CN113830074A - Intelligent driving vehicle longitudinal and transverse control method and system based on limit working condition - Google Patents

Intelligent driving vehicle longitudinal and transverse control method and system based on limit working condition Download PDF

Info

Publication number
CN113830074A
CN113830074A CN202111144976.2A CN202111144976A CN113830074A CN 113830074 A CN113830074 A CN 113830074A CN 202111144976 A CN202111144976 A CN 202111144976A CN 113830074 A CN113830074 A CN 113830074A
Authority
CN
China
Prior art keywords
driving vehicle
intelligent driving
optimal control
state
control quantity
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111144976.2A
Other languages
Chinese (zh)
Inventor
高洪波
万远龙
王源源
朱菊萍
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute of Advanced Technology University of Science and Technology of China
Original Assignee
Institute of Advanced Technology University of Science and Technology of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Institute of Advanced Technology University of Science and Technology of China filed Critical Institute of Advanced Technology University of Science and Technology of China
Priority to CN202111144976.2A priority Critical patent/CN113830074A/en
Publication of CN113830074A publication Critical patent/CN113830074A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/02—Control of vehicle driving stability
    • 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
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0013—Optimal controllers
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/40—Coefficient of friction

Abstract

The invention provides a longitudinal and transverse control method and system of an intelligent driving vehicle based on limit working conditions, which comprises the following steps: step 1: analyzing an intelligent driving vehicle and establishing a dynamic model; step 2: selecting a state variable and an input and output variable according to the dynamic model to obtain a state equation; and step 3: selecting a state variable function as a performance index to obtain a performance index function, and listing out constraint conditions; and 4, step 4: obtaining system state information, and solving an optimization problem consisting of a performance index function, a state equation and constraint conditions to obtain the current optimal control quantity; and 5: and taking the current optimal control quantity as control input to obtain the state information of the system at the next moment, and establishing a new optimization problem to obtain the optimal control quantity at the next moment. The invention can convert the nonlinear problem in the intelligent driving vehicle dynamic model into linear problem solution, and can conveniently process the multivariable problem in the intelligent driving vehicle dynamic model.

Description

Intelligent driving vehicle longitudinal and transverse control method and system based on limit working condition
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a longitudinal and transverse control method and system of an intelligent driving vehicle based on a limit working condition.
Background
The intelligent driving is a current hot industry and a future automobile development direction, various intelligent automobile competitions are always available at home and abroad to promote the development of intelligent driving technologies, various automatic driving technologies tend to be mature, and tracking control of the intelligent automobile is a key technology of the automatic driving technology and is a research hotspot all the time. In a practical scenario, the limit condition is a high incidence area of an automobile accident, especially an ice surface. In winter, most urban vehicle control in China must face the control problem brought by the ice surface. Even for manual driving, ice surface is one of the high causes of accidents; for intelligent driving, the problems caused by ice surface are not negligible. The popularization of intelligent driving must overcome the control problem in extreme conditions such as ice. For the working condition with low friction coefficient such as ice surface, a proper algorithm is needed to realize the longitudinal and transverse control of the intelligent vehicle.
Model Predictive Control (MPC) is based on a predictive model, sampling the state of the controlled body at time T, and for a short period of time in the future [ T, T + T ], the calculation is a control strategy that minimizes the cost. The control strategy only realizes the first step, then the system state under the obtained optimal control strategy is taken, and a new control strategy is calculated according to the new state. In each time step, the model prediction control needs repeated prediction and optimization, and the solution of the optimization problem is used as the output of the controller to act on the controlled object. By solving the optimization problem in each time step, the overall control strategy can be obtained.
For the problems of multivariable, coupling of longitudinal and transverse control, road surface constraint, model nonlinearity and the like in intelligent driving longitudinal and transverse control, a prediction model of model predictive control is based on a multivariable state equation, constraint conditions can be added to limit the actual working condition when solving an optimization problem, and the nonlinear problem can be solved through linearization, so that the model predictive control can be well suitable for the longitudinal and transverse control of intelligent driving.
Patent document CN112693449A (application number: CN202110102987.8) discloses a transverse and longitudinal coupling control method under the limit working condition of an unmanned vehicle, which comprises the following steps: constructing an NMPC transverse and longitudinal coupling control prediction model, a transverse NMPC control model and a longitudinal PID control model; forming a first controller by the NMPC transverse and longitudinal coupling control prediction model and a corresponding vehicle model and a performance evaluation index function thereof; combining the transverse NMPC control model and the corresponding vehicle model and performance evaluation index function thereof with the longitudinal PID control model to form a second controller; and in each control period, judging the current running condition according to the current speed and the curvature of the road, and selecting the first controller or the second controller to control the speed of the vehicle and the front wheel steering angle.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a longitudinal and transverse control method and system of an intelligent driving vehicle based on extreme working conditions.
The invention provides an intelligent driving vehicle longitudinal and transverse control method based on limit working conditions, which comprises the following steps:
step 1: analyzing an intelligent driving vehicle and establishing a dynamic model;
step 2: selecting a state variable and an input and output variable according to the dynamic model to obtain a state equation;
and step 3: selecting a state variable function as a performance index to obtain a performance index function, and listing out constraint conditions;
and 4, step 4: obtaining system state information, and solving an optimization problem consisting of a performance index function, a state equation and constraint conditions to obtain the current optimal control quantity;
and 5: and taking the current optimal control quantity as control input to obtain the state information of the system at the next moment, and establishing a new optimization problem to obtain the optimal control quantity at the next moment.
Preferably, in the equation of state, where the state variable is ξ (t), the input quantity is u (t), s (t) is the slip ratio, and μ (t) is the friction coefficient, then:
Figure BDA0003285115820000021
u(k)＝u(k-1)+Δu(k)
wherein: ξ (k +1) represents the state variable at time k + 1.
Preferably, the time period range selected by the performance indicator function is the same as the time period range of the constraint condition, and is from the current time point t to the future time point t + H of the systempWherein: hpIs a preset prediction time span.
Preferably, the solution optimization problem is as follows: calculating a future time period t +1 to t + H according to the dynamic model and the constraint conditionspA minimum value of an error function of the predicted output and the reference output;
obtaining the optimal control quantity sequence u*(k)，k＝t,…,t+Hp-1；
Then, the optimal control quantity u is selected*(t) as a current control input.
Preferably, the current optimal control quantity u is obtained*And (t), taking the state variable as a control input to obtain a next state variable xi (t +1) of the system, and establishing an optimization problem at the t +1 moment by xi (t +1) to solve a next optimal control quantity.
The invention provides an intelligent driving vehicle longitudinal and transverse control system based on limit working conditions, which comprises:
module M1: analyzing an intelligent driving vehicle and establishing a dynamic model;
module M2: selecting a state variable and an input and output variable according to the dynamic model to obtain a state equation;
module M3: selecting a state variable function as a performance index to obtain a performance index function, and listing out constraint conditions;
module M4: obtaining system state information, and solving an optimization problem consisting of a performance index function, a state equation and constraint conditions to obtain the current optimal control quantity;
module M5: and taking the current optimal control quantity as control input to obtain the state information of the system at the next moment, and establishing a new optimization problem to obtain the optimal control quantity at the next moment.
Preferably, in the equation of state, where the state variable is ξ (t), the input quantity is u (t), s (t) is the slip ratio, and μ (t) is the friction coefficient, then:
Figure BDA0003285115820000031
u(k)＝u(k-1)+Δu(k)
wherein: ξ (k +1) represents the state variable at time k + 1.
Preferably, the time period range selected by the performance indicator function is the same as the time period range of the constraint condition, and is from the current time point t to the future time point t + H of the systempWherein: hpIs a preset prediction time span.
Preferably, the solution optimization problem is as follows: calculating a future time period t +1 to t + H according to the dynamic model and the constraint conditionspA minimum value of an error function of the predicted output and the reference output;
obtaining the optimal control quantity sequence u*(k)，k＝t,…,t+Hp-1；
Then, the optimal control quantity u is selected*(t) as a current control input.
Preferably, the current optimal control quantity u is obtained*And (t), taking the state variable as a control input to obtain a next state variable xi (t +1) of the system, and establishing an optimization problem at the t +1 moment by xi (t +1) to solve a next optimal control quantity.
Compared with the prior art, the invention has the following beneficial effects:
the invention can convert the nonlinear problem in the intelligent driving vehicle dynamic model into linear problem solution, and can conveniently process the multivariable problem in the intelligent driving vehicle dynamic model; and the problem of multiple constraints in the intelligent driving vehicle dynamics model can be conveniently processed.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of a model prediction algorithm;
FIG. 3 is a simplified two degree of freedom smart driving vehicle model diagram.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example (b):
as shown in fig. 1 and 2, the method mainly realizes longitudinal and transverse control of the intelligent driving vehicle under the limit working condition based on the model predictive control algorithm, and comprises the following steps:
s1: analyzing the intelligent vehicle and establishing a corresponding dynamic model, namely a prediction model;
s2: selecting a proper variable as a state variable according to the dynamic model, and selecting an input variable and an output variable to obtain a state equation;
s3: selecting a proper state variable function as a performance index to obtain a performance index function, and listing out constraint conditions;
s4: obtaining system state information, solving an optimization problem consisting of a performance index function, a state equation and constraint conditions to obtain a current optimal control quantity sequence U*(k)；
S5: the current optimal control quantity u*(t) as control input, obtaining the state information xi (t +1) of the system at the next moment, then establishing a new optimization problem to obtain the optimal control quantity at the next moment, and continuously repeating the process.
Preferably, a simplified two-degree-of-freedom bicycle model based on vehicle dynamics is selected, and a complete intelligent vehicle dynamics model for control is established.
Specifically, the intelligent vehicle is analyzed and a corresponding dynamic model is established as follows, which is obtained from the conservation of force and moment according to fig. 3:
Figure BDA0003285115820000041
Figure BDA0003285115820000042
Figure BDA0003285115820000043
from an analysis of its velocity:
Figure BDA0003285115820000044
analysis of tire longitudinal and lateral velocities yields:
Figure BDA0003285115820000045
Figure BDA0003285115820000046
Figure BDA0003285115820000051
Figure BDA0003285115820000052
wherein:
Figure BDA0003285115820000053
Figure BDA0003285115820000054
analysis of the forces on the tire gave:
Fy＝Flsinδ+Fccosδ,Fx＝Flcosδ-Fcsinδ…………(11)
force F on the front and rear wheelsl、FcConsists of:
Fl＝fl(α,s,μ,Fz),Fc＝fc(α,s,μ,Fz)…………(12)
wherein the tire slip angle α:
Figure BDA0003285115820000055
slip ratio s:
Figure BDA0003285115820000056
stress of a tire on a z axis:
Figure BDA0003285115820000057
equations (1) - (15) are expressed as:
Figure BDA0003285115820000058
wherein:
Figure BDA0003285115820000059
is a state variable, u ═ δf]For controlling variables, Fl、FcX, Y is the abscissa and ordinate of the vehicle in the inertial coordinate system, a and b are the distance from the front wheel to the inertial center and the distance from the rear wheel to the inertial center, s is the slip ratio, and delta isr、δfYaw angle of the front wheels with respect to the vehicle body, yaw angle of the rear wheels with respect to the vehicle body, and ψ is an inertial coordinate of the vehicle body with respect to the vehicle bodyIs the angle of rotation, and alpha is the tire slip angle.
Preferably, the current state information of the system is acquired, a proper state variable is selected according to the dynamic model, constraint conditions are listed, the model is discretized, and the nonlinear model needs to be linearized, wherein the specific examples are as follows:
(16) can be rewritten to the following form using suitable linearization and discretization methods:
δξk+1,t＝Atδξk+1,t+Btδuk,t…………(17)
δαk,t＝Ctδξk+1,t+Dtδuk,t…………(18)
wherein: k is t, …, t + Hp，HpTo predict the length of time.
Preferably, a proper cost function is selected as a performance index to obtain a performance index function, and the performance index function is listed in a constraint condition, wherein the cost function selects a sum of squares of variables, which is as follows:
the cost function is:
Figure BDA0003285115820000061
wherein:
Figure BDA0003285115820000062
wherein: k is t, …, t + Hp
Constraint conditions are as follows:
uk,t＝u(t-1)+δuk,t…………(21)
ut-1,t＝u(t-1)…………(22)
Δuk,t＝uk,t-uk-1,t…………(23)
δf,min≤uk,t≤δf,max…………(24)
δΔf,min≤Δuk,t≤Δδf,max…………(25)
wherein: k is t, …, t + Hc-1
And meanwhile, the slip rate meets the following requirements:
δαmin-∈≤δαk,t≤δαmax+∈,k＝t,…,t+Hp…………(26)
for t + HcTo t + HpThe middle part:
Δuk,t＝0,k＝t+Hc,…,t+Hp…………(27)
simultaneously, the method comprises the following steps:
∈≥0…………(28)
δξt,t＝0…………(29)
preferably, the optimization problem composed of the future output performance index function, the future state equation and the future constraint condition is solved to obtain the future optimal control quantity sequence u*(k) In (3), the optimization problem is solved as a quadratic programming problem, and the specific optimization problem needing to be solved obtained from (17) to (29) is as follows:
Figure BDA0003285115820000071
Subj.toδξk+1,t＝Atδξk+1,t+Btδuk,t…………(31)
δαk,t＝Ctδξk+1,t+Dtδuk,t…………(32)
Figure BDA0003285115820000072
uk,t＝u(t-1)+δuk,t…………(34)
ut-1,t＝u(t-1)…………(35)
Δuk,t＝uk,t-uk-1,t…………(36)
δf,min≤uk,t≤δf,max…………(37)
δΔf,min≤Δuk,t≤Δδf,max…………(38)
wherein: (34) - (38) satisfying k ═ t, …, t + Hc-1。
δαmin-∈≤δαk,t≤δαmax+∈,k＝t,…,t+Hp…………(39)
Δuk,t＝0,k＝t+Hc,…,t+Hp…………(40)
∈≥0…………(41)
δξt,t＝0…………(42)
The optimal control sequence can be obtained by solving
Figure BDA0003285115820000073
The current optimal control quantity u*(t) is used as a control input, state information ξ (t +1) of the system at the next moment is collected and fed back to the module M2 as follows:
the current optimal control quantity can be obtained by selecting the current optimal control increment:
Figure BDA0003285115820000074
and then the state delta xi of the system at the next moment can be obtained from (17) - (18)t+1,tAnd carrying out next optimization solution.
The invention provides a model predictive control-based longitudinal and transverse control method for an intelligent driving vehicle under a limit working condition. The intelligent vehicle dynamic model is based on a two-degree-of-freedom bicycle model, a system state equation is obtained by the dynamic model through linearization and discretization, and finally optimization problem is changed into a quadratic programming problem. The method is easy to process the multi-constraint and multivariable intelligent vehicle longitudinal and transverse control problem, and has important significance on the intelligent vehicle longitudinal and transverse control problem.
The invention provides a model predictive control-based intelligent driving vehicle longitudinal and transverse control system under the limit working condition, which comprises:
module M1: analyzing the intelligent vehicle and establishing a corresponding dynamic model, namely a prediction model;
module M2: selecting a proper variable as a state variable according to the dynamic model, and selecting an input variable and an output variable to obtain a state equation;
module M3: selecting a proper state variable function as a performance index to obtain a performance index function, and listing out constraint conditions;
module M4: obtaining system state information, solving an optimization problem consisting of a performance index function, a state equation and constraint conditions to obtain a current optimal control quantity sequence U*(k)。
Module M5: the current optimal control quantity u*(t) as control input, obtaining the state information ξ (t +1) of the system at the next moment, and feeding back the state information of the system to the module M4, thereby establishing a new optimization problem to obtain the optimal control quantity at the next moment, and continuously repeating the process.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (10)

1. A longitudinal and transverse control method of an intelligent driving vehicle based on limit working conditions is characterized by comprising the following steps:
step 1: analyzing an intelligent driving vehicle and establishing a dynamic model;
step 2: selecting a state variable and an input and output variable according to the dynamic model to obtain a state equation;
and step 3: selecting a state variable function as a performance index to obtain a performance index function, and listing out constraint conditions;
and 4, step 4: obtaining system state information, and solving an optimization problem consisting of a performance index function, a state equation and constraint conditions to obtain the current optimal control quantity;
and 5: and taking the current optimal control quantity as control input to obtain the state information of the system at the next moment, and establishing a new optimization problem to obtain the optimal control quantity at the next moment.
2. The intelligent driving vehicle longitudinal and transverse control method based on the limit condition is characterized in that in the state equation, the state variable is ξ (t), the input quantity is u (t), s (t) is the slip ratio, and μ (t) is the friction coefficient, then:
Figure FDA0003285115810000011
u(k)＝u(k-1)+Δu(k)
wherein: ξ (k +1) represents the state variable at time k + 1.
3. The intelligent driving vehicle longitudinal and transverse control method based on the limit condition as claimed in claim 1, wherein the time period range selected by the performance index function is the same as the time period range of the constraint condition, and is from the current time point t to the future time point t + H of the systempWherein: hpIs a preset prediction time span.
4. The intelligent driving vehicle longitudinal and transverse control method based on the extreme working condition as claimed in claim 3, wherein the optimization problem is solved as follows: calculating a future time period t +1 to t + H according to the dynamic model and the constraint conditionspA minimum value of an error function of the predicted output and the reference output;
obtaining the optimal control quantity sequence u*(k)，k＝t,…,t+Hp-1；
Then, the optimal control quantity u is selected*(t) as a current control input.
5. The extreme condition-based intelligent driving vehicle longitudinal and transverse control method according to claim 4, wherein the current optimal control quantity u is obtained*And (t), taking the state variable as a control input to obtain a next state variable xi (t +1) of the system, and establishing an optimization problem at the t +1 moment by xi (t +1) to solve a next optimal control quantity.
6. The utility model provides an intelligence driving vehicle indulges horizontal control system based on extreme condition which characterized in that includes:
module M1: analyzing an intelligent driving vehicle and establishing a dynamic model;
module M2: selecting a state variable and an input and output variable according to the dynamic model to obtain a state equation;
module M3: selecting a state variable function as a performance index to obtain a performance index function, and listing out constraint conditions;
module M4: obtaining system state information, and solving an optimization problem consisting of a performance index function, a state equation and constraint conditions to obtain the current optimal control quantity;
module M5: and taking the current optimal control quantity as control input to obtain the state information of the system at the next moment, and establishing a new optimization problem to obtain the optimal control quantity at the next moment.
7. An intelligent driving vehicle longitudinal and transverse control system based on limit conditions as claimed in claim 6, wherein in the state equation, the state variable is ξ (t), the input quantity is u (t), s (t) is slip ratio, μ (t) is friction coefficient, and then:
Figure FDA0003285115810000021
u(k)＝u(k-1)+Δu(k)
wherein: ξ (k +1) represents the state variable at time k + 1.
8. The extreme condition-based intelligent driving vehicle longitudinal and transverse control system according to claim 6, wherein the time period range selected by the performance index function is the same as the time period range of the constraint condition, namely the current time point t to the future time point t + H of the systempWherein: hpIs a preset prediction time span.
9. The extreme condition-based intelligent driving vehicle longitudinal and transverse control system according to claim 8, wherein the optimization problem is solved as follows: calculating a future time period t +1 to t + H according to the dynamic model and the constraint conditionspA minimum value of an error function of the predicted output and the reference output;
obtaining the optimal control quantity sequence u*(k)，k＝t,…,t+Hp-1；
Then, the optimal control quantity u is selected*(t) as a current control input.
10. The extreme condition-based intelligent driving vehicle longitudinal and transverse control system according to claim 9, wherein the current optimal control quantity u is obtained*And (t), taking the state variable as a control input to obtain a next state variable xi (t +1) of the system, and establishing an optimization problem at the t +1 moment by xi (t +1) to solve a next optimal control quantity.
CN202111144976.2A 2021-09-28 2021-09-28 Intelligent driving vehicle longitudinal and transverse control method and system based on limit working condition Pending CN113830074A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111144976.2A CN113830074A (en) 2021-09-28 2021-09-28 Intelligent driving vehicle longitudinal and transverse control method and system based on limit working condition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111144976.2A CN113830074A (en) 2021-09-28 2021-09-28 Intelligent driving vehicle longitudinal and transverse control method and system based on limit working condition

Publications (1)

Publication Number Publication Date
CN113830074A true CN113830074A (en) 2021-12-24

Family

ID=78967135

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111144976.2A Pending CN113830074A (en) 2021-09-28 2021-09-28 Intelligent driving vehicle longitudinal and transverse control method and system based on limit working condition

Country Status (1)

Country Link
CN (1) CN113830074A (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2261093A1 (en) * 2009-06-01 2010-12-15 Ford Global Technologies, LLC Method and system for predictive yaw stability control for automobile
CN107161207A (en) * 2017-05-08 2017-09-15 江苏大学 A kind of intelligent automobile Trajectory Tracking Control System and control method based on active safety
CN107521496A (en) * 2017-09-08 2017-12-29 吉林大学 A kind of transverse and longitudinal of vehicle coordinates control track follow-up control method
CN108674414A (en) * 2018-07-02 2018-10-19 清华大学 A kind of intelligent automobile Trajectory Tracking Control method of limiting condition
CN109080608A (en) * 2018-06-04 2018-12-25 东南大学 A kind of braking force control method of automatic driving vehicle in sleet wet-skid road surface emergency braking
CN112693449A (en) * 2021-01-26 2021-04-23 湖南大学 Transverse and longitudinal coupling control method under limit working condition of unmanned vehicle

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2261093A1 (en) * 2009-06-01 2010-12-15 Ford Global Technologies, LLC Method and system for predictive yaw stability control for automobile
CN107161207A (en) * 2017-05-08 2017-09-15 江苏大学 A kind of intelligent automobile Trajectory Tracking Control System and control method based on active safety
CN107521496A (en) * 2017-09-08 2017-12-29 吉林大学 A kind of transverse and longitudinal of vehicle coordinates control track follow-up control method
CN109080608A (en) * 2018-06-04 2018-12-25 东南大学 A kind of braking force control method of automatic driving vehicle in sleet wet-skid road surface emergency braking
CN108674414A (en) * 2018-07-02 2018-10-19 清华大学 A kind of intelligent automobile Trajectory Tracking Control method of limiting condition
CN112693449A (en) * 2021-01-26 2021-04-23 湖南大学 Transverse and longitudinal coupling control method under limit working condition of unmanned vehicle

Similar Documents

Publication Publication Date Title
Borrelli et al. An MPC/hybrid system approach to traction control
Cao et al. Trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics
CN112622903B (en) Longitudinal and transverse control method for autonomous vehicle in vehicle following driving environment
CN108248605A (en) The transverse and longitudinal control method for coordinating that a kind of intelligent vehicle track follows
Xu et al. Model predictive control for lane keeping system in autonomous vehicle
CN112141101B (en) Method and system for pre-aiming safety path based on CNN and LSTM
Koga et al. Realization of different driving characteristics for autonomous vehicle by using model predictive control
Song et al. Combined longitudinal and lateral control for automated lane guidance of full drive-by-wire vehicles
CN113433942B (en) Long-axis vehicle path tracking control method based on optimal course angle
CN111930112A (en) Intelligent vehicle path tracking control method and system based on MPC
Ercan et al. An adaptive and predictive controller design for lateral control of an autonomous vehicle
Meiling et al. Model predictive control for UGV trajectory tracking based on dynamic model
CN111086510A (en) Front wheel steering vehicle lane keeping control method based on prediction function control
CN114185272A (en) Model prediction control-based adaptive speed intelligent vehicle path tracking method with preview characteristic
CN114394092B (en) Hybrid traffic flow automobile cooperative adaptive cruise control method based on vehicle-to-vehicle communication
Nestlinger et al. Bumpless transfer for convenient lateral car control handover
Feraco et al. Combined lane keeping and longitudinal speed control for autonomous driving
CN111845738B (en) Vehicle path tracking control method based on double-model combination
Wang et al. Vehicle automatic lane changing based on model predictive control
CN111413979B (en) Automobile track tracking control method based on rapid model prediction
CN113830074A (en) Intelligent driving vehicle longitudinal and transverse control method and system based on limit working condition
Li et al. Path tracking control based on the prediction of tire state stiffness using the optimized steering sequence
CN113561976B (en) Vehicle energy-saving prediction adaptive cruise control method and device based on feedback optimization
Wang et al. Application of stochastic model predictive control to modeling driver steering skills
Jonsson et al. Fuel optimized predictive following in low speed conditions

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20211224

RJ01 Rejection of invention patent application after publication