CN106476806B  Cooperating type selfadaption cruise system algorithm based on traffic information  Google Patents
Cooperating type selfadaption cruise system algorithm based on traffic information Download PDFInfo
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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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Classifications

 B—PERFORMING OPERATIONS; TRANSPORTING
 B60—VEHICLES IN GENERAL
 B60W—CONJOINT CONTROL OF VEHICLE SUBUNITS 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 SUBUNIT
 B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular subunit, e.g. of systems using conjoint control of vehicle subunits, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
 B60W30/14—Adaptive cruise control

 B—PERFORMING OPERATIONS; TRANSPORTING
 B60—VEHICLES IN GENERAL
 B60W—CONJOINT CONTROL OF VEHICLE SUBUNITS 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 SUBUNIT
 B60W40/00—Estimation or calculation of nondirectly 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
 B60W40/10—Estimation or calculation of nondirectly 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
 B60W40/107—Longitudinal acceleration

 B—PERFORMING OPERATIONS; TRANSPORTING
 B60—VEHICLES IN GENERAL
 B60W—CONJOINT CONTROL OF VEHICLE SUBUNITS 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 SUBUNIT
 B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular subunit, e.g. process diagnostic or vehicle driver interfaces
 B60W2050/0001—Details of the control system
 B60W2050/0019—Control system elements or transfer functions
 B60W2050/0028—Mathematical models, e.g. for simulation
 B60W2050/0031—Mathematical model of the vehicle

 B—PERFORMING OPERATIONS; TRANSPORTING
 B60—VEHICLES IN GENERAL
 B60W—CONJOINT CONTROL OF VEHICLE SUBUNITS 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 SUBUNIT
 B60W2520/00—Input parameters relating to overall vehicle dynamics
 B60W2520/10—Longitudinal speed

 B—PERFORMING OPERATIONS; TRANSPORTING
 B60—VEHICLES IN GENERAL
 B60W—CONJOINT CONTROL OF VEHICLE SUBUNITS 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 SUBUNIT
 B60W2520/00—Input parameters relating to overall vehicle dynamics
 B60W2520/10—Longitudinal speed
 B60W2520/105—Longitudinal acceleration

 B—PERFORMING OPERATIONS; TRANSPORTING
 B60—VEHICLES IN GENERAL
 B60W—CONJOINT CONTROL OF VEHICLE SUBUNITS 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 SUBUNIT
 B60W2554/00—Input parameters relating to objects
 B60W2554/80—Spatial relation or speed relative to objects
 B60W2554/801—Lateral distance

 B—PERFORMING OPERATIONS; TRANSPORTING
 B60—VEHICLES IN GENERAL
 B60W—CONJOINT CONTROL OF VEHICLE SUBUNITS 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 SUBUNIT
 B60W2554/00—Input parameters relating to objects
 B60W2554/80—Spatial relation or speed relative to objects
 B60W2554/804—Relative longitudinal speed
Abstract
The present invention relates to a kind of cooperating type selfadaption 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 selfability 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, multipleobjection 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
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 invehicle 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.Vehiclemounted 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 infrastructurebased 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 selfadaption 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 invehicle 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 selfadaption 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 selfability 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,
Multipleobjection 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 selfadaption cruise system algorithm based on traffic information, specific to wrap
Include following steps:
1) according to carfollowing 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 a_{des}And 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 a_{des}；
Three rank discrete state equations models of follow the bus system:
X (t+1)=Ax (t)+B_{u}u(t)+B_{w}w(t)
Wherein:
W (t)=[a_{p}(t),d_{s}(t)]^{T}, x (t)=[Δ d (t), Δ d_{sl}(t),Δd_{s}(t),v_{p}(t),v_{h}(t),a_{h}(t)]^{T},
In formula: from vehicle and leading vehicle distance Δ d (t), from vehicle and signal lamp distance, delta d_{sl}(t), front motorcade length Δ d_{s}
(t), preceding vehicle speed v_{p}(t), from vehicle speed v_{h}(t), from vehicle acceleration a_{h}(t) it is used as state variable x (t), and front truck acceleration
a_{p}(t) and front motorcade length d_{s}(t) system disturbance w (t), T are used as_{s}For the systematic sampling time, taking 0.001s, u (t) is lower layer
The input a of control_{des}；τ is time constant；
It is derived from vehicle and leading vehicle distance Δ d (t), from vehicle and front truck relative velocity v_{rel}(t), from vehicle speed v_{h}(t), from vehicle plus
Speed a_{h}(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 target_{y}, selection ginseng
Examine track y_{r}；
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 (t1),
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 (t1), i=0,1,2 ... p1；
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 reintroduced 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, u_{min}、u_{max}For vehicle acceleration ability, Δ u_{min}、Δu_{max}For acceleration change amount, y_{min}、y_{max}
For the constraint of output variable in carfollowing 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 selfadaption 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, carfollowing 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 calculating_{des}And pass to lower layer's actuator.A is being realized in lower layer's control_{des}When need it is inverse longitudinal dynamic by vehicle
Mechanical model realizes desired acceleration a to control accelerator open degree and brake pressure_{des}。
In the actual process, the input a of lower layer's controller_{des}It 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)+B_{u}u(t)+B_{w}w(t) (2)
Wherein:
W (t)=[a_{p}(t),d_{s}(t)]^{T}, x (t)=[Δ d (t), Δ d_{sl}(t),Δd_{s}(t),v_{p}(t),v_{h}(t),a_{h}(t)]^{T},
In formula: vehicle and leading vehicle distance Δ d (t) are selected from, from vehicle and signal lamp distance, delta d_{sl}(t), front motorcade length Δ
d_{s}(t), preceding vehicle speed v_{p}(t), from vehicle speed v_{h}(t), from vehicle acceleration a_{h}(t) it is used as state variable x (t), and front truck is accelerated
Spend a_{p}(t) and front motorcade length d_{s}(t) system disturbance w (t), T are used as_{s}For the systematic sampling time, take under 0.001s, u (t) be
The input a of layer control_{des}。
In addition it is selected from vehicle and leading vehicle distance Δ d (t), from vehicle and front truck relative velocity v_{rel}(t), from vehicle speed v_{h}(t),
From vehicle acceleration a_{h}(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 w_{y}Also difference is answered, in addition y (t) reference locus y_{r}It 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 w_{y}And y_{r}。
Judge as shown in Figure 2 using front truck or fleet as the flow chart of target.At the green light stage, work as v_{h}·t_{rt}>Δd_{sl}When
(t_{rt}For signal lamp remaining time) it is judged as and can passes through；v_{h}·t_{rt}≤Δd_{sl}≤v_{set}·t_{rt}When (v_{set}To set cruising speed)
Judge whether front truck intention passes through, if front truck acceleration a_{p}(t) it is greater than zero, then it is assumed that front truck is ready to pass through, a_{p}(t) less than zero
It is not ready to pass through；Work as v_{set}·t_{rt}≤Δd_{sl}When think to pass through；It is judged to not passing through when red light phase.In addition logical when determining
It is outofdate, then it is obstructed outofdate using front truck as tracking target, continue to judge front truck acceleration a_{p}Accelerate with the reference in MPC algorithm
Spend a_{r}Size relation, work as a_{r}≤a_{p}Shi Ze is tracking target with front fleet, otherwise using front truck as tracking target wherein a_{r}For
y_{r}In 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 d_{safe}, i.e. Δ d (t) >=d_{safe}
Here d_{safe}Take 2m.
1) the control target of front truck is tracked
When using front truck as target, performance weights w_{y1}Should more focus on leading vehicle distance Δ d, from vehicle and front truck relative velocity
v_{rel}And from vehicle desired acceleration a_{h}, target is that the distance from vehicle and front truck is allowed to level off to ideal distance d_{des}And relative velocity v_{rel}
0 is leveled off to, from vehicle desired acceleration a_{h}It levels off to referring to acceleration a_{r1}, i.e. Δ d → d_{des}
v_{rel}→0
a_{h}→a_{r1}
Acceleration adopts by reference a kind of linear follow the bus pilot model:
a_{r1}=k_{V}.v_{rel}+k_{D}.Δd_{error} (4)
K in formula_{V}, k_{D}For model coefficient, respectively 0.25 and 0.02, Δ d_{error}For the difference of actual range and ideal distance
Value, ideal distance d_{des}It is got by spacing policy calculation, here using the constant time headway in variable spacing strategy.
Δd_{des}(t)=t_{h}.v_{h}(t)+d_{0} (5)
Δd_{des}(t) it is and front truck actual range, t_{h}For 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, d_{0}For 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 w_{y2}More stress and front fleet end distance, delta d_{s}, from vehicle speed
v_{h}, and from vehicle acceleration a_{h}.Target at this time is from vehicle speed v_{h}Level off to 0, the distance, delta d with front fleet_{sl}Δd_{s}Approach
In d_{0}, from vehicle desired acceleration a_{h}It levels off to referring to acceleration a_{r2}, i.e.,
Δd_{sl}Δd_{s}→d_{0}
v_{h}→0
a_{h}→a_{r2}
Acceleration a_{h}Adopt 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 v_{b}Curve:
Reference acceleration a can be obtained by above formula_{r2} ^{*}:
However vehicle actual speed v_{h}It may be greater than or less than v_{b}, i.e., the acceleration needed at this time be greater than or be less than a_{r2} ^{*}
's.Assuming that with v at L0 in front of the terminal_{b}Speed traveling, according to formula (8), the acceleration needed at this time are as follows:
Similar, when with speed v_{h}≠v_{b}When 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 curve_{r}.This curve is reference curve y_{r}(t),
It is product of the setting value Jing Guo online softening.Here most commonly used first order exponential version is used:
y_{r}(t+i)=α^{i}y(t)+(1α^{i})y_{r} (12)
The coefficient that α is 0 to 1, i are ith 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 carfollowing 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 formula^{2}For square of A matrix, and so on, A^{p}For 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 (t1) 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 (t1), i=0,1,2 ... p1
In MPC problem as, problem to be optimized is write to the value function of a weighted type:
Wherein w_{y}For the weight matrix of system output, w_{u}For the weight of system input, w_{Δu}For 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 abovementioned 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, u_{min}、u_{max}For vehicle acceleration ability, Δ u_{min}、Δu_{max}For acceleration change amount, y_{min}、y_{max}For 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
Δd_{s}Two 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 
w_{y1}  diag([15,3,0.1,5])  w_{y2}  diag([0.1,0.1,3,5]) 
w_{u}  1  w_{Δu}  0.1 
y_{min}  [2,1,0,6]  y_{max}  [Inf,1,20,3] 
u_{min}  6  u_{max}  3 
Δu_{min}  2  Δu_{max}  2 
v_{set}  20  ρ  0.8 
v^{y} _{min}  [0,1,0,0.1]  v^{y} _{max}  [1,1,0,0.1] 
v^{u} _{min}  0.5  v^{u} _{max}  0.5 
v^{Δu} _{min}  1  v^{Δu} _{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:
d_{s}=10+5.5t_{i}(t_{i}=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 always^{2}, LQR maximum value reached 3.5m/s^{2}, 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/
s^{3}Left and right, this has very big influence to riding comfort, and MPC control changes due to suffering restraints in 3~3m/s^{3}Left 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 lookup 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 selfadaption cruise system algorithm based on traffic information, which is characterized in that specifically comprise the following steps:
1) according to carfollowing 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 data_{des}And 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 pressure_{des}；
Three rank discrete state equations models of follow the bus system:
X (t+1)=Ax (t)+B_{u}u(t)+B_{w}w(t)
Wherein:
W (t)=[a_{p}(t),d_{s}(t)]^{T}, x (t)=[Δ d (t), Δ d_{sl}(t),Δd_{s}(t),v_{p}(t),v_{h}(t),a_{h}(t)]^{T},
In formula: from vehicle and leading vehicle distance Δ d (t), from vehicle and signal lamp distance, delta d_{sl}(t), front motorcade length Δ d_{s}(t), preceding
Vehicle speed v_{p}(t), from vehicle speed v_{h}(t), from vehicle acceleration a_{h}(t) it is used as state variable x (t), and front truck acceleration a_{p}(t)
With front motorcade length d_{s}(t) system disturbance w (t), T are used as_{s}For the systematic sampling time, taking 0.001s, u (t) is lower layer's control
Input a_{des}；τ is time constant；
It is derived from vehicle and leading vehicle distance Δ d (t), from vehicle and front truck relative velocity v_{rel}(t), from vehicle speed v_{h}(t), from vehicle acceleration a_{h}
(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 target_{y}, select reference locus
y_{r}；
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 (t1) 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 (t1), i=0,1,2 ... p1；
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 reintroduced 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, u_{min}、u_{max}For vehicle acceleration ability, Δ u_{min}、Δu_{max}For acceleration change amount, y_{min}、y_{max}
For the constraint of output variable in carfollowing 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.
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