CN113104036B - Vehicle cooperative formation control method based on undirected network system - Google Patents

Vehicle cooperative formation control method based on undirected network system Download PDF

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CN113104036B
CN113104036B CN202110353987.5A CN202110353987A CN113104036B CN 113104036 B CN113104036 B CN 113104036B CN 202110353987 A CN202110353987 A CN 202110353987A CN 113104036 B CN113104036 B CN 113104036B
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vehicle
formation
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undirected network
vehicles
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CN113104036A (en
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贺宜
冯奇
杨鑫炜
吴超仲
彭理群
陈韬
姜春生
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Wuhan University of Technology WUT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, 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
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details 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
    • B60W2050/0001—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

Abstract

The invention provides a vehicle cooperative formation control method based on a undirected network system. The invention constructs a vehicle undirected network topology model; constructing a total communication cost according to a communication cost set in the vehicle undirected network topology model, optimizing by using a prim algorithm by taking the minimum total communication cost as an optimization target of the undirected network topology model to obtain an optimized undirected network topology model; the vehicle acquires the position and state information of the vehicle through a sensor, and a longitudinal dynamic model of the vehicle is established. Acquiring position and state information of a self vehicle and a front vehicle through a sensor unit and a communication unit, and establishing a vehicle formation safe distance model; and finally, constructing the distributed controllers of the vehicles in the vehicle formation. The invention ensures that the vehicles in the vehicle formation can sense the state information of the surrounding vehicles in real time and simultaneously realizes the minimum communication cost, so that the vehicle cooperative formation control method is more in line with the actual requirements.

Description

Vehicle cooperative formation control method based on undirected network system
Technical Field
The invention belongs to the field of intelligent networked automobiles, and particularly relates to a vehicle cooperative formation control method based on a undirected network system.
Background
At present, as the production and maintenance of automobiles rapidly increase, the problems of energy consumption, environmental pollution, poor driving experience, and the like caused by traffic congestion are increasing. The vehicle cooperative formation control technology is used for forming vehicles in the neighborhood, acquiring information of surrounding vehicles in a vehicle-to-vehicle communication (V2V) or vehicle-to-road communication (V2I) mode and the like, adaptively adjusting the speed of the vehicles according to the acquired information, and finally enabling the whole vehicle fleet to reach the expected driving speed on the premise of meeting the safety of the vehicle fleet. Research shows that the vehicle cooperative control formation technology can obviously improve road traffic efficiency, slow down traffic jam and improve fuel economy, so that a traffic system achieves the purposes of safety, high efficiency and energy conservation.
However, the current research on vehicle cooperative formation control still has certain problems and disadvantages. Firstly, the topological structures related to the current vehicle queue research are too single, most of the topological structures focus on the communication among single-lane vehicles, and the intelligent interconnection condition of the vehicles in the vehicle networking environment is not considered, so that the networking analysis of various topological structures is lacked; secondly, in practice, delay of V2V communication is inevitable, and communication delay and communication uncertainty have a significant influence on the stability of vehicle cooperative formation control. Therefore, finding a communication network topology with the minimum cost is particularly important for the safety and stability of vehicle cooperative formation control.
Disclosure of Invention
The invention aims to overcome the defects that the conventional vehicle cooperative formation control technology only focuses on a few common topological structures, communication cost is not considered, and communication redundancy is easily caused, and provides a vehicle cooperative formation control method and system based on an undirected network topological structure.
To achieve the above object, the present invention provides a vehicle cooperative formation control method based on a undirected network system to overcome or alleviate the drawbacks of the prior art.
The system is a vehicle cooperative formation control method based on a directionless network system, and is characterized in that a vehicle queue is regarded as a dynamic system which is composed of a plurality of single vehicle nodes, individual vehicles are controlled through information interaction among the nodes, and the individual vehicles are further coupled. Compared with the traditional vehicle control system, the system researches a plurality of control objects, namely the system is formed by a plurality of relatively independent intelligent agents through information flow interactive coupling; secondly, a system communication structure adopts a directionless network topology structure, and queue stability is improved while neighborhood vehicle information is fully utilized; then, considering the problem of communication cost of vehicle formation, selecting an optimal subgraph as the optimal information interaction topology of formation in the undirected network topology structure diagram, so that the communication cost of all members using the undirected network topology structure, such as total time delay, network overhead and stability, is minimum; and finally, the single control object adopts distributed control, namely, a single vehicle has certain communication and calculation capacity, and the vehicle makes a control decision in time according to the obtained vehicle information so as to achieve the aims of overall coordination of the whole system and improvement of queue efficiency.
The system of the invention needs to be installed in all vehicles in the vehicle formation, and can send the corresponding driving instruction to a specific vehicle in a point-to-point mode, and also can send the corresponding driving instruction to each vehicle in the vehicle formation in a broadcast mode.
The undirected network system comprises: the system comprises a plurality of sensing units, a plurality of wireless communication devices and a central processing unit;
the sensing unit is composed of a laser radar sensor, a speed sensor, an acceleration sensor, a wind speed sensor and a data processor;
the data processor is respectively connected with the laser radar sensor, the speed sensor, the acceleration sensor, the wind speed sensor and the wireless communication device in sequence in a wired mode; the wireless communication device is connected with the central processing unit in a wireless mode;
the laser radar sensor is mounted on the vehicle head, the speed sensor is mounted in a wheel of a vehicle, the wind speed sensor is mounted on the vehicle head, and the wireless communication device is mounted on the vehicle roof;
the laser radar sensor detects the distance between the laser radar sensor and the front vehicle and transmits the acquired distance between the vehicles to the data processor;
the speed sensor obtains the vehicle speed by detecting the rotating speed of the tire, and transmits the acquired vehicle speed to the data processor;
the acceleration sensor collects vehicle acceleration and transmits the collected vehicle acceleration to the data processor;
the wind speed sensor collects wind speed and transmits the collected wind speed of the vehicle to the data processor;
the data processor transmits vehicle driving state data to the central processing unit through the wireless communication device; the vehicle running state data includes: inter-vehicle distance, vehicle speed, vehicle acceleration, vehicle wind speed;
the vehicle cooperative formation control method comprises the following steps:
step 1: the vehicle running state information is collected through the sensing unit and is transmitted to the central processing unit through the wireless communication device;
step 2: constructing a vehicle undirected network topology model in a central processing unit;
and step 3: constructing a total communication cost according to a communication cost set in the vehicle undirected network topology model, optimizing by using a prim algorithm by taking the minimum total communication cost as an optimization target of the undirected network topology model to obtain an optimized undirected network topology model;
and 4, step 4: establishing a vehicle longitudinal dynamic model according to the collected vehicle running state information; establishing a vehicle formation safe distance model according to the collected position and speed information;
and 5: constructing a distributed controller of vehicles in a vehicle formation, solving the expected acceleration of the vehicles, and transmitting the expected acceleration information to a vehicle execution unit for control;
preferably, in step 1, the vehicle driving state information is:
Figure BDA0003003079410000031
wherein, the dataiAs the traveling state information of the i-th vehicle, Si,jThe distance information of the ith vehicle and the jth vehicle,
Figure BDA0003003079410000032
information on the speed of the i-th vehicle,
Figure BDA0003003079410000033
as acceleration information of the i-th vehicle, betaiIs wind speed information, εiThe vehicle node set comprises vehicle node information, wherein N is 1024 and represents the number of vehicle nodes in the vehicle node set;
the finished automobile parameter information is as follows:
εi＝{m，Fxf，Fxr，Rxf，Ap，f(ωe,θ)，τe，s}
where m is the mass of the vehicle, FxfLongitudinal tire force of front tire, FxrIs the longitudinal tire force at the rear tire, RxfIs the rolling resistance, R, of the front tirexrIs the rolling resistance of the rear tire, ApIs the forward area of the vehicle, i.e. the projected area of the vehicle in the direction of travel, f (ω)eθ) is a steady state torque characteristic function of the engine, τeIs the engine time constant; s is a complex frequency domain quantity; the vehicle state parameter data can be directly obtained by a manufacturer.
Preferably, the step 2 of defining the topological model of the undirected vehicle network is as follows:
Figure BDA0003003079410000034
V＝{vi}，0≤i≤N
E＝{ei,j}，0≤i≠j≤N
W＝{wi,j}，0≤i≠j≤N
wherein V represents a vehicle node set in the vehicle undirected network topology model, ViRepresenting the ith vehicle node in the vehicle node set, wherein N is 1024 representing the number of the vehicle nodes in the vehicle node set;
whereinE represents the set of edges in the undirected network topology model of the vehicle, Ei,jRepresenting whether communication exists between the ith vehicle node and the jth vehicle node in the vehicle node set;
if communication exists between the ith vehicle node and the jth vehicle node in the vehicle node set, ei,j1, otherwise ei,j＝0；
Wherein W represents a communication cost set in a vehicle undirected network topology model, and Wi,jRepresenting the communication cost between the ith vehicle node and the jth vehicle node in the vehicle node set;
preferably, the step 3 constructs a total communication cost according to the communication cost set in the vehicle undirected network topology model, wherein the total communication cost is as follows:
Figure BDA0003003079410000041
will communicate the cost wi,jEquivalent to the distance between the ith vehicle node and the jth vehicle node in the vehicle formation, namely:
wi,j＝Si,j，0≤i≠j≤N
in the formula wi,jRepresenting the communication cost between the ith vehicle node and the jth vehicle node in the vehicle node set; si,jRepresenting the distance between the ith vehicle node and the jth vehicle node in the vehicle node set; cost represents the total communication cost of the vehicle queue; e.g. of the typei,jRepresenting the communication situation between the ith vehicle node and the jth vehicle node in the vehicle formation, and if the ith vehicle node and the jth vehicle node in the vehicle formation can be connected by bidirectional information, at the moment ei,j1 otherwise ei,j0, N1024 represents the number of vehicle nodes in the set of vehicle nodes.
Step 3, optimizing by using a prim algorithm to obtain an optimized undirected network topology model:
step 3.1: initial order V ═ ViI is more than or equal to 0 and less than or equal to N is a node set in the vehicle formation,
Figure BDA0003003079410000042
step 3.2: selecting one vehicle node V from V optionally1And v is1Adding into
Figure BDA0003003079410000043
In the set V and
Figure BDA0003003079410000044
among the combinable sides, the side e with the smallest cost W is selected1,2E is to be1,2Adding E, v2Adding into
Figure BDA0003003079410000045
Step 3.3: repeating the steps until all the nodes in the initial set V are added
Figure BDA0003003079410000046
And E is finished when n-1 side exists in E.
Thereby obtaining an optimized undirected network topology model T ═ V*,E*,W*}。
Preferably, the vehicle longitudinal dynamic model in step 4 is:
Figure BDA0003003079410000047
wherein m is the mass of the vehicle;
Figure BDA0003003079410000048
is the vehicle acceleration; fxfIs the longitudinal tire force of the front tire; fxrIs the longitudinal tire force at the rear tire; faeroIs the equivalent longitudinal aerodynamic drag; rxfIs the rolling resistance of the front tire; rxrIs the rolling resistance of the rear tire; ρ is the mass density of air; cdIs the aerodynamic drag coefficient; a. thepIs the forward area of the vehicle, i.e. the projected area of the vehicle in the direction of travel;
Figure BDA0003003079410000051
is the vehicle longitudinal speed; beta is the wind speed; t isesOutputting torque for the engine at steady state; t iseIs a dynamic output torque; f (omega)eθ) is a steady state torque characteristic function of the engine; tau iseIs the engine time constant; s is a complex frequency domain quantity.
The vehicle safe distance model in the step 4 is as follows:
in vehicle formation, the control targets of individual vehicles are such that the following vehicle is required to keep the speed of the preceding vehicle consistent, and the distance between adjacent vehicles is kept at a desired distance, i.e., the distance between adjacent vehicles
Figure BDA0003003079410000052
Wherein subscript i represents the ith vehicle in the vehicle formation, subscript i-1 represents the ith-1 vehicle in the vehicle formation, subscript 0 represents the lead vehicle in the vehicle formation, and subscript j represents the jth vehicle in the vehicle formation;
Figure BDA0003003079410000053
represents the speed of vehicle i at time t;
Figure BDA0003003079410000054
represents the speed of the vehicle 0 at time t; x is the number ofi-1(t) represents the position of vehicle i-1 at time t; x is the number ofi(t) represents the position of the vehicle i at time t; ddesRepresents a desired safe distance between vehicles i and j; t is ti,jThe following vehicle distance between the vehicles i and j; di,jThe safe inter-vehicle distance between the vehicles i and j at rest, and N represents the number of vehicle nodes in the vehicle node set.
Preferably, the distributed controller in step 5 means that the local controller uses the position and state information of the vehicles in the neighborhood of the local controller at the vehicle i to control the node so as to achieve the expected target of the queue;
the controller is designed as follows:
Figure BDA0003003079410000055
in the formula: the subscript i represents the ith vehicle in the vehicle formation, and the subscript j represents the jth vehicle in the vehicle formation; gamma rayjAs a gain factor, γ is the closer the vehicle j is to the vehicle ijThe larger the size, the smaller the size otherwise;
Figure BDA0003003079410000056
acceleration of vehicle i at time t; x is the number ofj(t) is the initial position of vehicle j at time t; x is the number ofi(t) is the initial position of vehicle i at time t; t is ti,jThe time interval between vehicles i and j; l is the length of the vehicle body;
Figure BDA0003003079410000057
the speed of the rear vehicle at the time t;
Figure BDA0003003079410000058
the speed of the front vehicle at the time t is obtained; k is a radical of1、k2For different scaling factors, N represents the number of vehicle nodes in the set of vehicle nodes.
The invention has the advantages that a undirected network topology model considering communication cost is established, so that the communication cost is minimum while the vehicles in the vehicle formation can sense the state information of the surrounding vehicles in real time; and (3) establishing a vehicle dynamics model, a safe distance model and a distributed controller model to process and utilize information obtained by the vehicle, so that the vehicle cooperative formation control method is more in line with actual requirements.
Drawings
FIG. 1: the vehicle has no communication mode to the network topology structure.
FIG. 2: and optimizing the communication mode of the undirected network topology structure of the vehicle.
FIG. 3: a method flow diagram.
FIG. 4: a system flow diagram.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following describes an embodiment of the present invention with reference to fig. 1 to 4.
The system of the invention needs to be installed in all vehicles in a vehicle formation, can send corresponding driving instructions to specific vehicles in a point-to-point mode, and can also send corresponding driving instructions to each vehicle in the vehicle formation in a broadcast mode, and the system comprises the following steps: the system comprises a plurality of sensing units, a plurality of wireless communication devices and a central processing unit;
the sensing unit is composed of a laser radar sensor, a speed sensor, an acceleration sensor, a wind speed sensor and a data processor;
the data processor is respectively connected with the laser radar sensor, the speed sensor, the acceleration sensor, the wind speed sensor and the wireless communication device in sequence in a wired mode; the wireless communication device is connected with the central processing unit in a wireless mode;
the laser radar sensor is mounted on the vehicle head, the speed sensor is mounted in a wheel of a vehicle, the wind speed sensor is mounted on the vehicle head, and the wireless communication device is mounted on the vehicle roof;
the laser radar sensor is selected to be RS-LiDAR-32A, the distance between the laser radar sensor and a front vehicle is detected through the laser radar, and the collected distance between the vehicles is transmitted to the data processor;
the speed sensor is selected as SC461, the vehicle speed is obtained by detecting the rotating speed of the tire, and the acquired vehicle speed is transmitted to the data processor;
the acceleration sensor is selected to be G251, the acceleration of the vehicle is collected, and the collected acceleration of the vehicle is transmitted to the data processor;
the model of the wind speed sensor is RS-485, the wind speed is collected, and the collected wind speed of the vehicle is transmitted to the data processor;
the data processor is selected to be a high-pass Cellcon 820A and transmits the vehicle running state data to the central processing unit through the wireless communication device; the vehicle running state data includes: inter-vehicle distance, vehicle speed, vehicle acceleration, vehicle wind speed;
the invention provides a vehicle formation control method based on a directionless network topology structure, which specifically comprises the following steps:
step 1: the vehicle running state information is collected through the sensing unit and is transmitted to the central processing unit through the wireless communication device;
step 1, the vehicle running state information is as follows:
Figure BDA0003003079410000071
wherein, the dataiAs the traveling state information of the i-th vehicle, Si,jThe distance information of the ith vehicle and the jth vehicle,
Figure BDA0003003079410000072
information on the speed of the i-th vehicle,
Figure BDA0003003079410000073
as acceleration information of the i-th vehicle, betaiIs wind speed information, εiThe vehicle parameter information of the ith vehicle is obtained;
the finished automobile parameter information is as follows:
εi＝{m，Fxf，Fxr，Rxf，Ap，f(ωe,θ)，τe，s}
where m is the mass of the vehicle, FxfLongitudinal tire force of front tire, FxrIs the longitudinal tire force at the rear tire, RxfIs the rolling resistance, R, of the front tirexrIs the rolling resistance of the rear tire, ApIs the forward area of the vehicle, i.e. the projected area of the vehicle in the direction of travel，f(ωeθ) is a steady state torque characteristic function of the engine, τeIs the engine time constant; s is a complex frequency domain quantity; the vehicle state parameter data can be directly obtained by a manufacturer.
Step 2: constructing a vehicle undirected network topology model in a central processing unit;
step 2, defining a vehicle undirected network topology model as follows:
Figure BDA0003003079410000074
V＝{vi}，0≤i≤N
E＝{ei,j}，0≤i≠j≤N
W＝{wi,j}，0≤i≠j≤N
wherein V represents a vehicle node set in the vehicle undirected network topology model, ViRepresenting the ith vehicle node in the vehicle node set, wherein N is 1024 representing the number of the vehicle nodes in the vehicle node set;
wherein E represents the set of edges in the vehicle undirected network topology model, Ei,jRepresenting whether communication exists between the ith vehicle node and the jth vehicle node in the vehicle node set;
if communication exists between the ith vehicle node and the jth vehicle node in the vehicle node set, ei,j1, otherwise ei,j＝0；
Wherein W represents a communication cost set in a vehicle undirected network topology model, and Wi,jRepresenting the communication cost between the ith vehicle node and the jth vehicle node in the vehicle node set;
and step 3: constructing a total communication cost according to a communication cost set in the vehicle undirected network topology model, optimizing by using a prim algorithm by taking the minimum total communication cost as an optimization target of the undirected network topology model to obtain an optimized undirected network topology model;
in step 3, the total communication cost is constructed according to the communication cost set in the vehicle undirected network topology model:
Figure BDA0003003079410000081
will communicate the cost wi,jEquivalent to the distance between the ith vehicle node and the jth vehicle node in the vehicle formation, namely:
wi,j＝Si,j，0≤i≠j≤N
in the formula wi,jRepresenting the communication cost between the ith vehicle node and the jth vehicle node in the vehicle node set; si,jRepresenting the distance between the ith vehicle node and the jth vehicle node in the vehicle node set; cost represents the total communication cost of the vehicle queue; e.g. of the typei,jRepresenting the communication situation between the ith vehicle node and the jth vehicle node in the vehicle formation, and if the ith vehicle node and the jth vehicle node in the vehicle formation can be connected by bidirectional information, at the moment ei,j1 otherwise ei,j0, N1024 represents the number of vehicle nodes in the set of vehicle nodes.
Step 3, optimizing by using a prim algorithm to obtain an optimized undirected network topology model:
step 3.1: initial order V ═ ViI is more than or equal to 0 and less than or equal to N is a node set in the vehicle formation,
Figure BDA0003003079410000082
step 3.2: selecting one vehicle node V from V optionally1And v is1Adding into
Figure BDA0003003079410000083
In the set V and
Figure BDA0003003079410000084
among the combinable sides, the side e with the smallest cost W is selected1,2E is to be1,2Adding E, v2Adding into
Figure BDA0003003079410000085
Step 3.3: repeating the steps until all the nodes in the initial set V are added
Figure BDA0003003079410000086
And E is finished when n-1 side exists in E.
Thereby obtaining an optimized undirected network topology model T ═ V*,E*,W*}。
And 4, step 4: establishing a vehicle longitudinal dynamic model according to the collected vehicle running state information; establishing a vehicle formation safe distance model according to the collected position and speed information;
the vehicle longitudinal dynamic model in the step 4 is as follows:
Figure BDA0003003079410000091
wherein m is the mass of the vehicle;
Figure BDA0003003079410000092
is the vehicle acceleration; fxfIs the longitudinal tire force of the front tire; fxrIs the longitudinal tire force at the rear tire; faeroIs the equivalent longitudinal aerodynamic drag; rxfIs the rolling resistance of the front tire; rxrIs the rolling resistance of the rear tire; ρ is the mass density of air; cdIs the aerodynamic drag coefficient; a. thepIs the forward area of the vehicle, i.e. the projected area of the vehicle in the direction of travel;
Figure BDA0003003079410000093
is the vehicle longitudinal speed; beta is the wind speed; t isesOutputting torque for the engine at steady state; t iseIs a dynamic output torque; f (omega)eθ) is a steady state torque characteristic function of the engine; tau iseIs the engine time constant; s is a complex frequency domain quantity.
The vehicle safe distance model in the step 4 is as follows:
in vehicle formation, the control targets of individual vehicles are such that the following vehicle is required to keep the speed of the preceding vehicle consistent, and the distance between adjacent vehicles is kept at a desired distance, i.e., the distance between adjacent vehicles
Figure BDA0003003079410000094
Wherein subscript i represents the ith vehicle in the vehicle formation, subscript i-1 represents the ith-1 vehicle in the vehicle formation, subscript 0 represents the lead vehicle in the vehicle formation, and subscript j represents the jth vehicle in the vehicle formation;
Figure BDA0003003079410000095
represents the speed of vehicle i at time t;
Figure BDA0003003079410000096
represents the speed of the vehicle 0 at time t; x is the number ofi-1(t) represents the position of vehicle i-1 at time t; x is the number ofi(t) represents the position of the vehicle i at time t; ddesRepresents a desired safe distance between vehicles i and j; t is ti,jThe following vehicle distance between the vehicles i and j; di,jFor the safe inter-vehicle distance between the vehicles i and j at rest, N1024 represents the number of vehicle nodes in the vehicle node set.
And 5: constructing a distributed controller of vehicles in a vehicle formation, solving the expected acceleration of the vehicles, and transmitting the expected acceleration information to a vehicle execution unit for control;
in the step 5, the distributed controller refers to a local controller at a vehicle i, and the local controller uses the position and state information of the vehicles in the neighborhood to control the node so as to achieve the expected target of the queue;
the controller is designed as follows:
Figure BDA0003003079410000101
in the formula: the subscript i represents the ith vehicle in the vehicle formation, and the subscript j represents the jth vehicle in the vehicle formation; gamma rayjAs a gain factor, γ is the closer the vehicle j is to the vehicle ijThe larger the size, the smaller the size otherwise;
Figure BDA0003003079410000102
acceleration of vehicle i at time t; x is the number ofj(t) is the initial position of vehicle j at time t; x is the number ofi(t) is the initial position of vehicle i at time t; t is ti,jThe time interval between vehicles i and j; l is the length of the vehicle body;
Figure BDA0003003079410000103
the speed of the rear vehicle at the time t;
Figure BDA0003003079410000104
the speed of the front vehicle at the time t is obtained; k is a radical of1、k2For different scaling factors, N-1024 represents the number of vehicle nodes in the vehicle node set.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (6)

1. A vehicle cooperative formation control method based on a undirected network system is characterized by comprising the following steps:
the undirected network system comprises: the system comprises a plurality of sensing units, a plurality of wireless communication devices and a central processing unit;
the sensing unit is composed of a laser radar sensor, a speed sensor, an acceleration sensor, a wind speed sensor and a data processor;
the data processor is respectively connected with the laser radar sensor, the speed sensor, the acceleration sensor, the wind speed sensor and the wireless communication device in sequence in a wired mode; the wireless communication device is connected with the central processing unit in a wireless mode;
the laser radar sensor is mounted on the vehicle head, the speed sensor is mounted in a wheel of a vehicle, the wind speed sensor is mounted on the vehicle head, and the wireless communication device is mounted on the vehicle roof;
the laser radar sensor detects the distance between the laser radar sensor and the front vehicle and transmits the acquired distance between the vehicles to the data processor;
the speed sensor obtains the vehicle speed by detecting the rotating speed of the tire, and transmits the acquired vehicle speed to the data processor;
the acceleration sensor collects vehicle acceleration and transmits the collected vehicle acceleration to the data processor;
the wind speed sensor collects wind speed and transmits the collected wind speed of the vehicle to the data processor;
the data processor transmits vehicle driving state data to the central processing unit through the wireless communication device; the vehicle running state data includes: inter-vehicle distance, vehicle speed, vehicle acceleration, vehicle wind speed;
the vehicle cooperative formation control method comprises the following steps:
step 1: the vehicle running state information is collected through the sensing unit and is transmitted to the central processing unit through the wireless communication device;
step 2: constructing a vehicle undirected network topology model in a central processing unit;
and step 3: constructing a total communication cost according to a communication cost set in the vehicle undirected network topology model, optimizing by using a prim algorithm by taking the minimum total communication cost as an optimization target of the undirected network topology model to obtain an optimized undirected network topology model;
and 4, step 4: establishing a vehicle longitudinal dynamic model according to the collected vehicle running state information; establishing a vehicle formation safe distance model according to the collected position and speed information;
and 5: and constructing a distributed controller of the vehicles in the vehicle formation, solving the expected acceleration of the vehicles, and transmitting the expected acceleration information to a vehicle execution unit for control.
2. The undirected network system-based vehicle collaborative formation control method according to claim 1,
step 1, the vehicle running state information is as follows:
Figure FDA0003506174340000021
wherein, the dataiAs the traveling state information of the i-th vehicle, Si，jThe distance information of the ith vehicle and the jth vehicle,
Figure FDA0003506174340000022
information on the speed of the i-th vehicle,
Figure FDA0003506174340000023
as acceleration information of the i-th vehicle, betaiIs wind speed information, εiThe vehicle node set comprises vehicle node information, wherein N is 1024 and represents the number of vehicle nodes in the vehicle node set;
the finished automobile parameter information is as follows:
εi＝{m，Fxf，Fxr，Rxf，Ap，f(ωe，θ)，τe，s}
where m is the mass of the vehicle, FxfLongitudinal tire force of front tire, FxrIs the longitudinal tire force at the rear tire, RxfIs the rolling resistance, R, of the front tirexrIs the rolling resistance of the rear tire, ApIs the forward area of the vehicle, i.e. the projected area of the vehicle in the direction of travel, f (ω)eθ) is a steady state torque characteristic function of the engine, τeIs the engine time constant; s is a complex frequency domain quantity; the vehicle state parameter data can be directly obtained by a manufacturer.
3. The undirected network system-based vehicle collaborative formation control method according to claim 1,
step 2, the step of constructing the vehicle undirected network topology model comprises the following steps:
Figure FDA0003506174340000024
V＝{vi}，0≤i≤N
E＝{ei，j}，0≤i≠j≤N
W＝{wi，j}，0≤i≠j≤N
wherein V represents a vehicle node set in the vehicle undirected network topology model, ViRepresenting the ith vehicle node in the vehicle node set, wherein N is 1024 representing the number of the vehicle nodes in the vehicle node set;
wherein E represents the set of edges in the vehicle undirected network topology model, Ei，jRepresenting whether communication exists between the ith vehicle node and the jth vehicle node in the vehicle node set;
if communication exists between the ith vehicle node and the jth vehicle node in the vehicle node set, ei，j1, otherwise ei，j＝0；
Wherein W represents a communication cost set in a vehicle undirected network topology model, and Wi，jAnd representing the communication cost between the ith vehicle node and the jth vehicle node in the vehicle node set.
4. The undirected network system-based vehicle collaborative formation control method according to claim 1,
in step 3, the total communication cost is constructed according to the communication cost set in the vehicle undirected network topology model:
Figure FDA0003506174340000031
will communicate the cost wi，jEquivalent to the distance between the ith vehicle node and the jth vehicle node in the vehicle formation, namely:
wi，j＝Si，j，0≤i≠j≤N
in the formula wi，jRepresenting the communication cost between the ith vehicle node and the jth vehicle node in the vehicle node set; si，jRepresenting the distance between the ith vehicle node and the jth vehicle node in the vehicle node set; cost represents the total communication cost of the vehicle queue; e.g. of the typei，jRepresenting the communication situation between the ith vehicle node and the jth vehicle node in the vehicle formation, and if the ith vehicle node and the jth vehicle node in the vehicle formation can be connected by bidirectional information, at the moment ei，j1 otherwise ei，j0, N1024 represents the number of vehicle nodes in the vehicle node set;
step 3, optimizing by using a prim algorithm to obtain an optimized undirected network topology model:
step 3.1: initial order V ═ ViI is more than or equal to 0 and less than or equal to N is a node set in the vehicle formation,
Figure FDA0003506174340000034
step 3.2: selecting one vehicle node V from V optionally1And v is1Adding into
Figure FDA0003506174340000035
In the set V and
Figure FDA0003506174340000036
among the combinable sides, the side e with the smallest cost W is selected1，2E is to be1，2Adding E, v2Adding into
Figure FDA0003506174340000037
Step 3.3: repeating the steps until all the nodes in the initial set V are added
Figure FDA0003506174340000038
When n-1 edges exist in the E, ending;
thereby obtaining an optimized undirected networkTopology model T ═ { V ═ V*，E*，W*}。
5. The undirected network system-based vehicle collaborative formation control method according to claim 1,
the vehicle longitudinal dynamic model in the step 4 is as follows:
Figure FDA0003506174340000032
wherein m is the mass of the vehicle;
Figure FDA0003506174340000033
is the vehicle acceleration; fxfIs the longitudinal tire force of the front tire; fxrIs the longitudinal tire force at the rear tire; faeroIs the equivalent longitudinal aerodynamic drag; rxfIs the rolling resistance of the front tire; rxrIs the rolling resistance of the rear tire; ρ is the mass density of air; cdIs the aerodynamic drag coefficient; a. thepIs the forward area of the vehicle, i.e. the projected area of the vehicle in the direction of travel;
Figure FDA0003506174340000041
is the vehicle longitudinal speed; beta is the wind speed; t isesOutputting torque for the engine at steady state; t iseIs a dynamic output torque; f (omega)eθ) is a steady state torque characteristic function of the engine; tau iseIs the engine time constant; s is a complex frequency domain quantity;
the safe distance model for vehicle formation in the step 4 is as follows:
in vehicle formation, the control targets of individual vehicles are such that the following vehicle is required to keep the speed of the preceding vehicle consistent, and the distance between adjacent vehicles is kept at a desired distance, i.e., the distance between adjacent vehicles
Figure FDA0003506174340000042
Wherein subscript i represents the ith vehicle in the vehicle formation, subscript i-1 represents the ith-1 vehicle in the vehicle formation, subscript 0 represents the lead vehicle in the vehicle formation, and subscript j represents the jth vehicle in the vehicle formation;
Figure FDA0003506174340000043
represents the speed of vehicle i at time t;
Figure FDA0003506174340000044
represents the speed of the vehicle 0 at time t; x is the number ofi-1(t) represents the position of vehicle i-1 at time t; x is the number ofi(t) represents the position of the vehicle i at time t; ddesRepresents a desired safe distance between vehicles i and j; t is ti，jThe following vehicle distance between the vehicles i and j; di，jThe safe inter-vehicle distance between the vehicles i and j at rest, and N represents the number of vehicle nodes in the vehicle node set.
6. The undirected network system-based vehicle collaborative formation control method according to claim 1,
in the step 5, the distributed controller refers to a local controller at a vehicle i, and the local controller uses the position and state information of the vehicles in the neighborhood to control the nodes so as to achieve the expected target of the queue;
the controller is designed as follows:
Figure FDA0003506174340000045
0≤i≠j≤N
in the formula: the subscript i represents the ith vehicle in the vehicle formation, and the subscript j represents the jth vehicle in the vehicle formation; gamma rayjAs a gain factor, γ is the closer the vehicle j is to the vehicle ijThe larger the size, the smaller the size otherwise;
Figure FDA0003506174340000046
acceleration of vehicle i at time t; x is the number ofj(t) is the initial position of vehicle j at time t; x is the number ofi(t) is the initial position of vehicle i at time t; t is ti，jThe time interval between vehicles i and j; l is the length of the vehicle body;
Figure FDA0003506174340000047
the speed of the rear vehicle at the time t;
Figure FDA0003506174340000048
the speed of the front vehicle at the time t is obtained; k is a radical of1、k2For different scaling factors, N represents the number of vehicle nodes in the set of vehicle nodes.
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