) tracking target is selected, the output weight w that performance vectors y (t) to be optimized stresses is determined according to targety, selection ginseng Examine track yr
3) three rank discrete state equations models of the follow the bus system proposed according to step 1), used in the quantity of state y of t moment (t) following prediction is made to quantity of state y (t+i) future behaviour at t+i moment, is solved:
Wherein:
H only has mathematical meaning in formula, and without concrete meaning, p is to predict time domain length, Δ u (t)=u (t)-u (t-1), Indicate that control variable quantity, (t+i | t) represent the prediction in t moment to moment t+i, in the prediction of future horizon, due to disturbing w (t+i) unpredictable, it is assumed that in prediction time domain: w (t+i)=w (t-1), i=0,1,2 ... p-1;
4) it after the weight of setting system input u and system input change the weight of △ u, is write problem to be optimized as one and is added (y, u, Δ u) calculate the difference of the calculated result and reference curve in prediction time domain, system input to the value function J of power form And the summation of input slew rate and respective weights product, total value is minimized so that reality output reaches close to reference curve;
5) constraint of the constraint and vehicle parameter formed according to the selected tracking target of step 2), is re-introduced into slack variable ε, obtains To the constraint condition of system output, system input, system input variation, as shown in formula (15):
For in (15) constraint lower bound coefficient of relaxation,For Constrain the coefficient of relaxation in the upper bound, umin、umaxFor vehicle acceleration ability, Δ umin、ΔumaxFor acceleration change amount, ymin、ymax For the constraint of output variable in car-following model;
6) by the optimization problem of top level control become solve meet under constraint condition minimize value function J Δ u (t+i | T), (t+i | t) prediction of the representative in t moment to moment t+i, and using header element as output, wherein ρ is slack variable weight system Number:
Vehicle is controlled according to this algorithm.
The beneficial effects of the present invention are: the present invention is based on the cooperating type self-adaption cruise system algorithms of traffic information, subtract Lack because certain radical type drivers is followed also with radical, can to reach according to surrounding so as to cause from the driving behavior of vehicle Environment adjusted to reach an optimal speed.
Detailed description of the invention
Fig. 1 is in follow the bus system of the present invention from vehicle and front truck and the longitudinal dynamics relation schematic diagram of front fleet;
Fig. 2 is that the present invention judges using front truck or fleet as the flow chart of target;
Fig. 3 is that 20 groups of experimental datas of the invention calculate average speed curve graph;
Fig. 4 is analogous diagram one of the present invention;
Fig. 5 is analogous diagram two of the present invention;
Fig. 6 is analogous diagram three of the present invention;
Fig. 7 is analogous diagram four of the present invention;
Fig. 8 is that fuel consumption model of the present invention uses engine mockup included in CarSim;
Fig. 9 is fuel consumption comparison diagram under two kinds of control algolithms of the invention.
Specific embodiment
One, car-following model
The control design case of CACC system is divided into upper and lower level control: upper layer is according to the received distance of sensor institute, the number such as speed Acceleration a it is expected according to calculatingdesAnd pass to lower layer's actuator.A is being realized in lower layer's controldesWhen need it is inverse longitudinal dynamic by vehicle Mechanical model realizes desired acceleration a to control accelerator open degree and brake pressuredes
In the actual process, the input a of lower layer's controllerdesIt can be by an one order inertia system come table with reality output a Show:
τ is time constant, takes 0.5 here.
From vehicle and front truck and the longitudinal dynamics relation schematic diagram of front fleet in follow the bus system as shown in Figure 1, can obtain Three rank discrete state equations models of following follow the bus system out:
X (t+1)=Ax (t)+Buu(t)+Bww(t) (2)
Wherein:
W (t)=[ap(t),ds(t)]T, x (t)=[Δ d (t), Δ dsl(t),Δds(t),vp(t),vh(t),ah(t)]T,
In formula: vehicle and leading vehicle distance Δ d (t) are selected from, from vehicle and signal lamp distance, delta dsl(t), front motorcade length Δ ds(t), preceding vehicle speed vp(t), from vehicle speed vh(t), from vehicle acceleration ah(t) it is used as state variable x (t), and front truck is accelerated Spend ap(t) and front motorcade length ds(t) system disturbance w (t), T are used assFor the systematic sampling time, take under 0.001s, u (t) be The input a of layer controldes
In addition it is selected from vehicle and leading vehicle distance Δ d (t), from vehicle and front truck relative velocity vrel(t), from vehicle speed vh(t), From vehicle acceleration ah(t) it is used as output variable y (t), obtains output equation:
Y (t)=Cx (t) (3)
Wherein:
Two, the algorithm based on Model Predictive Control is established
Since CACC system joined the interaction of traffic information so that system ACC it is single with front truck be tracking target On the basis of, it is more fleet to be waited with front or the preceding crossing of traffic lights is tracking target, so the system needs under different operating conditions Switching is between several targets to realize active adjustment speed this purpose.
1, the selection of objects ahead
When due to choosing different tracking targets, the output weighted that performance vectors y (t) to be optimized stresses, so power Value wyAlso difference is answered, in addition y (t) reference locus yrIt is also required to change according to the difference of tracking target.So system has received signal After lamp state and upcoming traffic information it needs to be determined that current tracking target, to choose different wyAnd yr
Judge as shown in Figure 2 using front truck or fleet as the flow chart of target.At the green light stage, work as vh·trt>ΔdslWhen (trtFor signal lamp remaining time) it is judged as and can passes through;vh·trt≤Δdsl≤vset·trtWhen (vsetTo set cruising speed) Judge whether front truck intention passes through, if front truck acceleration ap(t) it is greater than zero, then it is assumed that front truck is ready to pass through, ap(t) less than zero It is not ready to pass through;Work as vset·trt≤ΔdslWhen think to pass through;It is judged to not passing through when red light phase.In addition logical when determining It is out-of-date, then it is obstructed out-of-date using front truck as tracking target, continue to judge front truck acceleration apAccelerate with the reference in MPC algorithm Spend arSize relation, work as ar≤apShi Ze is tracking target with front fleet, otherwise using front truck as tracking target wherein arFor yrIn reference acceleration.
2, CACC controls target analysis
Although the performance that CACC is able to ascend has very much, most basic and most important target is still guarantee safety, Therefore to guarantee from vehicle and front truck spacing Δ d (t) always not less than a safe distance dsafe, i.e. Δ d (t) >=dsafe
Here dsafeTake 2m.
1) the control target of front truck is tracked
When using front truck as target, performance weights wy1Should more focus on leading vehicle distance Δ d, from vehicle and front truck relative velocity vrelAnd from vehicle desired acceleration ah, target is that the distance from vehicle and front truck is allowed to level off to ideal distance ddesAnd relative velocity vrel 0 is leveled off to, from vehicle desired acceleration ahIt levels off to referring to acceleration ar1, i.e. Δ d → ddes
vrel→0
ah→ar1
Acceleration adopts by reference a kind of linear follow the bus pilot model:
ar1=kV.vrel+kD.Δderror (4)
K in formulaV, kDFor model coefficient, respectively 0.25 and 0.02, Δ derrorFor the difference of actual range and ideal distance Value, ideal distance ddesIt is got by spacing policy calculation, here using the constant time headway in variable spacing strategy.
Δddes(t)=th.vh(t)+d0 (5)
Δddes(t) it is and front truck actual range, thFor time headway, the vehicle team travelled on same lane is referred to In column, time interval of the two continuous vehicle headstock ends by a certain section, d0For braking to stopping when apart from leading vehicle distance.
2) control target when front fleet or crossing is tracked
When using front fleet as target, performance weights wy2More stress and front fleet end distance, delta ds, from vehicle speed vh, and from vehicle acceleration ah.Target at this time is from vehicle speed vhLevel off to 0, the distance, delta d with front fleetsl-ΔdsApproach In d0, from vehicle desired acceleration ahIt levels off to referring to acceleration ar2, i.e.,
Δdsl-Δds→d0
vh→0
ah→ar2
Acceleration ahAdopt by reference following method, use actual measurement, the obtained pilot model of fitting.
The pilot model is the model that stopping how being braked for a static target, based on MATLAB, CarSim, The experiment porch of dSPACE software and a set of sieve skill steering wheel, to measure braking of the driver to the outer static target of 200m or so Journey.Three drivers are shared respectively to brake the stationary vehicle at the l=0 of front under 35 to 60km/h random initial velocity Then 20 groups of experimental datas are calculated average speed curve by experiment, as shown in figure 3, the overstriking curve in figure is average speed Curve:
The rate curve is fitted to distance l and speed vbCurve:
Reference acceleration a can be obtained by above formular2 *:
However vehicle actual speed vhIt may be greater than or less than vb, i.e., the acceleration needed at this time be greater than or be less than ar2 * 's.Assuming that with v at L0 in front of the terminalbSpeed traveling, according to formula (8), the acceleration needed at this time are as follows:
Similar, when with speed vh≠vbWhen at L0 speed travel, calculate to obtain acceleration are as follows:
Formula (9) is brought into formula (10), is obtained:
In addition in MPC, it is contemplated that the dynamic characteristic of process is led in order to avoid the big variation of input and output in process Current output y (t) is often allowed to reach setting value y along desired easy curver.This curve is reference curve yr(t), It is product of the setting value Jing Guo online softening.Here most commonly used first order exponential version is used:
yr(t+i)=αiy(t)+(1-αi)yr (12)
The coefficient that α is 0 to 1, i are i-th of the time point predicted in time domain, and index variation form α is smaller, and reference locus is rung Answer speed it is faster reach setting value.Here α takes 0.9.(α is smaller here, it is envisaged that curve declines more precipitous, so energy It is enough faster to reach setting value)
3, the prediction of car-following model
According to three rank discrete state equations models of follow the bus system presented above, state of the CACC system based on t moment Amount y (t) makes following prediction to quantity of state y (t+i) future behaviour at t+i moment, defaults following input here in prediction It does not do and changes in domain:
Here h and j does not have practical significance, is only that mathematics is used, last calculated result is summed twice without h and j Be after calculating it is no, can simply be calculated with i=1.
It is solved by the formula:
Wherein:
A in formula2For square of A matrix, and so on, ApFor the p power of A matrix, h only has mathematical meaning, without specific Meaning, p are prediction time domain length, and Δ u (t)=u (t)-u (t-1) indicates control variable quantity, (t+i | t) it represents in t moment pair The prediction of moment t+i.It is unpredictable due to disturbance w (t+i) in the prediction of future horizon, it is assumed that in prediction time domain It is interior: w (t+i)=w (t-1), i=0,1,2 ... p-1
In MPC problem as, problem to be optimized is write to the value function of a weighted type:
Wherein wyFor the weight matrix of system output, wuFor the weight of system input, wΔuFor the weight of system input variation. Calculate the difference of the calculated result and reference curve in prediction time domain, system input and input slew rate and respective weights product Summation, minimize total value so that reality output reaches close to reference curve.
So far, constraint above-mentioned and the constraint of some vehicle parameters are rearranged, and introduce slack variable ε,For in (15) constraint lower bound coefficient of relaxation, For in constraint The coefficient of relaxation on boundary, umin、umaxFor vehicle acceleration ability, Δ umin、ΔumaxFor acceleration change amount, ymin、ymaxFor follow the bus The constraint of output variable in model,
Here slack variable effect is appropriate increase restriction range, is prevented because the big acceleration and deceleration of front truck lead to portion Real data is divided to exceed set constraint, so as to cause the situation of no solution.But for the safety for guaranteeing follow the bus, adjust the distance Δ d and ΔdsTwo parts keep hard constraint, to guarantee safety.So the optimization problem of top level control has so far reformed into solution completely The Δ u (t+i | t) of value function J is minimized under sufficient constraint condition, and using header element as exporting, wherein ρ is slack variable weight Coefficient:
s.t.
It constrains (15) (16)
Three, application examples
In order to verify the algorithm, carry out associative simulation using MATLAB/Simulink and CarSim, vehicle parameter is using default Setting, and compared with LQR (linear quadratic planning) control algolithm, the algorithm is always using front truck as tracking target.
1, parameter setting, such as following table
Parameter Numerical value Parameter Numerical value
wy1 diag([15,3,0.1,5]) wy2 diag([0.1,0.1,3,5])
wu 1 wΔu 0.1
ymin [2,-1,0,-6] ymax [Inf,1,20,3]
umin -6 umax 3
Δumin -2 Δumax 2
vset 20 ρ 0.8
vy min [0,-1,0,-0.1] vy max [1,1,0,0.1]
vu min -0.5 vu max 0.5
vΔu min -1 vΔu max 1
2, simulation analysis
Emulate operating condition are as follows: 200m receives beacon signal before signal lamp, and front signal light is red light remaining time 50s, front truck first remain a constant speed movement brake again it is static become fleet a part, front motorcade length situation of change as express Formula:
ds=10+5.5ti(ti=2,5,12.5) (17)
Motorcade length initial length be 10m, after 2s, 5s and 12.5s have respectively vehicle be added fleet.The simulation result such as following figure 4, shown in 5,6,7:
By Fig. 4,5 as it can be seen that when 0 to 7s front truck moves with uniform velocity, having had begun under MPC control from vehicle is braked in advance Control and leading vehicle distance become larger, and also make corresponding deceleration after big deceleration is made in front truck, following distance starts to reduce, preceding Vehicle is braked completely to slowly close to front truck, eventually stopping at front truck 3m, realize the switching of target and put down after static Steady follow the bus;LQR control it is lower made since vehicle it is whole after small speed adjustment keep following front truck, following distance fluctuate compared with It is relatively small for MPC, also illustrate it is always using front truck as target.
As seen from Figure 6, more gentle compared with LQR control and maximum from vehicle acceleration change under MPC algorithm control of the present invention Value is no more than -3m/s always2, LQR maximum value reached -3.5m/s2, and the shortcomings that this is also LQR, i.e., it cannot be to control target It is constrained.Simultaneously it can also be seen that the variation range of the rate of acceleration change of LQR is especially big in Fig. 7, minimum has reached -14m/ s3Left and right, this has very big influence to riding comfort, and MPC control changes due to suffering restraints in -3~3m/s3Left and right, Be conducive to riding comfort.
Fuel consumption model as shown in Figure 8 is using engine mockup figure is carried in CarSim, the model is according to current vehicle Accelerator open degree and engine speed table look-up to obtain fuel consumption rate, and then obtain the fuel consumption of whole process.Such as Fig. 9 As it can be seen that under the operating condition, MPC algorithm control of the present invention from vehicle fuel consumption in 0.0038kg or so, controlled than LQR algorithm Save about 11.63% from the 0.0043kg of vehicle.

Claims (1)

1. a kind of cooperating type self-adaption cruise system algorithm based on traffic information, which is characterized in that specifically comprise the following steps:
1) according to car-following model, determine output equation: system using upper and lower level control, upper layer according to sensor institute it is received away from Expectation acceleration a is calculated from, speed, acceleration datadesAnd lower layer's actuator is passed to, lower layer's control is inverse longitudinal by vehicle Kinetic model realizes desired acceleration a to control accelerator open degree and brake pressuredes
Three rank discrete state equations models of follow the bus system:
X (t+1)=Ax (t)+Buu(t)+Bww(t)
Wherein:
W (t)=[ap(t),ds(t)]T, x (t)=[Δ d (t), Δ dsl(t),Δds(t),vp(t),vh(t),ah(t)]T,
In formula: from vehicle and leading vehicle distance Δ d (t), from vehicle and signal lamp distance, delta dsl(t), front motorcade length Δ ds(t), preceding Vehicle speed vp(t), from vehicle speed vh(t), from vehicle acceleration ah(t) it is used as state variable x (t), and front truck acceleration ap(t) With front motorcade length ds(t) system disturbance w (t), T are used assFor the systematic sampling time, taking 0.001s, u (t) is lower layer's control Input ades;τ is time constant;
It is derived from vehicle and leading vehicle distance Δ d (t), from vehicle and front truck relative velocity vrel(t), from vehicle speed vh(t), from vehicle acceleration ah (t) it is used as output variable y (t), obtains output equation:
Y (t)=Cx (t)
Wherein:
2) tracking target is selected, the output weight w that performance vectors y (t) to be optimized stresses is determined according to targety, select reference locus yr
3) three rank discrete state equations models of the follow the bus system proposed according to step 1), the quantity of state y (t) used in t moment is to t Quantity of state y (t+i) future behaviour at+i moment makes following prediction, is solved:
Wherein:
H only has mathematical meaning in formula, and without concrete meaning, p is prediction time domain length, and Δ u (t)=u (t)-u (t-1) is indicated Variable quantity is controlled, (t+i | t) represents the prediction in t moment to moment t+i, in the prediction of future horizon, due to disturbing w (t+ I) unpredictable, it is assumed that in prediction time domain: w (t+i)=w (t-1), i=0,1,2 ... p-1;
4) after the weight of setting system input u and system input change the weight of △ u, problem to be optimized is write as a weighting shape Formula value function J (y, u, Δ u), calculate prediction time domain in calculated result and reference curve difference, system input and it is defeated Enter the summation of change rate and respective weights product, minimizes total value so that reality output reaches close to reference curve;
5) constraint of the constraint and vehicle parameter formed according to the selected tracking target of step 2), is re-introduced into slack variable ε, is The constraint condition of system output, system input, system input variation, as shown in formula (15):
For in (15) constraint lower bound coefficient of relaxation,For Constrain the coefficient of relaxation in the upper bound, umin、umaxFor vehicle acceleration ability, Δ umin、ΔumaxFor acceleration change amount, ymin、ymax For the constraint of output variable in car-following model;
6) optimization problem of top level control is become to the Δ u (t+i | t) for solving and meeting and minimizing value function J under constraint condition, (t+i | t) prediction of the representative in t moment to moment t+i, and using header element as output, wherein ρ is slack variable weight coefficient:
Vehicle is controlled according to this algorithm.
CN201610945654.0A 2016-10-26 2016-10-26 Cooperating type self-adaption cruise system algorithm based on traffic information CN106476806B (en)

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