CN113183957A - Vehicle control method, device and equipment and automatic driving vehicle - Google Patents

Vehicle control method, device and equipment and automatic driving vehicle Download PDF

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Publication number
CN113183957A
CN113183957A CN202110567584.0A CN202110567584A CN113183957A CN 113183957 A CN113183957 A CN 113183957A CN 202110567584 A CN202110567584 A CN 202110567584A CN 113183957 A CN113183957 A CN 113183957A
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vehicle
feedback
corner
control
front wheel
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骆振兴
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Qianhai Qijian Technology Shenzhen Co ltd
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Qianhai Qijian Technology Shenzhen Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/14—Adaptive cruise control
    • 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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
    • 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
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems

Abstract

The embodiment of the application provides a vehicle control method, a device and equipment and an automatic driving vehicle, wherein the method comprises the following steps: acquiring the current running speed of the vehicle, and then determining a group of feedback gains according to the current running speed and the feedback gain information; and determining a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed according to a group of feedback gains, further determining a front wheel expected corner corresponding to the current running speed, and generating a control command according to the front wheel expected corner to control the vehicle to run according to a preset tracking track. According to the vehicle control method, the calculation amount for obtaining the expected corner of the front wheel can be reduced by calculating the optimal feedback gain matrix off line, the influence on vehicle control is considered from multiple aspects, the tracking precision of the vehicle running track is improved, and the transverse control effect on the vehicle is better.

Description

Vehicle control method, device and equipment and automatic driving vehicle
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, and a device for controlling a vehicle, and an autonomous vehicle.
Background
With the rapid development of intelligent automobiles and intelligent transportation systems, automatic driving becomes a product of deep integration of the automobile industry and new-generation information technologies such as artificial intelligence, internet of things, high-performance computing and the like, and is a main direction of the current intelligent and networking development of the global automobile and transportation travel field. The three parts supplement each other to realize safe, comfortable, energy-saving and efficient automatic driving of the intelligent vehicle. The method is characterized in that vehicle transverse path tracking control is used as the last ring of automatic driving, and on the premise of ensuring the stability, safety and comfort of the vehicle, a steering system is controlled to enable the vehicle to run along an expected path, and the functional safety of the vehicle is in the most important position, so that the method is the key for realizing the core value of the vehicle.
Currently, vehicle transverse path tracking control is based on an optimal control method of an LQR (linear quadratic regulator) to calculate an expected steering angle in real time, and a steering command is generated according to the steering angle to control an automatic driving vehicle to drive according to a preset track. However, the convergence times of the iterative method are uncontrollable, the time required for iterative calculation is long, and particularly in a low-speed driving scene, so that the method has poor control effect on the transverse path tracking control of the automatic driving vehicle.
Disclosure of Invention
The embodiment of the application provides a vehicle control method, a device and equipment and an automatic driving vehicle, an optimal feedback gain matrix can be calculated off line, the calculation amount of the expected rotation angle of a front wheel is reduced, the feedforward control of a vehicle path tracking track is considered when the expected rotation angle of the front wheel is calculated, the transverse displacement control, the steering wheel hysteresis control, the curvature feedforward control of the tracking track and the road inclination feedforward control are also considered, the tracking precision of a vehicle driving track is improved, and the transverse control effect of the vehicle is better.
A first aspect of an embodiment of the present application provides a vehicle control method, including:
acquiring the current running speed of the vehicle;
determining a set of feedback gains according to the current running speed and the feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of the linear quadratic controller, and the control parameters comprise a positive semi-definite matrix and a positive definite matrix;
determining a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed according to a group of feedback gains;
determining a front wheel expected steering angle corresponding to the current running speed according to the feedback steering angle, the feedforward steering angle and the correction steering angle;
and generating a control command according to the expected turning angle of the front wheel to control the vehicle to run according to a preset tracking track.
In one embodiment, determining a feedback steering angle, a feed-forward steering angle, and a correction steering angle corresponding to the current driving speed based on a set of feedback gains includes:
determining a feedback corner according to an optimal feedback gain matrix consisting of a group of feedback gains and a state vector;
determining a feedforward corner according to the final value theorem, one feedback gain in a group of feedback gains, the preset curvature of the tracking track and the attribute parameters of the vehicle;
and determining a correction corner according to preset configuration parameters, the front wheel corner at the current moment, the front wheel corner at the previous moment and a control cycle, wherein the front wheel corner at the current moment and the front wheel corner at the previous moment are acquired by a sensor arranged on a vehicle chassis.
In one embodiment, the method further comprises:
according to m, L1、L2、I、C1、C2、V、
Figure BDA0003081329350000021
y and delta determine a two-degree-of-freedom kinetic equation of the vehicle, wherein m is the mass of the whole vehicle and L1Is the distance of the center of mass to the front axis, L2Is the distance from the center of mass to the rear axle, I is the moment of inertia of the whole vehicle, C1For front tyre cornering stiffnessDegree C2The cornering power of the rear tire, V the running speed,
Figure BDA0003081329350000022
the transverse offset speed is, y is the transverse offset, and delta is the expected turning angle of the front wheel;
processing a two-degree-of-freedom kinetic equation of the vehicle according to a second Liya Ponuf stable method to obtain a Riccati equation;
carrying out iterative calculation on the Riccati equation according to different driving speeds of the vehicle to obtain a converged transfer function;
and determining an optimal feedback gain matrix corresponding to each running speed according to the transfer function, the control parameters and the attribute parameters of the vehicle, wherein the optimal feedback gain matrix comprises a group of feedback gains.
In one embodiment, before processing the vehicle state equations to obtain the Riccati equation according to the second Liasian Voronoi stability method, the method includes:
according to e1、e2、
Figure BDA0003081329350000039
ψdes、
Figure BDA0003081329350000038
ψ、
Figure BDA0003081329350000031
And
Figure BDA0003081329350000032
processing a two-degree-of-freedom kinetic equation of the vehicle to obtain a path tracking deviation state equation, wherein the path tracking deviation state equation represents the response of the path tracking deviation corresponding to the preset front wheel steering angle, and e1For lateral deviation, e2As the yaw angle deviation, there is a deviation,
Figure BDA0003081329350000033
in order to be a lateral speed deviation,
Figure BDA0003081329350000034
as the yaw-rate deviation, there is,
Figure BDA0003081329350000035
as yaw rate, psidesIs the yaw angle.
In one embodiment, in accordance with e1、e2、
Figure BDA0003081329350000036
And
Figure BDA0003081329350000037
after the vehicle two-degree-of-freedom kinetic equation is processed to obtain a path tracking deviation state equation, the method comprises the following steps:
and expanding the path tracking deviation state equation according to a preset integral error term of the transverse displacement deviation to obtain a continuous state equation.
In one embodiment, the method further comprises:
discretizing the continuous state equation to obtain a discrete state equation;
and processing the discrete state equation according to the second Liasia Ponux stability method to obtain a Riccati equation.
In one embodiment, after the path tracking deviation state equation is extended according to the integral error term of the preset transverse displacement deviation to obtain a continuous state equation, the method includes:
and judging the stability of the continuous state equation according to the first Liya Punuo method.
A second aspect of the present application provides a vehicle control apparatus including:
the acquisition module is used for acquiring the current running speed of the vehicle;
the processing module is used for determining a group of feedback gains according to the current running speed and the feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of the linear quadratic controller, and the control parameters comprise a positive semi-definite matrix and a positive definite matrix;
the processing module is used for determining a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed according to a group of feedback gains;
the processing module is used for determining a front wheel expected steering angle corresponding to the current running speed according to the feedback steering angle, the feedforward steering angle and the correction steering angle;
and the control module is used for generating a control instruction according to the expected turning angle of the front wheel to control the vehicle to run according to a preset tracking track.
A third aspect of the application provides a vehicle control apparatus comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, carries out the steps of the method as claimed in any one of the preceding claims.
A fourth aspect of the present application provides an autonomous vehicle comprising: the vehicle comprises a vehicle body and a control device, wherein the control device controls the vehicle body to run by adopting any one of the control methods.
The embodiment of the application provides a vehicle control method, a device and equipment and an automatic driving vehicle, wherein the method comprises the following steps: the linear quadratic control system acquires the current running speed of the vehicle through a sensor arranged on the vehicle, and then determines a group of feedback gains according to the current running speed and the feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of the linear quadratic controller, the control parameters comprise a positive semi-fixed weighting matrix and a positive fixed weighting matrix, a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed are determined according to a group of feedback gains, and finally a front wheel expected corner corresponding to the current running speed is determined according to the feedback corner, the feedforward corner and the correction corner, so that a control instruction is generated according to the front wheel expected corner to control the vehicle to run according to a preset tracking track. According to the vehicle control method, the calculation amount for obtaining the expected rotation angle of the front wheel can be reduced by calculating the optimal feedback gain matrix off line, and the feedforward control of the vehicle path tracking track, the transverse displacement control, the steering wheel hysteresis control, the curvature feedforward control of the tracking track and the road inclination feedforward control are considered when the expected rotation angle of the front wheel is calculated, so that the tracking precision of the vehicle running track is improved, and the transverse control effect of the vehicle is better.
Drawings
FIG. 1 is a schematic flow chart diagram of a vehicle control method in one embodiment;
FIG. 2 is a schematic flow chart of a vehicle control method in another embodiment;
FIG. 3 is a schematic diagram of a process for solving Riccati's equation in another embodiment;
FIG. 4 is a block diagram showing the construction of a vehicle control apparatus according to one embodiment;
fig. 5 is an internal structural view of a vehicle control apparatus in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
And the automatic driving vehicle transverse control calculates the expected turning angle of the front wheel in real time according to the preset tracking track and the vehicle positioning information, and generates a control instruction to control the vehicle to run according to the preset tracking track. At present, a linear quadratic control method is widely adopted for lateral control of an automatic driving vehicle, but the linear quadratic control method has the following problems: (1) the optimal feedback gain matrix is generally obtained by solving the Riccati equation on line, but the convergence times of the iterative method are uncontrollable, the time required by iterative calculation is long, and particularly in a low-speed driving scene, so that the method has poor control effect on the transverse path tracking control of the automatic driving vehicle. (2) The prior automatic driving vehicle transverse control system mostly adopts the method of introducing the road curvature feedforward compensation to reduce the steady-state error, does not consider the transverse steady-state error caused by the road inclination, and reduces the quality of the vehicle transverse control system; (3) the attribute parameters of the vehicle have certain uncertainty, and the steady-state error of the lateral control of the vehicle cannot be completely eliminated. Based on the problems in the prior art, the present application provides a vehicle control method, as shown in fig. 1, specifically including the following steps:
and S102, acquiring the current running speed of the vehicle.
The current running speed of the vehicle can be obtained according to a sensor arranged on the vehicle and transmitted to the linear quadratic controller, and the linear quadratic controller calculates a desired turning angle of the front wheel corresponding to the current running speed according to the current running speed of the vehicle, so that the tracking accuracy of a preset tracking track of the vehicle is improved.
S104, determining a group of feedback gains according to the current running speed and the feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of the linear quadratic controller, and the control parameters comprise a positive semi-definite weighting matrix and a positive definite weighting matrix.
The driving speed and the feedback gain are in a one-to-one correspondence relationship, and the feedback gain is pre-stored in the linear quadratic controller through off-line calculation, and the feedback gain information may be, for example, a table of correspondence relationship between the driving speed and the feedback gain as shown below:
Figure BDA0003081329350000061
Figure BDA0003081329350000071
and after the running speed of the vehicle is obtained, obtaining a feedback gain corresponding to the running speed by inquiring the corresponding relation table of the running speed and the feedback gain, and then quickly calculating according to the feedback gain to obtain the expected turning angle of the front wheel. The feedback gain does not need to be calculated through the Riccati equation according to the vehicle speed in real time, and then the expected rotation angle of the front wheel is calculated according to the feedback gain, so that the calculation amount is greatly reduced.
And S106, determining a feedback steering angle, a feedforward steering angle and a correction steering angle corresponding to the current running speed according to a group of feedback gains.
The feedback corner is obtained based on the LQR best control theory and is used for representing feedback control for performing transverse optimal control on the vehicle; the feedforward corner is used for representing the feedforward control of the curvature change of the vehicle running road on the vehicle, the transverse deviation tends to be zero and is obtained in a stable state according to a final value theorem, and the compensation of the feedforward corner is used for eliminating the steady-state error caused by the lateral wind received by the vehicle or the curvature change of the vehicle running road. The correction steering angle is used to take into account the influence of the steering wheel hysteresis on the vehicle control. The influence brought when the vehicle is controlled is considered from multiple aspects, and therefore the accuracy of vehicle control is improved.
And S108, determining a desired steering angle of the front wheel corresponding to the current running speed according to the feedback steering angle, the feedforward steering angle and the correction steering angle.
The final output expected corner of the front wheel is obtained by summing the feedback corner, the feedforward corner and the correction corner, the influence of different factors on vehicle control is considered, the influence angles under the influence of different factors are calculated, the output expected corner of the front wheel is finally determined, and the control of the vehicle is more accurate.
And S110, generating a control command according to the expected turning angle of the front wheels to control the vehicle to run according to a preset tracking track.
And the linear quadratic controller controls the front wheels to turn according to the expected rotation angle of the front wheels according to the control instruction, so as to realize the tracking of the preset tracking track.
In one embodiment, the method how to determine the feedback steering angle, the feed-forward steering angle and the correction steering angle corresponding to the current driving speed according to a set of feedback gains includes the following steps:
determining a feedback corner according to an optimal feedback gain matrix consisting of a group of feedback gains and a state vector;
determining a feedforward corner according to the final value theorem, one feedback gain in a group of feedback gains, the preset curvature of the tracking track and the attribute parameters of the vehicle;
and determining a correction corner according to preset configuration parameters, the front wheel corner at the current moment, the front wheel corner at the previous moment and a control cycle, wherein the front wheel corner at the current moment and the front wheel corner at the previous moment are acquired by a sensor arranged on a vehicle chassis.
The feedback corner is obtained by multiplying the most feedback matrix formed by a group of feedback gains by a preset state vector and then inverting the multiplication result. Therefore, after a group of feedback gains are obtained according to the current running speed, the feedback turning angle can be quickly calculated, and the control efficiency of the vehicle is improved.
The feedforward corner is determined according to a final value theorem, one feedback gain in a group of feedback gains, the curvature of a preset tracking track and the attribute parameters of the vehicle, the calculation process needs more parameters, the considered influence factors are more comprehensive, and the calculated feedforward corner is more accurate.
When the correction steering angle is calculated, the required parameters are preset or directly acquired, data are easy to acquire, the calculation result of the correction steering angle can be obtained more quickly, and the control efficiency of the vehicle is further improved.
The following specifically describes how to obtain the feedback gain, and the calculation process of obtaining the feedback rotation angle, the feedforward rotation angle, and the correction rotation angle according to the parameters such as the feedback gain:
first, according to m, L1、L2、I、C1、C2、V、
Figure BDA0003081329350000081
y、ψ、
Figure BDA0003081329350000082
And delta determinationA two-degree-of-freedom kinetic equation of the vehicle, wherein m is the mass of the whole vehicle and L1Is the distance of the center of mass to the front axis, L2Is the distance from the center of mass to the rear axle, I is the moment of inertia of the whole vehicle, C1For front tire cornering stiffness, C2V is the running speed,
Figure BDA0003081329350000096
is the lateral offset velocity, y is the lateral offset, ψ is the heading angle of the vehicle,
Figure BDA00030813293500000920
δ is the desired turning angle of the front wheels, which is the heading angular velocity of the vehicle. The two-degree-of-freedom kinetic equation of the vehicle is as follows:
Figure BDA0003081329350000091
according to e1、e2、
Figure BDA0003081329350000099
ψdes、
Figure BDA00030813293500000910
ψ、
Figure BDA0003081329350000097
And
Figure BDA0003081329350000098
processing the vehicle state equation to obtain a path tracking deviation state equation, wherein e1For lateral deviation, e2As the yaw angle deviation, there is a deviation,
Figure BDA00030813293500000911
in order to be a lateral speed deviation,
Figure BDA00030813293500000912
as the yaw-rate deviation, there is,
Figure BDA00030813293500000913
as yaw rate, psidesIs the yaw angle.
Wherein e is2＝ψ-ψdes、
Figure BDA00030813293500000914
In order to reduce the vehicle track tracking deviation, the vehicle state equation is processed to obtain a path tracking deviation state equation, and the path tracking deviation state equation can analyze the response of the path tracking deviation under the given front wheel rotation angle. First to e1、e2、
Figure BDA00030813293500000919
Is derived and then according to e1、e2、
Figure BDA00030813293500000915
And
Figure BDA00030813293500000916
ψdes、
Figure BDA00030813293500000917
ψ、
Figure BDA00030813293500000918
processing the vehicle state equation to obtain a path tracking deviation state equation (2):
Figure BDA0003081329350000092
wherein the content of the first and second substances,
Figure BDA0003081329350000093
Figure BDA0003081329350000094
Figure BDA0003081329350000095
since the vehicle is affected by the side wind and/or encounters rough road surface during the driving process, in order to keep the vehicle running in the middle of the road all the time, the lateral displacement deviation of the vehicle under the influence of the side wind and the road condition is considered. In particular, an integral error term defining the lateral displacement deviation
Figure BDA0003081329350000101
And a state vector
Figure BDA0003081329350000102
Expanding the path tracking deviation state equation to obtain a continuous state equation:
Figure BDA0003081329350000106
wherein A, B, C is an expansion matrix:
Figure BDA0003081329350000103
since the continuous equation of state can only be used if it is stable, the stability of the continuous equation of state needs to be determined by rewriting equation (3) as:
Figure BDA0003081329350000104
calculating vehicle speeds at [ -20,50 ] at different vehicle speeds]In the m/s range and the vehicle speed can not be equal to 0, verify [ B, AB, A ]2B，A3B]And if the continuous state equations are all full rank, the continuous state equations are considered to be stable. At the same time, the vehicle speed is calculated to be [ -20,50 ] at different vehicle speeds]In the m/s range and the vehicle speed is not equal to 0, verify [ C, CA%2，CA3]TAnd if the continuous state equations are all full rank, the continuous state equations are considered to be stable.
In order to facilitate the calculation of a computer, discretization processing needs to be performed on a continuous state equation to obtain a discrete state equation:
Figure BDA0003081329350000105
wherein A isd＝1+A*Ts，Bd＝B*Ts，CdTs is the vehicle control period.
Based on the formula (4), the response characteristic of the path tracking deviation of the vehicle under the front wheel corner control input can be analyzed, according to the optimal control theory, the expected response characteristic is that the path tracking deviation can quickly and stably approach zero and keep balance, and meanwhile, the front wheel corner control input is as small as possible, so that the problem of multi-objective optimal control is converted. In the range of 0 to infinity, the optimum target J is set so that the tracking deviation and the input tire rotational angle control amount are weighted to the minimum. The optimized objective function is a weighted sum of the accumulated tracking offset and the accumulated control input of the tracking process, as shown in (5):
Figure BDA0003081329350000111
j is an optimal target, Q is a semi-positive definite weighting matrix, R is a positive definite weighting matrix, Q and R are usually taken as diagonal matrices, the enlargement of matrix elements in Q represents that the expected tracking deviation can quickly approach zero, and the enlargement of matrix elements in R represents that the control input is as small as possible. So the first term in equation (5) optimizes the objective
Figure BDA0003081329350000112
Representing cumulative magnitude of path deviation of tracking process, second optimization objective
Figure BDA0003081329350000113
Representing the loss of control energy to track the process.
According to the LQR optimal control theory, the formula (5) is optimized and solved to obtain a linear function related to the state vector e:
Figure BDA0003081329350000114
wherein S is a transfer function iteratively calculated according to the ricati equation.
The Riccati equation is obtained by processing the formula (4) according to the second Liasia Ponoff stability method:
Figure BDA0003081329350000115
as shown in fig. 3, fig. 3 is a flowchart of iteratively calculating the ricacies equation to obtain the transfer function S.
Since the equation of continuous state of formula (3) has both state feedback control and external disturbance of road curvature change, it is necessary to calculate the feedback rotation angle by the optimal control theory. Specifically, a state feedback regulator may be preset, closed-loop optimal control is realized through state feedback, and an optimal feedback gain matrix is obtained according to the linear function:
Figure BDA0003081329350000116
K＝[K1，K2，K3，K4，K5]the optimal feedback gain matrix is obtained according to different vehicle running speeds and a transfer function S, and matrix elements in the optimal feedback gain matrix represent feedback gains (K)1，K2，K3，K4，K5)。
According to the LQR feedback control principle, the feedback turn angle delta can be obtained by the feedback matrix obtained by the calculationfeedThe calculation formula of (c) is as follows: ,
δfeed＝δ*＝-Ke (7)
as shown in fig. 2, since the interference caused by the road curvature change, which is the dynamic change characteristic of the path itself, is not considered in the process of solving the feedback rotation angle by the LQR feedback control, if the front wheel rotation angle of the vehicle is controlled only according to the feedback rotation angle, a steady-state error may existIn order to eliminate the steady state error, a feedforward rotation angle delta is also addedff，δffThe calculation formula (4) is obtained by processing the formula (4) according to a final value theorem:
Figure BDA0003081329350000121
where ρ is the predicted road curvature, K4Is one of a set of adjustment coefficients in the feedback matrix K.
If the hysteresis of the steering wheel is not considered, the front wheel steering angle is δ ═ δfeed+δffHowever, in consideration of the steering wheel hysteresis, the input front wheel steering angle needs to be corrected, and the corrected steering angle is obtained according to the following equation (9):
Figure BDA0003081329350000122
where δ (K +1) is the front wheel corner at the current time, δ (K) is the front wheel corner at the previous time, and KdAnd Ts is a control period for preset configuration parameters.
Therefore, the final desired steering angle of the front wheels corresponding to the formal speed of the vehicle is calculated from (10):
δ＝δfeed+δff+δdelay (10)
according to the calculation process, a plurality of feedback gains corresponding to the running speeds of different vehicles are calculated in an off-line mode, real-time calculation is not needed in the running process of the vehicles, the problems that iteration convergence times of the Riccati equation are not controllable in a low-speed running scene, and time required by iteration calculation is long are solved, the required front wheel rotating angle of the vehicle can be obtained quickly according to the running speed of the vehicle, and the vehicle can be controlled better.
It should be understood that although the various steps in the flow charts of fig. 1-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 4, there is provided a vehicle control apparatus including:
the acquisition module is used for acquiring the current running speed of the vehicle;
the processing module is used for determining a group of feedback gains according to the current running speed and the feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of the linear quadratic controller, and the control parameters comprise a positive semi-definite matrix and a positive definite matrix;
the processing module is used for determining a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed according to a group of feedback gains;
the processing module is used for determining a front wheel expected steering angle corresponding to the current running speed according to the feedback steering angle, the feedforward steering angle and the correction steering angle;
and the control module is used for generating a control instruction according to the expected turning angle of the front wheel to control the vehicle to run according to a preset tracking track.
In another embodiment, the processing module is specifically configured to determine a feedback rotation angle according to an optimal feedback gain matrix composed of a set of feedback gains and a state vector; determining a feedforward corner according to the final value theorem, one feedback gain in a group of feedback gains, the preset curvature of the tracking track and the attribute parameters of the vehicle; and determining a correction corner according to preset configuration parameters, the front wheel corner at the current moment, the front wheel corner at the previous moment and a control cycle, wherein the front wheel corner at the current moment and the front wheel corner at the previous moment are acquired by a sensor arranged on a vehicle chassis.
In another embodiment, the processing module is specifically configured to process the data according to m, L1、L2、I、C1、C2、V、
Figure BDA0003081329350000141
y and delta determine a two-degree-of-freedom kinetic equation of the vehicle, wherein m is the mass of the whole vehicle and L1Is the distance of the center of mass to the front axis, L2Is the distance from the center of mass to the rear axle, I is the moment of inertia of the whole vehicle, C1For front tire cornering stiffness, C2The cornering power of the rear tire, V the running speed,
Figure BDA0003081329350000142
the transverse offset speed is, y is the transverse offset, and delta is the expected turning angle of the front wheel; processing a two-degree-of-freedom kinetic equation of the vehicle according to a second Liya Ponuf stable method to obtain a Riccati equation; carrying out iterative calculation on the Riccati equation according to different driving speeds of the vehicle to obtain a converged transfer function; and determining an optimal feedback gain matrix corresponding to each running speed according to the transfer function, the control parameters and the attribute parameters of the vehicle, wherein the optimal feedback gain matrix comprises a group of feedback gains.
In another embodiment, the processing module is specifically adapted to operate according to e1、e2、
Figure BDA0003081329350000145
ψdes、
Figure BDA0003081329350000146
ψ、
Figure BDA0003081329350000144
And
Figure BDA0003081329350000143
processing a two-degree-of-freedom kinetic equation of a vehicle to obtain a path tracking deviation state equationThe path tracking deviation state equation characterizes a response of the path tracking deviation corresponding to the preset front wheel steering angle, wherein e1For lateral deviation, e2As the yaw angle deviation, there is a deviation,
Figure BDA0003081329350000147
in order to be a lateral speed deviation,
Figure BDA0003081329350000148
as the yaw-rate deviation, there is,
Figure BDA0003081329350000149
as yaw rate, psidesIs the yaw angle.
In another embodiment, the processing module is specifically configured to expand the path tracking deviation state equation according to an integral error term of a preset lateral displacement deviation to obtain a continuous state equation.
In another embodiment, the processing module is specifically configured to perform discretization processing on the continuous state equation to obtain a discrete state equation; and processing the discrete state equation according to the second Liasia Ponux stability method to obtain a Riccati equation.
In another embodiment, the processing module is specifically configured to perform the step of determining the stability of the continuous state equation according to the lya probov first method.
For specific limitations of the vehicle control device, reference may be made to the above limitations of the vehicle control method, which are not described herein again. The respective modules in the vehicle control apparatus described above may be realized in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, as shown in fig. 5, there is provided a vehicle control apparatus comprising a memory having a computer program stored therein and a processor that, when executing the computer program, performs the steps of:
acquiring the current running speed of the vehicle;
determining a set of feedback gains according to the current running speed and the feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of the linear quadratic controller, and the control parameters comprise a positive semi-definite matrix and a positive definite matrix;
determining a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed according to a group of feedback gains;
determining a front wheel expected steering angle corresponding to the current running speed according to the feedback steering angle, the feedforward steering angle and the correction steering angle;
and generating a control command according to the expected turning angle of the front wheel to control the vehicle to run according to a preset tracking track.
In another embodiment, the processor, when executing the computer program, further performs the steps of: determining a feedback corner according to an optimal feedback gain matrix consisting of a group of feedback gains and a state vector; determining a feedforward corner according to the final value theorem, one feedback gain in a group of feedback gains, the preset curvature of the tracking track and the attribute parameters of the vehicle; and determining a correction corner according to preset configuration parameters, the front wheel corner at the current moment, the front wheel corner at the previous moment and a control cycle, wherein the front wheel corner at the current moment and the front wheel corner at the previous moment are acquired by a sensor arranged on a vehicle chassis.
In another embodiment, the processor, when executing the computer program, further performs the steps of: according to m, L1、L2、I、C1、C2、V、
Figure BDA0003081329350000161
y and delta determine a two-degree-of-freedom kinetic equation of the vehicle, wherein m is the mass of the whole vehicle and L1Is the distance of the center of mass to the front axis, L2Is the distance from the center of mass to the rear axle, I is the moment of inertia of the whole vehicle, C1For front tire cornering stiffness, C2For the cornering stiffness of the rear tyre, V is the running speedThe degree of the magnetic field is measured,
Figure BDA0003081329350000162
the transverse offset speed is, y is the transverse offset, and delta is the expected turning angle of the front wheel; processing a two-degree-of-freedom kinetic equation of the vehicle according to a second Liya Ponuf stable method to obtain a Riccati equation; carrying out iterative calculation on the Riccati equation according to different driving speeds of the vehicle to obtain a converged transfer function; and determining an optimal feedback gain matrix corresponding to each running speed according to the transfer function, the control parameters and the attribute parameters of the vehicle, wherein the optimal feedback gain matrix comprises a group of feedback gains.
In another embodiment, the processor, when executing the computer program, further performs the steps of: according to e1、e2、
Figure BDA0003081329350000165
ψdes、
Figure BDA0003081329350000166
ψ、
Figure BDA0003081329350000163
And
Figure BDA0003081329350000164
processing a two-degree-of-freedom kinetic equation of the vehicle to obtain a path tracking deviation state equation, wherein the path tracking deviation state equation represents the response of the path tracking deviation corresponding to the preset front wheel steering angle, and e1For lateral deviation, e2As the yaw angle deviation, there is a deviation,
Figure BDA0003081329350000167
in order to be a lateral speed deviation,
Figure BDA0003081329350000168
as the yaw-rate deviation, there is,
Figure BDA0003081329350000169
as yaw rate, psidesIs horizontally swungAnd (4) an angle.
In another embodiment, the processor, when executing the computer program, further performs the steps of: and expanding the path tracking deviation state equation according to a preset integral error term of the transverse displacement deviation to obtain a continuous state equation.
In another embodiment, the processor, when executing the computer program, further performs the steps of: discretizing the continuous state equation to obtain a discrete state equation; and processing the discrete state equation according to the second Liasia Ponux stability method to obtain a Riccati equation.
In another embodiment, the processor, when executing the computer program, further performs the steps of: and judging the stability of the continuous state equation according to the first Liya Punuo method.
In one embodiment, there is also provided an autonomous vehicle comprising: the vehicle control device controls the vehicle body to run by adopting any one of the vehicle control methods. This application is not described in detail herein.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring the current running speed of the vehicle;
determining a set of feedback gains according to the current running speed and the feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of the linear quadratic controller, and the control parameters comprise a positive semi-definite matrix and a positive definite matrix;
determining a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed according to a group of feedback gains;
determining a front wheel expected steering angle corresponding to the current running speed according to the feedback steering angle, the feedforward steering angle and the correction steering angle;
and generating a control command according to the expected turning angle of the front wheel to control the vehicle to run according to a preset tracking track.
In another embodiment, the computer program when executed by the processor further performs the steps of: determining a feedback corner according to an optimal feedback gain matrix consisting of a group of feedback gains and a state vector; determining a feedforward corner according to the final value theorem, one feedback gain in a group of feedback gains, the preset curvature of the tracking track and the attribute parameters of the vehicle; and determining a correction corner according to preset configuration parameters, the front wheel corner at the current moment, the front wheel corner at the previous moment and a control cycle, wherein the front wheel corner at the current moment and the front wheel corner at the previous moment are acquired by a sensor arranged on a vehicle chassis.
In another embodiment, the computer program when executed by the processor further performs the steps of: according to m, L1、L2、I、C1、C2、V、
Figure BDA0003081329350000181
y and delta determine a two-degree-of-freedom kinetic equation of the vehicle, wherein m is the mass of the whole vehicle and L1Is the distance of the center of mass to the front axis, L2Is the distance from the center of mass to the rear axle, I is the moment of inertia of the whole vehicle, C1For front tire cornering stiffness, C2The cornering power of the rear tire, V the running speed,
Figure BDA0003081329350000182
the transverse offset speed is, y is the transverse offset, and delta is the expected turning angle of the front wheel; processing a two-degree-of-freedom kinetic equation of the vehicle according to a second Liya Ponuf stable method to obtain a Riccati equation; carrying out iterative calculation on the Riccati equation according to different driving speeds of the vehicle to obtain a converged transfer function; and determining an optimal feedback gain matrix corresponding to each running speed according to the transfer function, the control parameters and the attribute parameters of the vehicle, wherein the optimal feedback gain matrix comprises a group of feedback gains.
In another embodiment, the computer program when executed by the processor further performs the steps of: according to e1、e2、
Figure BDA0003081329350000185
ψdes、
Figure BDA0003081329350000186
ψ、
Figure BDA0003081329350000183
And
Figure BDA0003081329350000184
processing a two-degree-of-freedom kinetic equation of the vehicle to obtain a path tracking deviation state equation, wherein the path tracking deviation state equation represents the response of the path tracking deviation corresponding to the preset front wheel steering angle, and e1For lateral deviation, e2As the yaw angle deviation, there is a deviation,
Figure BDA0003081329350000189
in order to be a lateral speed deviation,
Figure BDA0003081329350000187
as the yaw-rate deviation, there is,
Figure BDA0003081329350000188
as yaw rate, psidesIs the yaw angle.
In another embodiment, the computer program when executed by the processor further performs the steps of: and expanding the path tracking deviation state equation according to a preset integral error term of the transverse displacement deviation to obtain a continuous state equation.
In another embodiment, the computer program when executed by the processor further performs the steps of: discretizing the continuous state equation to obtain a discrete state equation; and processing the discrete state equation according to the second Liasia Ponux stability method to obtain a Riccati equation.
In another embodiment, the computer program when executed by the processor further performs the steps of: and judging the stability of the continuous state equation according to the first Liya Punuo method.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A control method of a vehicle, characterized by comprising:
acquiring the current running speed of the vehicle;
determining a set of feedback gains according to the current running speed and feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of the linear quadratic controller, wherein the control parameters comprise a positive semi-definite weighting matrix and a positive definite weighting matrix;
determining a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed according to the group of feedback gains;
determining a front wheel expected steering angle corresponding to the current running speed according to the feedback steering angle, the feedforward steering angle and the correction steering angle;
and generating a control instruction according to the expected turning angle of the front wheel to control the vehicle to run according to a preset tracking track.
2. The method of claim 1, wherein said determining a feedback turn angle, a feed-forward turn angle, and a correction turn angle corresponding to the current travel speed based on the set of feedback gains comprises:
determining the feedback corner according to an optimal feedback gain matrix consisting of the group of feedback gains and a state vector;
determining the feedforward corner according to a final value theorem, one feedback gain in the group of feedback gains, the curvature of the preset tracking track and the attribute parameters of the vehicle;
and determining a correction corner according to preset configuration parameters, a front wheel corner at the current moment, a front wheel corner at the previous moment and a control cycle, wherein the front wheel corner at the current moment and the front wheel corner at the previous moment are acquired through a sensor arranged on the vehicle chassis.
3. The method of claim 2, further comprising:
according to m, L1、L2、I、C1、C2、V、
Figure FDA0003081329340000011
y and delta determine a two-degree-of-freedom kinetic equation of the vehicle, wherein m is the mass of the whole vehicle and L1Is the distance of the center of mass to the front axis, L2Is the distance from the center of mass to the rear axle, I is the moment of inertia of the whole vehicle, C1For front tire cornering stiffness, C2The cornering power of the rear tire, V the running speed,
Figure FDA0003081329340000012
is the lateral offset speed, y is the lateral offset, δ is the desired turning angle of the front wheel;
processing the vehicle two-degree-of-freedom kinetic equation according to a second Liya Ponuf stable method to obtain a Riccati equation;
performing iterative calculation on the Riccati equation according to different driving speeds of the vehicle to obtain a converged transfer function;
and determining the optimal feedback gain matrix corresponding to each running speed according to the transfer function, the control parameters and the attribute parameters of the vehicle, wherein the optimal feedback gain matrix comprises a group of feedback gains.
4. The method of claim 3, wherein prior to said processing said vehicle state equations according to the second lya probov stable method to obtain the ricati equation, the method comprises:
according to e1、e2、
Figure FDA0003081329340000021
ψdes、
Figure FDA0003081329340000022
ψ、
Figure FDA0003081329340000023
And
Figure FDA0003081329340000024
to the two-degree-of-freedom power of the vehicleProcessing the mathematical equation to obtain a path tracking deviation state equation, wherein the path tracking deviation state equation represents the response of the path tracking deviation corresponding to the preset front wheel steering angle, and e1For lateral deviation, e2As the yaw angle deviation, there is a deviation,
Figure FDA0003081329340000025
in order to be a lateral speed deviation,
Figure FDA0003081329340000026
as the yaw-rate deviation, there is,
Figure FDA0003081329340000027
as yaw rate, psidesIs the yaw angle.
5. Method according to claim 4, characterized in that, in said method according to e1、e2、
Figure FDA0003081329340000028
ψdes、
Figure FDA0003081329340000029
ψ、
Figure FDA00030813293400000210
And
Figure FDA00030813293400000211
after the vehicle two-degree-of-freedom kinetic equation is processed to obtain a path tracking deviation state equation, the method comprises the following steps:
and expanding the path tracking deviation state equation according to a preset integral error term of the transverse displacement deviation to obtain a continuous state equation.
6. The method of claim 5, further comprising:
discretizing the continuous state equation to obtain a discrete state equation;
and processing the discrete state equation according to a second Liasia Ponux stability method to obtain the Riccati equation.
7. The method of claim 5, wherein after the expanding the path tracking offset state equation according to the integral error term of the preset lateral displacement offset to obtain a continuous state equation, the method comprises:
and judging the stability of the continuous state equation according to a Liya Ponugh first method.
8. A control apparatus of a vehicle, characterized by comprising:
the acquisition module is used for acquiring the current running speed of the vehicle;
the processing module is used for determining a group of feedback gains according to the current running speed and the feedback gain information; the feedback gain information includes a plurality of travel speeds and a set of feedback gains corresponding to each of the plurality of travel speeds; the feedback gain is determined according to preset control parameters of a linear quadratic controller, wherein the control parameters comprise a positive semi-definite matrix and a positive definite matrix;
the processing module is used for determining a feedback corner, a feedforward corner and a correction corner corresponding to the current running speed according to the group of feedback gains;
the processing module is used for determining a front wheel expected steering angle corresponding to the current running speed according to the feedback steering angle, the feedforward steering angle and the correction steering angle;
and the control module is used for generating a control instruction according to the expected turning angle of the front wheel to control the vehicle to run according to a preset tracking track.
9. A control device of a vehicle, characterized by comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An autonomous vehicle, comprising: a vehicle body and a control device, wherein the control device controls the vehicle body to travel by using the control method according to any one of claims 1 to 7.
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