CN117125079A - Vehicle control method and device, electronic equipment and storage medium - Google Patents
Vehicle control method and device, electronic equipment and storage medium Download PDFInfo
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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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/18172—Preventing, or responsive to skidding of wheels
-
- 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
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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
- 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
- B60W50/0097—Predicting future conditions
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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
- 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
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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
- 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
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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
- B60W2552/00—Input parameters relating to infrastructure
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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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/15—Road slope
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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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/30—Road curve radius
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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
- B60W2556/00—Input parameters relating to data
- B60W2556/45—External transmission of data to or from the vehicle
Abstract
The embodiment of the application discloses a control method and device of a vehicle, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring motion parameters and motion environment parameters of a vehicle, and controlling demand parameters of a driver on the vehicle; calculating a motion expected parameter of a driver according to the control demand parameter, calculating a motion state parameter according to the motion parameter, and calculating a target motion environment parameter according to the motion environment parameter; calculating a motion expected parameter and a motion state parameter through a model predictive control algorithm to obtain a yaw moment; and calculating driving torques of all wheels of the vehicle according to the yaw moment and the target motion environment parameters, and controlling the vehicle according to the driving torques of all the wheels. The technical scheme of the embodiment of the application can predict the ideal steering characteristic in the control time domain and obtain the optimal balance and control between the torque output, thereby optimizing the maneuverability and the energy consumption of the vehicle and reducing the slip ratio of the vehicle.
Description
Technical Field
The present application relates to the field of vehicle technologies, and in particular, to a vehicle control method and apparatus, an electronic device, and a computer readable storage medium.
Background
The distributed driving vehicle means that four wheels of the vehicle can be driven independently, and the distributed driving vehicle has the advantages of good trafficability, high efficiency, good operability and the like, but the control of the distributed driving vehicle is also a difficulty in the distributed driving vehicle, and the control of the distributed driving vehicle needs to consider the steering stability of the vehicle, and also considers the energy consumption of the vehicle and the abrasion of parts such as tires, which challenges the calculation of the additional yaw moment. In general control of a distributed driving vehicle, an additional yaw moment is calculated by using a feedback algorithm such as PID (Proportion Integration Differentiation, proportional, integral, derivative) according to a deviation between a target yaw rate and an actual yaw rate, so as to control the distributed driving vehicle, but this control scheme also has a certain temperature, when the deviation between the target yaw rate and the actual yaw rate is too large, the additional yaw moment becomes very large, resulting in an overshoot of torque, which may cause an increase in slip rate, increase energy consumption and component loss of the vehicle, and also results in a decrease in driving experience.
Disclosure of Invention
To solve the above technical problems, embodiments of the present application provide a method and apparatus for controlling a vehicle, an electronic device, and a computer readable storage medium, which aim to solve the technical problem of influence of overshoot of torque on a vehicle when driving a distributed driving vehicle by a feedback algorithm.
Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application.
According to an aspect of an embodiment of the present application, there is provided a control method of a vehicle, including:
acquiring motion parameters and motion environment parameters of a vehicle, and controlling demand parameters of a driver on the vehicle;
calculating a motion expected parameter of a driver according to the control demand parameter, calculating a motion state parameter according to the motion parameter, and calculating a target motion environment parameter according to the motion environment parameter;
calculating a motion expected parameter and a motion state parameter through a model predictive control algorithm to obtain a yaw moment;
and calculating driving torques of all wheels of the vehicle according to the yaw moment and the target motion environment parameters, and controlling the vehicle according to the driving torques of all the wheels.
In a further embodiment, calculating the driving torque to each wheel of the vehicle from the yaw moment and the target motion environment parameter includes:
acquiring an accelerator request of a driver to a vehicle, and calculating a longitudinal moment to the vehicle according to the accelerator request;
the driving torque for each wheel of the vehicle is calculated based on the longitudinal moment, the yaw moment and the target motion environment parameter.
In a further embodiment, calculating the driving torque to each wheel of the vehicle from the longitudinal moment, the yaw moment and the target motion environment parameter comprises:
and calculating longitudinal moment, yaw moment and target motion environment parameters through a preset optimization algorithm to obtain driving torques of all the wheels, so that when the vehicle is controlled through the driving torques of all the wheels, the attachment margin of the wheels is maximum and the efficiency is highest.
In further embodiments, the motion parameters include rotational speed of each wheel, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle yaw rate; the movement environment parameters comprise road curvature, road ramp and road surface parameters; calculating a motion state parameter from the motion parameter and calculating a target motion environment parameter from the motion environment parameter, comprising:
Inputting the motion parameters into a preset vehicle control reference model for processing to obtain the speed, mass and mass center slip angle of the vehicle, and taking the speed, mass and mass center slip angle as the motion state parameters; the vehicle control reference model is obtained based on a vehicle dynamics model with two degrees of freedom; and
and inputting the motion environment parameters into a vehicle control reference model for processing to obtain road surface adhesion coefficients and ramps, and taking the road surface adhesion coefficients and the ramps as target motion environment parameters.
In a further embodiment, the calculation of the motion desired parameter and the motion state parameter by the model predictive control algorithm to obtain the yaw moment comprises:
processing the motion expected parameter and the motion state parameter through a model predictive control algorithm to construct an optimized objective function; wherein the optimization objective function includes energy consumption and wear of a drive system of the vehicle, and optimization of steering characteristics;
and acquiring a weight coefficient matrix, and processing the optimized objective function through the weight coefficient matrix to obtain the yaw moment.
In a further embodiment, the motion desired parameter and the motion state parameter are processed by a model predictive control algorithm to construct an optimized objective function, comprising:
Constructing a system state, a control quantity and a system disturbance quantity of the vehicle according to the motion expected parameter and the motion state parameter;
acquiring the related information among the system state, the control quantity and the system disturbance quantity through a two-degree-of-freedom monorail model of the vehicle;
and constructing an optimization objective function according to the association information.
In a further embodiment, the processing of the optimization objective function by the weight coefficient matrix to obtain the yaw moment includes:
distributing the optimized objective function through the weight coefficient matrix to obtain an extremum objective function;
and calculating an optimal solution of the extremum objective function by an interior point method to obtain the yaw moment.
According to an aspect of an embodiment of the present application, there is provided a control device of a vehicle including:
the acquisition module is configured to acquire motion parameters and motion environment parameters of the vehicle and control demand parameters of a driver on the vehicle;
a first calculation module configured to calculate a motion expectation parameter of the driver according to the control demand parameter, calculate a motion state parameter according to the motion parameter, and calculate a target motion environment parameter according to the motion environment parameter;
the second calculation module is configured to calculate the motion expected parameter and the motion state parameter through a model predictive control algorithm to obtain a yaw moment;
And a third calculation module configured to calculate driving torques to the respective wheels of the vehicle based on the yaw moment and the target motion environment parameter, and to control the vehicle based on the driving torques of the respective wheels.
According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: one or more processors; and a storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement a control method of the vehicle as before.
According to an aspect of an embodiment of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions, which when executed by a processor of a computer, cause the computer to perform the method of controlling a vehicle as above.
According to an aspect of embodiments of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device performs the control method of the vehicle provided in the above-described various alternative embodiments.
In the technical scheme provided by the embodiment of the application, the control demand parameter, the motion parameter and the motion environment parameter of the vehicle are obtained, the control demand parameter, the motion parameter and the motion environment parameter are further calculated to obtain the motion expected parameter, the motion state parameter and the target motion environment parameter, and the yaw moment obtained by solving through the model predictive control algorithm is solved, so that the vehicle obtains optimal balance between the ideal steering characteristic and the torque output. And finally, calculating the driving torque of each wheel according to the yaw moment and the target motion environment parameter, and controlling each wheel to execute the corresponding driving torque to realize the distributed driving control of the vehicle. According to the application, the model predictive control algorithm is utilized to calculate the yaw moment, the motion state deviation and the change of the output torque of the vehicle in the short-time domain in the future can be predicted and calculated, and the optimal yaw moment is solved, so that the optimal balance and control between the ideal steering characteristic and the torque output of the vehicle in the prediction control time domain are obtained, the maneuverability and the energy consumption of the vehicle are optimized, and the slip rate of the vehicle is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 is a schematic illustration of an implementation environment in which the present application is directed;
FIG. 2 is a flow chart of a method of controlling a vehicle in accordance with the present application;
FIG. 3 is a flow chart of step S220 in one embodiment of the application;
FIG. 4 is a flow chart of step S230 in one embodiment of the application;
FIG. 5 is a flow chart of step S41 0 in one embodiment of the application;
FIG. 6 is a flow chart of step S420 in one embodiment of the application;
FIG. 7 is a flow chart of step S240 in one embodiment of the application;
fig. 8 is a block diagram of a control device of a vehicle according to the present application;
fig. 9 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Also to be described is: in the present application, the term "plurality" means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
Referring to fig. 1, fig. 1 is a schematic diagram of an implementation environment according to the present application. The implementation environment includes a distributed drive vehicle 110 and a server 120 deployed by a smart system, the distributed drive vehicle 110 and the server 120 communicating over a wireless network.
The server 120 stores a plurality of road information, and when the distributed driving vehicle 110 travels on a road, the road information of the road on which the vehicle is currently traveling can be obtained from the server as a motion environment parameter of the distributed driving vehicle 110. The server 120 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network ), and basic cloud computing services such as big data and an artificial intelligence platform, which are not limited herein.
Fig. 2 is a flowchart illustrating a control method of a vehicle according to an exemplary embodiment. The method may be applied to the implementation environment shown in fig. 1 and is specifically performed by vehicle 110 in the embodiment environment shown in fig. 1.
As shown in fig. 2, in an exemplary embodiment, the control method of the vehicle may include steps S2 to S240, which are described in detail as follows:
step S210, acquiring the motion parameters and the motion environment parameters of the vehicle, and the control demand parameters of the driver for the vehicle.
In the embodiment of the application, a sensor for acquiring the motion parameters of the vehicle is arranged on the vehicle, corresponding signal processing is carried out after the sensor signals related to the motion parameters are acquired, and the motion parameters are obtained, wherein the motion parameters comprise the rotating speed of each wheel, the transverse acceleration of the vehicle, the longitudinal acceleration of the vehicle and the yaw rate of the vehicle. Meanwhile, a sensor for acquiring control demand parameters of a driver on the vehicle is further arranged on the vehicle, corresponding signal processing is carried out after sensor signals related to the control demand parameters are acquired, and the control demand parameters are obtained, wherein the control demand parameters comprise an accelerator pedal signal, a brake pedal signal and a steering wheel corner.
The vehicle generates a request for acquiring the motion environment parameters and sends the request for acquiring the motion environment parameters to the intelligent system, the intelligent system acquires motion environment parameter signals corresponding to the request from the server according to the request for acquiring the motion environment parameters, the intelligent system returns the acquired motion environment parameter signals to the vehicle, the vehicle analyzes the received motion environment parameter signals and carries out corresponding signal processing to obtain the motion environment parameters, and the motion environment parameters comprise road curvature, road ramp, road surface parameters and the like.
Step S220, calculating a motion desired parameter of the driver according to the control demand parameter, calculating a motion state parameter according to the motion parameter, and calculating a target motion environment parameter according to the motion environment parameter.
In the embodiment of the application, the movement expected parameters of the driver are calculated according to the control demand parameters, wherein the movement expected parameters comprise the target vehicle speed and the target yaw rate, and the movement expected parameters represent the movement intention of the driver to the vehicle.
And calculating motion state parameters according to the motion parameters, wherein the motion state parameters comprise the speed, the mass and the centroid slip angle of the vehicle, and the motion state parameters represent the current motion state of the vehicle.
And calculating target motion environment parameters according to the motion environment parameters, wherein the target motion environment parameters comprise road surface adhesion coefficients and ramps, and the target motion environment parameters represent the current motion environment of the vehicle.
In an exemplary embodiment of the present application, referring to fig. 3, the motion parameters include the rotational speed of each wheel, the lateral acceleration of the vehicle, the longitudinal acceleration of the vehicle, the yaw rate of the vehicle; the movement environment parameters comprise road curvature, road ramp and road surface parameters; in step S220, the motion state parameter is calculated according to the motion parameter, and the target motion environment parameter is calculated according to the motion environment parameter, including steps S310 to S320, which are described in detail below:
step S310, inputting the motion parameters into a preset vehicle control reference model for processing to obtain the speed, mass and mass center slip angle of the vehicle, and taking the speed, mass and mass center slip angle as the motion state parameters; wherein the vehicle control reference model is obtained based on a two-degree-of-freedom vehicle dynamics model.
In the embodiment of the application, the motion state parameters are calculated according to the acquired motion parameters and by combining the vehicle control reference model.
Specifically, the vehicle control reference model adopts a vehicle dynamics model with two degrees of freedom, and a dynamics equation is established as shown in the following formula:
The kinematic equation is shown in the following formula:
longitudinal vehicle speed estimation based on kinematic model, and corresponding state vector is x= [ v ] x v y a x a y ] T The measurement vector is y= [ a ] x a y ] T The vehicle speed is estimated by adopting a Kalman filtering algorithm, and the vehicle speed is finally calculated by combining the acceleration or braking operation of a driver, namely an accelerator pedal signal, a brake pedal signal and the measured vehicle speed obtained by a GPS controller.
Centroid slip angle estimation based on vehicle dynamicsThe corresponding state vector is x= [ beta gamma v x ] T The measurement vector is y=a y And establishing a state space equation, and estimating the centroid slip angle by adopting a unscented Kalman filtering algorithm.
Based on a longitudinal dynamics model of the vehicle, the following formula is shown:
wherein M is the whole vehicle mass, v is the vehicle longitudinal speed, F t For driving force, F f For rolling resistance, F i For ramp resistance, F w Is air resistance; t (T) t For engine output torque, i 1 And i 2 The transmission ratio of the speed changer and the main speed reducer respectively, eta is transmission efficiency, r is effective radius of a driving wheel, T q For the driving wheel to output torque, f is the rolling resistance coefficient, generally regarded as a fixed constant, ρ is the air density, C d The air resistance coefficient is the air resistance coefficient, and A is the windward area of the automobile.
Thus, it is possible to obtain:
wherein F is err Systematic errors caused by some uncertain environmental disturbances in the longitudinal dynamics equations. To the left of the above formulaFor the longitudinal acceleration measured by the acceleration sensor, then:
order theWherein μ is an equivalent drag coefficient, the above formula can be rewritten as: />
Wherein:
y＝a senx ，
for the estimated vector in the aboveThe two items of the model (a) are respectively the reciprocal of the mass and the equivalent resistance coefficient, wherein the mass cannot change in the running process of the vehicle, the equivalent resistance coefficient can change along with different running environments of the vehicle such as a ramp, and the like, and then the least square method with forgetting factors is adopted to realize the estimation of the mass.
Step S320, the motion environment parameters are input into a vehicle control reference model for processing, road surface adhesion coefficients and slopes are obtained, and the road surface adhesion coefficients and the slopes are used as target motion environment parameters.
In the embodiment of the application, the motion environment parameters acquired from the intelligent linkage system are combined with the vehicle control reference model to calculate the target motion environment parameters.
Specifically, the mass of the vehicle is obtained through the calculation, the mass is substituted into a longitudinal dynamics equation, then the ramp and the mass are decoupled, the ramp can be further calculated based on a longitudinal dynamics model, and the ramp is estimated by adopting a Kalman filtering algorithm in combination with the obtained road ramp.
For a distributed drive vehicle, the torque and rotational speed of each wheel may be obtained from the motor controller, and thus the longitudinal force F of each wheel xi Acceleration a of vehicle x 、a y Yaw rate γ is allObservability of the quantity.
The three-degree-of-freedom dynamics model of the four-wheel vehicle is built as follows:
ma x ＝F x1 cosδ 1 +F x2 cosδ 2 +F x3 +F x4 -F y1 sinδ 1 -F y2 sinδ 2
ma y ＝F x1 sinδ 1 +F x2 sinδ 2 +F y1 cosδ 1 +F y2 cosδ 2 +F y3 +F y4
then, based on a magic formula tire model, a Kalman filter is constructed, wherein the state vector and the observation vector are respectively as follows:
x x ＝[μ 1max ，μ 2max ，μ 3max ，μ 4max ] T ，
and estimating the road surface adhesion coefficient of the vehicle by adopting a Kalman filtering algorithm and combining the road surface curvature and the road surface parameters of the intelligent camera.
Step S230, calculating a motion expected parameter and a motion state parameter through a model predictive control algorithm to obtain a yaw moment;
in the embodiment of the application, a model predictive control algorithm (Model Predictive Control, MPC) can solve a finite time open loop optimization problem on line according to the obtained current measurement information at each adopted moment, and act the first element of the obtained control sequence on the controlled object. At the next sampling moment, repeating the process, taking the new measured value as an initial condition for predicting the future dynamic state of the system at the moment, refreshing the optimization problem and solving again.
The motion expected parameter and the motion state parameter are calculated through a model predictive control algorithm, so that the motion state deviation and the change of the output torque of the vehicle in a future short-time domain can be predicted and calculated, and the yaw moment is further obtained.
In an exemplary embodiment of the present application, referring to fig. 4, in step S230, the motion expected parameter and the motion state parameter are calculated by the model predictive control algorithm to obtain the yaw moment, which includes step S410 and step S420, and is described in detail as follows:
step S410, processing the motion expected parameter and the motion state parameter through a model predictive control algorithm to construct an optimized objective function; the optimization objective function includes, among other things, the energy consumption and wear of the drive system of the vehicle, as well as the optimization of the steering characteristics.
In the embodiment of the application, the motion expected parameter and the motion state parameter are processed through the model predictive control algorithm, so that an optimized objective function is constructed, and the constructed optimized objective function has two items, wherein one item expresses the tracking error of the distributed driving system to the ideal steering characteristic in the predictive time domain, reflects the optimization to the steering characteristic, and the other item expresses the output yaw moment variation of the distributed driving system in the predictive control time domain, and reflects the energy consumption and abrasion of the driving system.
Step S420, a weight coefficient matrix is obtained, and the optimization objective function is processed through the weight coefficient matrix to obtain the yaw moment.
In the embodiment of the application, the weight coefficient matrix is arranged, and the weight coefficient matrix can distribute two items in the optimized objective function, so that the problem is converted into the problem of solving the quadratic programming extremum, and the yaw moment is obtained.
In an exemplary embodiment of the present application, referring to fig. 5, in step S410, the motion expected parameter and the motion state parameter are processed by the model predictive control algorithm to construct an optimized objective function, including steps S510 to S530, which are described in detail below:
step S510, constructing the system state, the control quantity and the system disturbance quantity of the vehicle according to the movement expected parameter and the movement state parameter.
In the embodiment of the application, the system state expression of the vehicle constructed according to the centroid slip angle and the yaw rate is as follows:
x＝[β，γ]
where β is the centroid slip angle of the vehicle and γ is the yaw rate of the vehicle.
The control amount according to the vehicle is expressed as follows:
u＝M z
wherein M is z Is the yaw moment experienced by the vehicle.
The system disturbance amount of the vehicle is expressed as follows:
d＝δ f
wherein delta f For the current steering angle input of the vehicle, d represents the current system disturbance amount of the driving system.
And step S520, obtaining the associated information among the system state, the control quantity and the system disturbance quantity through a two-degree-of-freedom single-rail model of the vehicle.
In the embodiment of the application, after the system state, the control quantity and the system disturbance quantity are obtained, the two-degree-of-freedom single-rail model of the vehicle can be obtained:
thus, the related information of the system state, the control quantity and the system disturbance quantity of the vehicle can be obtained, and the related information is as follows:
step S530, constructing an optimization objective function according to the association information.
In the embodiment of the application, let x r ＝[β d ，γ d ] T The reference yaw rate and the reference centroid slip angle representing the vehicle at time k, the reference states of the available vehicle are as follows:
the reference yaw rate and the reference centroid slip angle represent a target yaw rate and a target centroid slip angle that the vehicle is expected to achieve at the time k.
Taylor expansion is performed at the reference point and ignoring higher order terms may result in:
namely:
order theThe reconstruction of the above formula can be achieved:
is provided with
Order theDiscretizing the system can obtain:
wherein: a is that 1 ＝A 0 ×T+I，B 1 ＝B 0 ×T，D 1 ＝D 0 ×T。
Construction of new state quantityLet Δu (k) =u (k) -u (k-1), the system prediction output be η (k), and it is possible to obtain:
ξ(k+1)＝Aξ(k)+BΔu(k)+Dd(k)
η(k)＝Cξ(k)
Wherein:the prediction time domain of the model prediction control algorithm is N p Control the time domain to be N c The state quantity is N x The control amount is N u The disturbance quantity is N d 。
Is provided with
H(t)＝[η(k+1|t)，η(k+2|t)，…η(k+N p |t)] T
ΔU(t)＝[Δu(k|t)，Δu(k+1|t)，…，Δu(k+N c -1|t)] T
D(t)＝[d(k|t)，d(k+1|t)，…，d(k+N c -1|t)] T
The method can obtain: h (t) =phi ζ (k) +theta Δu (t) +delta D (t)
The construction optimization objective function is shown as follows:
the constructed optimization objective function has two items, wherein the first item expresses the tracking error of the distributed driving system to the ideal steering characteristic in the prediction time domain, reflects the optimization to the steering characteristic, and the second item expresses the output yaw moment variation of the distributed driving system in the prediction control time domain, and reflects the energy consumption and abrasion of the driving system.
In an exemplary embodiment of the present application, referring to fig. 6, in step S420, the optimization objective function is processed through the weight coefficient matrix to obtain the yaw moment, which includes step S610 and step S620, and is described in detail as follows:
and step S610, distributing the optimized objective function through a weight coefficient matrix to obtain an extremum objective function.
In the embodiment of the application, the weight coefficient matrix is shown as follows:
Γ x ＝diag(Γ x，1 ，Γ x，2 ，…，Γ x，p )
Γ u ＝diag(Γ u，1 ，Γ u，2 ，…，Γ u，m )
and distributing two terms of the optimized objective function through the weight coefficient matrix, so that the problem is converted into the problem of solving the quadratic programming extremum, and the extremum objective function is obtained.
And S620, calculating an optimal solution of the extremum objective function through an interior point method to obtain the yaw moment.
In the embodiment of the application, a punishment function is arranged in the interior point method and used for describing the convex set, and the interior point method searches the optimal solution by traversing the internal feasible region. And solving an optimal solution to the quadratic programming problem by an interior point method, so as to obtain an optimal yaw moment.
Step S240, calculating driving torques for the respective wheels of the vehicle according to the yaw moment and the target motion environment parameter, and controlling the vehicle according to the driving torques for the respective wheels.
In the embodiment of the application, after the yaw moment is calculated, the driving torque of each vehicle is calculated according to the yaw moment and the target motion environment parameter, and the calculated driving torque is output to the motor controller, so that the motor controller drives each vehicle according to the received driving torque.
In the embodiment of the application, the control demand parameters of a driver, the motion parameters of the vehicle and the motion environment parameters are identified by acquiring the sensor signals arranged on the vehicle and the data in the intelligent linkage system, the motion state parameters and the target motion environment parameters in the current state are obtained by calculating the built-in vehicle motion reference model, then the motion state errors of the vehicle in the prediction time domain and the output torque in the control time domain are obtained by solving the model prediction control algorithm, and the distribution of the two is regulated by the weight coefficient matrix, so that the optimization problem of solving the quadratic programming extremum is converted, and the obtained yaw moment is obtained, so that the optimal balance between the ideal steering characteristic and the torque output of the vehicle is obtained. And finally, calculating the driving torque of each wheel according to the yaw moment and the target motion environment parameter, and controlling each wheel to execute the corresponding driving torque to realize the distributed driving control of the vehicle.
In the existing vehicle control scheme, the calculation of the yaw moment is based on the feedback control of the vehicle state, the target yaw angular speed deviation and the centroid side deviation angle deviation are utilized to solve the yaw moment through a feedback control algorithm, and the feedback control algorithm is adopted to possibly cause the problems that when the target deviation is large, the control quantity is too large, the torque overshoot is caused, the slip rate is increased, and the energy consumption and the abrasion are increased. The application utilizes the model predictive control algorithm to calculate the yaw moment, can predict and calculate the motion state deviation and the change of the output torque of the vehicle in the short time domain in the future, and solves the optimal yaw moment, so that the optimal balance and control between the ideal steering characteristic and the torque output of the vehicle in the predictive control time domain are obtained, the maneuverability and the energy consumption of the vehicle are optimized, and the slip rate of the vehicle is reduced.
In an exemplary embodiment of the present application, referring to fig. 7, the driving torque for each wheel of the vehicle is calculated according to the yaw moment and the target motion environment parameter in step S240, including steps S710 to S720, which are described in detail as follows:
step S710, a throttle request of a driver to the vehicle is obtained, and a longitudinal moment to the vehicle is calculated according to the throttle request.
In the embodiment of the application, the throttle request of a driver to the vehicle is obtained, and the longitudinal moment of the control requirement of the vehicle is calculated according to the throttle request.
Step S720, calculating driving torque for each wheel of the vehicle according to the longitudinal moment, the yaw moment and the target motion environment parameter.
In the embodiment of the application, the driving torque of each wheel of the vehicle is jointly calculated according to the calculated longitudinal moment, yaw moment and target motion environment parameters, so that the finally calculated driving torque can be more accurate.
In an exemplary embodiment of the present application, the driving torque to each wheel of the vehicle is calculated according to the longitudinal moment, the yaw moment, and the target motion environment parameter in step S720, including the following steps, which are described in detail as follows:
and calculating longitudinal moment, yaw moment and target motion environment parameters through a preset optimization algorithm to obtain driving torques of all the wheels, so that when the vehicle is controlled through the driving torques of all the wheels, the attachment margin of the wheels is maximum and the efficiency is highest.
In the embodiment of the application, an optimization algorithm is preset, the optimization algorithm takes the maximum attachment margin and the highest efficiency of each wheel as targets, longitudinal moment, yaw moment and target motion environment parameters are calculated by the optimization algorithm, the optimal driving torque of each wheel is calculated, then the calculated driving torque is output to a motor controller, and the motor controller controls the corresponding wheel according to the driving torque to realize the control of the vehicle.
In an exemplary embodiment of the present application, referring to fig. 8, fig. 8 is a control apparatus of a vehicle according to an exemplary embodiment, including:
an acquisition module 810 configured to acquire a motion parameter and a motion environment parameter of the vehicle, and a control demand parameter of the driver for the vehicle;
a first calculation module 820 configured to calculate a motion desire parameter of the driver according to the control demand parameter, calculate a motion state parameter according to the motion parameter, and calculate a target motion environment parameter according to the motion environment parameter;
the second calculation module 830 is configured to calculate the motion expected parameter and the motion state parameter through a model prediction control algorithm, so as to obtain a yaw moment;
the third calculation module 840 is configured to calculate driving torques to the respective wheels of the vehicle based on the yaw moment and the target motion environment parameter, and to control the vehicle based on the driving torques of the respective wheels.
In one exemplary embodiment of the application, the third calculation module 840 includes:
the acquisition sub-module is configured to acquire an accelerator request of a driver to the vehicle and calculate a longitudinal moment to the vehicle according to the accelerator request;
a first calculation sub-module configured to calculate driving torques for respective wheels of the vehicle based on the longitudinal moment, the yaw moment, and the target motion environment parameter.
In one exemplary embodiment of the application, a first computing sub-module includes:
the first calculation unit is configured to calculate the longitudinal moment, the yaw moment and the target motion environment parameters through a preset optimization algorithm to obtain driving torques of all the wheels, so that when the vehicle is controlled through the driving torques of all the wheels, the attachment margin of the wheels is maximum and the efficiency is highest.
In one exemplary embodiment of the application, the motion parameters include rotational speed of each wheel, lateral acceleration of the vehicle, longitudinal acceleration of the vehicle, yaw rate of the vehicle; the movement environment parameters comprise road curvature, road ramp and road surface parameters; the first computing module 820 includes:
the first input sub-module is configured to input motion parameters into a preset vehicle control reference model for processing to obtain the speed, mass and mass center slip angle of the vehicle, and the speed, mass and mass center slip angle are used as motion state parameters; the vehicle control reference model is obtained based on a vehicle dynamics model with two degrees of freedom; and
and the second input submodule is configured to input the motion environment parameters into the vehicle control reference model for processing to obtain road surface adhesion coefficients and the ramp, and the road surface adhesion coefficients and the ramp are used as target motion environment parameters.
In one exemplary embodiment of the present application, the second calculation module 830 includes:
the construction submodule is configured to process the motion expected parameter and the motion state parameter through a model prediction control algorithm and construct an optimized objective function; wherein the optimization objective function includes energy consumption and wear of a drive system of the vehicle, and optimization of steering characteristics;
and the processing sub-module is configured to acquire a weight coefficient matrix, and process the optimized objective function through the weight coefficient matrix to obtain the yaw moment.
In one exemplary embodiment of the application, a building sub-module comprises:
a first construction unit configured to construct a system state, a control amount, and a system disturbance amount of the vehicle according to the movement expectation parameter and the movement state parameter;
the association information unit is configured to obtain association information among the system state, the control quantity and the system disturbance quantity through a two-degree-of-freedom single-rail model of the vehicle;
and a second construction unit configured to construct an optimization objective function based on the association information.
In one exemplary embodiment of the application, a processing sub-module includes:
the distribution unit is configured to distribute the optimized objective function through the weight coefficient matrix to obtain an extremum objective function;
And the second calculation unit is configured to calculate the optimal solution of the extremum objective function through the interior point method to obtain the yaw moment.
It should be noted that, the apparatus provided in the foregoing embodiments and the method provided in the foregoing embodiments belong to the same concept, and the specific manner in which each module and unit perform the operation has been described in detail in the method embodiments, which is not repeated herein.
The embodiment of the application also provides electronic equipment, which comprises: one or more processors; and a storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the control method of the vehicle provided in the respective embodiments described above.
Fig. 9 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.
It should be noted that, the computer system 900 of the electronic device shown in fig. 9 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 9, the computer system 900 includes a central processing unit (Central Processing Unit, CPU) 901 which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 902 or a program loaded from a storage portion 908 into a random access Memory (Random Access Memory, RAM) 903, for example, performing the method described in the above embodiment. In the RAM 903, various programs and data required for system operation are also stored. The CPU 901, ROM 902, and RAM 903 are connected to each other through a bus 904. An Input/Output (I/O) interface 905 is also connected to bus 904.
The following components are connected to the I/O interface 905: an input section 906 including a keyboard, a mouse, and the like; an output section 907 including a speaker and the like, such as a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and the like; a storage portion 908 including a hard disk or the like; and a communication section 909 including a network interface card such as a LAN (local area network) card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as needed. Removable media 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed as needed into the storage section 908.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. When the computer program is executed by a Central Processing Unit (CPU) 901, various functions defined in the system of the present application are performed.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
Another aspect of the application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.
Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device performs the control method of the vehicle provided in the above-described respective embodiments;
the control method of the vehicle comprises the following steps:
acquiring motion parameters and motion environment parameters of a vehicle, and controlling demand parameters of a driver on the vehicle;
calculating a motion expected parameter of a driver according to the control demand parameter, calculating a motion state parameter according to the motion parameter, and calculating a target motion environment parameter according to the motion environment parameter;
Calculating a motion expected parameter and a motion state parameter through a model predictive control algorithm to obtain a yaw moment;
and calculating driving torques of all wheels of the vehicle according to the yaw moment and the target motion environment parameters, and controlling the vehicle according to the driving torques of all the wheels.
The foregoing is merely illustrative of the preferred embodiments of the present application and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make corresponding variations or modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be defined by the claims.
Claims (10)
1. A control method of a vehicle, characterized by comprising:
acquiring motion parameters and motion environment parameters of a vehicle, and controlling demand parameters of a driver on the vehicle;
calculating a motion expected parameter of the driver according to the control demand parameter, calculating a motion state parameter according to the motion parameter, and calculating a target motion environment parameter according to the motion environment parameter;
calculating the motion expected parameter and the motion state parameter through a model predictive control algorithm to obtain a yaw moment;
And calculating driving torques of all wheels of the vehicle according to the yaw moment and the target motion environment parameter, and controlling the vehicle according to the driving torques of all the wheels.
2. The method of claim 1, wherein said calculating a driving torque for each wheel of said vehicle based on said yaw moment and said target motion environment parameter comprises:
acquiring an accelerator request of the driver to the vehicle, and calculating a longitudinal moment to the vehicle according to the accelerator request;
and calculating driving torque of each wheel of the vehicle according to the longitudinal moment, the yaw moment and the target motion environment parameter.
3. The method of claim 2, wherein said calculating drive torque to each wheel of said vehicle based on said longitudinal moment, said yaw moment, and said target motion environment parameter comprises:
and calculating the longitudinal moment, the yaw moment and the target motion environment parameters through a preset optimization algorithm to obtain driving torques of all the wheels, so that when the vehicle is controlled through the driving torques of all the wheels, the attachment margin of the wheels is maximum and the efficiency is highest.
4. The method of claim 1, wherein the motion parameters include rotational speed of each wheel, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle yaw rate; the motion environment parameters comprise road curvature, road ramp and road surface parameters; the calculating the motion state parameter according to the motion parameter and the calculating the target motion environment parameter according to the motion environment parameter comprises the following steps:
inputting the motion parameters into a preset vehicle control reference model for processing to obtain the speed, mass and mass center slip angle of the vehicle, and taking the speed, mass and mass center slip angle as motion state parameters; the vehicle control reference model is obtained based on a vehicle dynamics model with two degrees of freedom;
and inputting the motion environment parameters into the vehicle control reference model for processing to obtain road surface adhesion coefficients and ramps, and taking the road surface adhesion coefficients and the ramps as the target motion environment parameters.
5. The method of claim 1, wherein said calculating said motion desire parameter and said motion state parameter by a model predictive control algorithm to obtain a yaw moment comprises:
Processing the motion expected parameters and the motion state parameters through a model predictive control algorithm to construct an optimized objective function; wherein the optimization objective function includes energy consumption and wear of a drive system of the vehicle, and optimization of steering characteristics;
and acquiring a weight coefficient matrix, and processing the optimized objective function through the weight coefficient matrix to obtain the yaw moment.
6. The method of claim 5, wherein said processing said motion desired parameter and said motion state parameter by a model predictive control algorithm to construct an optimized objective function comprises:
constructing a system state, a control quantity and a system disturbance quantity of the vehicle according to the motion expected parameter and the motion state parameter;
obtaining the association information among the system state, the control quantity and the system disturbance quantity through a two-degree-of-freedom single-rail model of the vehicle;
and constructing the optimization objective function according to the association information.
7. The method of claim 5, wherein said processing said optimization objective function through said weight coefficient matrix to obtain said yaw moment comprises:
Distributing the optimized objective function through the weight coefficient matrix to obtain an extremum objective function;
and calculating the optimal solution of the extremum objective function by an interior point method to obtain the yaw moment.
8. A control device for a vehicle, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is configured to acquire motion parameters and motion environment parameters of a vehicle and control demand parameters of a driver on the vehicle;
a first calculation module configured to calculate a movement expectation parameter of the driver according to the control demand parameter, calculate a movement state parameter according to the movement parameter, and calculate a target movement environment parameter according to the movement environment parameter;
the second calculation module is configured to calculate the motion expected parameter and the motion state parameter through a model predictive control algorithm to obtain a yaw moment;
and a third calculation module configured to calculate driving torques for respective wheels of the vehicle based on the yaw moment and the target motion environment parameter, and to control the vehicle based on the driving torques for the respective wheels.
9. An electronic device, comprising:
one or more processors;
Storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the method of controlling a vehicle as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon computer-readable instructions that, when executed by a processor of a computer, cause the computer to perform the method of controlling a vehicle according to any one of claims 1 to 7.
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