CN114212083B - Online optimal scheduling network-connected vehicle multi-target self-adaptive cruise control method - Google Patents

Online optimal scheduling network-connected vehicle multi-target self-adaptive cruise control method Download PDF

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CN114212083B
CN114212083B CN202210054097.9A CN202210054097A CN114212083B CN 114212083 B CN114212083 B CN 114212083B CN 202210054097 A CN202210054097 A CN 202210054097A CN 114212083 B CN114212083 B CN 114212083B
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vehicle
matrix
target
control
state
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CN114212083A (en
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宋秀兰
柴伟豪
王轲
董汉聪
何德峰
卢为党
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Zhejiang University of Technology ZJUT
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Zhejiang University of Technology ZJUT
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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
    • 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
    • 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
    • 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
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The invention discloses an online optimal dispatching network-connected vehicle multi-target self-adaptive cruise control method, which comprises the steps of obtaining vehicle running information between a target vehicle and a front vehicle, and representing each control target based on the vehicle running information; generating comprehensive tracking signals of the target vehicle based on each control target, and representing a state vector according to signal errors of the comprehensive tracking signals; generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on the cost function, and solving the state feedback control law based on the optimization problem equation to minimize signal errors; and calculating control input corresponding to the solved state feedback control law, and driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period. The invention solves the problem that a plurality of control targets in the adaptive cruise control of the networked vehicle have contradiction, and ensures the safety and the passenger comfort of the vehicle while reducing the following distance of the vehicle.

Description

Online optimal scheduling network-connected vehicle multi-target self-adaptive cruise control method
Technical Field
The application relates to the technical field of adaptive cruise control, in particular to an online optimal scheduling network-connected vehicle multi-target adaptive cruise control method.
Background
The network-connected vehicle self-adaptive cruise control is a control method for acquiring vehicle running information (position, speed, acceleration and the like) of a front vehicle and a vehicle through a wireless communication network (special short-range communication, cellular vehicle networking and the like) and a vehicle-mounted sensor (radar, laser radar, ultrasonic sensor and the like), then establishing a vehicle dynamics state space model, inputting the acquired front vehicle and vehicle running information into the model, calculating proper throttle opening or brake control quantity, regulating the vehicle distance between the vehicles, controlling the speed and the acceleration of the vehicle and realizing the automatic driving of the vehicle. When a networked vehicle travels along a road following a preceding vehicle, in order to ensure the following target of the vehicle, the speed of the vehicle needs to be kept consistent with the preceding vehicle, and the inter-vehicle distance is kept at a small distance so as to follow the preceding vehicle for cruising, but this makes it impossible to ensure passenger comfort and vehicle safety. For example, when the state of the front vehicle is frequently changed, in order to ensure the following performance of the vehicle, the vehicle is also frequently accelerated or braked, so that the comfort of passengers is further affected. In addition, the internet-connected vehicle can keep a small distance from the front vehicle in order to ensure the following performance target, which can cause the vehicle to have a rear-end collision accident under the emergency braking condition.
For the above reasons, the current internet-connected vehicle multi-target adaptive cruise control process needs to consider three control targets of vehicle following performance, vehicle safety and passenger comfort. However, the existing network-connected vehicle multi-target adaptive cruise control method has the problem that the conflict among a plurality of targets cannot be ensured at the same time, and most of the network-connected vehicle multi-target adaptive cruise control method uses a model prediction method, but the model prediction method is difficult to realize the steady operation of all road conditions of the network-connected vehicle adaptive cruise.
Disclosure of Invention
In order to solve the problems, the embodiment of the application provides an online optimal scheduling network-connected vehicle multi-target self-adaptive cruise control method.
In a first aspect, an embodiment of the present application provides an online optimized dispatching internet-connected vehicle multi-target adaptive cruise control method, where the method includes:
acquiring vehicle running information between a target vehicle and a front vehicle, and representing each control target of the network-connected vehicle self-adaptive cruise control system based on the vehicle running information;
generating a comprehensive tracking signal of the target vehicle based on each control target, and representing a state vector according to a signal error of the comprehensive tracking signal;
generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation to minimize the signal error;
And calculating control input corresponding to the solved state feedback control law, and driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period.
Preferably, the obtaining vehicle running information between the target vehicle and the preceding vehicle, and characterizing each control target of the network-connected vehicle adaptive cruise control system based on the vehicle running information, includes:
acquiring vehicle running information between a target vehicle and a front vehicle, and establishing a network-connected vehicle kinematics equation based on the vehicle running information to represent the relative distance between the target vehicle and the front vehicle, the relative speed between the target vehicle and the front vehicle and the acceleration of the target vehicle;
and characterizing each control target of the networked vehicle adaptive cruise control system based on the relative distance, the relative speed and the acceleration, wherein the control targets comprise passenger comfort, vehicle safety and vehicle following performance.
Preferably, the generating a comprehensive tracking signal of the target vehicle based on each control target, and characterizing a state vector according to a signal error of the comprehensive tracking signal, includes:
setting adjustable real weight parameters, respectively distributing the adjustable real weight parameters to the control targets, and generating comprehensive tracking signals of the target vehicle based on the control targets and the adjustable real weight parameters;
And characterizing a state vector according to the signal error of the comprehensive tracking signal.
Preferably, the characterizing a state vector according to the signal error of the integrated tracking signal includes:
constructing a first transfer matrix according to the relative distance, the relative speed and the acceleration, and generating a discrete state space model of the networked vehicle self-adaptive cruise control system based on the first transfer matrix;
and constructing a third transpose matrix based on the signal error of the integrated tracking signal and a second transpose matrix, and converting the discrete state space model into a discrete augmented state space model according to the third transpose matrix, wherein the third transpose matrix is a state vector of the discrete augmented state space model, and the second transpose matrix is a transpose matrix of the first transpose matrix.
Preferably, the generating a state feedback control law according to the state vector includes:
defining a first solving matrix and a second solving matrix, constructing a state feedback control matrix according to the first solving matrix and the second solving matrix, and generating a state feedback control law based on the state feedback control matrix and the state vector.
Preferably, the solving the state feedback control law based on the optimization problem equation to minimize the signal error includes:
Initializing each adjustable real weight parameter, and calculating an initial state vector;
solving the first solving matrix and the second solving matrix based on the optimization problem equation and preset inequality constraint of each linear matrix, and calculating and solving the state feedback control law;
a control input is determined based on the state feedback control law to minimize the signal error.
Preferably, the method further comprises:
when the next control period is entered, the control input is recalculated and the step of driving the tracked preceding vehicle motion state of the target vehicle based on the control input in the current control period is repeated.
In a second aspect, an embodiment of the present application provides an online optimized dispatching internet-connected vehicle multi-target adaptive cruise control device, where the device includes:
the acquisition module is used for acquiring vehicle running information between a target vehicle and a front vehicle and representing each control target of the network-connected vehicle self-adaptive cruise control system based on the vehicle running information;
the generation module is used for generating a comprehensive tracking signal of the target vehicle based on each control target and representing a state vector according to a signal error of the comprehensive tracking signal;
The solving module is used for generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation so as to minimize the signal error;
and the control module is used for calculating control input corresponding to the solved state feedback control law, and driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method as provided in the first aspect or any one of the possible implementations of the first aspect when the computer program is executed.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as provided by the first aspect or any one of the possible implementations of the first aspect.
The beneficial effects of the invention are as follows: 1. the comprehensive tracking signal is designed, the feedback control law is designed to ensure that the error between the state of the vehicle and the tracking signal is minimum, and a plurality of control targets of the vehicle are ensured, so that the problem that a plurality of control targets are contradictory in the adaptive cruise control of the networked vehicle is solved, the following distance of the vehicle is reduced, and meanwhile, the safety and the passenger comfort of the vehicle are ensured.
2. The adjustable parameters of the vehicle multi-target self-adaptive cruise control system are less, the operation is convenient, the calculation is simple, the calculation speed is high, and the instantaneity is good.
3. And the feedback control law is calculated based on the inequality of the linear matrix, so that the calculation speed is high and the instantaneity is good.
4. By means of online calculation, the optimal control law can be calculated according to the real-time state change of the vehicle to adjust the state of the vehicle, and therefore multi-target self-adaptive cruising of the vehicle is better achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an online optimized dispatching network-connected vehicle multi-target adaptive cruise control method according to an embodiment of the present application;
fig. 2 is a schematic diagram of a vehicle-to-vehicle distance change curve between a target vehicle and a preceding vehicle in a vehicle multi-target adaptive cruise control according to an embodiment of the present application;
FIG. 3 is a graph of target vehicle speed variation in a vehicle multi-target adaptive cruise control according to an embodiment of the present application;
FIG. 4 is a graph of target vehicle control input change in a vehicle multi-target adaptive cruise control according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of an online optimized dispatching network-connected vehicle multi-target adaptive cruise control device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In the following description, the terms "first," "second," and "first," are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The following description provides various embodiments of the present application, and various embodiments may be substituted or combined, so that the present application is also intended to encompass all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then the present application should also be considered to include embodiments that include one or more of all other possible combinations including A, B, C, D, although such an embodiment may not be explicitly recited in the following.
The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the application. Various examples may omit, replace, or add various procedures or components as appropriate. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Referring to fig. 1, fig. 1 is a schematic flow chart of an online optimized dispatching network-connected vehicle multi-target adaptive cruise control method according to an embodiment of the present application. In an embodiment of the present application, the method includes:
s101, acquiring vehicle running information between a target vehicle and a front vehicle, and representing each control target of the network-connected vehicle self-adaptive cruise control system based on the vehicle running information.
The execution subject of the application may be a cloud server of an online vehicle adaptive cruise control system.
In the embodiment of the application, in the running process of the target vehicle, the vehicle running information between the target vehicle and the front vehicle is acquired through the vehicle-mounted sensor, and the vehicle running information is uploaded to the cloud server. After the cloud server acquires the vehicle running information, the control targets of the network-connected vehicle self-adaptive cruise control system are respectively represented on the basis of the vehicle running information, so that the control targets can practically represent the accuracy and real-time requirements of the vehicle multi-target self-adaptive cruise control field on the vehicle running information such as vehicle safety distance, relative speed, acceleration control and the like.
In one embodiment, step S101 includes:
acquiring vehicle running information between a target vehicle and a front vehicle, and establishing a network-connected vehicle kinematics equation based on the vehicle running information to represent the relative distance between the target vehicle and the front vehicle, the relative speed between the target vehicle and the front vehicle and the acceleration of the target vehicle;
and characterizing each control target of the networked vehicle adaptive cruise control system based on the relative distance, the relative speed and the acceleration, wherein the control targets comprise passenger comfort, vehicle safety and vehicle following performance.
According to the method and the device for controlling the vehicle, an online vehicle kinematics equation can be established according to vehicle running information, the relative distance between the target vehicle and the front vehicle, the relative speed between the target vehicle and the front vehicle and the acceleration of the target vehicle can be respectively represented through the online vehicle kinematics equation, and then the cloud server can represent each control target based on the data.
Specifically, the first calculation formula of the kinematic equation of the networked vehicle is as follows:
wherein the variable k represents the moment of time,respectively representing the position and the speed of the target vehicle;respectively representing the position and the speed of the front vehicle; / >Respectively representing acceleration and control input of the target vehicle; />Representing a vehicle dynamics constant of the target vehicle; />Representing a sampling period; />The relative distance and relative speed of the target vehicle from the preceding vehicle, respectively.
The second calculation formula for characterizing passenger comfort is:
the third calculation formula for characterizing the safety of the vehicle is:
the fourth calculation formula for characterizing the vehicle following performance is:
wherein,representing a maximum acceleration of the target vehicle; />Representing the acceleration variation amount of the target vehicle; />Representing a minimum safe distance of the vehicle; />Representing the headway of the target vehicle; />Respectively representing an ideal vehicle distance and an ideal speed difference between a target vehicle and a front vehicle; />And representing the error between the ideal vehicle distance and the actual vehicle distance of the target vehicle and the front vehicle.
S102, generating a comprehensive tracking signal of the target vehicle based on each control target, and representing a state vector according to a signal error of the comprehensive tracking signal.
In the embodiment of the application, after each control target is determined by characterization, in order to solve the problem that contradiction exists among a plurality of control targets, the cloud server integrates each control target to generate the comprehensive tracking signal, so that the safety of the vehicle, the comfort of passengers and the like can be ensured while the following distance of the vehicle is reduced in the subsequent process of carrying out specific calculation on the comprehensive tracking signal. Because in the actual situation, an error exists between the ideal comprehensive tracking signal and the actual comprehensive tracking signal, the signal error of the comprehensive tracking signal is determined according to the characterization calculation formula corresponding to the generated comprehensive tracking signal, and the state vector of the vehicle is characterized by the signal error.
In one embodiment, step S102 includes:
setting adjustable real weight parameters, respectively distributing the adjustable real weight parameters to the control targets, and generating comprehensive tracking signals of the target vehicle based on the control targets and the adjustable real weight parameters;
and characterizing a state vector according to the signal error of the comprehensive tracking signal.
In the embodiment of the application, the adjustable real weight parameter is setThe fifth calculation formula corresponding to the designed comprehensive tracking signal of the vehicle is as follows:
wherein,representing the designed integrated tracking signal of the host vehicle, < >>Indicating the acceleration of the front vehicle.
In one embodiment, the characterizing a state vector according to the signal error of the integrated tracking signal includes:
constructing a first transfer matrix according to the relative distance, the relative speed and the acceleration, and generating a discrete state space model of the networked vehicle self-adaptive cruise control system based on the first transfer matrix;
and constructing a third transpose matrix based on the signal error of the integrated tracking signal and a second transpose matrix, and converting the discrete state space model into a discrete augmented state space model according to the third transpose matrix, wherein the third transpose matrix is a state vector of the discrete augmented state space model, and the second transpose matrix is a transpose matrix of the first transpose matrix.
In the embodiment of the application, a first transformation matrix is constructed according to the relative distance, the relative speed and the accelerationAccording to the first transfer matrix and the first calculation formula, a discrete state space model of the adaptive cruise control system of the networked vehicle can be obtained, and a sixth calculation formula corresponding to the discrete state space model is as follows:
wherein,。
the signal error of the integrated tracking signal isThe definition is generated with a third transposed matrix->Further, the discrete state space model is converted into a discrete augmentation state space model, and a seventh corresponding calculation formula is as follows:
wherein,；
。
the state vector can be obtained through the finally obtained discrete augmentation state space modelAnd (5) performing iterative calculation.
S103, generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation so as to minimize the signal error.
In the embodiment of the application, according to the state vector which is characterized by calculation, a state feedback control law can be designed and generated, and an optimization problem equation with the state feedback control law can be generated according to a cost function. In the foregoing process, the calculation data such as the state feedback control law and the state vector are all calculation expressions calculated and characterized by the vehicle running information. After the optimization problem equation is generated, the state feedback control law is actually solved and calculated, so that the signal error of the finally obtained state feedback control law under the current vehicle running information is minimized.
In one embodiment, the generating a state feedback control law according to the state vector includes:
defining a first solving matrix and a second solving matrix, constructing a state feedback control matrix according to the first solving matrix and the second solving matrix, and generating a state feedback control law based on the state feedback control matrix and the state vector.
In the embodiment of the application, a first solving matrix is definedAnd a second solving matrix->A state feedback control matrix can be constructed according to the first solving matrix and the second solving matrix>According to the state feedback control matrix and the state vector, the design and generation of the state feedback control law can be realized>。
The cost function is that an eighth calculation formula corresponding to the optimization problem equation is as follows:
wherein,is a system transfer function; />Is given +.>Performance limits;；/>；；/>；/>the real number weight parameters are adjustable, namely tracking error weight and control input weight; j represents a time step predicted backward at the current k moment; l represents the time step from zero to k-1.
In one embodiment, the solving the state feedback control law based on the optimization problem equation to minimize the signal error includes:
Initializing each adjustable real weight parameter, and calculating an initial state vector;
solving the first solving matrix and the second solving matrix based on the optimization problem equation and preset inequality constraint of each linear matrix, and calculating and solving the state feedback control law;
a control input is determined based on the state feedback control law to minimize the signal error.
In the embodiment of the application, the matrix can be initialized by initializing each adjustable real weight parameterFurthermore, the initial state vector +.>. According to the first solving matrix and the second solving matrix, the optimization problem equation can be adjusted to +.>By giving->、/>And->It is possible to constraint the first solution matrix based on the optimization problem equation and the respective linear matrix inequality>And a second solving matrix->Solving to calculate a state feedback control matrix +.>Then calculate the state feedback control law +.>Further, a control input ∈>。
In the present application, three linear matrix inequality constraint formulas are provided, and the corresponding ninth calculation formula, tenth calculation formula and eleventh calculation formula are as follows:
wherein,to relax the variable, take the value of [0,1 ] ]The method comprises the steps of carrying out a first treatment on the surface of the I is an identity matrix.
S104, calculating control input corresponding to the solved state feedback control law, and driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period.
In the embodiment of the application, after solving the state feedback control law, the cloud server can determine the control input corresponding to the state feedback control law, and the control input obtained at the moment is the optimal control input capable of reducing the following distance of the vehicle and guaranteeing the safety of the vehicle and the comfort of passengers under the current vehicle running information. Therefore, the cloud server drives the tracked front vehicle movement state of the target vehicle according to the finally calculated control input in the current control period.
In one embodiment, the method further comprises:
when the next control period is entered, the control input is recalculated and the step of driving the tracked preceding vehicle motion state of the target vehicle based on the control input in the current control period is repeated.
In the embodiment of the application, at the next control period, the real-time vehicle running information of the target vehicle and the preceding vehicle is re-acquired and a new control input is calculated And the multi-target self-adaptive cruising of the network-connected vehicle is realized by repeating the steps.
An actual operation process corresponding to the online optimized dispatching internet-connected vehicle multi-target self-adaptive cruise control method is as follows:
1. input deviceDistance of safety->Dynamics constant of own vehicle->Sampling period->=0.1 s, headway +.>=1s，/>Constraint->=1, relaxation variable->Control input constraint =0.6->Acceleration of the front vehicle 0 to 64 seconds +.>After which->。
2. Calculating a state feedback control matrix of the multi-target adaptive cruise system of the vehicle at the moment k=1 through a written calculation program, and substituting input parameters into a linear matrix inequalityObtaining the compliance
Then calculate the state feedback control matrix。
3. After the state feedback control matrix is obtained, the control input is further calculatedDriving the vehicle to track the motion state of the front vehicle; in the next control cycle, the real-time vehicle driving information of the host vehicle and the preceding vehicle is acquired again and a new control input +.>And the multi-target self-adaptive cruising of the network-connected vehicle is realized by repeating the steps.
The actual control effect is shown in fig. 2, 3 and 4, and fig. 2 is a vehicle distance change curve between the vehicle and the front vehicle in the multi-target adaptive cruise control of the vehicle; FIG. 3 is a graph of the speed change of the vehicle in a multi-target adaptive cruise control of the vehicle; FIG. 4 is a graph of control input variation for a host vehicle in a multi-target adaptive cruise control for the vehicle.
The online optimally scheduled internet-connected vehicle multi-target adaptive cruise control device provided in the embodiment of the present application will be described in detail with reference to fig. 5. It should be noted that, the online optimally scheduled internet-connected vehicle multi-target adaptive cruise control device shown in fig. 5 is used to execute the method of the embodiment shown in fig. 1 of the present application, and for convenience of explanation, only the relevant parts of the embodiment of the present application are shown, and specific technical details are not disclosed, please refer to the embodiment shown in fig. 1 of the present application.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an online optimized dispatching network-connected vehicle multi-target adaptive cruise control device according to an embodiment of the present application. As shown in fig. 5, the apparatus includes:
the acquiring module 501 is configured to acquire vehicle running information between a target vehicle and a preceding vehicle, and characterize each control target of the networked vehicle adaptive cruise control system based on the vehicle running information;
a generating module 502, configured to generate a comprehensive tracking signal of the target vehicle based on each control target, and characterize a state vector according to a signal error of the comprehensive tracking signal;
a solving module 503, configured to generate a state feedback control law according to the state vector, generate an optimization problem equation of the state feedback control law based on a cost function, and solve the state feedback control law based on the optimization problem equation, so as to minimize the signal error;
And the control module 504 is used for calculating control input corresponding to the solved state feedback control law, and driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period.
In one embodiment, the acquisition module 501 includes:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring vehicle running information between a target vehicle and a front vehicle, establishing a network vehicle kinematics equation based on the vehicle running information and representing the relative distance between the target vehicle and the front vehicle, the relative speed between the target vehicle and the front vehicle and the acceleration of the target vehicle;
the first characterization unit is used for characterizing each control target of the network-connected vehicle adaptive cruise control system based on the relative distance, the relative speed and the acceleration, wherein the control targets comprise passenger comfort, vehicle safety and vehicle following performance.
In one implementation, the generating module 502 includes:
the setting unit is used for setting adjustable real weight parameters, and generating comprehensive tracking signals of the target vehicle based on the control targets and the adjustable real weight parameters after the adjustable real weight parameters are respectively distributed to the control targets;
And the second characterization unit is used for characterizing the state vector according to the signal error of the comprehensive tracking signal.
In one embodiment, the generating module 502 further includes:
the first construction unit is used for constructing a first transfer matrix according to the relative distance, the relative speed and the acceleration, and generating a discrete state space model of the networked vehicle adaptive cruise control system based on the first transfer matrix;
the second construction unit is configured to construct a third transposed matrix based on the signal error of the integrated tracking signal and a second transposed matrix, and convert the discrete state space model into a discrete augmented state space model according to the third transposed matrix, where the third transposed matrix is a state vector of the discrete augmented state space model, and the second transposed matrix is a transposed matrix of the first transposed matrix.
In one embodiment, the solving module 503 includes:
the definition unit is used for defining a first solving matrix and a second solving matrix, constructing a state feedback control matrix according to the first solving matrix and the second solving matrix, and generating a state feedback control law based on the state feedback control matrix and the state vector.
In one embodiment, the solving module 503 further includes:
the first calculating unit is used for initializing each adjustable real weight parameter and calculating an initial state vector;
the second calculation unit is used for solving the first solving matrix and the second solving matrix based on the optimization problem equation and preset inequality constraint of each linear matrix, and calculating and solving the state feedback control law;
and a third calculation unit for determining a control input based on the state feedback control law to minimize the signal error.
In one embodiment, the apparatus further comprises:
and the repeating module is used for recalculating the control input when entering the next control period and repeating the step of driving the target vehicle to track the front vehicle motion state based on the control input in the current control period.
It will be apparent to those skilled in the art that the embodiments of the present application may be implemented in software and/or hardware. "Unit" and "module" in this specification refer to software and/or hardware capable of performing a specific function, either alone or in combination with other components, such as Field programmable gate arrays (Field-Programmable Gate Array, FPGAs), integrated circuits (Integrated Circuit, ICs), etc.
The processing units and/or modules of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
Referring to fig. 6, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the method in the embodiment shown in fig. 1. As shown in fig. 6, the electronic device 600 may include: at least one central processor 601, at least one network interface 604, a user interface 603, a memory 605, at least one communication bus 602.
Wherein the communication bus 602 is used to enable connected communications between these components.
The user interface 603 may include a Display screen (Display), a Camera (Camera), and the optional user interface 603 may further include a standard wired interface, a wireless interface.
The network interface 604 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the central processor 601 may comprise one or more processing cores. The central processor 601 connects various parts within the entire electronic device 600 using various interfaces and lines, performs various functions of the terminal 600 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 605, and invoking data stored in the memory 605. Alternatively, the central processor 601 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The central processor 601 may integrate one or a combination of several of a central processor (Central Processing Unit, CPU), an image central processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It should be understood that the modem may not be integrated into the cpu 601 and may be implemented by a single chip.
The Memory 605 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 605 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 605 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 605 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 605 may also optionally be at least one storage device located remotely from the aforementioned central processor 601. As shown in fig. 6, an operating system, network communication modules, user interface modules, and program instructions may be included in memory 605, which is a type of computer storage medium.
In the electronic device 600 shown in fig. 6, the user interface 603 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the central processor 601 may be used to invoke the online optimally scheduled networked vehicle multi-target adaptive cruise control application stored in the memory 605 and specifically:
Acquiring vehicle running information between a target vehicle and a front vehicle, and representing each control target of the network-connected vehicle self-adaptive cruise control system based on the vehicle running information;
generating a comprehensive tracking signal of the target vehicle based on each control target, and representing a state vector according to a signal error of the comprehensive tracking signal;
generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation to minimize the signal error;
and calculating control input corresponding to the solved state feedback control law, and driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method. The computer readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be performed by hardware associated with a program that is stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.

Claims (5)

1. An online optimally scheduled internet-connected vehicle multi-target self-adaptive cruise control method, which is characterized by comprising the following steps:
Acquiring vehicle running information between a target vehicle and a front vehicle, and representing each control target of the network-connected vehicle self-adaptive cruise control system based on the vehicle running information;
generating a comprehensive tracking signal of the target vehicle based on each control target, and representing a state vector according to a signal error of the comprehensive tracking signal;
generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation to minimize the signal error;
calculating control input corresponding to the solved state feedback control law, and driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period;
the method for obtaining the vehicle running information between the target vehicle and the front vehicle and representing each control target of the network-connected vehicle self-adaptive cruise control system based on the vehicle running information comprises the following steps:
acquiring vehicle running information between a target vehicle and a front vehicle, and establishing a network-connected vehicle kinematics equation based on the vehicle running information to represent the relative distance between the target vehicle and the front vehicle, the relative speed between the target vehicle and the front vehicle and the acceleration of the target vehicle;
Characterizing control targets of the networked vehicle adaptive cruise control system based on the relative distance, relative speed, and acceleration, the control targets including passenger comfort, vehicle safety, and vehicle following;
the generating a comprehensive tracking signal of the target vehicle based on each control target, and characterizing a state vector according to a signal error of the comprehensive tracking signal, includes:
setting adjustable real weight parameters, respectively distributing the adjustable real weight parameters to the control targets, and generating comprehensive tracking signals of the target vehicle based on the control targets and the adjustable real weight parameters;
representing a state vector according to the signal error of the comprehensive tracking signal;
the signal error characterization state vector according to the integrated tracking signal comprises:
constructing a first transfer matrix according to the relative distance, the relative speed and the acceleration, and generating a discrete state space model of the networked vehicle self-adaptive cruise control system based on the first transfer matrix;
constructing a third transpose matrix based on the signal error of the integrated tracking signal and a second transpose matrix, and converting the discrete state space model into a discrete augmented state space model according to the third transpose matrix, wherein the third transpose matrix is a state vector of the discrete augmented state space model, and the second transpose matrix is a transpose matrix of the first transpose matrix;
The generating a state feedback control law according to the state vector comprises the following steps:
defining a first solving matrix and a second solving matrix, constructing a state feedback control matrix according to the first solving matrix and the second solving matrix, and generating a state feedback control law based on the state feedback control matrix and the state vector;
the solving the state feedback control law based on the optimization problem equation to minimize the signal error includes:
initializing each adjustable real weight parameter, and calculating an initial state vector;
solving the first solving matrix and the second solving matrix based on the optimization problem equation and preset inequality constraint of each linear matrix, and calculating and solving the state feedback control law;
a control input is determined based on the state feedback control law to minimize the signal error.
2. The method according to claim 1, wherein the method further comprises:
when the next control period is entered, the control input is recalculated and the step of driving the tracked preceding vehicle motion state of the target vehicle based on the control input in the current control period is repeated.
3. An online optimally scheduled networked vehicle multi-target adaptive cruise control device, the device comprising:
The acquisition module is used for acquiring vehicle running information between a target vehicle and a front vehicle and representing each control target of the network-connected vehicle self-adaptive cruise control system based on the vehicle running information;
the generation module is used for generating a comprehensive tracking signal of the target vehicle based on each control target and representing a state vector according to a signal error of the comprehensive tracking signal;
the solving module is used for generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation so as to minimize the signal error;
the control module is used for calculating control input corresponding to the solved state feedback control law, and driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period;
wherein, the acquisition module includes:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring vehicle running information between a target vehicle and a front vehicle, establishing a network vehicle kinematics equation based on the vehicle running information and representing the relative distance between the target vehicle and the front vehicle, the relative speed between the target vehicle and the front vehicle and the acceleration of the target vehicle;
The first characterization unit is used for characterizing each control target of the networked vehicle adaptive cruise control system based on the relative distance, the relative speed and the acceleration, wherein the control targets comprise passenger comfort, vehicle safety and vehicle following performance;
the generation module comprises:
the setting unit is used for setting adjustable real weight parameters, and generating comprehensive tracking signals of the target vehicle based on the control targets and the adjustable real weight parameters after the adjustable real weight parameters are respectively distributed to the control targets;
the second characterization unit is used for characterizing a state vector according to the signal error of the comprehensive tracking signal;
the generation module further includes:
the first construction unit is used for constructing a first transfer matrix according to the relative distance, the relative speed and the acceleration, and generating a discrete state space model of the networked vehicle adaptive cruise control system based on the first transfer matrix;
a second construction unit, configured to construct a third transposed matrix based on the signal error of the integrated tracking signal and a second transposed matrix, and convert the discrete state space model into a discrete augmented state space model according to the third transposed matrix, where the third transposed matrix is a state vector of the discrete augmented state space model, and the second transposed matrix is a transposed matrix of the first transposed matrix;
The solving module comprises:
the definition unit is used for defining a first solving matrix and a second solving matrix, constructing a state feedback control matrix according to the first solving matrix and the second solving matrix, and generating a state feedback control law based on the state feedback control matrix and the state vector;
the solution module further includes:
the first calculating unit is used for initializing each adjustable real weight parameter and calculating an initial state vector;
the second calculation unit is used for solving the first solving matrix and the second solving matrix based on the optimization problem equation and preset inequality constraint of each linear matrix, and calculating and solving the state feedback control law;
and a third calculation unit for determining a control input based on the state feedback control law to minimize the signal error.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-2 when the computer program is executed.
5. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-2.
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