CN115963836A - Path tracking and vehicle body posture cooperative control method - Google Patents
Path tracking and vehicle body posture cooperative control method Download PDFInfo
- Publication number
- CN115963836A CN115963836A CN202310027042.3A CN202310027042A CN115963836A CN 115963836 A CN115963836 A CN 115963836A CN 202310027042 A CN202310027042 A CN 202310027042A CN 115963836 A CN115963836 A CN 115963836A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- roll
- mass
- threshold value
- deviation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Abstract
The invention discloses a path tracking and vehicle body posture cooperative control method, which comprises the following steps: step 1, planning an expected path in real time based on road information acquired by a sensor; step 2, performing feedforward control on the vehicle based on the vehicle inverse dynamics model, and preferentially meeting two control targets with large influence on driving safety, namely path tracking precision and rollover resistance; and step 3, setting a comprehensive evaluation module for evaluating the feedforward control effect of the previous control period to obtain the distribution weight of each performance, and step 4, continuously repeating closed-loop control from a sensor in the feedforward control module to a feedback regulation module in one control period until the period is finished and entering the next period from the feedforward control module. Has the advantages that: by using the inverse dynamics model, the defects of high order of the forward dynamics model, difficult determination of system parameters, strong nonlinearity of a chassis execution system and the like are overcome, the reaction speed and the initial output precision of the system are improved, and the buffeting of the output quantity is reduced.
Description
Technical Field
The invention relates to a path tracking control method, and particularly provides a path tracking and vehicle body posture cooperative control method.
Background
In recent years, with the rapid development of artificial intelligence, chips and sensors, intellectualization and networking become research hotspots in the automobile industry, and powerful power is injected for continuous upgrading of the automobile industry. The intelligent automobile has the advantages of being capable of predicting driving behaviors, reducing the occurrence rate of traffic accidents, improving the commuting efficiency and the like, and simultaneously challenges the stability and development of moral and ethics and laws and regulations, so that the requirement of the public on the safety of the intelligent driving automobile is obviously higher than that of the traditional automobile.
In the aspect of motion control, the driving safety of the intelligent automobile mainly comprises the following two aspects: accurately driving along the expected path, and avoiding collision and exceeding a lane line; when the driving direction is changed, the automobile body does not turn over laterally or sideslip, namely, the posture and the operation stability of the automobile body are kept in a certain range.
In the current stage, motion control of most intelligent automobiles guarantees path tracking accuracy under the condition of not considering body posture control and operation stability, namely, only the first point of meeting driving safety is met. However, in the actual path tracking process, mutual coupling between the vehicle body posture and the path tracking accuracy is restricted, and the path tracking accuracy is maintained and improved, and the rapid deterioration of the vehicle body posture and the operation stability is possibly accompanied, so that the probability of rollover and sideslip is increased.
For the above problems, the existing research continuously corrects the control quantities such as the vehicle speed, the steering angle, the vehicle body posture and the like by applying the modern control method and based on the information such as the vehicle azimuth, the vehicle posture and the like obtained by the sensor, so as to ensure the path tracking precision and the anti-roll safety to a certain extent.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of large initial error, system buffeting and the like of the existing control method, the invention provides a path tracking and body posture cooperative control method based on a vehicle inverse dynamic model, the inverse dynamic model capable of accurately reflecting the transverse-vertical motion coupling characteristic of an automatic driving automobile is constructed and used, the defects of high order of a forward dynamic model, difficult determination of system parameters, strong nonlinearity of a chassis execution system and the like are overcome, the control flow of the automatic driving automobile is highly matched, the system reaction speed and the initial output precision are improved, and the buffeting of output quantity is reduced.
The technical scheme is as follows:
a path tracking and vehicle body posture cooperative control method comprises the following steps:
processing the sensor data to acquire quantitative evaluation of path tracking precision, vehicle body posture maintaining performance, vibration comfort, rollover resistance, sideslip resistance and the like, and performing normalization processing;
the sensor data corresponding to each index comprises: path tracking accuracy corresponding to lateral displacement deviation y e Threshold value, lateral azimuth deviation e threshold value and yaw velocity threshold value w e Obtaining the entropy of the index and further obtaining the weight of each index;
multiplying the path tracking precision and the vibration comfort level index weight by coefficients k1 and k2 according to the selection of a driver on the deviation of the driving performance to obtain a comprehensive evaluation index H, wherein k1+ k2=2, if the driving performance deviates to motion, k1 is 2, if the driving performance deviates to comfort, k1=0, and otherwise, the linear adjustment is carried out between 0 and 2; if the comprehensive index H after entropy weight fusion is less than 0.8, allowing to enter the feedforward control of the next period; if H is more than 0.8 and less than 1, suspending feedforward control and only keeping the feedback control module; if H is larger than 1, reminding a driver to intervene;
and if the driving state parameter exceeds the early warning value, early warning and prompting a driver to intervene, and directly jumping out of the control period to enter the next period.
Further, the specific operation flow of step 2 is as follows:
according to the selection of a driver on the driving style, the bias of performance indexes of path tracking precision, vehicle body posture holding performance, vibration comfort, anti-rollover performance and anti-sideslip performance in the driving process is determined;
the vehicle-mounted computer determines performance requirements according to conditions such as roads, traffic flow density and the like acquired by the sensor and the driving style, and distributes weights of control parameters of the path tracking system and the vehicle body attitude system under the current working condition;
taking the path tracking precision and the anti-rollover performance as constraint conditions, and solving the transverse displacement deviation y representing the constraint conditions based on the weight distribution of each performance e Threshold value, lateral azimuth deviation e threshold value and yaw velocity threshold value w e ；
The method comprises the steps of obtaining current displacement deviation and road curvature by means of noise point filtering, multi-frame fusion and main characteristic clustering on a point cloud picture obtained by a laser radar and an image obtained by a vehicle-mounted camera, inputting the current displacement deviation, a road curvature and transverse displacement deviation ye threshold value, a transverse azimuth deviation e threshold value and a yaw angle speed threshold value x meters ahead into a transverse inverse dynamics model, and obtaining a vehicle speed v meeting the tracking accuracy requirement x And a pre-aiming distance x e (ii) a Wherein the value of x meters is the value of the average vehicle speed of the past 2s multiplied by 5;
the roll angle and the roll angle speed are measured in real time through a vehicle-mounted triaxial accelerometer, a roll threshold value TTR value is obtained through dynamic balance analysis and is input into a vertical inverse dynamics model, and the vehicle speed v meeting the anti-rollover performance is obtained x And a pre-aiming distance x e ；
Vehicle speed v meeting the requirements of path tracking accuracy and rollover resistance x And a pre-aiming distance x e When there is intersection, the vehicle speed v is selected according to the driving style selected by the driver x And a pre-aiming distance x e Controlling the throttle opening, the gear and the brake through a longitudinal inverse dynamics model to control the vehicle speed;
if the vehicle speed v meets the requirements of path tracking precision and rollover resistance x And a pre-aiming distance x e And if no intersection exists, warning is sent to the driver and the driver directly enters a feedback regulation system.
Further, the specific control logic of step 3 is as follows:
when the path tracking precision, the operation stability and the comfort level all meet the requirements, the path tracking is normally carried out by feed forward and feedback;
when the path tracking precision is lower than a threshold value, but the operation stability and the vehicle body posture meet the requirements, reducing the pre-aiming distance to perform tracking precision compensation;
when the path tracking precision does not meet the requirement, and lane changing conditions and no rollover danger exist, attitude compensation is performed on the operation stability and the comfort level;
when the path tracking precision does not meet the requirement, lane changing conditions exist and the lane changing has rollover danger, the vehicle is decelerated and corrected, and attitude compensation is carried out;
when the tracking precision is not satisfied and no lane change condition exists, performing tracking precision compensation on the vehicle, and performing attitude compensation if the operation stability is deteriorated;
when the vehicle body attitude compensation and the path tracking precision compensation can not reach the generalized control target, early warning, braking and deceleration are carried out, and a driver is prompted to intervene.
Further, with the path tracking accuracy, the operation stability and the roll safety as constraint conditions, a roll time TTR value and a lateral displacement deviation y are calculated based on the road conditions, the driving state and the performance requirement deviation e Substituting a threshold value and a transverse azimuth deviation e threshold value into a vertical inverse dynamics model by using a roll time limit TTR value, and substituting a transverse displacement deviation y e Threshold value, lateral azimuth deviation e threshold value brought into lateral inversionMechanical model, respectively solving vehicle speed v satisfying real-time constraint condition x And a pre-aiming distance x e ；
Vehicle speed v solved by two inverse dynamic model x And a pre-aiming distance x e Respectively forming closed planes on the two-dimensional plane, and selecting proper vehicle speed v in the overlapping area of the two closed planes according to the real-time performance requirement x And a pre-aiming distance x e And outputting the signal to an actuating mechanism.
Further, taking the road parameters, the driving state parameters and the cooperative control constraint conditions as input, the method specifically comprises the following steps: front road curvature, current vehicle orientation deviation y e The displacement deviation e, the yaw angular velocity, the maximum displacement deviation threshold value and the maximum azimuth deviation threshold value are output as the vehicle speed v meeting the requirements of path tracking precision and operation stability x And a pre-aiming distance x e ；
The vertical inverse dynamics model inputs the vehicle body roll angle speed and roll TTR time limit threshold and outputs the vehicle speed v meeting the roll safety x And a pre-aiming distance x e 。
Further, the inverse dynamics model obtaining method is as follows: according to the motion differential equations of a two-degree-of-freedom automobile open-loop system model and a preview tracking model, solving a forward dynamics model, obtaining input and output data sets for expressing path tracking precision under different road curvatures, different working conditions and different preview distances, and obtaining explicit expressions of input and output of a path tracking system;
on the basis, the generalized regression neural network is applied to the transverse inverse dynamics modeling of the automatic driving automobile, and the normal driving of the automobile is as follows: selecting an input and output set under the constraint conditions of no collision with the road boundary and no side turning, and extracting the roll angle, the roll angle speed and the transverse displacement deviation y of the vehicle e And (e) constructing a multi-input multi-output data set of the normal path tracking system of the automatic driving automobile, carrying out matching design on weighted summation of neuron data of a generalized regression neural network summation layer, and finally obtaining an inverse dynamics model which accurately reflects the transverse dynamics and the preview tracking characteristics of the automobile.
Further, an input and output set is obtained based on a two-degree-of-freedom model and a preview tracking model of the vehicle, and the method is realized by the following steps:
establishing a two-degree-of-freedom vehicle dynamics model based on a two-degree-of-freedom vehicle dynamics equation;
according to the parameter relations of the slip angle of the tire, the slip angle of the mass center of the vehicle, the yaw velocity, the distance between the mass center and the front and rear axes and the like, the motion differential equation of the linear two-degree-of-freedom model is obtained by combining the Newton second law:
wherein m is the mass of the whole vehicle, C f Cr is the linear yaw stiffness of the front and rear wheels, /) f 、l r Is the distance of the vehicle's center of mass from the front and rear axes, I Z Moment of inertia, delta, of the finished vehicle about the Z axis f Corner of front wheel, V x Longitudinal vehicle speed;
designing a preview error system; the inputs of the preview error model comprise the output mass center slip angle and the yaw rate of the vehicle dynamic model, and the external input road curvature rho and the preview distance x e And outputting the preview error model as a transverse displacement deviation ye and a transverse azimuth deviation e, and constructing a preview error model formula of the vehicle:
wherein y is the deviation of the distance between the pre-aiming point and the central line of the vehicle, v y Is the transverse velocity v of the vehicle x Longitudinal vehicle speed, r steering radius;
performing weight distribution on the transverse displacement deviation ye and the transverse azimuth deviation E by using an entropy weight fusion method based on data in a previous feedforward control period to obtain a comprehensive deviation E;
inputting the obtained comprehensive deviation E into a PID controller, performing time domain integration on the comprehensive deviation of at least two previous feedforward control periods, and using PID based on the integration resultControl method for outputting front wheel steering angle delta of supplementary vehicle f And the control quantity is used as a control quantity of a vehicle dynamic model, so that a closed loop control system for tracking the transverse motion of the path is formed.
Further, optimizing the weight by using the optimal individual to obtain the optimized weight;
substituting the optimized initial weight and threshold into a neural network, finding out an individual corresponding to the optimal fitness value through selection, intersection and variation operations, calculating network errors, modifying the weight and the threshold according to a calculation result, and finally achieving the required precision;
the measured value of the triaxial acceleration and the environment variable are used as input nodes, the environment variable is determined according to conditions, the specific value is determined after network training is finished, the pitching angle and the roll angle of the vehicle body are used as output nodes, and the weight and the threshold value are changed through the network training until the precision reaches the standard.
Furthermore, the relative quantity of the ratio of the current lateral acceleration of the vehicle to the real-time lateral limit acceleration is used as a rollover judging condition to replace the traditional method for predicting rollover by depending on the roll angle or the absolute quantity of the lateral acceleration;
the acceleration of the vehicle in the rollover critical state is as follows:
in the formula, a y Lateral acceleration of the position of the center of gravity, a y,L The critical lateral acceleration of the rollover at the position of the center of gravity, m is the mass of the whole vehicle, m s Is the sprung mass h cm The height of the center of gravity, T the wheel track, h the distance from the center of gravity to the center of roll,
is a roll angle
L d＝a y /a y,L
In the formula, a y Lateral acceleration as position of center of gravity, a y,L Critical lateral acceleration of the rollover at the position of the center of gravity, m is the mass of the whole vehicle, m s Is the sprung mass h cm The height of the center of gravity, T the wheel track, h the distance from the center of gravity to the center of roll,
is a roll angle
Based on L d Calculating the rollover time limit (namely TTR value) with the calculation step length of 10ms;
determining TTR threshold value for compensating vehicle body posture according to performance requirement deviation,
if the cooperative control performance requirement is biased to the tracking precision, the TTR threshold value is a larger value, so that the center of gravity of the whole vehicle is lower when the vehicle steers;
if the cooperative control demand is biased towards comfort, the TTR threshold takes a smaller value, and the TTR threshold is in direct proportion to the vehicle speed.
Further, when the path tracking accuracy is lower than a set threshold but neither the steering stability nor the comfort metric satisfies the set threshold, the steering stability and the comfort of the vehicle are considered preferentially, and the vehicle body attitude compensation is performed on the vehicle, and the vehicle body attitude compensation control method is realized through the following steps:
the posture of the vehicle body is improved by controlling the roll of the vehicle, and the roll moment generated by the roll is composed of three parts:
1. roll moment M caused by centrifugal force of sprung mass Φr I；
2. Anti-roll moment M caused by spring load mass gravity ΦrII ；
3. Roll moment M caused by centrifugal force of non-suspended mass ΦrIII ；
In addition, the vertical load during rolling is transferred between the left and right wheel loads, and a load transfer moment M is generated ZF 、M ZR (ii) a When the vehicle body is in a roll state, the left and right suspension frames apply additional opposite forces delta f to the active suspension actuators in the current state, and an anti-roll moment M can be formed af The roll of the vehicle can be restrained;
roll moment M caused by centrifugal force of sprung mass Φr Ⅰ Comprises the following steps:
M Φr Ⅰ ＝m s ·a y ·h
M Φr Ⅰ roll moment, m, due to the centrifugal force of the sprung mass s Is a sprung mass, a y The acceleration of the mass center of the vehicle body is taken as h, and the height of the mass center is taken as h;
sprung mass gravity induced anti-roll moment M Φr II Comprises the following steps:
M Φr II roll moment, m, caused by the centrifugal force of the sprung mass s Is the sprung mass, e is the transverse distance of the centre of mass to the wheel, a y Is the acceleration of the center of mass of the vehicle body, h g The height from the connecting position of the suspension and the vehicle body on the lower side of the vehicle body to the mass center,
is the vehicle body roll angle;
roll moment M caused by centrifugal force of non-suspended mass ΦrIII Comprises the following steps:
M ΦrIII ＝-Fuy(h 0 -r)
M ΦrIII the roll moment caused by unsprung mass, F the static friction force of the road surface against the wheel pointing to the steering center, u the vehicle speed, y the transverse dimension of the vehicle body, h 0 Is the height of the center of mass, and r is the radius of the wheel;
when the vehicle is tilted, the vertical load is transferred between the left and right wheels, and a load transfer moment M is generated ZF 、M ZR Comprises the following steps:
M ZF ＝(F rRF -F rLF )·B/2
M ZR ＝(F rRR -F rLR )·B/2
M ZF for front-side wheel transfer torque, M ZR For rear side wheel transfer torque, F rRF For transferring force to the right front wheel, F rLF Is the left rear side wheel transfer force, F rRR For right rear side wheel transfer force, F rRR And B is the width of the vehicle body;
taking a moment of a longitudinal center line of a vehicle body, namely:
M Φr I -M ΦrII +M ΦrIII +M ZF +M ZR ＝M af
M ΦrI roll moment caused by centrifugal force of sprung mass, M ΦrII Roll moment caused by centrifugal force of sprung mass, M ΦrIII Roll moment due to unsprung mass, M ZF For front-side wheel transfer torque, M ZR For rear side wheel transfer torque, M af An anti-roll moment provided to the actuator.
Has the advantages that: the invention provides a path tracking and vehicle body posture cooperative control method, which constructs and uses an inverse dynamics model capable of accurately reflecting the transverse-vertical motion coupling characteristic of an automatic driving vehicle, overcomes the defects of high order, difficult determination of system parameters, strong nonlinearity of a chassis execution system and the like of a forward dynamics model, highly conforms to the control flow of the automatic driving vehicle, improves the reaction speed and the initial output precision of the system, and reduces the buffeting of output quantity.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a logical road map for vehicle path tracking and body attitude cooperative control according to the present invention.
FIG. 2 is a road driving scene diagram of the intelligent vehicle according to the invention.
FIG. 3 is a flow chart of the vehicle dynamics inverse model construction of the present invention.
FIG. 4 is a two-degree-of-freedom dynamic model diagram of the vehicle according to the present invention.
FIG. 5 is a diagram of a preview error model according to the present invention.
Fig. 6 is a schematic diagram of the roll moment generation of the present invention.
Fig. 7 is a diagram showing the effect of compensating the attitude of the vehicle body according to the present invention.
FIG. 8 is a diagram illustrating the effect of path tracking compensation according to the present invention.
FIG. 9 is a diagram illustrating the effect of path tracking compensation according to the present invention.
FIG. 10 is a diagram illustrating the effect of path tracking compensation according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the present invention, unless otherwise expressly stated or limited, "above" or "below" a first feature means that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact with each other via another feature therebetween. Also, the first feature being "on," "above" and "over" the second feature includes the first feature being directly on and obliquely above the second feature, or merely indicating that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature includes the first feature being directly under and obliquely below the second feature, or simply meaning that the first feature is at a lesser elevation than the second feature.
As shown in fig. 1 and 2, a path tracking and vehicle body posture cooperative control method includes the following steps:
according to the selection of a driver on the driving style, the bias of performance indexes of path tracking precision, vehicle body posture holding performance, vibration comfort, anti-rollover performance and anti-sideslip performance in the driving process is determined;
the vehicle-mounted computer determines performance requirements according to conditions of roads, traffic flow density and the like acquired by the sensors and the driving style, makes normalized evaluation indexes on path tracking precision, vehicle body posture maintaining performance, vibration comfort, anti-rollover performance and anti-sideslip performance, and determines distribution weights of control parameters of a path tracking system and a vehicle body posture system under the current working condition;
taking the path tracking precision and the anti-rollover performance as constraint conditions, and solving the transverse displacement deviation y representing the constraint conditions based on the weight distribution of each performance e Threshold value, lateral azimuth deviation e threshold value and yaw velocity threshold value w e ；
The method comprises the steps of obtaining current displacement deviation and road curvature by means of noise point filtering, multi-frame fusion and main feature clustering of a point cloud picture obtained by a laser radar and an image obtained by a vehicle-mounted camera, inputting the current displacement deviation, the road curvature and transverse displacement deviation ye threshold value, transverse azimuth deviation e threshold value and yaw velocity threshold value x meters ahead into a transverse inverse dynamics model, and obtaining a vehicle speed v meeting the tracking accuracy requirement x And a pre-aiming distance x e (ii) a Wherein x m is a value ofThe value of the average vehicle speed of past 2s is multiplied by 5;
data measured by a vehicle-mounted triaxial accelerometer in real time are subjected to time domain integration and frequency domain integration to obtain a roll angle and a roll angle speed, a roll threshold value TTR value is obtained through dynamic balance analysis and is input into a vertical inverse dynamics model, and a vehicle speed v meeting the anti-rollover performance is obtained x And a pre-aiming distance x e；
Vehicle speed v meeting the requirements of path tracking accuracy and rollover resistance x And a pre-aiming distance x e When there is intersection, the vehicle speed v is selected according to the driving style selected by the driver x And a pre-aiming distance x e Controlling the throttle opening, the gear and the brake through a longitudinal inverse dynamics model to control the vehicle speed;
if the vehicle speed v meets the requirements of path tracking precision and rollover resistance x And a pre-aiming distance x e If the intersection is not formed, a warning is sent to the driver and the driver directly enters a feedback regulation system;
taking sensor data processed every 0.01s in the previous feedforward control period of 0.4s, and respectively carrying out normalization processing on path tracking precision, vehicle body posture maintaining performance, vibration comfort, anti-rollover performance and anti-sideslip performance;
the sensor data corresponding to each index comprises: path tracking accuracy corresponding to lateral displacement deviation y e Threshold value, lateral azimuth deviation e threshold value and yaw velocity threshold value w e Obtaining the entropy of the index and further obtaining the weight of each index;
multiplying the path tracking precision and the vibration comfort level index weight by coefficients k1 and k2 according to the selection of a driver on the deviation of the driving performance to obtain a comprehensive evaluation index H, wherein k1+ k2=2, if the driving performance deviates to motion, k1 is 2, if the driving performance deviates to comfort, k1=0, and other conditions are linearly adjusted between 0 and 2; if the comprehensive index H after entropy weight fusion is less than 0.8, allowing to enter feedforward control of the next period; if H is more than 0.8 and less than 1, suspending feedforward control and only keeping the feedback control module; if H is larger than 1, reminding a driver to intervene; the specific control logic is as follows:
when the path tracking precision, the operation stability and the comfort level all meet the requirements, the path tracking is normally carried out by feed forward and feedback;
when the path tracking precision is lower than a threshold value, but the operation stability and the vehicle body posture meet the requirements, reducing the pre-aiming distance to compensate the tracking precision;
when the path tracking precision does not meet the requirement, and the lane changing condition and the rollover danger do not exist, performing attitude compensation on the operation stability and the comfort level;
when the path tracking precision does not meet the requirement, the lane change condition exists and the lane change has the rollover risk, the vehicle is decelerated and corrected and the attitude compensation is carried out;
when the tracking precision is not satisfied and no lane change condition exists, performing tracking precision compensation on the vehicle, and performing attitude compensation if the operation stability is deteriorated;
when the vehicle body attitude compensation and the path tracking precision compensation can not reach the generalized control target, early warning, braking and decelerating and prompting the intervention of a driver;
and if the driving state parameter exceeds the early warning value, early warning and prompting a driver to intervene, and directly jumping out of the control period to enter the next period.
Calculating an initial output quantity by a feedforward control module at the beginning of each control period and outputting the initial output quantity by an actuating mechanism;
then entering a feedback control module, acquiring vehicle position and orientation information running state parameters in real time by using a three-axis accelerometer and a laser radar, judging the running state of the vehicle, and correspondingly selecting a vehicle chassis executing mechanism control logic in feedback control;
if the comprehensive evaluation index H of the feedforward control is larger than 1, namely the control quantity meeting the cooperative control requirement cannot be output at the initial moment of the control period, early warning is given to prompt a driver to pay attention to intervention at any time, and meanwhile, feedback control is carried out.
The feedforward control only outputs the controlled variable at the initial moment of each control period, and then enters the feedback control, the feedback control step length is 10ms, and one control period is 400ms.
Calculating a roll time limit TTR value and a lateral displacement deviation y based on the road condition, the driving state and the performance requirement deviation by taking the path tracking precision, the operation stability and the roll safety as constraint conditions e Threshold value and transverse azimuth deviation e threshold value, substituting the roll time limit TTR value into the vertical inverse dynamics model, and substituting the transverse displacement deviation y e Substituting a threshold value and a transverse azimuth deviation e threshold value into a transverse inverse dynamics model, and respectively solving the vehicle speed v meeting the real-time constraint condition x And a pre-aiming distance x e ；
Vehicle speed v solved by two inverse dynamic model x And a pre-aiming distance x e Respectively forming closed planes on the two-dimensional plane, and selecting proper vehicle speed v in the overlapping area of the two closed planes according to the real-time performance requirement x And a pre-aiming distance x e And outputting the output to an actuating mechanism.
The method takes road parameters, driving state parameters and cooperative control constraint conditions as input, and specifically comprises the following steps: front road curvature, current vehicle orientation deviation y e The displacement deviation e, the yaw angular velocity, the maximum displacement deviation threshold value and the maximum azimuth deviation threshold value are output as the vehicle speed v meeting the requirements of path tracking precision and operation stability x And a pre-aiming distance x e ；
The vertical inverse dynamics model inputs the roll angle speed and roll TTR time limit threshold of the vehicle body and outputs the vehicle speed v meeting the roll safety x And a pre-aiming distance x e 。
According to the motion differential equations of a two-degree-of-freedom automobile open-loop system model and a preview tracking model, solving a forward dynamics model, obtaining input and output data sets for expressing path tracking precision under different road curvatures, different working conditions and different preview distances, and obtaining explicit expressions of input and output of a path tracking system;
on the basis, a Generalized Regression Neural Network (GRNN) is applied to transverse inverse dynamics modeling of the automatic driving automobile, and the normal driving of the automobile is as follows: selecting an input and output set under the constraint conditions of no collision with the road boundary and no side turning, and extracting the roll angle, the roll angle speed and the transverse displacement deviation y of the vehicle e And constructing a multi-input multi-output data set of the automatic driving automobile normal path tracking system by the transverse azimuth deviation e, carrying out matching design on GRNN summation layer neuron data weighted summation, and finally obtaining an inverse dynamics model which accurately reflects the transverse dynamics and the pre-aiming tracking characteristics of the automobile.
The method comprises the following steps of obtaining an input and output set based on a two-degree-of-freedom model and a preview tracking model of a vehicle, and realizing the following steps:
as shown in fig. 3, a two-degree-of-freedom vehicle dynamics model is established based on a two-degree-of-freedom vehicle dynamics equation;
according to the parameter relations of the slip angle of the tire, the slip angle of the mass center of the vehicle, the yaw velocity, the distance between the mass center and the front and rear axes and the like, the motion differential equation of the linear two-degree-of-freedom model is obtained by combining the Newton second law:
wherein m is the mass of the whole vehicle, C f 、C r Linear cornering stiffness of the front and rear wheels, /) f 、l r Is the distance of the vehicle's center of mass from the front and rear axes, I Z Moment of inertia, delta, of the finished vehicle about the Z axis f Corner of front wheel, V x Longitudinal vehicle speed;
as shown in fig. 4, the design of the preview error system; the inputs of the preview error model comprise the output mass center slip angle and the yaw rate of the vehicle dynamic model, and the external input road curvature rho and the preview distance x e The outputs of the preview error model are the transverse displacement deviation ye and the transverse azimuth deviation e, and the preview error model of the vehicle is constructedThe formula:
wherein y is the deviation of the distance between the pre-aiming point and the central line of the vehicle, v y Is the transverse velocity v of the vehicle x For longitudinal vehicle speed, r is the steering radius.
Performing weight distribution on the transverse displacement deviation ye and the transverse azimuth deviation E by using an entropy weight fusion method based on data in the past 0.4s to obtain a comprehensive deviation E;
inputting the obtained comprehensive deviation E into a PID controller, performing time domain integration on the past 2s comprehensive deviation, and outputting the front wheel turning angle delta of the supplementary vehicle by using a PID control method based on the integration result f As a control quantity of a vehicle dynamic model, a closed loop control system for path tracking transverse motion is formed.
The driving state monitoring module is characterized in that:
optimizing the weight by using the optimal individual to obtain the optimized weight;
substituting the optimized initial weight and threshold into a neural network, finding out an individual corresponding to the optimal fitness value through selection, intersection and variation operations, calculating network errors, modifying the weight and the threshold according to a calculation result, and finally achieving the required precision;
the measured value of the triaxial acceleration and the environment variable are used as input nodes, the environment variable is determined according to conditions, the specific values are determined after the network training is finished, the pitching angle and the roll angle of the vehicle body are used as output nodes, and the weight and the threshold are changed through the network training until the precision reaches the standard.
The relative quantity of the ratio of the current lateral acceleration of the vehicle to the real-time lateral limit acceleration is used as a rollover judging condition to replace the traditional method of predicting rollover by depending on the roll angle or the absolute quantity of the lateral acceleration;
the acceleration of the vehicle in the rollover critical state is as follows:
in the formula, a y Lateral acceleration as position of center of gravity, a y,L Critical lateral acceleration of the rollover at the position of the center of gravity, m is the mass of the whole vehicle, m s Is the sprung mass h cm The height of the center of gravity, T the wheel track, h the distance from the center of gravity to the center of roll,
at a roll angle
L d ＝a y /a y,L
In the formula, a y Lateral acceleration as position of center of gravity, a y,L Critical lateral acceleration of the rollover at the position of the center of gravity, m is the mass of the whole vehicle, m s Is sprung mass, h cm The height of the center of gravity, T the wheel track, h the distance from the center of gravity to the center of roll,
in combination with a side inclination>
Based on L d Calculating the rollover time limit (namely TTR value) with the calculation step length of 10ms;
determining TTR threshold value for compensating vehicle body posture according to performance requirement deviation,
if the cooperative control performance requirement is biased to the tracking precision, the TTR threshold value is a larger value, so that the center of gravity of the whole vehicle is lower when the vehicle steers;
if the cooperative control demand is biased towards comfort, the TTR threshold is a smaller value, and the TTR threshold is in direct proportion to the vehicle speed.
When the path tracking precision is lower than a set threshold value, but the control stability and the comfort degree value do not meet the set threshold value, the control stability and the comfort of the vehicle are considered preferentially, and the vehicle body attitude compensation is carried out on the vehicle, and the vehicle body attitude compensation control method is realized through the following steps:
the posture of the vehicle body is improved by controlling the roll of the vehicle, and the roll moment generated by the roll is composed of three parts:
1. roll moment M caused by sprung mass centrifugal force ΦrΙ ；
2. Anti-roll moment M caused by spring load mass gravity ΦrII ；
3. Roll moment M caused by centrifugal force of non-suspended mass ΦrIII ；
Further, as shown in fig. 5 and 6, the vertical load at the time of rolling is transferred between the left and right wheel loads, and a load transfer moment M is generated ZF 、M ZR (ii) a When the vehicle body is in a roll state, the left and right suspension frames apply additional opposite forces delta f to the active suspension actuators in the current state, and an anti-roll moment M can be formed af The roll of the vehicle can be suppressed;
roll moment M caused by centrifugal force of sprung mass ΦrⅠ Comprises the following steps:
M ΦrⅠ ＝m s ·a y ·h
M ΦrI roll moment, m, caused by the centrifugal force of the sprung mass s Is a sprung mass, a y The acceleration of the mass center of the vehicle body is taken as h, and the height of the mass center is taken as h;
sprung mass gravity induced anti-roll moment M Φr II Comprises the following steps:
M Φr II roll moment, m, caused by the centrifugal force of the sprung mass s Is the sprung mass, e is the transverse distance of the centre of mass to the wheel, a y Is the acceleration of the center of mass of the vehicle body, h g The height from the connecting position of the suspension and the vehicle body at the lower side of the vehicle body to the center of mass,
is the vehicle body roll angle;
roll moment M caused by centrifugal force of non-suspended mass ΦrIII Comprises the following steps:
M ΦrIII ＝-Fuy(h 0 -r)
M ΦrIII the roll moment caused by unsprung mass, F the static friction force of the road surface against the wheel pointing to the steering center, u the vehicle speed, y the transverse dimension of the vehicle body, h 0 Is the height of the center of mass and r is the radius of the wheel.
When the vehicle is tilted, the vertical load is transferred between the left and right wheels to generate load transfer moment M ZF 、M ZR Comprises the following steps:
M ZF ＝(F rRF -F rLF )·B/2
M ZR ＝(F rRR -F rLR )·B/2
M ZF for front-side wheel transfer torque, M ZR For rear side wheel transfer torque, F rRF Transfer force to the right front side wheel, F rLF Is the left rear side wheel transfer force, F rRR Is a right rear side wheel transfer force, F rRR And B is the width of the vehicle body.
Taking a moment of a longitudinal center line of a vehicle body, namely:
M Φr I -M Φr II +M Φr III +M ZF +M ZR ＝M af
M Φr I roll moment caused by centrifugal force of sprung mass, M Φr II Roll moment due to centrifugal force of sprung mass, M ΦrIII Roll moment, M, due to unsprung mass ZF For front-side wheel transfer torque, M ZR For rear side wheel transfer torque, M af An anti-roll moment provided to the actuator.
Restraining vertical vibration of the vehicle by using a ceiling control strategy; the ceiling control force is as follows:
wherein f is si The ceiling control force is adopted; c. C sky For ceiling damping, Z si The absolute speed of the vertical movement of the vehicle body is;
controlling the force of the ceilingf si And the control force compensation amount f fi After addition, the ideal semi-active control force F is obtained ci Namely:
F ci ＝f si +f fi
F ci for semi-active control of force, f si For ceiling control of force, f fi To control the amount of force compensation.
F ci And A zi The mathematical relationship of (1) is as follows:
in order to ensure that the flow area of the adjustable damping valve is within a reasonable range, the maximum flow area A of the adjustable damping valve is set according to experience max ＝0.00006m 2 Minimum flow area A of the adjustable damping valve min ＝0m 2 。
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A path tracking and vehicle body posture cooperative control method is characterized by comprising the following steps:
step 1, starting a path tracking and vehicle body posture cooperative controller, and planning out an expected path in real time based on road information acquired by a sensor;
step 2, the vehicle-mounted computer performs feedforward control on the vehicle based on the vehicle inverse dynamics model according to the driving requirement and the road condition, and preferably meets two control targets with great influence on driving safety, namely path tracking precision and anti-rollover performance;
step 3, setting a comprehensive evaluation module for evaluating the feedforward control effect of the previous control period, and firstly obtaining the weights of path tracking precision, vehicle body posture holding performance, vibration comfort, anti-rollover performance and anti-sideslip performance in real time by using an entropy weight fusion method, wherein the method comprises the following steps:
processing the sensor data to acquire quantitative evaluation of path tracking precision, vehicle body posture maintaining performance, vibration comfort, rollover resistance, sideslip resistance and the like, and performing normalization processing;
the sensor data corresponding to each index comprises: path tracking accuracy versus lateral displacement deviation y e Threshold value, lateral azimuth deviation e threshold value and yaw velocity threshold value w e Obtaining the entropy of the index and further obtaining the weight of each index;
multiplying the path tracking precision and the vibration comfort level index weight by coefficients k1 and k2 according to the selection of a driver on the deviation of the driving performance to obtain a comprehensive evaluation index H, wherein k1+ k2=2, if the driving performance deviates to motion, k1 is 2, if the driving performance deviates to comfort, k1=0, and otherwise, the linear adjustment is carried out between 0 and 2; if the comprehensive index H after entropy weight fusion is less than 0.8, allowing to enter the feedforward control of the next period; if H is more than 0.8 and less than 1, suspending feedforward control and only keeping the feedback control module; if H is larger than 1, reminding a driver to intervene;
step 4, in a control period, the slowest working step length of a sensor in the feedforward control module is a negative feedback control step length, and closed-loop control from the sensor in the feedforward control module to the feedback regulation module is continuously repeated until the period is finished and the next period is started from the feedforward control module;
and if the driving state parameter exceeds the early warning value, early warning and prompting a driver to intervene, and directly jumping out of the control period to enter the next period.
2. The path-tracking and vehicle-body-posture cooperative control method according to claim 1, characterized in that: the specific operation flow of the step 2 is as follows:
according to the selection of a driver on the driving style, the bias of performance indexes of path tracking precision, vehicle body posture holding performance, vibration comfort, anti-rollover performance and anti-sideslip performance in the driving process is determined;
the vehicle-mounted computer determines the performance requirement and the distribution weight of the control parameters of the path tracking system and the vehicle body attitude system under the current working condition according to the conditions of roads, traffic flow density and the like acquired by the sensor and the driving style;
taking the path tracking precision and the anti-rollover performance as constraint conditions, and solving the transverse displacement deviation y representing the constraint conditions based on the weight distribution of each performance e Threshold value, lateral azimuth deviation e threshold value and yaw velocity threshold value w e ；
The method comprises the steps of obtaining current displacement deviation and road curvature by means of noise point filtering, multi-frame fusion and main characteristic clustering on a point cloud picture obtained by a laser radar and an image obtained by a vehicle-mounted camera, inputting the current displacement deviation, a road curvature and transverse displacement deviation ye threshold value, a transverse azimuth deviation e threshold value and a yaw angle speed threshold value x meters ahead into a transverse inverse dynamics model, and obtaining a vehicle speed v meeting the tracking accuracy requirement x And a pre-aiming distance x e (ii) a Wherein the value of x meters is the value of the average vehicle speed of the past 2s multiplied by 5;
the roll angle and the roll angle speed are measured in real time through a vehicle-mounted three-axis accelerometer, a roll threshold value TTR value is obtained through dynamic balance analysis and is input into a vertical inverse dynamics model, and the vehicle speed v meeting the anti-rollover performance is obtained x And a pre-aiming distance x e ；
Vehicle speed v meeting the requirements of path tracking precision and rollover resistance x And a pre-aiming distance x e When there is intersection, the vehicle speed v is selected according to the driving style selected by the driver x And a pre-aiming distance x e And by longitudinal counterpowerThe learning model controls the throttle opening, the gear and the brake to control the vehicle speed;
if the vehicle speed v meets the requirements of path tracking precision and rollover resistance x And a pre-aiming distance x e And if no intersection exists, warning is sent to the driver and the driver directly enters a feedback regulation system.
3. The path-tracking and vehicle-body-posture cooperative control method according to claim 1, characterized in that: the specific control logic of the step 3 is as follows:
when the path tracking precision, the operation stability and the comfort level all meet the requirements, the path tracking is normally carried out by feed forward and feedback;
when the path tracking precision is lower than a threshold value, but the operation stability and the vehicle body posture meet the requirements, reducing the pre-aiming distance to compensate the tracking precision;
when the path tracking precision does not meet the requirement, and the lane changing condition and the rollover danger do not exist, performing attitude compensation on the operation stability and the comfort level;
when the path tracking precision does not meet the requirement, lane changing conditions exist and the lane changing has rollover danger, the vehicle is decelerated and corrected, and attitude compensation is carried out;
when the tracking precision does not meet and the lane changing condition does not exist, the tracking precision compensation is carried out on the vehicle, and if the operation stability is deteriorated, the attitude compensation is carried out;
when the vehicle body attitude compensation and the path tracking precision compensation can not reach the generalized control target, early warning, braking and deceleration are carried out, and a driver is prompted to intervene.
4. The path-tracking and vehicle-body-posture cooperative control method according to claim 1, characterized in that: taking path tracking accuracy, control stability and roll safety as constraint conditions, and calculating a roll time limit TTR value and a lateral displacement deviation y based on road conditions, driving states and performance demand deviation e Substituting a threshold value and a transverse azimuth deviation e threshold value into a vertical inverse dynamics model by using a roll time limit TTR value, and substituting a transverse displacement deviation y e Substituting a threshold value and a transverse azimuth deviation e threshold value into a transverse inverse dynamics model, and respectively solving the condition of meeting real-time constraint stripsSpeed v of the member x And a pre-aiming distance x e ；
Vehicle speed v solved by two inverse dynamic model x And a pre-aiming distance x e Respectively forming closed planes on the two-dimensional plane, and selecting proper vehicle speed v in the overlapping area of the two closed planes according to the real-time performance requirement x And a pre-aiming distance x e And outputting the signal to an actuating mechanism.
5. The path-tracking and vehicle-body-posture cooperative control method according to claim 4, characterized in that: the method takes road parameters, driving state parameters and cooperative control constraint conditions as input, and specifically comprises the following steps: front road curvature, current vehicle orientation deviation y e The displacement deviation e, the yaw angular velocity, the maximum displacement deviation threshold value and the maximum azimuth deviation threshold value are output as the vehicle speed v meeting the requirements of path tracking precision and operation stability x And a pre-aiming distance x e ；
The vertical inverse dynamics model inputs the vehicle body roll angle speed and roll TTR time limit threshold and outputs the vehicle speed v meeting the roll safety x And a pre-aiming distance x e 。
6. The path-tracking and vehicle-body-posture cooperative control method according to claim 4 or 5, characterized in that: the inverse dynamics model obtaining method comprises the following steps: according to the motion differential equation of a two-degree-of-freedom automobile open-loop system model and a preview tracking model, solving a forward dynamics model, acquiring input and output data sets for expressing path tracking precision under different road curvatures, different working conditions and different preview distances, and obtaining explicit expression of input and output of a path tracking system;
on the basis, the generalized regression neural network is applied to the transverse inverse dynamics modeling of the automatic driving automobile, and the vehicle normally runs as follows: selecting an input and output set under the constraint conditions of no collision with the road boundary and no side turning, and extracting the roll angle, the roll angle speed and the transverse displacement deviation y of the vehicle e And transverse azimuth deviation e, constructing a multi-input multi-output data set of the normal path tracking system of the automatic driving automobile, and aiming at wide rangeAnd carrying out matching design by weighted summation of neuron data of the semantic regression neural network summation layer to finally obtain an inverse dynamics model which accurately reflects the transverse dynamics and the preview tracking characteristics of the vehicle.
7. The path-tracking and vehicle-body-posture cooperative control method according to claim 6, characterized in that: an input and output set is obtained based on a vehicle two-degree-of-freedom model and a preview tracking model, and the method is realized by the following steps:
establishing a two-degree-of-freedom vehicle dynamics model based on a two-degree-of-freedom vehicle dynamics equation;
according to the parameter relations of the slip angle of the tire, the slip angle of the mass center of the vehicle, the yaw velocity, the distance between the mass center and the front and rear axes and the like, the motion differential equation of the linear two-degree-of-freedom model is obtained by combining the Newton second law:
wherein m is the mass of the whole vehicle, C f 、C r Linear cornering stiffness of the front and rear wheels, /) f 、l r Is the distance of the vehicle's center of mass from the front and rear axes, I Z Moment of inertia, delta, of the entire vehicle about the Z axis f Corner of front wheel, V x Longitudinal vehicle speed;
designing a preview error system; the inputs of the preview error model comprise the output mass center slip angle and the yaw rate of the vehicle dynamic model, and the external input road curvature rho and the preview distance x e And outputting the preview error model as a transverse displacement deviation ye and a transverse azimuth deviation e, and constructing a preview error model formula of the vehicle:
wherein y is the deviation of the distance between the pre-aiming point and the central line of the vehicle, v y Is the transverse velocity v of the vehicle x Longitudinal vehicle speed, r steering radius;
performing weight distribution on the transverse displacement deviation ye and the transverse azimuth deviation E by using an entropy weight fusion method based on data in a previous feedforward control period to obtain a comprehensive deviation E;
inputting the obtained comprehensive deviation E into a PID controller, performing time domain integration on the comprehensive deviation of at least two previous feedforward control periods, and outputting the front wheel corner delta of the supplementary vehicle by using a PID control method based on the integration result f And the control quantity is used as a control quantity of a vehicle dynamic model, so that a closed loop control system for tracking the transverse motion of the path is formed.
8. The path-tracking and vehicle-body-posture cooperative control method according to claim 1, the running-state monitoring module, characterized in that:
optimizing the weight by using the optimal individual to obtain the optimized weight;
substituting the optimized initial weight and threshold into a neural network, finding out an individual corresponding to the optimal fitness value through selection, intersection and variation operations, calculating network errors, modifying the weight and the threshold according to a calculation result, and finally achieving the required precision;
the measured value of the triaxial acceleration and the environment variable are used as input nodes, the environment variable is determined according to conditions, the specific value is determined after network training is finished, the pitching angle and the roll angle of the vehicle body are used as output nodes, and the weight and the threshold value are changed through the network training until the precision reaches the standard.
9. The path-tracking and vehicle-body-posture cooperative control method according to claim 1, characterized in that: the relative quantity of the ratio of the current lateral acceleration of the vehicle to the real-time lateral limit acceleration is used as a rollover judging condition to replace the traditional method of predicting rollover by depending on the roll angle or the absolute quantity of the lateral acceleration;
the acceleration of the vehicle in the rollover critical state is as follows:
in the formula, a y Lateral acceleration as position of center of gravity, a y,L Critical lateral acceleration of the rollover at the position of the center of gravity, m is the mass of the whole vehicle, m s Is the sprung mass h cm The height of the center of gravity, T the wheel track, h the distance from the center of gravity to the center of inclination,
at a roll angle
L d ＝a y /a y,L
In the formula, a y Lateral acceleration of the position of the center of gravity, a y,L Critical lateral acceleration of the rollover at the position of the center of gravity, m is the mass of the whole vehicle, m s Is the sprung mass h cm The height of the center of gravity, T the wheel track, h the distance from the center of gravity to the center of roll,
is a roll angle
Based on L d Calculating rollover time limit (TTR value) with the calculation step length of 10ms;
determining TTR threshold value for compensating vehicle body posture according to performance requirement deviation,
if the cooperative control performance requirement is biased to the tracking precision, the TTR threshold value is a larger value, so that the center of gravity of the whole vehicle is lower when the vehicle steers;
if the cooperative control demand is biased towards comfort, the TTR threshold is a smaller value, and the TTR threshold is in direct proportion to the vehicle speed.
10. The path-tracking and vehicle-body-posture cooperative control method according to claim 1, characterized in that: when the path tracking precision is lower than a set threshold value, but the control stability and the comfort degree value do not meet the set threshold value, the control stability and the comfort of the vehicle are considered preferentially, and the vehicle body attitude compensation is carried out on the vehicle, and the vehicle body attitude compensation control method is realized through the following steps:
the posture of the vehicle body is improved by controlling the roll of the vehicle, and the roll moment generated by the roll is composed of three parts:
1. roll moment M caused by centrifugal force of sprung mass ΦrΙ ；
2. Anti-roll moment M caused by spring-loaded mass gravity ΦrⅡ ；
3. Roll moment M caused by centrifugal force of non-suspended mass ΦrⅢ ；
In addition, the vertical load is transferred between the left and right wheels during rolling, and a load transfer moment M is generated ZF 、M ZR (ii) a When the vehicle body is in a roll state, the left and right suspension frames apply additional opposite forces delta f to the active suspension actuators in the current state, and an anti-roll moment M can be formed af The roll of the vehicle can be restrained;
roll moment M caused by sprung mass centrifugal force ΦrⅠ Comprises the following steps:
M ΦrⅠ ＝m s ·a y ·h
M ΦrⅠ roll moment, m, caused by the centrifugal force of the sprung mass s Is a sprung mass, a y The acceleration of the mass center of the vehicle body is taken as h, and the height of the mass center is taken as h;
sprung mass gravity induced anti-roll moment M ΦrⅡ Comprises the following steps:
M ΦrⅡ roll moment, m, caused by the centrifugal force of the sprung mass s Is the sprung mass, e is the transverse distance of the centre of mass to the wheel, a y Is the acceleration of the center of mass of the vehicle body, h g The height from the connecting position of the suspension and the vehicle body at the lower side of the vehicle body to the center of mass,
is the vehicle body roll angle;
roll moment M caused by centrifugal force of non-suspended mass ΦrⅢ Comprises the following steps:
M ΦrⅢ ＝-Fuy(h 0 -r)
M ΦrⅢ the roll moment caused by unsprung mass, F the static friction force of road surface to wheel pointing to the steering center, u the vehicle speed, y the transverse dimension of the vehicle body, h 0 Is the height of the center of mass, and r is the radius of the wheel;
when the vehicle is tilted, the vertical load is transferred between the left and right wheels, and a load transfer moment M is generated ZF 、M ZR Comprises the following steps:
M ZF ＝(F rRF -F rLF )·B/2
M ZR ＝(F rRR -F rLR )·B/2
M ZF for the front side wheel transfer moment, M ZR For rear side wheel transfer torque, F rRF For transferring force to the right front wheel, F rLF Is the left rear side wheel transfer force, F rRR Is a right rear side wheel transfer force, F rRR And B is the width of the vehicle body;
taking a moment of a longitudinal center line of a vehicle body, namely:
M ΦrⅠ -M ΦrⅡ +M ΦrⅢ +M ZF +M ZR ＝M af
M ΦrⅠ roll moment caused by centrifugal force of sprung mass, M ΦrⅡ Roll moment caused by centrifugal force of sprung mass, M ΦrⅢ Roll moment due to unsprung mass, M ZF For front-side wheel transfer torque, M ZR For rear side wheel transfer torque, M af An anti-roll moment provided to the actuator.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310027042.3A CN115963836A (en) | 2023-01-09 | 2023-01-09 | Path tracking and vehicle body posture cooperative control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310027042.3A CN115963836A (en) | 2023-01-09 | 2023-01-09 | Path tracking and vehicle body posture cooperative control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115963836A true CN115963836A (en) | 2023-04-14 |
Family
ID=87354733
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310027042.3A Pending CN115963836A (en) | 2023-01-09 | 2023-01-09 | Path tracking and vehicle body posture cooperative control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115963836A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116198522A (en) * | 2023-05-05 | 2023-06-02 | 江苏大学 | Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions |
-
2023
- 2023-01-09 CN CN202310027042.3A patent/CN115963836A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116198522A (en) * | 2023-05-05 | 2023-06-02 | 江苏大学 | Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions |
| CN116198522B (en) * | 2023-05-05 | 2023-08-08 | 江苏大学 | Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tagne et al. | Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation | |
| CN110654195B (en) | Vehicle, vehicle suspension system and adjusting method and device thereof | |
| JP4143104B2 (en) | Vehicle control device | |
| CN111016893B (en) | Intelligent vehicle extensible game lane keeping self-adaptive cruise control system and control method under congestion environment | |
| US20140195112A1 (en) | Adaptive Active Suspension System With Road Preview | |
| Hima et al. | Trajectory tracking for highly automated passenger vehicles | |
| CN112141101B (en) | Method and system for pre-aiming safety path based on CNN and LSTM | |
| CN107015477A (en) | Vehicle route tracking H ∞ control methods based on feedback of status | |
| CN110962849A (en) | Curve self-adaptive cruise method | |
| CN114379583A (en) | Automatic driving vehicle trajectory tracking system and method based on neural network dynamics model | |
| CN115963836A (en) | Path tracking and vehicle body posture cooperative control method | |
| Dekkata et al. | Improved steering and adaptive cruise control for autonomous vehicles using model predictive control | |
| CN112578672A (en) | Unmanned vehicle trajectory control system based on chassis nonlinearity and trajectory control method thereof | |
| EP4000973A1 (en) | The use of neural networks in control systems | |
| JP4766152B2 (en) | Vehicle travel control device | |
| Lee et al. | An investigation on the integrated human driver model for closed-loop simulation of intelligent safety systems | |
| CN115298045A (en) | Vehicle control device, vehicle control method, and vehicle control system | |
| JP7446434B2 (en) | Suspension control device and suspension device control method | |
| Salvi et al. | Stabilization of vertical motion of a vehicle on bumpy terrain using deep reinforcement learning | |
| Dekkata | Steering and adaptive cruise control for autonomous vehicles using model predictive control | |
| Hirao et al. | A semi-active suspension system using ride control based on bi-linear optimal control theory and handling control considering roll feeling | |
| CN114179818A (en) | Intelligent automobile transverse control method based on adaptive preview time and sliding mode control | |
| Yakub et al. | Autonomous ground vehicle of path following control through model predictive control with feed forward controller | |
| Pauca et al. | Automated computing and tracking of the maximum velocity on a certain road sector | |
| Zhuang et al. | Model-Predictive-Control-Based Simultaneous Trajectory Tracking and Speed Control for Intelligent Vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |