CN116206447A - Intelligent network-connected vehicle intersection ecological driving control method - Google Patents

Intelligent network-connected vehicle intersection ecological driving control method Download PDF

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CN116206447A
CN116206447A CN202310167370.3A CN202310167370A CN116206447A CN 116206447 A CN116206447 A CN 116206447A CN 202310167370 A CN202310167370 A CN 202310167370A CN 116206447 A CN116206447 A CN 116206447A
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胡笳
冯永威
张子晗
王浩然
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Tongji University
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Abstract

The invention relates to an intelligent network-connected vehicle intersection ecological driving control method, which comprises the following steps: based on GPS technology and V2I communication, traffic information is collected in real time; globally linearizing a vehicle dynamics model based on the simulated sampled vehicle trajectory data; combining intersection signal timing constraint and a global linearization vehicle dynamics model to serve as constraint conditions, and constructing an ecological driving optimization control model based on a spatial domain; based on a model predictive control technology, traffic information acquired in real time is input into an ecological driving optimal control model, an ecological driving optimal motion track is generated through iterative dynamic solution and real-time planning, and an automatic driving control instruction is updated in real time, so that the vehicle runs according to the ecological driving optimal motion track. Compared with the prior art, the intelligent network vehicle running track planning method and system can accurately and rapidly conduct decision planning on the intelligent network vehicle running track, and therefore efficient and green passing of the intelligent network vehicle at an urban intersection is achieved.

Description

Intelligent network-connected vehicle intersection ecological driving control method
Technical Field
The invention relates to the technical field of network-connected automatic driving automobile traffic control, in particular to an intelligent network-connected vehicle intersection ecological driving control method.
Background
The transportation field is one of the main sources of energy consumption and pollution emissions worldwide. In 2014, carbon dioxide emissions from the transportation industry account for 20.5% of the global total, and are inferior to the electric and thermal industries. In 2016, the energy consumed by the transportation industry accounts for 26% of the total energy demand worldwide, and this proportion has been rising to date. The energy consumption and carbon dioxide emission of road traffic are more than 72% and 80% of the whole transportation industry. Therefore, solving the problems of traffic energy consumption and pollution emission is not easy.
The development of ecological driving assistance systems is currently the main countermeasure to solve the problems of traffic energy consumption and pollution. The ecological driving auxiliary system is based on the network automatic driving technology, and fuel consumption is saved by optimizing driving distance, adjusting driving speed, reducing parking times and the like. Ecological driving is mainly divided into ecological cruising driving and green speed guiding at intersections, wherein the ecological cruising driving is suitable for scenes without signal lamp control, such as expressways and expressways; the latter considers the influence of the intersection signal lamp, and realizes the purpose of saving oil consumption by coordinating acceleration, speed and intersection signal timing of the network-connected automatic driving automobile. With the development of the vehicle-road cooperative technology, the network-connected automatic driving vehicle can acquire signal timing information in real time, which also plays a great pushing role in the development of the green speed guiding technology of the intersection.
Under urban road environment, intersection signal control often cuts off the normal operation of vehicles, so that the running efficiency and energy consumption of the vehicles are greatly influenced, and how to plan the vehicle track in real time so that the vehicles can pass through the intersection in a low-energy-consumption and high-efficiency state is a major research focus on the decision control problem of the intelligent network-connected vehicles at present. The existing ecological driving auxiliary system for realizing the green speed guidance of the intersection mainly adopts an optimal control method, but has the following obvious defects:
(1) The existing ecological driving technology only considers the longitudinal kinematic characteristics of the vehicle, mostly ignores transverse dynamics and kinematics, and more rarely considers the longitudinal and transverse coupling characteristics of the vehicle operation, so that the theoretical modeling is not matched with the actual operation of the vehicle, and the model error is larger.
(2) In the existing ecological driving technical method considering the longitudinal and transverse coupling characteristics of the vehicle, the real-time performance and the accuracy of vehicle control are affected to a certain extent because the vehicle dynamics system has the characteristics of nonlinearity and high coupling, and the current method is difficult to realize the closed-loop feedback control of the vehicle dynamics system with high efficiency and high accuracy.
(3) The existing ecological driving technology based on optimization control lacks an optimization means for a control time domain, and the control time domain is often given in advance through means such as simulation experiments, so that the flexibility of vehicle driving strategy selection is limited, the energy consumption saving of a vehicle cannot be optimized, and meanwhile, the mobility of the vehicle is greatly lost.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an intelligent network-connected vehicle intersection ecological driving control method which is based on a global linearization vehicle dynamics model of massive analog sampling data, and constructs a model prediction control algorithm based on a spatial domain, so that real-time decision planning of the intelligent network-connected vehicle driving track in an urban road environment is realized, and efficient and green passing of the intelligent network-connected vehicle at the urban intersection is realized.
The aim of the invention can be achieved by the following technical scheme: an intelligent network-connected vehicle intersection ecological driving control method comprises the following steps:
s1, communicating with V2I (Vehicle to Infrastructure, vehicles and infrastructure) based on a GPS technology, and collecting traffic information in real time;
s2, based on the vehicle track data of the analog sampling, a vehicle dynamics model is globally linearized;
s3, combining intersection signal timing constraint and a global linearization vehicle dynamics model to serve as constraint conditions, and constructing an ecological driving optimization control model based on a space domain;
s4, inputting the traffic information acquired in real time into an ecological driving optimal control model based on a model predictive control technology, generating an ecological driving optimal motion track through iterative dynamic solution and real-time planning, and updating an optimal automatic driving control instruction in real time to enable the vehicle to run according to the ecological driving optimal motion track.
Further, in the step S1, the intelligent network-connected vehicle acquires and collects traffic information from the vehicle-mounted GPS device, the vehicle-mounted sensing communication device and the road side sensing communication module.
Further, the traffic information collected in the step S1 includes a host vehicle position coordinate, a vehicle speed, a yaw angle, an environmental vehicle position coordinate, a vehicle speed, and intersection signal timing information.
Further, the step S2 is specifically to adopt a nonlinear dynamics model with high coupling in the longitudinal and transverse directions to simulate sampling track data offline, and take the simulated sampling track data as input based on Koopman operator to globally linearize the vehicle dynamics model.
Further, the step S2 includes the steps of:
s21, selecting a basis function vector and amplifying a state vector;
nonlinear vehicle dynamics model x k+1 ＝f(x k ，u k ) Wherein, the method comprises the steps of, wherein,
Figure BDA0004096350180000031
expressed as a vehicle state vector at the kth time, including the longitudinal and transverse position coordinates, the longitudinal and transverse speed, the yaw angle, and the yaw rate of the vehicle, +.>
Figure BDA0004096350180000032
The vehicle control input vector at the kth moment comprises the acceleration and the front wheel rotation angle of the vehicle;
by augmenting the state vector x according to Koopman linear operator theory k The original nonlinear dynamics model tends to be linearized by the dimension of (a) that is, there is a linearity calculation
Figure BDA0004096350180000033
And the base function phi, makeThe formula is established:
Figure BDA0004096350180000034
selecting radial basis function psi and linear function
Figure BDA0004096350180000035
As a basis function phi, i.e
Figure BDA0004096350180000036
Wherein, psi is i (x k )＝||x k -x i || 2 log(||x k -x i ||)，
Figure BDA0004096350180000037
x i A parameter vector selected randomly;
then the original state vector
Figure BDA0004096350180000038
Amplified to +.>
Figure BDA0004096350180000039
z k ＝(x k ，ψ 1 (x k )，…，ψ N (x k ))；
Linear Koopman calculation
Figure BDA00040963501800000310
Nonlinear power system x k+1 ＝f(x k ，u k ) Conversion to a Linear Power System z k+1 ＝Az k +Bu k A and B are parameter matrixes to be solved;
s22, performing off-line analog sampling on track data;
based on the virtual simulation platform, K (K>>N, m, N) control inputs U, constituting a control input data set u= [ U ] 1 ，...，u k ，...，u K ]The control input in U is sequentially acted on the nonlinear dynamics system x k+1 ＝f(x k ，u k ) Then the state sequence x= [ X ] is obtained 1 ，...，x k ，...，x K+1 ]Substituting the state sequences into the radial basis functions in sequence, the extended state sequence Z= [ Z ] can be obtained 1 ，...，z k ，...，z K ]Y= [ z ] 2 ，...，z k ，...，z K+1 ]；
S23, global linearization of a vehicle dynamics model;
describing a undetermined parameter matrix A and B of the linear dynamics system by using a least square problem:
Figure BDA00040963501800000311
the solution of the least squares problem is
Figure BDA00040963501800000312
And substituting the track data of the analog sampling into the equation to obtain parameter matrixes A and B, and thus, completing global linearization of the nonlinear vehicle dynamics model.
Further, the step S3 specifically includes the following steps:
s31, taking energy consumption and maneuverability of the intelligent network-connected vehicle into consideration, and constructing a model cost function;
s32, establishing corresponding signal timing constraint according to the state of the current signal lamp and the red light and green light states;
s33, the space stationarity of intelligent network vehicles passing through the intersection is considered, the time domain model is converted into a space domain model, the longitudinal position of the vehicle is used as a bottom layer variable, the time is used as a system state variable, and the ecological driving optimization control model based on the space domain is constructed.
Further, the model cost function in the step S31 specifically includes:
Figure BDA0004096350180000041
the corresponding conversion into a vector matrix form is as follows:
Figure BDA0004096350180000042
wherein beta is 0 、β 1 、β 2 Is a weight coefficient; v represents the longitudinal speed of the intelligent network vehicle and is the variable to be solved; v des The expected speed of the intelligent network connection; m is intelligent network connection quality; a is longitudinal acceleration, which is the variable to be solved; f is the magnitude of the force blocked by the vehicle; s is(s) tf Is the last longitudinal position of the vehicle; l is the longitudinal distance of the vehicle from the stopping line; t is t 0 The initial moment when the vehicle arrives at the intersection is a known quantity; t is t f For the moment that the vehicle passes through the intersection, the cost function integral number outer term and the first term in the integral number are used for describing the mobility requirement of the vehicle, and the second term in the integral number is used for describing the energy consumption requirement of the vehicle; q (Q) tf And Q, S are weight coefficient matrixes.
Further, the signal timing constraint in the step S32 is specifically:
if the current time is a red light, t f The following constraints need to be satisfied:
Figure BDA0004096350180000043
wherein r is rest G is green light time;
if the current time is green light, t f The following constraints need to be satisfied:
when g rest When not less than τ, t f ≤g rest +t 0 ；
When g rest When < τ, t f ≥g rest +r+t 0 ；
Wherein g rest The remaining green time; r is red light time; tau is the key time, defined byThe maximum limiting acceleration of the vehicle and the maximum speed limit are determined.
Further, the step S33 is specifically to convert the time domain model into a spatial domain model by the following conversion formula:
Figure BDA0004096350180000044
based on the transformation mode, t 0 →t f Conversion of an uncertain time interval of (a) to s 0 The original free time domain optimal control problem is converted into a constrained fixed space domain optimal control problem in the fixed space interval of L.
Further, the specific process of iterative dynamic solution in step S4 is as follows:
s41, discretizing a model cost function to obtain a weight coefficient matrix Q of each space step k ，S k ，k＝(0，1，....，M)；
S42, setting iteration times i=1, converging a threshold epsilon, and juxtaposing an initial value of a control input sequence U as U 0 ；
S43, reversely calculating control law gain:
when k=m, the gain matrix P is set M ＝Q M ；
From k=m-1 to k=1, the following equation is calculated in reverse recursion:
Figure BDA0004096350180000051
s44, forward calculation control input u k ：
Figure BDA0004096350180000052
z k+1 ＝Az k +Bu k
Obtaining a control input sequence U and a state sequence Z;
s45, judging whether the current time state meets the signal timing constraint condition, and executing the step S46 if the current time state meets the signal timing constraint condition; if not, correcting the control input U according to the boundary value of the constraint condition, and then executing step S46;
s46, judging convergence, if meeting U-U 0 The I is less than or equal to epsilon, an optimal solution is obtained, and the solution is finished; if not, let U 0 = U, i =i+1, and the process returns to step S43.
Compared with the prior art, the invention has the following advantages:
1. the invention provides a data-driven intelligent network-connected vehicle intersection ecological driving control method, which firstly considers the nonlinear and longitudinal coupling effects of a vehicle dynamics model, adopts mass simulation sampling data, and globally linearizes the vehicle dynamics model based on Koopman operator so as to improve the execution efficiency of a decision control algorithm; and the space-time coupling characteristic of the urban road is considered, an ecological driving optimization control model based on a space domain is constructed, a model prediction control algorithm is combined, GPS and V2I communication data are used as model input, acceleration, braking and front wheel rotation angle are used as control instructions, accurate and rapid decision planning of the intelligent network vehicle running track is realized, and therefore efficient and green passing of the intelligent network vehicle at an urban intersection is realized.
2. The method fully considers the longitudinal and transverse coupling effect of the nonlinear vehicle dynamics model, adopts the Koopman operator theory global linearization vehicle dynamics model, and can effectively improve the subsequent calculation solution efficiency, thereby realizing the real-time rolling optimization control of the intelligent network-connected vehicle.
3. According to the method, the influence of signal timing constraint on the running of the vehicle under the urban intersection environment is considered, and the space-time coupling effect of the urban road is considered, so that a model prediction control method based on a space domain is constructed, the problem of time uncertainty is converted into the problem of space certainty, the influence of a free control time domain on model solving can be eliminated, and the reliability of a model solving result is ensured.
4. The invention gives consideration to the energy consumption and the driving maneuverability of the vehicle, and implements the double-objective optimization of the energy consumption and the driving maneuverability so as to construct a model cost function. The energy consumption of the driving is saved, the energy conservation and the emission reduction are realized, the loss of the motor performance of the vehicle is as small as possible, and the driving of the vehicle is ensured to be environment-friendly and efficient.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples.
Examples
As shown in fig. 1, the ecological driving control method for the intersection of the intelligent network-connected vehicles comprises the following steps:
s1, communicating with V2I based on a GPS technology, and collecting traffic information in real time;
s2, based on the vehicle track data of the analog sampling, a vehicle dynamics model is globally linearized;
s3, combining intersection signal timing constraint and a global linearization vehicle dynamics model to serve as constraint conditions, and constructing an ecological driving optimization control model based on a space domain;
s4, inputting the traffic information acquired in real time into an ecological driving optimal control model based on a model predictive control technology, generating an ecological driving optimal motion track through iterative dynamic solution and real-time planning, and updating an optimal automatic driving control instruction in real time to enable the vehicle to run according to the ecological driving optimal motion track.
By applying the technical scheme, the main content of the embodiment comprises the following steps:
step 1: based on GPS technology and V2I communication, traffic information (main car information, environment vehicle information, signal timing information and the like) is collected in real time.
In step 1, the intelligent network-connected vehicle may acquire information according to the vehicle-mounted GPS device, the vehicle-mounted sensing communication device, and the road side sensing communication module, where the information includes: the main vehicle position coordinates, the vehicle speed, the yaw angle, the environment vehicle position coordinates, the vehicle speed and intersection signal timing information.
In order to collect the above information, in this embodiment, the applied intelligent network vehicle should have a vehicle information collection device, a communication device, a vehicle database, and a vehicle control unit; the intelligent signal lamp is provided with an information sensing device, a communication device and a road side data processing device.
Step 2: based on the vehicle track data of the simulation sampling, the vehicle dynamics model is globally linearized so as to improve the calculation efficiency of the decision control algorithm.
In the step 2, the sampling track data is simulated offline by adopting a nonlinear dynamics model with high coupling in the longitudinal and transverse directions, and then the vehicle dynamics model is linearized globally by taking the simulated sampling track data as input based on a Koopman operator. The method comprises the following steps:
step 2.1: the basis function vector selection and the amplification of the state vector. Recording the accurate nonlinear vehicle dynamics model as x k+1 ＝f(x k ，u k ) Wherein, the method comprises the steps of, wherein,
Figure BDA0004096350180000071
the vehicle state vector, which is expressed as the kth time, includes the longitudinal and lateral position coordinates, the longitudinal and lateral speed, the yaw angle, and the yaw rate of the vehicle. />
Figure BDA0004096350180000072
The vehicle control input vector at the kth time includes the acceleration and the front wheel rotation angle of the vehicle. According to the Koopman linear operator theory, the state vector x can be amplified k The original nonlinear dynamics model tends to be linearized by the dimension of (2), i.e. there is linearity +.>
Figure BDA0004096350180000073
And a base function phi, the following formula is established:
Figure BDA0004096350180000074
selecting radial basis function psi and linear function
Figure BDA0004096350180000075
As a basis function phi, i.e
Figure BDA0004096350180000076
Wherein, psi is i (x k )＝||x k -x i || 2 log(||x k -x i ||)，
Figure BDA0004096350180000077
x i Is a randomly selected parameter vector.
Then the original state vector
Figure BDA0004096350180000078
Can be amplified to->
Figure BDA0004096350180000079
z k ＝(x k ，ψ 1 (x k )，…，ψ N (x k ))。
Linear Koopman operator
Figure BDA00040963501800000710
Nonlinear power system x k+1 ＝f(x k ，u k ) Can be converted into a linear power system z k+1 ＝Az k +Bu k A and B are parameter matrixes to be solved.
Step 2.2: the trajectory data is analog sampled offline. Based on the virtual simulation platform, K (K>>N, m, N) control inputs U, constituting a control input data set u= [ U ] 1 ，...，u k ，...，u K ]The control input in U is sequentially acted on the nonlinear dynamics system x k+1 ＝f(x k ，u k ) The state sequence x= [ X ] can be obtained 1 ，...，x k ，...，x K+1 ]Substituting the state sequences into the radial basis functions in sequence, the extended state sequence Z= [ Z ] can be obtained 1 ，...，z k ，...，z K ]Y= [ z ] 2 ，...，z k ，...，z K+1 ]。
Step 2.3: the vehicle dynamics model is globally linearized. The undetermined parameter matrix a, B of the linear dynamics system can be described by the following least squares problem:
Figure BDA00040963501800000711
the solution of the least squares problem is
Figure BDA00040963501800000712
And substituting the data of the analog sampling into the equation, the parameter matrix A, B can be obtained, and the global linearization of the nonlinear vehicle dynamics model is completed.
Step 3: and constructing an ecological driving optimization control model based on a spatial domain aiming at the intersection signal timing constraint.
In step 3, aiming at the intersection signal timing constraint, the space-time coupling characteristic of the road is considered, and an ecological driving optimization control model based on a space domain is constructed. The method comprises the following steps:
step 3.1: and (5) constructing a model cost function. The model cost function mainly considers the energy consumption and mobility of the intelligent network-connected vehicle, and the mathematical description of the cost function J can be given by the following formula:
Figure BDA0004096350180000081
wherein beta is 0 、β 1 、β 2 Is a weight coefficient; v represents the longitudinal speed of the intelligent network vehicle and is the variable to be solved; v des The expected speed of the intelligent network connection; m is intelligent network connection quality; a is longitudinal acceleration, which is the variable to be solved; f is the magnitude of the force blocked by the vehicle; s is(s) tf Is the last longitudinal position of the vehicle; l is the longitudinal distance of the vehicle from the stopping line; t is t 0 The initial moment when the vehicle arrives at the intersection is a known quantity; t is t f The time when the vehicle passes through the intersection is the waiting quantity. The cost function is used for describing the mobility requirement of the vehicle by an integral number outer term and a first term in the integral number, and is used for describing the mobility requirement of the vehicle by a second term in the integral numberEnergy consumption requirements.
The cost function may be further converted into the form of a vector matrix:
Figure BDA0004096350180000082
wherein Q is tf Q, S are weight coefficient matrices each having beta 0 、β 1 、β 2 Constant of M, f, etc.
Step 3.2: signal timing constraints are established. The signal timing is discussed in a classification mode according to the state (red light or green light) of the current signal lamp, and the constraint mathematical description is as follows:
if the current time is a red light, t f The following constraints need to be satisfied:
Figure BDA0004096350180000083
wherein r is rest G is green light time, which is the remaining red light time.
If the current time is green light, t f The following constraints need to be satisfied:
when g rest When not less than τ, t f ≤g rest +t 0
When g rest When < τ, t f ≥g rest +r+t 0
Wherein g rest The remaining green time; r is red light time; τ is the critical time, determined by the maximum limiting acceleration of the vehicle and the maximum speed limit.
Step 3.3: conversion of the time domain model to the spatial domain model. Since in the time domain optimization control model, time t f -t 0 Is variable and is constrained by signal timing, so that the solution of the time domain model has a certain difficulty. The space stationarity of intelligent network vehicles passing at the intersection is considered, so that the time domain model can be converted into the space domain model to be solved, the longitudinal position of the vehicle is taken as a bottom variable, and the time is taken as a system state variableThe model is converted from the last state free problem to the last state fixed problem. The transformation mode is as follows:
Figure BDA0004096350180000084
based on the transformation mode, t 0 →t f Conversion of an uncertain time interval of (a) to s 0 A fixed spatial interval of L. The original free time domain optimization control problem is converted into a fixed space domain optimization control problem with constraint.
Step 4: based on the thought of model predictive control, an iterative dynamic programming algorithm is designed for real-time programming of the optimal motion trail of ecological driving, and automatic driving control instructions are optimized in an online rolling mode.
In step 4, the iterative dynamic programming algorithm comprises the following steps:
step 4.1: discretizing the cost function to obtain a weight coefficient matrix Q of each space step k ，S k ，(k＝0，1，....，M)。
Step 4.2: setting the iteration number i=1, converging the threshold epsilon, and setting the initial value of the control input sequence U as U 0 。
Step 4.3: inversely calculating the control law gain:
when k=m, the gain matrix P is set M ＝Q M ；
From k=m-1 to k=1, the following equation is calculated in reverse recursion:
Figure BDA0004096350180000091
step 4.4: forward calculation control input u k ：
Figure BDA0004096350180000092
z k+1 ＝Az k +Bu k
Obtaining a control input sequence U and a state sequence Z
Step 4.5: judging whether the solved time state meets the signal timing constraint condition, and executing the step 4.6 if the solved time state meets the signal timing constraint condition; if not, the control input U is corrected according to the boundary value of the constraint condition, and then step 4.6 is executed.
Step 4.6: and (5) judging convergence. If meeting U-U 0 The I is less than or equal to epsilon, the optimal solution is obtained, and the algorithm is finished; if the formula is not satisfied, let U 0 = U, i =i+1, returning to step 4.3.
In summary, the technical scheme realizes a vehicle dynamics system linearization scheme based on simulated track data and a spatial domain model predictive control scheme facing to an urban intersection based on a vehicle longitudinal and transverse coupling dynamics model, a Koopman operator theory, an optimization control theory and a model predictive control method, and can accurately and rapidly make decision and plan on the running track of an intelligent network vehicle, thereby guaranteeing the mobility and ecology of the intelligent network vehicle running in the urban intersection environment.

Claims (10)

1. The intelligent network-connected vehicle intersection ecological driving control method is characterized by comprising the following steps of:
s1, communicating with V2I based on a GPS technology, and collecting traffic information in real time;
s2, based on the vehicle track data of the analog sampling, a vehicle dynamics model is globally linearized;
s3, combining intersection signal timing constraint and a global linearization vehicle dynamics model to serve as constraint conditions, and constructing an ecological driving optimization control model based on a space domain;
s4, inputting the traffic information acquired in real time into an ecological driving optimal control model based on a model predictive control technology, generating an ecological driving optimal motion track through iterative dynamic solution and real-time planning, and updating an optimal automatic driving control instruction in real time to enable the vehicle to run according to the ecological driving optimal motion track.
2. The method for ecologically controlling the intersection of intelligent network-connected vehicles according to claim 1, wherein in step S1, the intelligent network-connected vehicles acquire collected traffic information from the vehicle-mounted GPS device, the vehicle-mounted sensing communication device and the road-side sensing communication module.
3. The intelligent network-connected vehicle intersection ecological driving control method according to claim 1, wherein the traffic information collected in the step S1 includes a host vehicle position coordinate, a vehicle speed, a yaw angle, an environmental vehicle position coordinate, a vehicle speed, and intersection signal timing information.
4. The intelligent network-connected vehicle intersection ecological driving control method according to claim 1, wherein the step S2 is specifically to use a nonlinear dynamics model with high coupling in the longitudinal and transverse directions to simulate sampling trajectory data offline, and based on Koopman operator, to take simulated sampling trajectory data as input, and to linearize the vehicle dynamics model globally.
5. The intelligent network-connected vehicle intersection ecological driving control method according to claim 4, wherein the step S2 comprises the steps of:
s21, selecting a basis function vector and amplifying a state vector;
nonlinear vehicle dynamics model x k+1 ＝f(x k ，u k ) Wherein, the method comprises the steps of, wherein,
Figure FDA0004096350170000011
expressed as a vehicle state vector at the kth time, including the longitudinal and transverse position coordinates, the longitudinal and transverse speed, the yaw angle, and the yaw rate of the vehicle, +.>
Figure FDA0004096350170000012
The vehicle control input vector at the kth moment comprises the acceleration and the front wheel rotation angle of the vehicle;
by augmenting the state vector x according to Koopman linear operator theory k Is to line the original nonlinear dynamics model by the dimension of (a)Sexualization, i.e. the presence of linear operators
Figure FDA0004096350170000013
And a base function phi, the following formula is established:
Figure FDA0004096350170000014
selecting radial basis function psi and linear function
Figure FDA0004096350170000021
As a basis function phi, i.e
Figure FDA0004096350170000022
Wherein, psi is i (x k )＝||x k -x i || 2 log(||x k -x i ||)，
Figure FDA0004096350170000023
x i A parameter vector selected randomly;
then the original state vector
Figure FDA0004096350170000024
Amplified to +.>
Figure FDA0004096350170000025
z k ＝(x k ，ψ 1 (x k )，…，ψ N (x k ))；
Linear Koopman operator
Figure FDA0004096350170000026
Nonlinear power system x k+1 ＝f(x k ，u k ) Conversion to a Linear Power System z k+1 ＝Az k +Bu k A and B are all to be solvedA parameter matrix;
s22, performing off-line analog sampling on track data;
based on the virtual simulation platform, K (K>>N, m, N) control inputs U, constituting a control input data set u= [ U ] 1 ，...，u k ，...，u K ]The control input in U is sequentially acted on the nonlinear dynamics system x k+1 ＝f(x k ，u k ) Then the state sequence x= [ X ] is obtained 1 ，...，x k ，...，x K+1 ]Substituting the state sequences into the radial basis functions in sequence, the extended state sequence Z= [ Z ] can be obtained 1 ，...，z k ，...，z K ]Y= [ z ] 2 ，...，z k ，...，z K+1 ]；
S23, global linearization of a vehicle dynamics model;
describing a undetermined parameter matrix A and B of the linear dynamics system by using a least square problem:
Figure FDA0004096350170000027
the solution of the least squares problem is
Figure FDA0004096350170000029
And substituting the track data of the analog sampling into the equation to obtain parameter matrixes A and B, and thus, completing global linearization of the nonlinear vehicle dynamics model.
6. The method for controlling ecological driving at an intersection of an intelligent network-connected vehicle according to claim 5, wherein the step S3 specifically comprises the following steps:
s31, taking energy consumption and maneuverability of the intelligent network-connected vehicle into consideration, and constructing a model cost function;
s32, establishing corresponding signal timing constraint according to the state of the current signal lamp and the red light and green light states;
s33, the space stationarity of intelligent network vehicles passing through the intersection is considered, the time domain model is converted into a space domain model, the longitudinal position of the vehicle is used as a bottom layer variable, the time is used as a system state variable, and the ecological driving optimization control model based on the space domain is constructed.
7. The intelligent network-connected vehicle intersection ecological driving control method according to claim 6, wherein the model cost function in the step S31 is specifically:
Figure FDA0004096350170000028
the corresponding conversion into a vector matrix form is as follows:
Figure FDA0004096350170000031
wherein beta is 0 、β 1 、β 2 Is a weight coefficient; v represents the longitudinal speed of the intelligent network vehicle and is the variable to be solved; v des The expected speed of the intelligent network connection; m is intelligent network connection quality; a is longitudinal acceleration, which is the variable to be solved; f is the magnitude of the force blocked by the vehicle; s is(s) tf Is the last longitudinal position of the vehicle; l is the longitudinal distance of the vehicle from the stopping line; t is t 0 The initial moment when the vehicle arrives at the intersection is a known quantity; t is t f For the moment that the vehicle passes through the intersection, the cost function integral number outer term and the first term in the integral number are used for describing the mobility requirement of the vehicle, and the second term in the integral number is used for describing the energy consumption requirement of the vehicle; q (Q) tf And Q, S are weight coefficient matrixes.
8. The intelligent network-connected vehicle intersection ecological driving control method according to claim 6, wherein the signal timing constraint in the step S32 is specifically:
if the current time is a red light, t f The following constraints need to be satisfied:
Figure FDA0004096350170000032
wherein r is rest G is green light time;
if the current time is green light, t f The following constraints need to be satisfied:
when g rest When not less than τ, t f ≤g rest +t 0 ；
When g rest When < τ, t f ≥g rest +r+t 0 ；
Wherein g rest The remaining green time; r is red light time; τ is the critical time, determined by the maximum limiting acceleration of the vehicle and the maximum speed limit.
9. The intelligent network vehicle intersection ecological driving control method according to claim 8, wherein the step S33 is specifically to convert the time domain model into the space domain model by the following conversion formula:
Figure FDA0004096350170000033
based on the transformation mode, t 0 →t f Conversion of an uncertain time interval of (a) to s 0 The original free time domain optimal control problem is converted into a constrained fixed space domain optimal control problem in the fixed space interval of L.
10. The intelligent network-connected vehicle intersection ecological driving control method according to claim 7, wherein the specific process of iterative dynamic solution in the step S4 is as follows:
s41, discretizing a model cost function to obtain a weight coefficient matrix Q of each space step k ，S k ，k＝(0，1，....，M)；
S42, setting the iteration number i=1,convergence threshold epsilon and initial value of juxtaposed control input sequence U of U 0 ；
S43, reversely calculating control law gain:
when k=m, the gain matrix P is set M ＝Q M ；
From k=m-1 to k=1, the following equation is calculated in reverse recursion:
Figure FDA0004096350170000041
s44, forward calculation control input u k ：
Figure FDA0004096350170000042
z k+1 ＝Az k +Bu k
Obtaining a control input sequence U and a state sequence Z;
s45, judging whether the current time state meets the signal timing constraint condition, and executing the step S46 if the current time state meets the signal timing constraint condition; if not, correcting the control input U according to the boundary value of the constraint condition, and then executing step S46;
s46, judging convergence, if meeting U-U 0 The I is less than or equal to epsilon, an optimal solution is obtained, and the solution is finished; if not, let U 0 = U, i =i+1, and the process returns to step S43.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN116524722B (en) * 2023-06-26 2023-10-13 浙江大学 Mixed traffic flow-oriented vehicle ecological driving control method and electronic equipment

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