CN112758109A - Transverse tracking steady state deviation compensation method and device - Google Patents
Transverse tracking steady state deviation compensation method and device Download PDFInfo
- Publication number
- CN112758109A CN112758109A CN202110380282.2A CN202110380282A CN112758109A CN 112758109 A CN112758109 A CN 112758109A CN 202110380282 A CN202110380282 A CN 202110380282A CN 112758109 A CN112758109 A CN 112758109A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- state deviation
- steady state
- deviation
- compensation
- 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.)
- Granted
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- 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
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Human Computer Interaction (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention discloses a method and a device for compensating transverse tracking steady-state deviation, relates to the technical field of automatic driving, and mainly aims to improve the robustness and adaptability of a transverse control algorithm by adding an interference self-compensation link on the control architecture level and realize the compensation of the transverse tracking steady-state deviation under the condition of not needing a vehicle model and not considering the delay of a steering mechanism. The method for compensating the transverse tracking steady-state deviation comprises the following steps: acquiring real-time information of vehicle running based on a sensor of the vehicle; judging whether the vehicle carries out steady state deviation compensation or not according to the real-time information and a preset composite interference estimation condition; if the steady state deviation compensation is executed, determining the steady state deviation of the vehicle by using an exponential weighted moving average algorithm according to the vehicle track; and processing the steady state deviation by utilizing a preset feedback controller to obtain the front wheel deflection angle to be compensated of the vehicle.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a method and a device for compensating transverse tracking steady-state deviation.
Background
With the deep advance of the internet, big data, communication technology and artificial intelligence, the automatic driving of automobiles becomes a key technology for solving the current problems of traffic jam and traffic safety. The lateral tracking of the vehicle is one of the key technologies of automatic driving, and is the basis for ensuring the safe and effective driving of the vehicle in a complicated and variable road environment. Since the vehicle itself is a complex system with high coupling, strong nonlinearity and hysteresis, and is interfered and influenced by road conditions, air resistance or tire characteristics under extreme conditions during driving, the problem of lateral tracking control becomes a difficult point in the automatic driving technology.
The autonomous vehicle is influenced by factors such as system uncertainty and complex external interference in the path tracking process, and stable tracking deviation may exist. The factors such as system uncertainty and complex external interference are comprehensively regarded as composite interference, and in order to eliminate the lateral tracking steady-state deviation, an estimated value conforming to the interference needs to be obtained through calculation, and then rotation angle compensation is carried out. In the method for compensating the lateral tracking turning angle of the automatic driving path, for example, a method based on a Model Reference Adaptive System (MRAS), a method based on a Nonlinear Disturbance Observer (NDO), etc., a vehicle Model is usually required to be based on when the lateral control Disturbance compensation is performed, or the influence of delay of a steering mechanism is considered, but the models of deployed vehicles are not consistent in the actual application process, so that the large-scale deployment of the deployed vehicles is difficult, and the mobility of the compensation method is poor.
Disclosure of Invention
In view of the above problems, the present invention provides a method and an apparatus for compensating a lateral tracking steady-state deviation, and mainly aims to improve the robustness and adaptability of a lateral control algorithm by adding an interference self-compensation link in a control architecture level, and to realize the compensation of the lateral tracking steady-state deviation without a vehicle model and considering the delay of a steering mechanism.
In order to achieve the purpose, the invention mainly provides the following technical scheme:
in a first aspect, the present invention provides a method for compensating a lateral tracking steady-state deviation, the method comprising:
acquiring real-time information of vehicle running based on a sensor of the vehicle;
judging whether the vehicle carries out steady state deviation compensation or not according to the real-time information and a preset composite interference estimation condition;
if the steady state deviation compensation is executed, determining the steady state deviation of the vehicle by using an exponential weighted moving average algorithm according to the vehicle track;
and processing the steady state deviation by utilizing a preset feedback controller to obtain the front wheel deflection angle to be compensated of the vehicle.
Preferably, determining the steady state deviation of the vehicle by using an exponentially weighted moving average algorithm according to the vehicle trajectory comprises:
acquiring track tracking deviation in the running process of a vehicle, wherein the track tracking deviation comprises transverse tracking deviation, course tracking deviation and front wheel deflection angle;
calculating the average front wheel deflection angle of the vehicle from the current position to the next position by using the transverse tracking deviation and the course tracking deviation;
judging whether the vehicle meets a preset front wheel deflection angle compensation condition or not according to the front wheel deflection angle and the average front wheel deflection angle;
and if so, calculating the steady state deviation of the vehicle by using an exponential weighted moving average algorithm.
Preferably, before calculating the steady state deviation of the vehicle using the exponentially weighted moving average algorithm, the method further comprises:
determining weight coefficients in an exponentially weighted moving average algorithm, comprising: setting fixed weights and setting adjustable weights.
Preferably, the setting of the adjustable weight includes:
calculating the mean square error of the data according to the trajectory tracking deviation data in a preset time period;
if the mean square error is larger than a preset upper limit threshold, setting the weight as a first weight;
if the mean square error is smaller than a preset lower limit threshold, setting the weight as a second weight;
and if the mean square error is between the upper limit threshold and the lower limit threshold, determining the weight by using a preset algorithm.
Preferably, before the steady state deviation is processed by a preset feedback controller to obtain the front wheel slip angle to be compensated of the vehicle, the method further comprises:
and determining the feedback gain of the feedback controller to the transverse tracking deviation and the feedback gain of the feedback controller to the course tracking deviation by using a pole configuration mode, wherein the input of the feedback controller is the steady state deviation, and the input of the feedback controller is the compensated front wheel deflection angle of the vehicle.
Preferably, the vehicle-based sensor acquiring real-time information of vehicle driving includes:
collecting environment perception information in the running process of a vehicle, wherein the environment perception information at least comprises obstacle information, road environment information and lane line information in the surrounding environment where the vehicle is located;
real-time position information, navigation information and vehicle information of a CAN bus carried in the floor control of the vehicle are determined based on the running state information of the vehicle.
Preferably, the determining whether the vehicle performs steady state deviation compensation according to the real-time information and a preset combined interference estimation condition includes:
judging whether the driving track of the vehicle on the preset distance section is a straight line or not;
judging whether the running speed of the vehicle is within a preset speed interval or not;
judging whether the track tracking deviation of the vehicle is within a preset range or not;
and when the judgment conditions are met, determining that the vehicle performs steady state deviation compensation, and otherwise, determining that the vehicle does not perform steady state deviation compensation.
In a second aspect, the present invention provides a lateral tracking steady state deviation compensation apparatus, comprising:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring real-time information of vehicle running based on a sensor of the vehicle;
the judging unit is used for judging whether the vehicle carries out steady state deviation compensation or not according to the real-time information obtained by the obtaining unit and a preset composite interference estimation condition;
the prediction unit is used for determining the steady-state deviation of the vehicle by using an exponential weighted moving average algorithm according to the vehicle track if the judgment unit determines to execute the steady-state deviation compensation;
and the compensation unit is used for processing the steady state deviation obtained by the prediction unit by utilizing a preset feedback controller to obtain the to-be-compensated front wheel deflection angle of the vehicle.
Preferably, the prediction unit includes:
the system comprises an acquisition module, a tracking module and a control module, wherein the acquisition module is used for acquiring track tracking deviation in the running process of a vehicle, and the track tracking deviation comprises transverse tracking deviation, course tracking deviation and front wheel deflection;
the calculation module is used for calculating the average front wheel deflection angle of the vehicle from the current position to the next position by utilizing the transverse tracking deviation and the course tracking deviation obtained by the acquisition module;
the judging module is used for judging whether the vehicle meets a preset front wheel deflection angle compensation condition or not according to the front wheel deflection angle and the average front wheel deflection angle obtained by the calculating module;
the calculation module is further used for calculating the steady state deviation of the vehicle by using an exponential weighted moving average algorithm if the judgment module determines that the front wheel deflection angle compensation condition is met.
Preferably, the prediction unit further includes:
a determining module for determining a weight coefficient in an exponentially weighted moving average algorithm before the calculating module calculates the steady state deviation of the vehicle using the exponentially weighted moving average algorithm, comprising: setting fixed weights and setting adjustable weights.
Preferably, the determining module sets an adjustable weight, specifically including:
calculating the mean square error of the data according to the trajectory tracking deviation data in a preset time period;
if the mean square error is larger than a preset upper limit threshold, setting the weight as a first weight;
if the mean square error is smaller than a preset lower limit threshold, setting the weight as a second weight;
and if the mean square error is between the upper limit threshold and the lower limit threshold, determining the weight by using a preset algorithm.
Preferably, before the compensating unit processes the steady state deviation by using a preset feedback controller to obtain a front wheel slip angle to be compensated of the vehicle, the apparatus further comprises:
and the determining unit is used for determining the feedback gain of the feedback controller to the transverse tracking deviation and the feedback gain of the feedback controller to the course tracking deviation by utilizing a pole configuration mode, wherein the input of the feedback controller is the steady state deviation, and the input of the feedback controller is the compensated front wheel deflection angle of the vehicle.
Preferably, the acquiring unit includes:
the acquisition module is used for acquiring environment perception information in the running process of the vehicle, and at least comprises obstacle information, road environment information and lane line information in the surrounding environment where the vehicle is located;
and the determining module is used for determining the real-time position information, the navigation information and the vehicle information of the CAN bus carried in the bottom layer control of the vehicle based on the running state information of the vehicle.
Preferably, the judging unit includes:
the first judgment module is used for judging whether a running track of the vehicle on a preset distance section is a straight line or not;
the second judgment module is used for judging whether the running speed of the vehicle is within a preset speed interval or not;
the third judgment module is used for judging whether the track tracking deviation of the vehicle is within a preset range or not;
and the determining module is used for determining that the vehicle carries out steady state deviation compensation when the judging conditions are met, and otherwise, determining that the vehicle does not carry out steady state deviation compensation.
In another aspect, the present invention further provides a processor, configured to execute a program, where the program executes the above-mentioned method for compensating for lateral tracking steady-state deviation.
In another aspect, the present invention further provides a storage medium, where the storage medium is used to store a computer program, where the computer program controls, when running, an apparatus in which the storage medium is located to execute the above-mentioned method for compensating for the lateral tracking steady-state deviation.
By means of the technical scheme, the transverse tracking steady state deviation compensation method and device provided by the invention can be applied to an automatic driving platform to compensate the transverse tracking steady state deviation of the vehicle. The method comprises the steps of comprehensively judging whether a current vehicle meets a preset composite interference estimation condition or not by collecting running state information of the vehicle in real time, and determining whether to perform steady state deviation compensation or not. And if the vehicle is subjected to steady state deviation compensation, the steady state deviation value of the vehicle needs to be estimated, the current steady state deviation of the vehicle is determined by using an exponential weighted moving average algorithm, and the preset feedback controller is applied to carry out post-processing on the steady state deviation to obtain the front wheel deflection angle to be compensated of the vehicle. Compared with the vehicle transverse tracking steady-state compensation method in the prior art, the method provided by the invention has the advantages that an interference self-compensation link is added on the control architecture level, and the robustness and the adaptability of a transverse control algorithm are improved. Meanwhile, real-time information of vehicle running can be fully utilized as feed-forward, so that an ideal running state of the vehicle is obtained. In addition, the compensation method does not use a vehicle model, does not need to consider the influence of delay of a steering mechanism and the like, is simple and visual, and has universality.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings.
Fig. 1 is a schematic flow chart of a lateral tracking steady-state deviation compensation method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another method for compensating for lateral tracking steady-state deviation according to an embodiment of the present invention;
FIG. 3 is a graph of simulation results for lateral tracking steady state offset compensation in an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a lateral tracking steady-state deviation compensation apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of another lateral tracking steady-state deviation compensation device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The method in the examples of the present invention will be described in detail below.
The embodiment of the invention provides a method for compensating transverse tracking steady-state deviation, and fig. 1 is a schematic flow chart of the method for compensating transverse tracking steady-state deviation, which specifically comprises the following steps:
and S101, acquiring real-time information of vehicle running based on a vehicle sensor.
In this embodiment, the sensors of the vehicle are mainly environment sensing devices of the vehicle, such as sensor devices of a laser radar, an ultrasonic radar, a camera, and the like, and the real-time information acquired by the environment sensing devices includes obstacle information, road environment information, lane line information, and the like in the surrounding environment where the vehicle is located.
In addition, the real-time information of the vehicle running also comprises real-time position information of the vehicle, navigation information and vehicle information of a CAN bus carried in the bottom layer control, specifically, the position and navigation information of the vehicle mainly comprise positioning and speed information of the vehicle and the like, and the positioning information of the vehicle CAN be acquired by combining a GPS (global positioning system) with an inertial navigation IMU (inertial measurement unit); the vehicle information based on the CAN bus is that the CAN bus carried in the vehicle floor control is used for transmitting the vehicle state to other components, such as the vehicle accelerator opening degree, the brake pedal angle, the gear position, the steering wheel angle and the like, so as to collect the state information of the vehicle position, the vehicle speed, the vehicle steering and the like in real time.
And S102, judging whether the vehicle carries out steady state deviation compensation or not according to the real-time information and the preset composite interference estimation condition.
When the vehicle is subjected to steady state deviation compensation, the composite interference which causes the vehicle to have the steady state deviation needs to be determined to reach a certain preset condition, and the vehicle is compensated when the composite interference meets the preset condition. In this step, the preset combined interference estimation condition is a condition for judging whether the vehicle satisfies the steady-state deviation compensation. The method specifically comprises the following steps: whether the running track of the vehicle is a straight line or not; whether the running speed of the vehicle is within a preset speed interval or not; whether the trajectory tracking deviation of the vehicle is within a preset range or not, and the like, and the specific judgment condition can be preset according to the actual requirement. The running track, speed, track tracking deviation and other state information of the vehicle are determined according to the real-time information of the vehicle.
And when the judgment result in the step is that the steady state deviation compensation is executed, continuing to execute the step S103, otherwise, returning to the previous step to continue to acquire the real-time information of the vehicle.
And S103, if the steady state deviation compensation is executed, determining the steady state deviation of the vehicle by using an exponential weighted moving average algorithm according to the vehicle track.
An Exponential Weighted Moving Average (EWMA) used in this step is a data analysis method for full data that assigns higher weight to the nearest data and reduces the weight of data farther from the current time point, and this method assigns a weight that exponentially decays with time to each sample, so that the average obtained by calculation can reflect the latest statistical information of the data.
Specifically, in this step, the data processed by the EWMA is vehicle trajectory data, which includes lateral tracking deviation, heading tracking deviation, and front wheel slip angle obtained based on the vehicle trajectory data. The basic idea of applying EWMA to compensate the steady state deviation of the vehicle lateral tracking is as follows: under ideal conditions, the heading tracking deviation and the front wheel slip angle of the vehicle are both zero when the vehicle is stably running on a straight road. If the course tracking deviation or the front wheel slip angle of the vehicle is not zero at this time, it is considered that these deviations are caused by a compound disturbance D (a calibration deviation, an external disturbance, etc.). The mean value of the course tracking deviation and the front wheel deflection angle of the vehicle running on the straight road is used as the estimated value of the composite interference, and the estimated value is compensated, so that the transverse tracking steady-state deviation can be eliminated. Therefore, the step is to calculate the steady state deviation value required to be compensated by the vehicle according to the current running track of the vehicle and real-time information by using the EWMA.
And S104, processing the steady state deviation by using a preset feedback controller to obtain the front wheel deflection angle to be compensated of the vehicle.
The feedback controller in the step is designed aiming at the vehicle kinematic model, and the feedback controller realizes the self-compensation of the steady state deviation so as to obtain more accurate front wheel deflection angle of the vehicle to be compensated. The step is that the steady state deviation of the vehicle acts on the feedback controller, the feedback controller optimizes the front wheel deflection angle value to be compensated and feeds back the front wheel deflection angle value to the composite disturbance estimation condition, when the front wheel deflection angle is not compensated, the compensation is stopped, and the parameters in the composite disturbance estimation condition are reset.
Based on the embodiment shown in fig. 1, it can be seen that the method for compensating for the transverse tracking steady-state deviation provided by the invention comprehensively judges whether the current vehicle meets the preset composite interference estimation condition by acquiring the running state information of the vehicle in real time, so as to determine whether to perform steady-state deviation compensation on the current vehicle. And if the vehicle is subjected to steady state deviation compensation, the steady state deviation value of the vehicle needs to be estimated, the current steady state deviation of the vehicle is determined by using an exponential weighted moving average algorithm, and the preset feedback controller is applied to carry out post-processing on the steady state deviation to obtain the front wheel deflection angle to be compensated of the vehicle. Compared with the vehicle transverse tracking steady-state compensation method in the prior art, the method provided by the invention has the advantages that an interference self-compensation link is added on the control architecture level, and the robustness and the adaptability of a transverse control algorithm are improved. Meanwhile, real-time information of vehicle running can be fully utilized as feed-forward, so that an ideal running state of the vehicle is obtained. In addition, the compensation method does not use a vehicle model, does not need to consider the influence of delay of a steering mechanism and the like, is simple and visual, and has universality.
Further, as a refinement and extension of the lateral tracking steady state deviation compensation method shown in fig. 1, particularly for the determination of the vehicle steady state deviation and the detailed description of the feedback controller, please refer to the embodiment described in fig. 2 specifically, the method may include:
s201, acquiring real-time information of vehicle running based on a sensor of the vehicle.
In this embodiment, the obtaining of the real-time information includes collecting environment sensing information in a vehicle driving process, and determining real-time position information and navigation information of the vehicle based on the vehicle driving state information. The details of which have been described in the embodiment shown in fig. 1 and will not be described herein again.
S202, judging whether the vehicle carries out steady state deviation compensation or not according to the real-time information and the preset composite interference estimation condition.
The preset multiple interference estimation condition in this embodiment is described in the embodiment shown in fig. 1, and the specific determination process is as follows:
judging whether the driving track of the vehicle on a preset distance section is a straight line or not, wherein the preset distance section is set in a self-defined mode according to the state of the vehicle and the actual requirement, and when the preset distance section is the straight line, the condition is met;
judging whether the running speed of the vehicle is within a preset speed interval, wherein the speed interval is also set by self-definition according to actual requirements, and when the running speed of the vehicle is within the preset speed interval, determining that the condition is met;
judging whether the track tracking deviation of the vehicle is within a preset range, namely whether the deviation between the actual track of the vehicle and the navigation track is within the preset range, and if so, determining that the condition is met;
the three conditions can be judged synchronously or progressively one by one when the judgment is yes, when all the judgment conditions are met, the vehicle is determined to perform the steady state deviation compensation, otherwise, when one condition is not met, the vehicle is determined not to perform the steady state deviation compensation.
And S203, acquiring the track tracking deviation in the running process of the vehicle.
When determining to execute the steady state deviation compensation, the step determines the current trajectory tracking deviation of the vehicle according to the running trajectory of the vehicle and the navigation information, and specifically comprises the following steps: lateral tracking deviation, course tracking deviation and front wheel deflection angle.
And S204, calculating the average front wheel slip angle of the vehicle from the current position to the next position by using the transverse tracking deviation and the heading tracking deviation.
Wherein the next position is the position of the vehicle after one or more sampling periods.
And S205, judging whether the vehicle meets a preset front wheel deflection angle compensation condition or not according to the front wheel deflection angle and the average front wheel deflection angle.
Wherein, the front wheel slip angle is the front wheel slip angle obtained in step 203, the difference between the front wheel slip angle and the average front wheel slip angle obtained in step 204 is compared, and when the front wheel slip angle is within the preset range, it is determined that the front wheel slip angle satisfies the preset front wheel slip angle compensation condition, and step 206 is executed.
And S206, calculating the steady state deviation of the vehicle by using an exponential weighted moving average algorithm.
In executing this step, it is further required to determine a weight coefficient in the exponentially weighted moving average algorithm, and the setting of the weight coefficient can be specifically divided into a fixed weight and an adjustable weight.
Specifically, for setting the fixed weight, the fixed weight may be performed in an infinite time domain. In calculating the steady state deviation of the vehicle, it is desirable that the newly measured data be weighted higher and the weight of the data be decreased over time. When the window length N approaches infinity, an exponentially weighted moving average acting in an infinite time domain is obtained, which can be expressed as:
wherein the content of the first and second substances,
is an estimate of the composite interference and,
is the weight of exponential decay, satisfies
。
A larger value means a higher weight for the current sample value, i.e. a lower weight for the past measurement values. The stronger the timeliness of the EWMA at this point, and the weaker the EWMA will be otherwise.
The larger the weight is, the lower the past measurements are weighted, the less stationary the stationary will decrease, and vice versa the stationary will increase.
For setting adjustable weight, the Mean Square Error (MSE) of data can be calculated according to the trajectory tracking deviation data in a preset time period; if the mean square error is larger than a preset upper limit threshold, setting the weight as a first weight; if the mean square error is smaller than a preset lower limit threshold, setting the weight as a second weight; and if the mean square error is between the upper limit threshold and the lower limit threshold, determining the weight by using a preset algorithm. The preset algorithm may be a fixed step number of adaptive adjustment weights, or adaptive adjustment weights based on MSE normalization.
Specifically, the weights are adaptively adjusted for a fixed number of steps. The Mean Squared Error (MSE) is determined by storing data over a period of time.
If MSE is greater than the upper threshold
Then, then
；
If the MSE is between the lower threshold
And upper threshold
And then:
if MSE is less than the lower threshold
Then, then
。
However, this method has the disadvantage that the weight adjustment is not flexible, and can only be reduced according to a fixed step size, which has a certain limitation. Therefore, in this embodiment, it is preferable that the weight is adaptively adjusted based on MSE normalization, and the specific determination method includes: the Mean Squared Error (MSE) is determined by storing data over a period of time.
If MSE is greater than the upper threshold
Then, then
；
If the MSE is between the lower threshold
And upper threshold
BetweenAnd then:
if MSE is less than the lower threshold
Then, then
。
The steady-state deviation of the vehicle is calculated using an exponentially weighted moving average algorithm based on the weights set above.
And S207, processing the steady state deviation by using a preset feedback controller to obtain a front wheel deflection angle to be compensated of the vehicle.
In this embodiment, for a system described by a state space equation:
a feedback controller is designed for this purpose:
wherein the content of the first and second substances,
is a reference input, and for transverse path tracking, can be a reference corner or a reference curvature of a reference point on a desired path, so as to construct a closed loop system.
By substituting formula (4) for formula (3), it is possible to obtain:
for the open-loop system shown in equation (3), the poles of the open-loop transfer function are known to be the eigenvalues of the system matrix a from modern control theory. For the closed loop system shown in equation (5), the system matrix is transformed from A to A-BF. The dynamic behavior of the closed-loop system is mainly determined by its closed-loop poles, i.e. the eigenvalues of the system matrix a-BF. Therefore, a proper feedback gain matrix F can be selected, so that poles of the closed-loop system are all in the left half complex plane, and the stability of the system is ensured. Similarly, by configuring a Pole (Pole) of the closed-loop system, a feedback gain matrix F can also be obtained, and further a feedback control quantity can be obtained.
Specifically, in this embodiment, a pole allocation method is applied to compensate the vehicle deflection angle control quantity, and the trajectory error model of the continuous state space is
The above equation can be written as:
discretizing a continuous state space by adopting an accurate method:
order to
The following can be obtained:
order to
，
And substituting into formula (8) to obtain:
wherein the index matrix
The expandable representation is:
then, the error model can be discretized as:
wherein the content of the first and second substances,
as the wheel base of the vehicle,
is the speed at which the vehicle is traveling,
is the algorithm sampling period.
Order to
Then, then
Then
Is provided with
，
Being poles of a closed-loop system, i.e.
The characteristic value of (1) is
Comparing equation (12) and equation (13), we can obtain:
from the formulas (12) to (14), the relationship between the pole position and the corner feedback gain can be obtained
Wherein the content of the first and second substances,
is the feedback gain to the lateral tracking offset,
is the feedback gain to the heading tracking bias.
Therefore, the feedback controller designed aiming at the vehicle kinematic model is adopted, and the steady state deviation of the vehicle acts on the feedback controller designed by the invention, so that the front wheel deflection angle control quantity is compensated. And finally, outputting the compensated front wheel deflection angle. And when the front wheel slip angle compensation condition is not met, resetting parameters in the composite interference estimation condition and returning to the front wheel slip angle.
Further, fig. 3 schematically shows a simulation result diagram of the transversal tracking steady-state deviation compensation based on the exponentially weighted moving average adaptive weight in the embodiment of the present invention, and the embodiment of the present invention performs verification of the transversal tracking steady-state deviation compensation algorithm based on the exponentially weighted moving average adaptive weight in simulation software according to the establishment of the feedback controller. Specifically, the function is based on development of a dev branch version of an xcontrol packet, and parameter debugging, performance testing and performance optimization are performed subsequently. Adding a LateraCtrL Comp function class in the existing module, and performing compensation correction on lat _ ctrl _ cmd _ reinforced _ angle generated by the transverse control calculation, wherein a detailed parameter configuration list is shown in Table 1:
TABLE 1 list of lateral tracking steady state offset compensation parameters
According to the parameter configuration list, simulation tests are performed according to the flow steps of the embodiment shown in fig. 1 or fig. 2, and the variation values of the lateral tracking deviation and the reference steering angle are shown in fig. 3. The simulation result shows that the method provided by the embodiment can achieve better effect in the compensation of the lateral tracking steady-state deviation.
Based on the same inventive concept, as an implementation of the method, the embodiment of the invention also provides a device for compensating the transverse tracking steady-state deviation, which mainly aims to improve the robustness and the adaptability of a transverse control algorithm by adding an interference self-compensation link on the control architecture level and realize the compensation of the transverse tracking steady-state deviation under the conditions of not needing a vehicle model and not considering the delay of a steering mechanism. For convenience of reading, details in the foregoing method embodiments are not described in detail again in this apparatus embodiment, but it should be clear that the apparatus in this embodiment can correspondingly implement all the contents in the foregoing method embodiments. Referring specifically to fig. 4, the apparatus may comprise:
an acquisition unit 31 for acquiring real-time information of vehicle travel based on a sensor of the vehicle;
a determining unit 32, configured to determine whether the vehicle performs steady-state deviation compensation according to the real-time information obtained by the obtaining unit 31 and a preset composite interference estimation condition;
a prediction unit 33, configured to determine a steady-state deviation of the vehicle according to a vehicle trajectory by using an exponential weighted moving average algorithm if the determination unit 32 determines that the steady-state deviation compensation is performed;
and the compensation unit 34 is configured to process the steady-state deviation obtained by the prediction unit 33 by using a preset feedback controller, so as to obtain a to-be-compensated front wheel slip angle of the vehicle.
Further, as shown in fig. 5, the prediction unit 33 includes:
the acquiring module 331 is configured to acquire a trajectory tracking deviation in a vehicle driving process, where the trajectory tracking deviation includes a lateral tracking deviation, a heading tracking deviation, and a front wheel slip angle;
a calculating module 332, configured to calculate an average front wheel slip angle of the vehicle from the current position to the next position by using the lateral tracking deviation and the heading tracking deviation obtained by the obtaining module 331;
a determining module 333, configured to determine whether the vehicle meets a preset front wheel slip angle compensation condition according to the front wheel slip angle and the average front wheel slip angle obtained by the calculating module 332;
the calculating module 332 is further configured to calculate a steady-state deviation of the vehicle by using an exponential weighted moving average algorithm if the determining module 333 determines that the front-wheel slip angle compensation condition is satisfied.
Further, as shown in fig. 5, the prediction unit 33 further includes:
a determining module 334, configured to determine the weight coefficients in the exponentially weighted moving average algorithm before the calculating module 332 calculates the steady-state deviation of the vehicle by using the exponentially weighted moving average algorithm, including: setting fixed weights, such as fixed weights in an infinite time domain; and setting adjustable weights, such as adaptive weight adjustment under a fixed step number and adaptive weight adjustment based on MSE normalization.
Further, the determining module 334 sets an adjustable weight, which specifically includes:
calculating the mean square error of the data according to the trajectory tracking deviation data in a preset time period;
if the mean square error is larger than a preset upper limit threshold, setting the weight as a first weight;
if the mean square error is smaller than a preset lower limit threshold, setting the weight as a second weight;
and if the mean square error is between the upper limit threshold and the lower limit threshold, determining the weight by using a preset algorithm.
Further, as shown in fig. 5, before the compensating unit 34 processes the steady state deviation by using a preset feedback controller to obtain the front wheel slip angle to be compensated of the vehicle, the apparatus further includes:
and the determining unit 35 is configured to determine a feedback gain of the feedback controller for the lateral tracking deviation and a feedback gain of the feedback controller for the heading tracking deviation by using a pole configuration mode, where an input of the feedback controller is the steady-state deviation, and an input of the feedback controller is a compensated vehicle front wheel slip angle.
Further, as shown in fig. 5, the acquiring unit 31 includes:
the acquisition module 311 is configured to acquire environment sensing information during the driving process of the vehicle, where the environment sensing information at least includes obstacle information, road environment information, and lane line information in the surrounding environment where the vehicle is located;
and a determining module 312, configured to determine real-time position information, navigation information, and vehicle information of a CAN bus mounted in the floor control of the vehicle based on the vehicle driving state information.
Further, as shown in fig. 5, the judging unit 32 includes:
the first judging module 321 is configured to judge whether a driving track of the vehicle on a preset distance segment is a straight line;
a second determination module 322, configured to determine whether a driving speed of the vehicle is within a preset speed interval;
a third judging module 323 for judging whether the trajectory tracking deviation of the vehicle is within a preset range;
and the determining module 324 is configured to determine that the vehicle performs the steady-state deviation compensation when the determination conditions are met, and otherwise, determine that the vehicle does not perform the steady-state deviation compensation.
Further, the present invention also provides a processor, configured to execute a program, where the program executes the method for compensating for the lateral tracking steady-state deviation provided in any one of the embodiments shown in fig. 1-2.
In another aspect, the present invention further provides a storage medium, where the storage medium is used to store a computer program, where the computer program when executed controls an apparatus where the computer readable storage medium is located to execute the method for compensating for the lateral tracking steady-state deviation according to any one of the embodiments shown in fig. 1-2.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of a data access method, apparatus and system according to embodiments of the present invention. The present invention may also be embodied as devices or device programs (e.g., computer programs and computer program products) for performing some or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (10)
1. A method of lateral tracking steady state offset compensation, the method comprising:
acquiring real-time information of vehicle running based on a sensor of the vehicle;
judging whether the vehicle carries out steady state deviation compensation or not according to the real-time information and a preset composite interference estimation condition;
if the steady state deviation compensation is executed, determining the steady state deviation of the vehicle by using an exponential weighted moving average algorithm according to the vehicle track;
and processing the steady state deviation by utilizing a preset feedback controller to obtain the front wheel deflection angle to be compensated of the vehicle.
2. The method of claim 1, wherein determining the steady state deviation of the vehicle using an exponentially weighted moving average algorithm from a vehicle trajectory comprises:
acquiring track tracking deviation in the running process of a vehicle, wherein the track tracking deviation comprises transverse tracking deviation, course tracking deviation and front wheel deflection angle;
calculating the average front wheel deflection angle of the vehicle from the current position to the next position by using the transverse tracking deviation and the course tracking deviation;
judging whether the vehicle meets a preset front wheel deflection angle compensation condition or not according to the front wheel deflection angle and the average front wheel deflection angle;
and if so, calculating the steady state deviation of the vehicle by using an exponential weighted moving average algorithm.
3. The method of claim 2, wherein prior to calculating the steady state deviation of the vehicle using an exponentially weighted moving average algorithm, the method further comprises:
determining weight coefficients in an exponentially weighted moving average algorithm, comprising: setting fixed weights and setting adjustable weights.
4. The method of claim 3, wherein setting the adjustable weight comprises:
calculating the mean square error of the data according to the trajectory tracking deviation data in a preset time period;
if the mean square error is larger than a preset upper limit threshold, setting the weight as a first weight;
if the mean square error is smaller than a preset lower limit threshold, setting the weight as a second weight;
and if the mean square error is between the upper limit threshold and the lower limit threshold, determining the weight by using a preset algorithm.
5. The method of claim 1, wherein before processing the steady state deviation with a preset feedback controller to obtain a front wheel slip angle of the vehicle to be compensated, the method further comprises:
and determining the feedback gain of the feedback controller to the transverse tracking deviation and the feedback gain of the feedback controller to the course tracking deviation by using a pole configuration mode, wherein the input of the feedback controller is the steady state deviation, and the input of the feedback controller is the compensated front wheel deflection angle of the vehicle.
6. The method of claim 1, wherein the vehicle-based sensor obtains real-time information of vehicle travel, comprising:
collecting environment perception information in the running process of a vehicle, wherein the environment perception information at least comprises obstacle information, road environment information and lane line information in the surrounding environment where the vehicle is located;
real-time position information, navigation information and vehicle information of a CAN bus carried in the floor control of the vehicle are determined based on the running state information of the vehicle.
7. The method of claim 1, wherein determining whether the vehicle is performing steady state bias compensation based on the real-time information and a predetermined combined interference estimation condition comprises:
judging whether the driving track of the vehicle on the preset distance section is a straight line or not;
judging whether the running speed of the vehicle is within a preset speed interval or not;
judging whether the track tracking deviation of the vehicle is within a preset range or not;
and when the judgment conditions are met, determining that the vehicle performs steady state deviation compensation, and otherwise, determining that the vehicle does not perform steady state deviation compensation.
8. A lateral tracking steady state deviation compensation apparatus, comprising:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring real-time information of vehicle running based on a sensor of the vehicle;
the judging unit is used for judging whether the vehicle carries out steady state deviation compensation or not according to the real-time information obtained by the obtaining unit and a preset composite interference estimation condition;
the prediction unit is used for determining the steady-state deviation of the vehicle by using an exponential weighted moving average algorithm according to the vehicle track if the judgment unit determines to execute the steady-state deviation compensation;
and the compensation unit is used for processing the steady state deviation obtained by the prediction unit by utilizing a preset feedback controller to obtain the to-be-compensated front wheel deflection angle of the vehicle.
9. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to perform the lateral tracking steady-state deviation compensation method of any one of claims 1 to 7 when running.
10. A computer-readable storage medium storing a computer program, wherein the computer program is configured to control an apparatus in which the computer-readable storage medium is located to execute the method of lateral tracking steady-state deviation compensation according to any one of claims 1-7 when the computer program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110380282.2A CN112758109B (en) | 2021-04-09 | 2021-04-09 | Transverse tracking steady state deviation compensation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110380282.2A CN112758109B (en) | 2021-04-09 | 2021-04-09 | Transverse tracking steady state deviation compensation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112758109A true CN112758109A (en) | 2021-05-07 |
| CN112758109B CN112758109B (en) | 2021-07-27 |
Family
ID=75691390
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110380282.2A Active CN112758109B (en) | 2021-04-09 | 2021-04-09 | Transverse tracking steady state deviation compensation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112758109B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115453858A (en) * | 2022-10-18 | 2022-12-09 | 扬州万方科技股份有限公司 | Double-vehicle linkage control capturing and tracking system and operation method thereof |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090095562A1 (en) * | 2007-10-16 | 2009-04-16 | Yoshiyuki Yasui | Steering control device for vehicle |
| JP2011123822A (en) * | 2009-12-14 | 2011-06-23 | Tsubakimoto Chain Co | Transfer vehicle and program |
| CN104787039A (en) * | 2015-04-13 | 2015-07-22 | 电子科技大学 | Car body stable control method of four-wheel independent drive electric car |
| CN106643800A (en) * | 2016-12-27 | 2017-05-10 | 上海司南卫星导航技术股份有限公司 | Course angle error calibration method and automatic navigation driving system |
| CN106681335A (en) * | 2017-01-22 | 2017-05-17 | 无锡卡尔曼导航技术有限公司 | Obstacle-avoiding route planning and control method for unmanned agricultural machine driving |
| CN108001447A (en) * | 2017-11-23 | 2018-05-08 | 江苏大学 | A kind of intelligent vehicle path trace front wheel angle compensating control method |
| CN108622104A (en) * | 2018-05-07 | 2018-10-09 | 湖北汽车工业学院 | A kind of Trajectory Tracking Control method for automatic driving vehicle |
| CN108657179A (en) * | 2017-03-27 | 2018-10-16 | 株式会社斯巴鲁 | The travel controlling system of vehicle |
| CN108932840A (en) * | 2018-07-17 | 2018-12-04 | 北京理工大学 | Automatic driving vehicle urban intersection passing method based on intensified learning |
| CN109606352A (en) * | 2018-11-22 | 2019-04-12 | 江苏大学 | A kind of tracking of vehicle route and stability control method for coordinating |
| CN110341688A (en) * | 2019-05-31 | 2019-10-18 | 驭势科技（北京）有限公司 | A kind of front wheel angle compensation method, device, mobile unit and storage medium |
| CN111703436A (en) * | 2020-08-18 | 2020-09-25 | 北京主线科技有限公司 | Control method and device for automatically driving vehicle |
-
2021
- 2021-04-09 CN CN202110380282.2A patent/CN112758109B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090095562A1 (en) * | 2007-10-16 | 2009-04-16 | Yoshiyuki Yasui | Steering control device for vehicle |
| JP2011123822A (en) * | 2009-12-14 | 2011-06-23 | Tsubakimoto Chain Co | Transfer vehicle and program |
| CN104787039A (en) * | 2015-04-13 | 2015-07-22 | 电子科技大学 | Car body stable control method of four-wheel independent drive electric car |
| CN106643800A (en) * | 2016-12-27 | 2017-05-10 | 上海司南卫星导航技术股份有限公司 | Course angle error calibration method and automatic navigation driving system |
| CN106681335A (en) * | 2017-01-22 | 2017-05-17 | 无锡卡尔曼导航技术有限公司 | Obstacle-avoiding route planning and control method for unmanned agricultural machine driving |
| CN108657179A (en) * | 2017-03-27 | 2018-10-16 | 株式会社斯巴鲁 | The travel controlling system of vehicle |
| CN108001447A (en) * | 2017-11-23 | 2018-05-08 | 江苏大学 | A kind of intelligent vehicle path trace front wheel angle compensating control method |
| CN108622104A (en) * | 2018-05-07 | 2018-10-09 | 湖北汽车工业学院 | A kind of Trajectory Tracking Control method for automatic driving vehicle |
| CN108932840A (en) * | 2018-07-17 | 2018-12-04 | 北京理工大学 | Automatic driving vehicle urban intersection passing method based on intensified learning |
| CN109606352A (en) * | 2018-11-22 | 2019-04-12 | 江苏大学 | A kind of tracking of vehicle route and stability control method for coordinating |
| CN110341688A (en) * | 2019-05-31 | 2019-10-18 | 驭势科技（北京）有限公司 | A kind of front wheel angle compensation method, device, mobile unit and storage medium |
| CN111703436A (en) * | 2020-08-18 | 2020-09-25 | 北京主线科技有限公司 | Control method and device for automatically driving vehicle |
Non-Patent Citations (1)
| Title |
|---|
| 侯隽: "《我国期货市场保证金设置研究》", 30 June 2012, 天津大学出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115453858A (en) * | 2022-10-18 | 2022-12-09 | 扬州万方科技股份有限公司 | Double-vehicle linkage control capturing and tracking system and operation method thereof |
| CN115453858B (en) * | 2022-10-18 | 2023-11-10 | 扬州万方科技股份有限公司 | Dual-vehicle linkage control capturing and tracking system and operation method thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112758109B (en) | 2021-07-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jo et al. | GPS-bias correction for precise localization of autonomous vehicles | |
| CN110967991B (en) | Method and device for determining vehicle control parameters, vehicle-mounted controller and unmanned vehicle | |
| US10829114B2 (en) | Vehicle target tracking | |
| WO2012138286A1 (en) | Estimation of road inclination | |
| CN110346824B (en) | Vehicle navigation method, system and device and readable storage medium | |
| CN109415054B (en) | Apparatus for tracking vehicle path | |
| KR102024986B1 (en) | Apparatus and method for lane Keeping control | |
| JP7284268B2 (en) | Methods for Identifying Integrity Ranges | |
| CN113465628A (en) | Inertial measurement unit data compensation method and system | |
| CN112758109B (en) | Transverse tracking steady state deviation compensation method and device | |
| Strano et al. | Vehicle sideslip angle estimation via a Riccati equation based nonlinear filter | |
| CN112455446A (en) | Method, apparatus, electronic device, and storage medium for vehicle control | |
| CN112099378A (en) | Front vehicle lateral motion state real-time estimation method considering random measurement time lag | |
| CN113771857B (en) | Longitudinal speed estimation method and system for vehicle control | |
| KR101390776B1 (en) | Localization device, method and robot using fuzzy extended kalman filter algorithm | |
| JP7069624B2 (en) | Position calculation method, vehicle control method and position calculation device | |
| CN112046491B (en) | Method and device for estimating cornering stiffness of wheel, vehicle and readable storage medium | |
| Liu et al. | Vehicle state and parameter estimation based on double cubature Kalman filter algorithm | |
| Liu et al. | Vehicle state estimation based on adaptive fading unscented kalman filter | |
| JP7028223B2 (en) | Self-position estimator | |
| CN112776883A (en) | Enhanced vehicle operation | |
| JP7206883B2 (en) | Yaw rate corrector | |
| US20200172165A1 (en) | Vehicle wheel diagnostic | |
| Sugai et al. | Preview ride comfort control for electric active suspension | |
| Liu et al. | Vehicle state and parameter estimation based on adaptive robust unscented particle filter |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |