CN116215585A - Intelligent network-connected bus path tracking game control method and device - Google Patents

Intelligent network-connected bus path tracking game control method and device Download PDF

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CN116215585A
CN116215585A CN202310513369.1A CN202310513369A CN116215585A CN 116215585 A CN116215585 A CN 116215585A CN 202310513369 A CN202310513369 A CN 202310513369A CN 116215585 A CN116215585 A CN 116215585A
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path tracking
vehicle
control
game
intelligent
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CN116215585B (en
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范志先
李亮
陈振国
吴德喜
徐海柱
黄玉鹏
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Tsinghua University
Zhongtong Bus Holding Co Ltd
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Tsinghua University
Zhongtong Bus Holding Co Ltd
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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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • 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
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE 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
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • 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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  • Automation & Control Theory (AREA)
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Abstract

The application discloses an intelligent network-connected bus path tracking game control method and device, wherein the method comprises the following steps: constructing a dynamic two-degree-of-freedom vehicle model of an automobile system according to actual parameters of the intelligent network bus; constructing a road model according to the road information, and constructing a vehicle-road model by combining a vehicle model with two degrees of freedom of dynamics of an automobile system and the road model; based on a quadratic optimal theory, constructing a cost function of intelligent driving domain path tracking control and chassis domain stability control based on a vehicle-road model; based on the cost function, intelligent driving domain path tracking control and chassis domain stability control are combined with the Stannberg closed-loop game, the intelligent driving domain is used as a leader of the game, the chassis domain is used as a follower of the game, and the optimal control strategy is solved. Therefore, the problems that in the related art, due to the fact that understeer or oversteer is generated by single-wheel braking, control accuracy of path tracking is reduced, safety and stability of a vehicle are reduced and the like are solved.

Description

Intelligent network-connected bus path tracking game control method and device
Technical Field
The application relates to the technical field of intelligent driving, in particular to an intelligent network-connected bus path tracking game control method and device.
Background
In the related art, an intelligent driving domain performs real-time planning on a vehicle running track and path tracking control on the planned track, and a chassis domain covers a transmission, running, steering and braking system, for example, an intelligent automobile chassis can comprise a drive-by-wire, a brake-by-wire and a steering-by-wire for controlling transverse and longitudinal movement of the vehicle and a suspension-by-wire, when an intelligent network bus encounters sudden destabilization in the high-speed running process, the chassis domain stability control system is momentarily involved, and the vehicle returns to a stable state through single-wheel braking.
However, in the related art, because the single-wheel braking generates understeer or oversteer, the control accuracy of path tracking is reduced, and a large gap is generated between the vehicle state and the control target, so that the safety and stability of the vehicle are reduced, and the driving requirement of a user cannot be met, so that the problem is to be solved.
Disclosure of Invention
The application provides an intelligent network-connected bus path tracking game control method and device, which are used for solving the problems that in the related art, due to insufficient steering or excessive steering caused by single-wheel braking, the control precision of path tracking is reduced, a large gap is generated between the state of a vehicle and a control target, the safety and stability of the vehicle are reduced, and the driving requirement of a user cannot be met.
An embodiment of a first aspect of the present application provides an intelligent network-connected bus path tracking game control method, including the following steps: constructing a dynamic two-degree-of-freedom vehicle model of an automobile system according to actual parameters of the intelligent network bus; constructing a road model according to road information, and constructing a vehicle-road model by combining the vehicle system dynamics two-degree-of-freedom vehicle model and the road model; based on a quadratic form optimal theory, constructing a cost function of intelligent driving domain path tracking control and chassis domain stability control based on the vehicle-road model; based on the cost function, intelligent driving domain path tracking control and chassis domain stability control are combined with the Stannberg closed-loop game, the intelligent driving domain is used as a leader of the game, the chassis domain is used as a follower of the game, and an optimal control strategy is solved.
Optionally, in an embodiment of the present application, the constructing a dynamic two-degree-of-freedom vehicle model of an automobile system according to actual parameters of an intelligent network bus includes: establishing a two-degree-of-freedom model state equation taking a front wheel of a vehicle as an input object; discretizing the two-degree-of-freedom model state equation to obtain a discrete vehicle dynamics equation.
Optionally, in one embodiment of the present application, the constructing a road model according to road information, and combining the two-degree-of-freedom vehicle model of the dynamics of the automobile system and the road model, includes: and adding the pre-aiming path information of the road information into the discrete vehicle dynamics equation to amplify the steering brake sharing type vehicle dynamics system through a pre-aiming dynamic process, so as to obtain the intelligent network-connected bus multi-target path tracking and amplifying system.
Optionally, in an embodiment of the present application, the intelligent network-connected bus multi-target path tracking and amplifying system is:
Figure SMS_1
，
wherein ,
Figure SMS_4
is a state coefficient matrix>
Figure SMS_7
Marks the parameter related to the front wheel rotation angle with a symbol +.>
Figure SMS_9
Is the current +>
Figure SMS_5
Time of day (I)>
Figure SMS_8
Is the current +>
Figure SMS_10
Time of day (I)>
Figure SMS_12
Subscript for augmenting state equation related parameters, ++>
Figure SMS_2
For vehicle-road state variables, +.>
Figure SMS_6
For control input +.>
Figure SMS_11
Matrix coefficients of>
Figure SMS_13
For control input +.>
Figure SMS_3
Is included in the matrix coefficients of (a).
Optionally, in an embodiment of the present application, the constructing a cost function of the intelligent driving domain path tracking control and the chassis domain stability control based on the quadratic optimal theory includes: and selecting the transverse position deviation and the course angle deviation at the pre-aiming point as weighting items of a steering system, and generating a cost function of the multi-target path tracking control problem by taking the ideal yaw rate of the automobile as the weighting item of braking control.
Optionally, in an embodiment of the present application, the combining intelligent driving domain path tracking control and chassis domain stability control with the stamina berg closed loop game based on the cost function, using the intelligent driving domain as a leader of the game and using the chassis domain as a follower of the game, and solving the optimal control strategy includes: in closed-loop Stank-berg game control, the leader and the follower meet a preset recurrence relation to derive a game control strategy of the intelligent driving domain and the chassis domain based on a Stank-berg feedback non-cooperative game theory, so as to obtain a unique feedback Stank-berg equilibrium solution.
An embodiment of a second aspect of the present application provides an intelligent networked passenger car path tracking game control device, including: the first construction module is used for constructing a dynamic two-degree-of-freedom vehicle model of the automobile system according to actual parameters of the intelligent network bus; the second construction module is used for constructing a road model according to road information and combining the two-degree-of-freedom vehicle model of the dynamics of the automobile system and the road model to construct a vehicle-road model; the construction module is used for constructing a cost function of intelligent driving domain path tracking control and chassis domain stability control based on the vehicle-road model based on a quadratic optimal theory; and the calculation module is used for combining intelligent driving domain path tracking control and chassis domain stability control with the Stannberg closed-loop game based on the cost function, taking the intelligent driving domain as a leader of the game and taking the chassis domain as a follower of the game, and solving an optimal control strategy.
Optionally, in one embodiment of the present application, the first construction module includes: the building unit is used for building a two-degree-of-freedom model state equation taking a front wheel of the vehicle as an input object; and the computing unit is used for discretizing the two-degree-of-freedom model state equation to obtain a discrete vehicle dynamics equation.
Optionally, in one embodiment of the present application, the second construction module includes: and the processing unit is used for adding the pre-aiming path information of the road information into the discrete vehicle dynamics equation so as to amplify the steering brake sharing type vehicle dynamics system through the pre-aiming dynamic process and obtain the intelligent network-connected bus multi-target path tracking and amplifying system.
Optionally, in an embodiment of the present application, the intelligent network-connected bus multi-target path tracking and amplifying system is:
Figure SMS_14
，
wherein ,
Figure SMS_16
is a state coefficient matrix>
Figure SMS_19
Marks the parameter related to the front wheel rotation angle with a symbol +.>
Figure SMS_22
Is the current +>
Figure SMS_18
Time of day (I)>
Figure SMS_21
Is the current +>
Figure SMS_23
Time of day (I)>
Figure SMS_25
Subscript for augmenting state equation related parameters, ++>
Figure SMS_17
For vehicle-road state variables, +.>
Figure SMS_20
For control input +.>
Figure SMS_24
Matrix coefficients of>
Figure SMS_26
For control input +.>
Figure SMS_15
Is included in the matrix coefficients of (a).
Optionally, in one embodiment of the present application, the building block includes: the construction unit is used for selecting the transverse position deviation and the course angle deviation at the pre-aiming point as weighting items of the steering system, taking the ideal yaw rate of the automobile as the weighting items of the braking control and generating a cost function of the multi-target path tracking control problem.
Optionally, in one embodiment of the present application, the computing module includes: and the deriving unit is used for enabling the leader and the follower to meet a preset recurrence relation in closed-loop Stankleber game control so as to derive a game control strategy of the intelligent driving domain and the chassis domain based on a Stankleber feedback non-cooperative game theory and obtain a unique feedback Stankleber equilibrium solution.
An embodiment of a third aspect of the present application provides an electronic device, including: the intelligent network bus path tracking game control method comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the program to realize the intelligent network bus path tracking game control method.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program that when executed by a processor implements an intelligent networked passenger car path tracking game control method as described above.
According to the embodiment of the application, an automobile system dynamics two-degree-of-freedom vehicle model can be constructed according to actual parameters of an intelligent network-connected passenger car, a road model is constructed according to road information, the automobile system dynamics two-degree-of-freedom vehicle model and the road model are combined to construct an automobile-road model, a cost function of intelligent driving domain path tracking control and chassis domain stability control is constructed based on an automobile-road model on the basis of a quadratic optimal theory, so that intelligent driving domain path tracking control and chassis domain stability control are combined with a Stankberg closed-loop game, an intelligent driving domain is used as a leader of the game, a chassis domain is used as a follower of the game, and an optimal control strategy is solved, so that the path tracking control precision is effectively improved, the safety and stability of the automobile are improved, and the driving and riding requirements of users are effectively met. Therefore, the problems that in the related art, due to the fact that understeer or oversteer is generated by single-wheel braking, control accuracy of path tracking is reduced, safety and stability of a vehicle are reduced, and driving requirements of users cannot be met are solved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flowchart of an intelligent networked passenger car path tracking game control method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a dynamic two-degree-of-freedom model of an automotive system according to one embodiment of the present application;
FIG. 3 is a schematic illustration of a Stankberg game control in accordance with one embodiment of the present application;
FIG. 4 is a schematic diagram of a pretightening theory design according to an embodiment of the present application;
FIG. 5 is a schematic illustration of the principle of a Stankberg game in accordance with one embodiment of the present application;
FIG. 6 is a schematic diagram illustrating parameter comparison of different path tracking control methods according to one embodiment of the present application;
FIG. 7 is a schematic structural diagram of an intelligent network-connected bus path tracking game control device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes an intelligent network-connected bus path tracking game control method and device according to the embodiment of the application with reference to the accompanying drawings. Aiming at the problems that in the related technology mentioned in the background technology center, due to insufficient steering or excessive steering caused by single-wheel braking, the control precision of path tracking is reduced, the safety and stability of a vehicle are reduced, and the driving requirement of a user cannot be met, the application provides an intelligent network bus path tracking game control method. Therefore, the problems that in the related art, due to the fact that understeer or oversteer is generated by single-wheel braking, control accuracy of path tracking is reduced, safety and stability of a vehicle are reduced, and driving requirements of users cannot be met are solved.
Specifically, fig. 1 is a schematic flow chart of an intelligent network bus path tracking game control method provided in an embodiment of the present application.
As shown in fig. 1, the intelligent network-connected bus path tracking game control method comprises the following steps:
in step S101, a dynamic two-degree-of-freedom vehicle model of the automobile system is constructed according to actual parameters of the intelligent network bus.
It can be understood that the embodiment of the application can construct a dynamic two-degree-of-freedom vehicle model of the automobile system according to the actual parameters of the intelligent network bus in the following steps, so that the executable of the intelligent network bus path tracking game control is effectively improved.
In one embodiment of the present application, the method for constructing the dynamic two-degree-of-freedom vehicle model of the automobile system according to the actual parameters of the intelligent network bus comprises the following steps: establishing a two-degree-of-freedom model state equation taking a front wheel of a vehicle as an input object; discretizing the two-degree-of-freedom model state equation to obtain a discrete vehicle dynamics equation.
In the actual implementation process, assuming that the tire side force of the vehicle is a linear function of the tire slip angle, the x-axis direction speed is unchanged, the influence of suspension characteristics is ignored, the default vehicle only moves parallel to the ground, no load is transferred, the influence of a steering system is ignored, and the front wheel steering angle is directly taken as an input.
Further, as shown in fig. 2, the embodiment of the present application may establish a two-degree-of-freedom model state equation using a front wheel of a vehicle as an input object, that is:
Figure SMS_27
，
wherein ,
Figure SMS_28
is a two-degree-of-freedom vehicle model state variable matrix, < >>
Figure SMS_29
For the variable matrix of the front wheel steering angle coefficient of the two-degree-of-freedom vehicle model,>
Figure SMS_30
direct yaw moment coefficient variable matrix for two-degree-of-freedom vehicle model>
Figure SMS_31
To distinguish symbols +.>
Figure SMS_32
For time (I)>
Figure SMS_33
Is a continuous system state variable +.>
Figure SMS_34
Is the front wheel corner.
wherein ,
Figure SMS_35
is a continuous system state variable, lateral velocity, yaw rate, lateral displacement, and yaw angle, respectively.
Then, the state equation coefficient matrix is as follows:
Figure SMS_36
，
furthermore, the embodiment of the present application may use the c2d command of MATLAB to discretize the two-degree-of-freedom model state equation in the above step, to obtain a discrete vehicle system, that is:
Figure SMS_37
wherein ,
Figure SMS_39
for the discrete state of the current time step +.>
Figure SMS_41
For the discrete state of the next time step, +.>
Figure SMS_43
、
Figure SMS_40
、/>
Figure SMS_42
Respectively by corresponding continuous time matrix->
Figure SMS_44
、/>
Figure SMS_45
、/>
Figure SMS_38
Is obtained by discrete bilinear transformation.
wherein ,
Figure SMS_46
，
wherein ,
Figure SMS_47
for the time step +.>
Figure SMS_48
Is time of
In step S102, a road model is constructed based on the road information, and a vehicle-road model is constructed in combination with the vehicle model of the two degrees of freedom of dynamics of the vehicle system and the road model.
It can be understood that the road model can be constructed according to the road information in the following steps, and the vehicle-road model is constructed by combining the vehicle model with the two degrees of freedom of the dynamics of the vehicle system and the road model, so that the vehicle is more reasonable in the aspects of path tracking and transverse stability control distribution, and the stability of the vehicle is effectively improved.
Wherein, in one embodiment of the present application, constructing a road model according to road information, and constructing a vehicle-road model in combination with a vehicle model of two degrees of freedom of dynamics of an automobile system and the road model, comprises: and adding the pre-aiming path information of the road information into a discrete vehicle dynamics equation to amplify the steering brake sharing type vehicle dynamics system through a pre-aiming dynamic process, so as to obtain the intelligent network bus multi-target path tracking and amplifying system.
For example, as shown in connection with fig. 3 and 4, fig. 4 is a road pre-aiming model, in which the pre-aiming distance is discretized to be fixed
Figure SMS_49
And a point for providing road information for the next control.
Then, the embodiment of the application can add the pre-aimed path information of the road information into a discrete vehicle dynamics equation, wherein the vehicle
Figure SMS_50
Lateral displacement of individual pretightening- >
Figure SMS_51
By shifting mailingThe memory generation, namely:
Figure SMS_52
wherein ,
Figure SMS_54
is->
Figure SMS_57
、/>
Figure SMS_59
Control input sign->
Figure SMS_55
Defining symbols for overall control objective +.>
Figure SMS_56
Is->
Figure SMS_58
Road information matrix of steps->
Figure SMS_60
For a shift register matrix>
Figure SMS_53
And the road information matrix to be updated at the current moment.
wherein ,
Figure SMS_61
，
wherein ,
Figure SMS_62
for controlling the target matrix->
Figure SMS_63
For the route marking, ++>
Figure SMS_64
For course angle mark, ++>
Figure SMS_65
For the lateral displacement to be a function of,
Figure SMS_66
is course angle, and->
Figure SMS_67
。/>
wherein ,
Figure SMS_68
Figure SMS_69
wherein ,
Figure SMS_70
for the furthest point control target value, +.>
Figure SMS_71
Is->
Figure SMS_72
Time of day (I)>
Figure SMS_73
For pretightening value, ++>
Figure SMS_74
To update the matrix +.>
Figure SMS_75
Is a shift register.
Then, the embodiment of the application can amplify the steering brake sharing type vehicle dynamics system through a pre-aiming dynamic process, so that the intelligent network-connected bus multi-target path tracking and amplifying system can be obtained, namely:
Figure SMS_76
，
wherein ,
Figure SMS_77
aiming point at the far-end of aiming area of two intelligent agents of intelligent driving area path tracking system and chassis area stability control system>
Figure SMS_78
For vehicle-road state variables, +.>
Figure SMS_79
The matrix is updated for the control objective.
wherein ：
Figure SMS_80
，
Figure SMS_81
，/>
Figure SMS_82
wherein ,
Figure SMS_83
for vehicle parameter state variables, +.>
Figure SMS_84
Weight is input for controlling the front wheel rotation angle, < +.>
Figure SMS_85
Weights are input for the control of the direct yaw moment, +.>
Figure SMS_86
Control target for intelligent driving domain ，/>
Figure SMS_87
Is a control target of the chassis domain.
Because the intelligent driving domain path tracking system and the chassis domain stability control system are both in an augmentation state with pre-aiming information of two intelligent agents in other areas
Figure SMS_88
In, the furthest pretightening point information +.>
Figure SMS_89
。
Wherein, in one embodiment of the present application, the intelligent network-connected bus multi-target path tracking augmentation system can be further simplified into:
Figure SMS_90
wherein ,
Figure SMS_91
for vehicle-road state variables, +.>
Figure SMS_92
。
In addition, in the intelligent network-connected bus multi-target path tracking and amplifying system
Figure SMS_93
Is->
Figure SMS_94
The system state variable of the moment, i.e.)>
Figure SMS_95
State variables of moment vehicle, road pre-aiming and stability target pre-aiming information, in mathematical formula, intelligent network-connected passenger car multi-target path tracking and amplifying system>
Figure SMS_96
Representing a matrix, namely:
Figure SMS_97
，
wherein ,
Figure SMS_98
for control input +.>
Figure SMS_99
Matrix coefficients of>
Figure SMS_100
For control input +.>
Figure SMS_101
Is included in the matrix coefficients of (a).
In step S103, based on the quadratic form optimal theory, a cost function of intelligent driving domain path tracking control and chassis domain stability control is constructed based on the vehicle-road model.
It can be understood that the embodiment of the application can construct the cost function of intelligent driving domain path tracking control and chassis domain stability control based on the vehicle-road model based on the quadratic optimal theory in the following steps, so that the stability and safety of the vehicle are effectively improved, and the driving experience of a user is improved.
In one embodiment of the present application, based on quadratic optimization theory, constructing a cost function of intelligent driving domain path tracking control and chassis domain stability control includes: and selecting the transverse position deviation and the course angle deviation at the pre-aiming point as weighting items of a steering system, and generating a cost function of the multi-target path tracking control problem by taking the ideal yaw rate of the automobile as the weighting item of braking control.
In some embodiments, as shown in fig. 3, the embodiments of the present application may select a lateral position deviation and a heading angle deviation at a pre-aiming point as a weighting term of a steering system, an ideal yaw rate of an automobile as a weighting term of braking control, and design a prediction and control time domain asN u The cost function of the step length multi-target path tracking control problem is as follows:
Figure SMS_102
，
wherein ,
Figure SMS_104
for the iterative accumulated value of the time instants +.>
Figure SMS_108
Designating a symbol for a state variable, +.>
Figure SMS_111
Defining a sign for the front wheel steering angle cost function, +.>
Figure SMS_105
Defining a sign for the cost function of the direct yaw moment, < ->
Figure SMS_107
and />
Figure SMS_112
Self-input weighting coefficients for steering and braking systems, respectively, < >>
Figure SMS_113
、/>
Figure SMS_106
Tracking error weighting matrix for steering and braking systems, respectively,>
Figure SMS_110
、/>
Figure SMS_114
respectively the first
Figure SMS_115
Weight matrix of time steering and braking system performance index functions, and +. >
Figure SMS_103
，/>
Figure SMS_109
。
wherein ,
Figure SMS_116
，/>
Figure SMS_117
，
wherein ,
Figure SMS_119
matrix is formed for the deviation of the front wheel angle, +.>
Figure SMS_122
Matrix is formed for the deviation of the direct yaw moment, +.>
Figure SMS_125
Constructing a transposed matrix of the matrix for the deviations of the front wheel corners,/->
Figure SMS_120
Constructing a transposed matrix of the matrix for the deviation of the direct yaw moment,/->
Figure SMS_123
Tracking target for control of front wheel corner, +.>
Figure SMS_126
Tracking target for control of direct yaw moment, +.>
Figure SMS_127
and />
Figure SMS_118
State weighting matrix for steering and braking systems, respectively, < >>
Figure SMS_121
and />
Figure SMS_124
The self-input weighting coefficients of the steering and braking systems, respectively.
In step S104, based on the cost function, intelligent driving domain path tracking control and chassis domain stability control are combined with the stamina closed loop game, the intelligent driving domain is used as a leader of the game, the chassis domain is used as a follower of the game, and the optimal control strategy is solved.
It can be understood that the embodiment of the application can combine intelligent driving domain path tracking control and chassis domain stability control in the following steps with the stoneberg closed-loop game based on the cost function, take the intelligent driving domain as a leader of the game and take the chassis domain as a follower of the game, and solve the optimal control strategy, so that the intelligent network bus has stability and reliability while tracking the path.
Optionally, in one embodiment of the present application, based on a cost function, the intelligent driving domain path tracking control and the chassis domain stability control are combined with the stoneberg closed loop game, the intelligent driving domain is used as a leader of the game, the chassis domain is used as a follower of the game, and the solving of the optimal control strategy includes: in closed-loop Steiner game control, a leader and a follower meet a preset recurrence relation to derive a game control strategy of an intelligent driving domain and a chassis domain based on an Steiner feedback non-cooperative game theory, so as to obtain a unique feedback Steiner equilibrium solution.
In some embodiments, for convenience of description, white noise and road reference information may be ignored, and the cost function of the intelligent network-connected bus multi-target path tracking and amplifying system and the multi-target path tracking control problem is defined as follows, and the control set of the active steering and braking system is defined as
Figure SMS_128
and />
Figure SMS_129
。
Further:
Figure SMS_130
，
wherein ,
Figure SMS_131
、/>
Figure SMS_132
and />
Figure SMS_133
Generalized definitions of state equation, front wheel steering angle and direct yaw moment, respectively.
As shown in fig. 3, in closed loop stoneberg game control, the leader and follower must satisfy the following recurrence relation, namely:
Figure SMS_134
，
wherein ,
Figure SMS_135
and />
Figure SMS_136
Definition of the state equation when the state is optimal and definition of the cost function when the direct yaw moment takes the optimal value, respectively +.>
Figure SMS_137
As a function of the value of the direct yaw moment, +.>
Figure SMS_138
Is the iterative control rate of the direct yaw moment.
Then there will be a series of optimal stoneberg game control strategies
Figure SMS_139
。
wherein ,
Figure SMS_140
wherein ,
Figure SMS_141
is a weight matrix of the direct yaw moment.
However, the optimal solution of intelligent driving domain control is a recursive solution set obtained on the basis of taking the chassis domain control decision into consideration, namely:
Figure SMS_142
wherein ,
Figure SMS_143
for the optimal solution of front wheel steering angle->
Figure SMS_144
As a function of the value of the front wheel angle, +.>
Figure SMS_145
For the definition of the equation of state,
Figure SMS_146
definition of the cost function for front wheel corner, +.>
Figure SMS_147
Definition of a cost function when an optimal value is obtained for the front wheel steering angle.
wherein ：
Figure SMS_148
，
likewise, the optimal solution of the chassis domain control is a recursive solution set obtained on the basis of taking into account intelligent driving domain control decisions, namely:
Figure SMS_149
wherein ：
Figure SMS_150
wherein ,
Figure SMS_151
、/>
Figure SMS_152
and />
Figure SMS_153
Cost functions of the optimal direct yaw moment, the state equation under the optimal control input and the direct yaw moment under the optimal front wheel steering angle, respectively +.>
Figure SMS_154
For the optimal front wheel angle +.>
Figure SMS_155
Is the optimal front wheel steering angle transposition.
Therefore, according to the embodiment of the application, the game control strategy of the intelligent driving domain and the chassis domain can be deduced based on the Stankleber feedback non-cooperative game theory, and for the special case of linear secondary countermeasures with strict convex cost functions, a unique feedback Stankleber equilibrium solution can be obtained, the current value of the equilibrium solution in a state is linear, and the form of the solution is as follows:
Figure SMS_156
wherein ,
Figure SMS_157
for controlling rate->
Figure SMS_158
A feedback stoneberg equilibrium solution for the front wheel corner,
Figure SMS_159
a feedback stoneberg equilibrium solution for the direct yaw moment.
And control rate
Figure SMS_160
The following relationship is satisfied, namely:
Figure SMS_161
，
wherein ,
Figure SMS_162
for front wheel steering/direct yaw moment +.>
Figure SMS_163
Time-of-day Richti equation solution>
Figure SMS_164
Control rate of feedback stonberg equilibrium solution for front wheel corner, +.>
Figure SMS_165
Control rate of feedback stoneberg equilibrium solution for direct yaw moment, +.>
Figure SMS_166
For front wheel steering/direct yaw moment +.>
Figure SMS_167
Time-of-day Richti equation solution>
Figure SMS_168
The control rate of the stonger equalization solution is fed back for the moment of the front wheel steering angle/direct yaw force.
For example, as shown in fig. 5, in the dynamic game evolution process, the intelligent network bus has stability and reliability while tracking a path according to the lateral stability working condition and road information and based on strategy interaction between the intelligent driving domain and the chassis domain.
For example, as shown in fig. 6, the state parameter curves of the vehicle during the experiment are respectively the lateral displacement, the yaw angle, the front wheel rotation angle, the centroid yaw angle, the additional yaw moment, the wheel cylinder pressure for the game control and the wheel cylinder pressure for the distributed control from (a) to (g), wherein the first four terms are compared by three experimental schemes, and the last three terms are data of the stability control input distributed control and the game control.
Then, as can be seen from fig. (a), the game control path tracking effect is best, the error is minimum, and the LQR path tracking control scheme without stability control has better tracking effect before 7 seconds, but obviously has instability after 7 seconds, severely deviates from the DLC road, while the distributed control has smaller error at 7-9 seconds, but has larger overall curve tracking error, and obviously lags the deviation position, and cannot finish the DLC road well.
In addition, the graph (b) is a yaw angle contrast curve, overall, game control tracking is optimal, distributed secondary, LQR active steering control without stability control has good tracking effect before 5 seconds, and after 5 seconds, the LQR active steering control is seriously deviated from a target yaw angle, and obviously has instability, so that excessive pursuit control effect under low attachment conditions easily causes instability of overall tracking control.
Finally, as can be seen from the graph (c), the front wheel steering angle of the game control is minimum, although the tracking offset of the distributed control path is smaller and the error is larger in the graph (a), the front wheel steering angle of the graph (a) is larger, the stability control is mainly used for seriously inhibiting the steering control, so that the benefit conflict between steering and braking is obvious, the maximum wheel cylinder pressure in the graph (g) can reach 0.8MPa, compared with the wheel cylinder pressure in the graph (f), the maximum wheel cylinder pressure value in the graph (g) is relatively larger, the yaw moment comparison curve in the graph (e) is larger, and particularly in the 6 th second, the yaw moment comparison curve of the graph (e) is up to-2500 n x m, which is 500n x m larger than the value of the game control, and the game control yaw moment curve of the game control is more coordinated as a whole, and the centroid side deviation angle is seen from the graph (d).
In summary, the embodiment of the application can define the intelligent driving domain and the chassis domain as two game participants, deduce the intelligent driving domain and the chassis domain interaction control strategy of the intelligent network bus by utilizing the dynamic game theory, and observe the control decision when the intelligent driving domain is taken as a leader to decide the control decision in the decision process, so that the chassis domain can decide the response according to the control decision of the intelligent driving domain system, wherein the specificity of the Stark primary game is that the leader can fully understand the dynamic strategy of the follower when planning the decision, and the leader can understand the cost function or the performance index function of the follower.
Therefore, the leader can expect the influence of the decision on the follower, the decision of the leader takes the cost function or the performance index function of the follower as constraint, and the leader obtains the maximum benefit, so that the globally optimal control solution of the two systems of the intelligent driving domain and the chassis domain is obtained, the distribution of the vehicle in the path tracking and the transverse stable control is more reasonable, and the safety and the stability of the intelligent network bus are improved.
According to the intelligent network bus path tracking game control method provided by the embodiment of the application, an automobile system dynamics two-degree-of-freedom vehicle model can be constructed according to actual parameters of the intelligent network bus, a road model is constructed according to road information, the automobile system dynamics two-degree-of-freedom vehicle model and the road model are combined to construct an automobile-road model, a cost function of intelligent driving domain path tracking control and chassis domain stability control is constructed based on the automobile-road model on the basis of a quadratic optimal theory, so that intelligent driving domain path tracking control and chassis domain stability control are combined with a ston closed-loop game, an intelligent driving domain is used as a leader of the game, a chassis domain is used as a follower of the game, an optimal control strategy is solved, the control accuracy of path tracking is further effectively improved, the safety and stability of the automobile are improved, and the driving demand of a user is effectively met. Therefore, the problems that in the related art, due to the fact that understeer or oversteer is generated by single-wheel braking, control accuracy of path tracking is reduced, safety and stability of a vehicle are reduced, and driving requirements of users cannot be met are solved.
The intelligent network-connected bus path tracking game control device according to the embodiment of the application is described with reference to the accompanying drawings.
Fig. 7 is a block schematic diagram of an intelligent networked passenger car path tracking game control device according to an embodiment of the present application.
As shown in fig. 7, the intelligent networked passenger car path tracking game control device 10 includes: a first construction module 100, a second construction module 200, a construction module 300, and a calculation module 400.
Specifically, the first construction module 100 is configured to implement a two-degree-of-freedom vehicle model for vehicle dynamics according to actual parameters of the intelligent network bus.
The second construction module 200 is used for constructing a road model according to road information and combining the two-degree-of-freedom vehicle model of the dynamics of the automobile system and the road model to construct a vehicle-road model.
The construction module 300 is configured to construct a cost function for intelligent driving domain path tracking control and chassis domain stability control based on a vehicle-road model based on a quadratic optimal theory.
The calculation module 400 is configured to combine intelligent driving domain path tracking control and chassis domain stability control with the stoneberg closed-loop game based on the cost function, use the intelligent driving domain as a leader of the game, and use the chassis domain as a follower of the game, so as to solve the optimal control strategy.
Optionally, in one embodiment of the present application, the first construction module 100 includes: a setup unit and a calculation unit.
The building unit is used for building a two-degree-of-freedom model state equation taking a front wheel of the vehicle as an input object.
And the computing unit is used for discretizing the state equation of the two-degree-of-freedom model to obtain a discrete vehicle dynamics equation.
Optionally, in one embodiment of the present application, the second construction module 200 includes: and a processing unit.
The processing unit is used for adding the pre-aiming path information of the road information into a discrete vehicle dynamics equation so as to amplify the steering brake sharing type vehicle dynamics system through the pre-aiming dynamic process, and a multi-target path tracking and amplifying system of the intelligent network bus is obtained.
Optionally, in one embodiment of the present application, the intelligent networked passenger car multi-target path tracking augmentation system is:
Figure SMS_169
，
wherein ,
Figure SMS_170
is a state quantity coefficient matrix->
Figure SMS_171
For vehicle-road state variables, +.>
Figure SMS_172
For control input +.>
Figure SMS_173
Matrix coefficients of>
Figure SMS_174
For control input +.>
Figure SMS_175
Is included in the matrix coefficients of (a).
Optionally, in one embodiment of the present application, the building module includes: and (5) constructing a unit.
The construction unit is used for selecting the transverse position deviation and the course angle deviation at the pre-aiming point as weighting items of the steering system, taking the ideal yaw rate of the automobile as the weighting items of the braking control and generating a cost function of the multi-target path tracking control problem.
Optionally, in one embodiment of the present application, the computing module includes: and a deriving unit.
The derivation unit is used for enabling a leader and a follower to meet a preset recurrence relation in closed-loop Stank-berg game control so as to derive a game control strategy of an intelligent driving domain and a chassis domain based on a Stank-berg feedback non-cooperative game theory and obtain a unique feedback Stank-berg equilibrium solution.
It should be noted that the foregoing explanation of the embodiment of the method for controlling the path tracking game of the intelligent network-connected bus is also applicable to the path tracking game control device of the intelligent network-connected bus of this embodiment, and will not be repeated herein.
According to the intelligent network bus path tracking game control device provided by the embodiment of the application, an automobile system dynamics two-degree-of-freedom vehicle model can be constructed according to actual parameters of the intelligent network bus, a road model is constructed according to road information, the automobile system dynamics two-degree-of-freedom vehicle model and the road model are combined to construct an automobile-road model, a cost function of intelligent driving domain path tracking control and chassis domain stability control is constructed based on the automobile-road model on the basis of a quadratic optimal theory, so that intelligent driving domain path tracking control and chassis domain stability control are combined with a ston Berger closed-loop game, an intelligent driving domain is used as a leader of the game, a chassis domain is used as a follower of the game, an optimal control strategy is solved, the control accuracy of path tracking is further effectively improved, the safety and stability of the automobile are improved, and the driving requirements of users are effectively met. Therefore, the problems that in the related art, due to the fact that understeer or oversteer is generated by single-wheel braking, control accuracy of path tracking is reduced, safety and stability of a vehicle are reduced, and driving requirements of users cannot be met are solved.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
a memory 801, a processor 802, and a computer program stored on the memory 801 and executable on the processor 802.
The processor 802 implements the intelligent internet-connected passenger car path tracking game control method provided in the above embodiment when executing the program.
Further, the electronic device further includes:
a communication interface 803 for communication between the memory 801 and the processor 802.
A memory 801 for storing a computer program executable on the processor 802.
The memory 801 may include high-speed RAM memory or may further include non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
If the memory 801, the processor 802, and the communication interface 803 are implemented independently, the communication interface 803, the memory 801, and the processor 802 may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 8, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 801, the processor 802, and the communication interface 803 are integrated on a chip, the memory 801, the processor 802, and the communication interface 803 may communicate with each other through internal interfaces.
The processor 802 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the intelligent networked passenger car path tracking game control method as described above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (14)

1. An intelligent network-connected bus path tracking game control method is characterized by comprising the following steps:
constructing a dynamic two-degree-of-freedom vehicle model of an automobile system according to actual parameters of the intelligent network bus;
constructing a road model according to road information, and constructing a vehicle-road model by combining the vehicle system dynamics two-degree-of-freedom vehicle model and the road model;
based on a quadratic form optimal theory, constructing a cost function of intelligent driving domain path tracking control and chassis domain stability control based on the vehicle-road model; and
based on the cost function, intelligent driving domain path tracking control and chassis domain stability control are combined with the Stannberg closed-loop game, the intelligent driving domain is used as a leader of the game, the chassis domain is used as a follower of the game, and an optimal control strategy is solved.
2. The intelligent network bus path tracking game control method according to claim 1, wherein the constructing the vehicle model with two degrees of freedom according to the actual parameters of the intelligent network bus comprises:
establishing a two-degree-of-freedom model state equation taking a front wheel of a vehicle as an input object;
discretizing the two-degree-of-freedom model state equation to obtain a discrete vehicle dynamics equation.
3. The intelligent networked passenger car path tracking game control method according to claim 1, wherein constructing a road model according to road information and combining the two-degree-of-freedom vehicle model of the automobile system dynamics and the road model to construct a vehicle-road model comprises:
and adding the pre-aiming path information of the road information into the discrete vehicle dynamics equation to amplify the steering brake sharing type vehicle dynamics system through a pre-aiming dynamic process, so as to obtain the intelligent network-connected bus multi-target path tracking and amplifying system.
4. The intelligent network bus path tracking game control method as set forth in claim 3, wherein the intelligent network bus multi-objective path tracking augmentation system is:
Figure QLYQS_1
，
wherein ,
Figure QLYQS_2
is a state coefficient matrix>
Figure QLYQS_7
Marks the parameter related to the front wheel rotation angle with a symbol +.>
Figure QLYQS_10
Is the current +>
Figure QLYQS_4
At the moment of time of day,
Figure QLYQS_11
is the current +>
Figure QLYQS_13
Time of day (I)>
Figure QLYQS_15
Subscript for augmenting state equation related parameters, ++>
Figure QLYQS_5
For vehicle-road state variables, +.>
Figure QLYQS_8
For control input +.>
Figure QLYQS_12
Matrix coefficients of>
Figure QLYQS_14
For control input +.>
Figure QLYQS_3
Matrix coefficients of>
Figure QLYQS_6
and />
Figure QLYQS_9
For different control inputs.
5. The intelligent network-connected bus path tracking game control method according to claim 1, wherein the constructing a cost function of intelligent driving domain path tracking control and chassis domain stability control based on a quadratic optimal theory comprises:
and selecting the transverse position deviation and the course angle deviation at the pre-aiming point as weighting items of a steering system, and generating a cost function of the multi-target path tracking control problem by taking the ideal yaw rate of the automobile as the weighting item of braking control.
6. The intelligent networked passenger car path tracking game control method according to claim 1, wherein the solving the optimal control strategy based on the cost function by combining intelligent driving domain path tracking control and chassis domain stability control with the stoneberg closed loop game, using the intelligent driving domain as a leader of the game and using the chassis domain as a follower of the game comprises:
In closed-loop Stank-berg game control, the leader and the follower meet a preset recurrence relation to derive a game control strategy of the intelligent driving domain and the chassis domain based on a Stank-berg feedback non-cooperative game theory, so as to obtain a unique feedback Stank-berg equilibrium solution.
7. An intelligent network-connected bus path tracking game control device, which is characterized by comprising:
the first construction module is used for constructing a dynamic two-degree-of-freedom vehicle model of the automobile system according to actual parameters of the intelligent network bus;
the second construction module is used for constructing a road model according to road information and combining the two-degree-of-freedom vehicle model of the dynamics of the automobile system and the road model to construct a vehicle-road model;
the construction module is used for constructing a cost function of intelligent driving domain path tracking control and chassis domain stability control based on the vehicle-road model based on a quadratic optimal theory; and
and the calculation module is used for combining intelligent driving domain path tracking control and chassis domain stability control with the Stannberg closed-loop game based on the cost function, taking the intelligent driving domain as a leader of the game and taking the chassis domain as a follower of the game, and solving an optimal control strategy.
8. The intelligent networked passenger vehicle path tracking gaming control device of claim 7, wherein the first configuration module comprises:
the building unit is used for building a two-degree-of-freedom model state equation taking a front wheel of the vehicle as an input object;
and the computing unit is used for discretizing the two-degree-of-freedom model state equation to obtain a discrete vehicle dynamics equation.
9. The intelligent networked passenger vehicle path tracking gaming control device of claim 7, wherein the second configuration module comprises:
and the processing unit is used for adding the pre-aiming path information of the road information into the discrete vehicle dynamics equation so as to amplify the steering brake sharing type vehicle dynamics system through the pre-aiming dynamic process and obtain the intelligent network-connected bus multi-target path tracking and amplifying system.
10. The intelligent networked passenger car path tracking game control device according to claim 9, wherein the intelligent networked passenger car multi-objective path tracking augmentation system is:
Figure QLYQS_16
，
wherein ,
Figure QLYQS_18
is a state coefficient matrix>
Figure QLYQS_23
Marks the parameter related to the front wheel rotation angle with a symbol +.>
Figure QLYQS_26
Is the current +>
Figure QLYQS_20
At the moment of time of day,
Figure QLYQS_22
is the current + >
Figure QLYQS_25
Time of day (I)>
Figure QLYQS_28
Subscript for augmenting state equation related parameters, ++>
Figure QLYQS_17
For vehicle-road state variables, +.>
Figure QLYQS_21
For control input +.>
Figure QLYQS_24
Matrix coefficients of>
Figure QLYQS_27
For control input +.>
Figure QLYQS_19
Is included in the matrix coefficients of (a).
11. The intelligent networked passenger vehicle path tracking game control device of claim 7, wherein the building module comprises:
the construction unit is used for selecting the transverse position deviation and the course angle deviation at the pre-aiming point as weighting items of the steering system, taking the ideal yaw rate of the automobile as the weighting items of the braking control and generating a cost function of the multi-target path tracking control problem.
12. The intelligent networked passenger vehicle path tracking game control device of claim 7, wherein the computing module comprises:
and the deriving unit is used for enabling the leader and the follower to meet a preset recurrence relation in closed-loop Stankleber game control so as to derive a game control strategy of the intelligent driving domain and the chassis domain based on a Stankleber feedback non-cooperative game theory and obtain a unique feedback Stankleber equilibrium solution.
13. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the intelligent networked passenger car path tracking game control method of any one of claims 1-6.
14. A computer readable storage medium having stored thereon a computer program, the program being executable by a processor for implementing the intelligent networked passenger car path tracking game control method of any of claims 1-6.
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