CN115284809B - Intelligent internet fleet active suspension control method and system and computer equipment - Google Patents

Intelligent internet fleet active suspension control method and system and computer equipment Download PDF

Info

Publication number
CN115284809B
CN115284809B CN202211224542.8A CN202211224542A CN115284809B CN 115284809 B CN115284809 B CN 115284809B CN 202211224542 A CN202211224542 A CN 202211224542A CN 115284809 B CN115284809 B CN 115284809B
Authority
CN
China
Prior art keywords
active suspension
road
profile information
road profile
vehicle
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.)
Active
Application number
CN202211224542.8A
Other languages
Chinese (zh)
Other versions
CN115284809A (en
Inventor
高小林
邱香
易星
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangxi University of Technology
Original Assignee
Jiangxi University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangxi University of Technology filed Critical Jiangxi University of Technology
Priority to CN202211224542.8A priority Critical patent/CN115284809B/en
Publication of CN115284809A publication Critical patent/CN115284809A/en
Application granted granted Critical
Publication of CN115284809B publication Critical patent/CN115284809B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
    • B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
    • B60G17/016—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input
    • B60G17/0165—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input to an external condition, e.g. rough road surface, side wind
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
    • B60G2400/80—Exterior conditions
    • B60G2400/82—Ground surface

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Vehicle Body Suspensions (AREA)

Abstract

The invention provides a method, a system and computer equipment for controlling an active suspension of an intelligent networked fleet, wherein the method for controlling the active suspension of the intelligent networked fleet comprises the following steps: constructing an active suspension dynamic model, and acquiring a model state space equation according to the active suspension dynamic model; performing navigation lane contour estimation on the current road by combining an estimator based on a model state space equation to obtain first road contour information and obtain second road contour information of the current road; calculating a first reference contrast value of the first road profile information and the second road profile information, and comparing the first reference contrast value with an allowable error threshold value; and switching the control mode of the active suspension of the pilot vehicle according to the comparison result. Through the method and the device, the accuracy of the second road profile information pre-aimed by the sensor can be detected by the pilot vehicle at any time, the problem of misjudgment of the vehicle caused by the fact that the second road profile information is inaccurate is avoided, and comfort, stability and driving safety are improved.

Description

Intelligent internet fleet active suspension control method and system and computer equipment
Technical Field
The invention relates to the technical field of motorcade control, in particular to an active suspension control method, an active suspension control system and computer equipment for an intelligent networked motorcade.
Background
With the high-speed development of electronic and electric appliance architectures of automobiles, the trend of electromotion, intellectualization and electric control of automobiles is obvious, and the development of suspensions is mainly embodied in the improvement of control forms, and can be divided into passive suspensions, semi-active suspensions and active suspensions.
At present, an active suspension determines future control input mainly according to the current state or output through a feedback control mode, and usually acquires road elevation information by utilizing a sensor pre-aiming mode, and the sensor pre-aiming method is fragile, can be confused by water, snow or other soft obstacles on a road surface, and is easily limited to short distance, namely, when the distance of an intelligent motorcade is very close, the sensor controlled by the active suspension pre-aiming cannot accurately detect the road elevation information of a pre-aiming point, so that the vehicle is judged wrongly.
Disclosure of Invention
Based on this, the present invention provides an active suspension control method, system and computer device for an intelligent networked fleet, so as to solve the above-mentioned deficiencies in the prior art.
In order to achieve the aim, the invention provides an active suspension control method of an intelligent networked fleet, which comprises the following steps:
constructing an active suspension dynamic model, and acquiring a model state space equation according to the active suspension dynamic model;
performing navigation lane contour estimation on the current road by combining an estimator based on the model state space equation to obtain first road contour information, and acquiring second road contour information of the current road;
calculating a first reference contrast value of the first road profile information and the second road profile information, and comparing the first reference contrast value with an allowable error threshold;
when the first reference contrast value is larger than or equal to the allowable error threshold value, switching the active suspension control mode of the piloting vehicle into a feedback control mode;
and when the first reference contrast value is smaller than the allowable error threshold value, switching the active suspension control mode of the pilot vehicle into a feedforward-feedback control mode.
Preferably, the expression of the model state space equation is as follows:
Figure 225929DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 476782DEST_PATH_IMAGE003
is the first derivative of the active suspension control system state variable, x (t) is the active suspension control system state variable; A. b and D are respectively a system matrix, a control matrix and a road surface input matrix, and can be obtained by an active suspension dynamic model; w (t) is the first road profile information; and u (t) is the acting force of the actuator under the action of the active suspension control system.
Preferably, the estimator uses a kalman filter estimator, and the step of performing the contour estimation of the piloting lane on the current road based on the model state space equation and by combining the estimator to obtain the first road contour information includes:
estimating the vehicle tire deflection of the pilot vehicle according to the model state space equation and a Kalman filtering estimator;
acquiring the unsprung mass speed of the pilot vehicle;
calculating first road profile information according to the vehicle tire deflection and the unsprung mass velocity;
the calculation formula of the first road profile information is as follows:
Figure 52119DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 388423DEST_PATH_IMAGE005
vertical vibration displacement of the tire at the time t;
Figure 707409DEST_PATH_IMAGE006
the vertical vibration speed of the tire at the moment t;
Figure 812768DEST_PATH_IMAGE007
inputting road surface contour disturbance displacement;
Figure 559007DEST_PATH_IMAGE008
the road profile is perturbed to the speed input.
Preferably, the step of estimating vehicle tire deflection of the pilot vehicle from the model state space equation and the kalman filter estimator comprises:
constructing an active suspension system control model based on the model state space equation;
constructing a state observer estimation model based on the active suspension system control model and combining a Kalman filtering estimation principle;
and estimating the vehicle tire deflection of the pilot vehicle by utilizing the state observer estimation model.
Preferably, the functional expression of the active suspension system control model is as follows:
Figure 117027DEST_PATH_IMAGE009
wherein C is an output matrix of the active suspension control system,
Figure 505283DEST_PATH_IMAGE010
is white gaussian noise at the time t,
Figure 465149DEST_PATH_IMAGE011
measuring error at the moment t, and y is the output quantity of the active suspension control system;
the functional expression of the state observation estimator model is as follows:
Figure 382290DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 693185DEST_PATH_IMAGE013
estimating a state variable estimate of the model for the state observer at time t,
Figure 353974DEST_PATH_IMAGE014
estimating a first derivative of a state variable estimate of a model for the state observer,
Figure 168346DEST_PATH_IMAGE015
the state observer gain at time t.
Preferably, the method further comprises:
acquiring third road profile information, and carrying out following road profile estimation on the current road to obtain fourth road profile information;
and comparing the second road profile information and the third road profile information with the fourth road profile information respectively, and switching an active suspension control mode corresponding to the following vehicle according to a comparison result.
Preferably, the step of comparing the second road profile information and the third road profile information with the fourth road profile information respectively, and switching the active suspension control mode corresponding to the following vehicle according to the comparison result includes:
respectively calculating a second reference contrast value of the third road contour information and the fourth road contour information and calculating a third reference contrast value of the second road contour information and the fourth road contour information;
when the second reference contrast value and the third reference contrast value are both greater than or equal to the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedback control mode;
when the second reference contrast value is greater than or equal to the allowable error threshold value and the third reference contrast value is smaller than the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedforward-feedback control mode and outputting the first road profile information;
when the second reference contrast value is smaller than the allowable error threshold value and the third reference contrast value is larger than or equal to the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedforward-feedback control mode and outputting the third road profile information;
and when the second reference contrast value and the third reference contrast value are both smaller than the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedforward-feedback control mode, and outputting the first road profile information or the third road profile information.
In order to achieve the above object, the present invention further provides an active suspension control system for an intelligent networked fleet, the system comprising:
the system comprises a construction module, a control module and a control module, wherein the construction module is used for constructing an active suspension dynamic model and acquiring a model state space equation according to the active suspension dynamic model;
the obtaining module is used for carrying out road contour estimation on the current road based on the model state space equation and by combining an estimator to obtain first road contour information and obtain second road contour information of the current road;
the calculation module is used for calculating a first reference contrast value of the first road profile information and the second road profile information and comparing the first reference contrast value with an allowable error threshold;
the first switching module is used for switching the active suspension control mode of the pilot vehicle into a feedback control mode when the first reference contrast value is greater than or equal to the allowable error threshold;
and the second switching module is used for switching the active suspension control mode of the pilot vehicle into a feedforward-feedback control mode when the first reference contrast value is smaller than the allowable error threshold value.
Preferably, the system further comprises:
the acquisition module is used for acquiring third road profile information and carrying out following road profile estimation on the current road to obtain fourth road profile information;
and the comparison module is used for comparing the second road profile information and the third road profile information with the fourth road profile information respectively and switching an active suspension control mode corresponding to the following vehicle according to a comparison result.
In order to achieve the above object, the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the intelligent networked fleet active suspension control method described in the above when executing the computer program.
The invention provides an active suspension control method, system and computer equipment for an intelligent networked vehicle fleet, wherein the method comprises the steps of estimating first road profile information by carrying out piloting on a current road according to the road profile, acquiring second road profile information of the current road according to a sensor pre-aiming mode, extracting corresponding first elevation information and second elevation information from the first road profile information and the second road profile information respectively, calculating a first reference comparison value of the first elevation information and the second elevation information, comparing the first reference comparison value with an allowable error threshold value, and switching an active suspension control mode according to the comparison result, so that a piloting vehicle can detect the accuracy of the second road profile information pre-aimed by the sensor at any time, the problem of wrong judgment of the vehicle caused by the fact that the second road profile information is inaccurate is solved, and comfort, stability and driving safety are improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flowchart of an active suspension control method for an intelligent internet fleet according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a preview sensor provided in accordance with a first embodiment of the present invention;
fig. 3 is a schematic diagram of an estimator provided in the first embodiment of the present invention;
FIG. 4 is a schematic diagram of an active suspension control method for an intelligent networked fleet according to a first embodiment of the present invention;
fig. 5 is a block diagram of an active suspension control system of an intelligent networked fleet according to a second embodiment of the present invention;
fig. 6 is a schematic hardware structure diagram of a computer device according to a third embodiment of the present invention.
The following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless otherwise defined, technical or scientific terms referred to herein should have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The use of the terms "including," "comprising," "having," and any variations thereof herein, is meant to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes the association relationship of the associated object, indicating that there may be three relationships, for example, "a and/or B" may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The first embodiment of the invention provides an active suspension control method for an intelligent networked fleet, which is suitable for intelligent networked vehicles, in particular to automatic driving vehicles lacking subjective evaluation feedback of drivers.
It can be understood that the current active suspension road sighting method mainly has three types, 1, 'look ahead' sensor sighting, and a 'look ahead' sensor is installed at the front end of an automobile by adopting reflected light (such as laser radar), ultrasonic wave or radar beam technology to measure road elevation information. The preview method of the sensor is fragile, and not only can be confused by water, snow or other soft obstacles on the road surface; the sensor controlled by active suspension pre-aiming control cannot accurately detect the road elevation information of a pre-aiming point when the distance of the intelligent motorcade is very short; 2) Wheelbase preview, the road profile of the rear axle is estimated from the response of the front wheel suspension by assuming that the rear wheel road input is the same as the front wheel, with an appropriate time delay. The method has a narrow application scene range and can only be applied to rear axle suspension control of vehicles with high coincidence ratio of front and rear axle paths; 3) The method comprises the steps of pre-aiming a piloted vehicle, networking the vehicles and carrying out cooperative development of multiple vehicles, pre-estimating the piloted vehicle, using the piloted vehicle as a prospective sensor, pre-estimating a road elevation profile, utilizing the advantages of the intelligent networked vehicle, and sharing pre-aiming information with other vehicles through networking. This method also risks errors in the estimation of the road profile, and if a loose obstacle is removed or cleared, or the following vehicle path in a smart fleet changes, resulting in a vehicle missing a discrete road event, the topography between different vehicles may change. By the method for controlling the active suspension of the intelligent internet-of-things fleet, the active suspension can provide higher comfort performance and performance safety performance under the conditions that the feedback of an intelligent internet-of-things vehicle driver is less and the road surface preview is inaccurate.
Fig. 1 is a flowchart of an active suspension control method of an intelligent networked fleet according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
s101, constructing an active suspension dynamic model, and acquiring a model state space equation according to the active suspension dynamic model;
wherein, according to Newton's second law, a 1/4 two-degree-of-freedom active suspension dynamic model of the pilot vehicle is established, and the suspension displacement in figure 2 is selected
Figure 521967DEST_PATH_IMAGE016
Spring loaded mass velocity
Figure 54579DEST_PATH_IMAGE017
Deflection of tire
Figure 784638DEST_PATH_IMAGE018
Figure 453517DEST_PATH_IMAGE019
Unsprung mass velocity
Figure 978039DEST_PATH_IMAGE020
Controlling system state variables for active suspension
Figure 997948DEST_PATH_IMAGE021
The unified processing active suspension control system dynamics model is a state space model as follows:
Figure 539DEST_PATH_IMAGE022
wherein, the first and the second end of the pipe are connected with each other,
Figure 789503DEST_PATH_IMAGE023
is the first derivative of the active suspension control system state variable,
Figure 750506DEST_PATH_IMAGE024
controlling a system state variable for the active suspension; A. b, D are the system matrix, control matrix, road surface input matrix separately, three matrices of the invention are all determined by suspension parameters such as intellectual motorcade sprung mass, unsprung mass, etc., can be got by the dynamic model of the initiative suspension;
Figure 992131DEST_PATH_IMAGE025
is the first road profile information;
Figure 798413DEST_PATH_IMAGE026
is the acting force of an actuator under the action of an active suspension control system.
Step S102, performing navigation lane contour estimation on a current road by combining an estimator based on the model state space equation to obtain first road contour information, and acquiring second road contour information of the current road;
the estimator uses a kalman filter estimator, as shown in fig. 3, and the kalman filter estimator includes: the system comprises an active suspension system control model determined by an active suspension control module of the intelligent networked fleet, an observer estimation model determined by the amount to be observed of the system, an observer gain determined by a Kalman filtering estimator, noise caused by a measurement system, a feedback gain determined by the active suspension control module, a comparison module for controlling the output measured value of the system and the output value of the observer estimation model, and road profile input.
It should be noted that the second road profile information is obtained by measuring with a first preview sensor installed on the pilot vehicle. The pre-aiming sensor adopts a solid laser radar which can measure the concave-convex road surface, and can realize the ultra-high precision measurement of 2mm, and can measure multiple points at the same time point and can also measure under the condition of high-speed running.
Step S103, calculating a first reference contrast value of the first road profile information and the second road profile information, and comparing the first reference contrast value with an allowable error threshold;
in order to reduce the influence of errors caused by time lag of the two modules, in this embodiment, a weighted root mean square value RMS of elevation information of the road profile is compared with the allowable error threshold, that is, the first reference contrast value is a weighted root mean square value between the first elevation information and the second elevation information.
It should be noted that first elevation information and second elevation information are extracted from the first road profile information and the second road profile information, respectively, and the first reference contrast value is a numerical value calculated from the first elevation information and the second elevation information.
Step S104, when the first reference contrast value is greater than or equal to the allowable error threshold value, switching the active suspension control mode of the pilot vehicle into a feedback control mode;
the feedback control mode is to adjust the system through feedback control after the disturbance input in the previous period acts on the disturbance input, so that the defect of control lag exists. The feedforward-feedback control is to switch in a feedforward control action before the suspension system receives the road profile input disturbance but has no effect, so that the influence of the road input disturbance on the control variable of the active suspension system can be counteracted at the disturbance point.
And S105, when the first reference contrast value is smaller than the allowable error threshold value, switching the active suspension control mode of the pilot vehicle into a feedforward-feedback control mode.
The feedforward-feedback control mode is to switch in a feedforward control action before the suspension system receives the road profile input disturbance but does not have the consequence, so that the influence of the road input disturbance on the control variable of the active suspension system can be counteracted at a disturbance point. It should be noted that the feedforward-feedback control mode can perform feedforward control on the premise that the feedforward-feedback control mode can sense (target) the input signal of the road profile in advance, so the feedforward-feedback control method can be used only when the module capable of predicting the road profile information is in operation, that is, when the first reference contrast value is smaller than the allowable error threshold value, the second road profile information predicted by the target is matched with the first road profile information estimated by the estimation module, and the second road profile information is the input of the feedforward-feedback control mode.
Through the steps, the contour of the lane of the current road is estimated to obtain the contour information of the first road, the contour information of the second road of the current road is obtained in a pre-aiming mode through the sensor, corresponding first elevation information and second elevation information are extracted from the contour information of the first road and the contour information of the second road respectively to calculate a first reference contrast value of the first elevation information and the second elevation information, the first reference contrast value is compared with an allowable error threshold value, and then an active suspension control mode is switched according to the comparison result, so that the accuracy of the contour information of the second road pre-aimed by the sensor can be detected by the pilot vehicle at any time, the problem of wrong judgment of the vehicle caused by the fact that the contour information of the second road is inaccurate is avoided, and the comfort, the stability and the driving safety are improved.
In some of these embodiments, the expression of the model state space equation is as follows:
Figure 710393DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 311139DEST_PATH_IMAGE003
is a first derivative of the active suspension control system state variable;
Figure 305640DEST_PATH_IMAGE028
controlling a system state variable for the active suspension; A. b and D are respectively a system matrix, a control matrix and a road surface input matrix, and can be obtained by an active suspension dynamic model;
Figure 650033DEST_PATH_IMAGE029
is the first road profile information;
Figure 413590DEST_PATH_IMAGE030
is the acting force of an actuator under the action of an active suspension control system.
In some embodiments, the estimator uses a kalman filter estimator, and the step of obtaining the first road profile information by performing the pilot lane road profile estimation on the current road based on the model state space equation and by combining the estimator comprises:
estimating the vehicle tire deflection of the pilot vehicle according to the model state space equation and a Kalman filtering estimator;
acquiring the unsprung mass speed of the pilot vehicle;
calculating first road profile information according to the vehicle tire deflection and the unsprung mass velocity;
it should be noted that, the pilot vehicle estimation module performs the first road profile information
Figure 185237DEST_PATH_IMAGE031
When estimating, the deflection of the tyre is based on the state of the piloted vehicle
Figure 667034DEST_PATH_IMAGE032
And unsprung mass velocity
Figure 80698DEST_PATH_IMAGE033
The combination of (a) and (b) to produce,
the calculation formula of the first road profile information is as follows:
Figure 698761DEST_PATH_IMAGE034
wherein the content of the first and second substances,
Figure 906888DEST_PATH_IMAGE035
vertical vibration displacement of the tire at the time t;
Figure 610402DEST_PATH_IMAGE036
the vertical vibration speed of the tire at the time t;
Figure 296598DEST_PATH_IMAGE037
inputting road profile interference displacement;
Figure 34747DEST_PATH_IMAGE038
inputting the road profile disturbance speed; the other symbols are as above.
The sprung mass velocity and the unsprung mass velocity can be measured by sensors which are acceleration sensors and are mounted 1 each on the vehicle suspension and near the front and rear bumpers, the tyre deflection
Figure 148197DEST_PATH_IMAGE039
It is not possible to make measurements with the sensor. In order to realize the road profile estimation function of the pilot vehicle estimation module, the observer estimation model shown in fig. 3 is adopted for tire deflection observation in the embodiment.
In some of these embodiments, the step of estimating vehicle tire deflection for the pilot vehicle from the model state space equations and a kalman filter estimator comprises:
constructing an active suspension system control model based on the model state space equation;
in step S101 of the present invention, a model state space equation has been determined, and in order to estimate the state quantity of the active suspension, the output quantity of the control system designed in the embodiment of the present invention needs to be measurable, that is, the output quantity is a measurement quantity, it should be noted that the measurement quantity is a measurement quantity
Figure 339006DEST_PATH_IMAGE040
Wherein C = [ 10 0; 01 0;0 0 01]Including suspension deflection
Figure 828894DEST_PATH_IMAGE041
Sprung mass velocity
Figure 421549DEST_PATH_IMAGE042
And unsprung mass velocity
Figure 237058DEST_PATH_IMAGE043
All three quantities can be measured by sensors.
Therefore, the control model of the pilot vehicle active suspension system including the output quantity is as follows:
Figure 915164DEST_PATH_IMAGE044
wherein C is an output matrix of the active suspension control system,
Figure 943163DEST_PATH_IMAGE045
is white gaussian noise at the time t,
Figure 390325DEST_PATH_IMAGE046
and y is the output quantity of the active suspension control system, and other symbols are consistent with the above.
The input and measured noise variance matrices are respectively:
Figure 111156DEST_PATH_IMAGE047
，
Figure 542138DEST_PATH_IMAGE048
。
in the above formula, the first and second carbon atoms are,
Figure 373827DEST_PATH_IMAGE049
representing functions of desired values, in which sensor errors and measured values
Figure 409917DEST_PATH_IMAGE050
The values are related to the white gaussian noise standard deviation.
Constructing a state observer estimation model based on the active suspension system control model and in combination with a Kalman filtering estimation principle;
wherein the functional expression of the state observation estimator model is as follows:
Figure 567228DEST_PATH_IMAGE051
in the formula (I), the compound is shown in the specification,
Figure 219927DEST_PATH_IMAGE052
the state observer estimates the state variable estimate of the model for time t,
Figure 589728DEST_PATH_IMAGE053
estimating a first derivative of a state variable estimate of a model for the state observer,
Figure 11482DEST_PATH_IMAGE054
the other symbols are consistent with the above for the gain of the state observer at time t.
It should be noted that, the gain of the state observation estimator model obtained by the basic principle of the kalman filter estimator is:
Figure 71186DEST_PATH_IMAGE055
where T is the matrix transpose symbol and W is the measurement noise variance matrix.
P (t) is the solution of the following Riccati equation (ricacati equation).
Figure 945602DEST_PATH_IMAGE056
Wherein, P (t) is the only definite solution of the Riccati equation, V is the input noise variance matrix, and other symbols are the same as above.
Therefore, the state observation estimator model in the embodiment of the invention outputs an estimated state vector containing the deflection of the tire, and the first road profile information of the pilot vehicle estimation module in the embodiment of the invention can be obtained by substituting the estimated state vector into the calculation formula of the first road profile information.
And estimating the vehicle tire deflection of the pilot vehicle by utilizing the state observer estimation model.
In some of these embodiments, the functional expression of the active suspension system control model is as follows:
Figure 384673DEST_PATH_IMAGE057
wherein C is an output matrix of the active suspension control system,
Figure 926513DEST_PATH_IMAGE058
is white gaussian noise at the time t,
Figure 894469DEST_PATH_IMAGE059
measuring errors at the time t, wherein y is the output quantity of the active suspension control system; other symbols are as above.
The functional expression of the state observation estimator model is as follows:
Figure 521759DEST_PATH_IMAGE060
wherein, the first and the second end of the pipe are connected with each other,
Figure 233363DEST_PATH_IMAGE061
estimating a state variable estimate of the model for the state observer at time t,
Figure 629710DEST_PATH_IMAGE053
estimating a first derivative of a state variable estimate of a model for the state observer,
Figure 34146DEST_PATH_IMAGE062
the state observer gain at time t.
In some of these embodiments, the method further comprises:
acquiring third road profile information, and carrying out following road profile estimation on the current road to obtain fourth road profile information;
and comparing the second road profile information and the third road profile information with the fourth road profile information respectively, and switching an active suspension control mode corresponding to a following vehicle according to a comparison result.
Specifically, the second road profile information is sent to a following vehicle shift register; measuring by a second preview sensor in a follow-up preview module to obtain the third road profile information, estimating the follow-up road profile of the current road to obtain fourth road profile information, and sending the third road profile information to a follow-up shift register; the second road contour information and the third road contour information are sent to a comparison module in a following vehicle through the shift register, and the fourth road contour information is directly sent to the comparison module; and comparing the second road profile information and the third road profile information with the fourth road profile information through the comparison module respectively, and switching an active suspension control mode corresponding to the following vehicle according to a comparison result.
It is understood that the follower shift register is a linear feedback shift register, the upper limit shift speed of which depends on the delay time of the shift unit, and the delay time of the follower shift register shift unit is determined by the intelligent fleet driving speed and the fleet distance.
The fourth road profile information is obtained by estimating the road profile through a following vehicle estimation module, and the estimation mode of the fourth road profile information is substantially the same as that of the first road profile information, and is not repeated herein.
In some embodiments, the step of comparing the second road profile information and the third road profile information with the fourth road profile information respectively, and switching the active suspension control mode corresponding to the following vehicle according to the comparison result comprises:
respectively calculating a second reference contrast value of the third road profile information and the fourth road profile information and calculating a third reference contrast value of the second road profile information and the fourth road profile information;
when the second reference contrast value and the third reference contrast value are both greater than or equal to the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedback control mode;
when the second reference contrast value is greater than or equal to the allowable error threshold value and the third reference contrast value is smaller than the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedforward-feedback control mode and outputting the first road profile information;
when the second reference contrast value is smaller than the allowable error threshold value and the third reference contrast value is larger than or equal to the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedforward-feedback control mode and outputting the third road profile information;
when the second reference contrast value and the third reference contrast value are both smaller than the allowable error threshold value, switching an active suspension control mode of the follower vehicle to a feedforward-feedback control mode, and outputting the first road profile information or the third road profile information.
In the following vehicle, the active suspension control mode of the following vehicle can be started by receiving second road profile information sent by the pilot vehicle as input, and the active suspension control mode of the following vehicle can also be started by taking third road profile information measured in real time by a second preview sensor in a preview module of the following vehicle as input. And the following vehicle active suspension control mode is started according to which road profile information is taken as input, and the judgment of the comparison result of the second reference contrast value and the third reference contrast value with the allowable error threshold value and the judgment of the following vehicle active suspension control mode are required.
It can be understood that, when the second reference contrast value and the third reference contrast value are both greater than or equal to the allowable error threshold, it indicates that the errors of the second road profile information and the third road profile information and the fourth road profile information are large and are not matched, so that the feedback control mode is selected, and active suspension control is automatically implemented without inputting the road profile information as a start signal.
In addition, when any one of the second reference contrast value and the third reference contrast value is smaller than the allowable error threshold, it indicates that the error between the corresponding road profile information smaller than the allowable error threshold is small, and the corresponding road profile information can be matched, that is, the corresponding road profile information can be input into the feedforward-feedback control mode as the start signal to start the mode to enter the operation.
As shown in fig. 4, in a preferred embodiment of the present application, a method for controlling an active suspension of an intelligent networked fleet is provided, which includes the following steps:
respectively establishing an active suspension dynamic model and a model state space equation of a pilot vehicle and a following vehicle;
acquiring first road profile information and second road profile information through a pilot vehicle preview sensor and a follow-up vehicle preview sensor respectively;
respectively estimating the road profile of the current road through a piloting vehicle estimation module and a following vehicle estimation module to obtain third road profile information and fourth road profile information;
calculating a first reference contrast value of the first road profile information and the third road profile information through a pilot vehicle road profile information comparison module, and synchronously comparing the first reference contrast value with an allowable error threshold value so that the pilot vehicle switches a corresponding pilot vehicle active suspension control mode according to a comparison result;
sending the third road profile information to a following vehicle shift register through the piloting lane profile information comparison module;
the second road profile information is sent to the follow-up vehicle shift register through the follow-up vehicle preview module, and the fourth road profile information is sent to a follow-up vehicle profile information comparison module through the follow-up vehicle estimation module;
the second road profile information and the third road profile information are sent to the following vehicle road profile information comparison module through the following vehicle shift register;
calculating a second reference contrast value of the second road profile information and the fourth road profile information and a third reference contrast value of the fourth road profile information and the third road profile information through the following road profile information comparison module;
and comparing the second reference comparison value and the third reference comparison value with the allowable error threshold value respectively through the following vehicle road profile information comparison module, so that the following vehicle switches the following vehicle active suspension control mode according to the comparison result.
Through the steps, the intelligent fleet navigator can detect whether the sensor road preview is accurate at any time, and can switch the control mode of the active suspension of the navigator when the sensor road preview has errors, so that the stability and the driving safety of the suspension performance of the navigator are ensured; the accuracy of road profile information transmitted by a pilot vehicle and road profile information pre-aimed by a sensor of an intelligent motorcade following vehicle can be detected at any time, and the intelligent motorcade following vehicle can be selected preferentially, so that the performance of an active suspension of the following vehicle can be improved; meanwhile, when the road information of the two vehicles is wrong, the control mode of the following vehicle active suspension can be switched, and the stability and the driving safety of the following vehicle suspension performance are guaranteed.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The second embodiment of the present application further provides an active suspension control system for an intelligent networked fleet, which is used to implement the first embodiment and the preferred embodiment, and the description of the system is omitted. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 5 is a block diagram of an active suspension control system of an intelligent networked fleet according to a second embodiment of the present application, as shown in fig. 5, the system includes:
the system comprises a construction module 10, a model state space equation and a dynamic model generation module, wherein the construction module is used for constructing an active suspension dynamic model and acquiring a model state space equation according to the active suspension dynamic model;
the obtaining module 20 is configured to perform road contour estimation on a current road based on the model state space equation and in combination with an estimator, obtain first road contour information, and obtain second road contour information of the current road;
a calculating module 30, configured to calculate a first reference contrast value of the first road profile information and the second road profile information, and compare the first reference contrast value with an allowable error threshold;
the first switching module 40 is used for switching the active suspension control mode of the piloting vehicle into a feedback control mode when the first reference contrast value is greater than or equal to the allowable error threshold;
a second switching module 50, configured to switch the active suspension control mode of the pilot vehicle to a feedforward-feedback control mode when the first reference contrast value is smaller than the allowable error threshold.
Through the steps, the first road profile information is estimated according to the piloting lane profile of the current road, the second road profile information of the current road is obtained in a sensor pre-aiming mode, corresponding first elevation information and second elevation information are extracted from the first road profile information and the second road profile information respectively to calculate a first reference contrast value of the first elevation information and the second elevation information, the first reference contrast value is compared with an allowable error threshold, and then an active suspension control mode is switched according to the comparison result, so that the accuracy of the second road profile information pre-aimed by the sensor can be detected by the piloting vehicle at any time, the problem of misjudgment of the vehicle caused by the fact that the second road profile information is inaccurate is avoided, and comfort, stability and driving safety are improved.
In some embodiments, the estimator employs a kalman filter estimator, and the obtaining module 20 includes:
the estimation unit is used for estimating the vehicle tire deflection of the pilot vehicle according to the model state space equation and the Kalman filtering estimator;
an acquisition unit configured to acquire an unsprung mass velocity of the pilot vehicle;
a first calculation unit for calculating first road profile information based on the vehicle tire deflection and the unsprung mass velocity.
In some of these embodiments, the first computing unit comprises:
the first construction subunit is used for constructing an active suspension system control model based on the model state space equation;
the second construction subunit is used for constructing a state observer estimation model based on the active suspension system control model and combined with a Kalman filtering estimation principle;
and the estimation subunit is used for estimating the vehicle tire deflection by utilizing the state observer estimation model.
In some of these embodiments, the system further comprises:
the acquisition module is used for acquiring third road profile information and estimating a following road profile of the current road to obtain fourth road profile information;
and the comparison module is used for comparing the second road profile information and the third road profile information with the fourth road profile information respectively and switching an active suspension control mode corresponding to the following vehicle according to a comparison result.
In some of these embodiments, the comparison module comprises:
a second calculation unit, configured to calculate a second reference contrast value of the third road contour information and the fourth road contour information, and calculate a third reference contrast value of the second road contour information and the fourth road contour information, respectively;
a first switching unit, configured to switch an active suspension control mode of the following vehicle to a feedback control mode when the second reference contrast value and the third reference contrast value are both greater than or equal to the allowable error threshold;
a second switching unit, configured to switch an active suspension control mode of the following vehicle to a feedforward-feedback control mode and output the first road profile information when the second reference contrast value is greater than or equal to the allowable error threshold and the third reference contrast value is smaller than the allowable error threshold;
a third switching unit, configured to switch an active suspension control mode of the following vehicle to a feedforward-feedback control mode and output the third road profile information when the second reference contrast value is smaller than the allowable error threshold and the third reference contrast value is greater than or equal to the allowable error threshold;
a fourth unit, configured to switch an active suspension control mode of the follower vehicle to a feedforward-feedback control mode and output the first road profile information or the third road profile information when the second reference contrast value and the third reference contrast value are both smaller than the allowable error threshold value.
The functions or operation steps of the modules and units when executed are substantially the same as those of the method embodiments, and are not described herein again.
The implementation principle and the generated technical effects of the active suspension control system for the intelligent networked fleet provided by the embodiment of the invention are the same as those of the method embodiment, and for brief description, reference can be made to corresponding contents in the method embodiment for the fact that no part of the device embodiment is mentioned.
It should be noted that the above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the above modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In addition, the method for controlling the active suspension of the intelligent networked fleet described in conjunction with fig. 1 according to the third embodiment of the present application can be implemented by a computer device. Fig. 6 is a hardware structure diagram of a computer device according to an embodiment of the present application.
The computer device may include a processor 62 and a memory 63 in which computer program instructions are stored.
Specifically, the processor 62 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
Memory 63 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 63 may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, magnetic tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 63 may include removable or non-removable (or fixed) media, where appropriate. The memory 63 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 63 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, memory 63 includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended Data Out Dynamic Random Access Memory (EDODRAM), a Synchronous Dynamic Random Access Memory (SDRAM), and the like.
Memory 63 may be used to store or cache various data files for processing and/or communication use, as well as possibly computer program instructions for execution by processor 62.
The processor 62 may implement any of the above-described methods of intelligent networked fleet active suspension control in embodiments by reading and executing computer program instructions stored in the memory 63.
In some of these embodiments, the computer device may also include a communication interface 64 and a bus 61. As shown in fig. 6, the processor 62, the memory 63, and the communication interface 64 are connected via the bus 61 to complete communication therebetween.
The communication interface 64 is used for realizing communication among modules, devices, units and/or equipment in the embodiment of the present application. The communication interface 64 may also enable communication with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The bus 61 comprises hardware, software, or both that couple the components of the computer device to one another. Bus 61 includes, but is not limited to, at least one of the following: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example and not limitation, bus 61 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a vlslave Bus, a Video Bus, or a combination of two or more of these suitable electronic buses. Bus 61 may include one or more buses where appropriate. Although specific buses are described and shown in the embodiments of the present application, any suitable buses or interconnects are contemplated by the present application.
The computer device may execute the intelligent networked fleet active suspension control method in the embodiment of the present application based on the acquired computer program, thereby implementing the intelligent networked fleet active suspension control method described in conjunction with fig. 1.
In addition, in combination with the method for controlling the active suspension of the smart internet fleet in the foregoing embodiment, the embodiment of the present application may be implemented by providing a readable storage medium. The readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the methods of intelligent networked fleet active suspension control described above.
All possible combinations of the technical features of the above embodiments may not be described for the sake of brevity, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. An active suspension control method for an intelligent networked fleet is characterized by comprising the following steps:
constructing an active suspension dynamic model, and acquiring a model state space equation according to the active suspension dynamic model;
performing navigation lane contour estimation on a current road based on the model state space equation and by combining an estimator to obtain first road contour information, and acquiring second road contour information of the current road through a first pre-aiming sensor of a navigation vehicle, wherein the first road contour information and the second road contour information are respectively input into an active suspension control system of the navigation vehicle;
the calculation formula of the first road profile information is as follows:
Figure 976864DEST_PATH_IMAGE001
wherein, the first and the second end of the pipe are connected with each other,
Figure 176902DEST_PATH_IMAGE002
vertical vibration displacement of the tire at the time t;
Figure 435845DEST_PATH_IMAGE003
the vertical vibration speed of the tire at the time t;
Figure 455753DEST_PATH_IMAGE004
inputting road profile interference displacement;
Figure 458344DEST_PATH_IMAGE005
inputting the road profile disturbance speed;
calculating a first reference contrast value of the first road profile information and the second road profile information, and comparing the first reference contrast value with an allowable error threshold;
when the first reference contrast value is larger than or equal to the allowable error threshold value, switching an active suspension control mode of the pilot vehicle into a feedback control mode;
when the first reference contrast value is smaller than the allowable error threshold value, switching the active suspension control mode of the pilot vehicle into a feedforward-feedback control mode;
the feedback control mode is to perform feedback control on the system after the disturbance input in the previous period acts, and the feedforward-feedback control mode is to switch in feedforward control before the suspension system receives the road profile input disturbance and does not act.
2. The intelligent networked fleet active suspension control method of claim 1, wherein said model state space equation is expressed as follows:
Figure 512888DEST_PATH_IMAGE006
wherein the content of the first and second substances,
Figure 208311DEST_PATH_IMAGE007
is the first derivative of the active suspension control system state variable, x (t) is the active suspension control system state variable; A. b and D are respectively a system matrix, a control matrix and a road surface input matrix, and can be obtained by an active suspension dynamic model; w (t) is the first road profile information; and u (t) is the acting force of the actuator under the action of the active suspension control system.
3. The active suspension control method of the intelligent networked fleet according to claim 2, wherein the estimator uses a kalman filter estimator, and the step of estimating the contour of the current road based on the model state space equation and the estimator to obtain the contour information of the first road comprises:
estimating the vehicle tire deflection of the pilot vehicle according to the model state space equation and a Kalman filtering estimator;
acquiring the unsprung mass speed of the pilot vehicle;
and calculating first road profile information according to the vehicle tire deflection and the unsprung mass velocity.
4. The intelligent networked fleet active suspension control method of claim 3, wherein said step of estimating vehicle tire deflection of said pilot vehicle from said model state space equation and a Kalman filter estimator comprises:
constructing an active suspension system control model based on the model state space equation;
constructing a state observer estimation model based on the active suspension system control model and combining a Kalman filtering estimation principle;
and estimating the vehicle tire deflection of the pilot vehicle by utilizing the state observer estimation model.
5. The intelligent networked fleet active suspension control method of claim 4, wherein said active suspension system control model is functionally expressed as follows:
Figure 715516DEST_PATH_IMAGE008
wherein C is an output matrix of the active suspension control system,
Figure 256219DEST_PATH_IMAGE009
is white gaussian noise at the time t,
Figure 165269DEST_PATH_IMAGE010
measuring error at the moment t, and y is the output quantity of the active suspension control system;
the functional expression of the state observation estimator model is as follows:
Figure 766015DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 26095DEST_PATH_IMAGE012
the state observer estimates the state variable estimate of the model for time t,
Figure 104909DEST_PATH_IMAGE013
estimating a first derivative of a state variable estimate of a model for the state observer,
Figure 868466DEST_PATH_IMAGE014
the state observer gain at time t.
6. The intelligent networked fleet active suspension control method of claim 1, further comprising:
constructing a follow-up vehicle active suspension dynamic model, acquiring a first model state space equation according to the follow-up vehicle active suspension dynamic model, performing follow-up vehicle road profile estimation on a current road by combining a first estimator based on the first model state space equation to obtain fourth road profile information, and acquiring third road profile information through a second pre-aiming sensor of a follow-up vehicle, wherein the third road profile information and the fourth road profile information are respectively input into an active suspension control system of the follow-up vehicle;
and comparing the second road profile information and the third road profile information with the fourth road profile information respectively, and switching an active suspension control mode corresponding to the following vehicle according to a comparison result.
7. The method according to claim 6, wherein the step of comparing the second road profile information and the third road profile information with the fourth road profile information respectively, and switching the active suspension control mode corresponding to the following vehicle according to the comparison result comprises:
respectively calculating a second reference contrast value of the third road contour information and the fourth road contour information and calculating a third reference contrast value of the second road contour information and the fourth road contour information;
when the second reference contrast value and the third reference contrast value are both greater than or equal to the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedback control mode;
when the second reference contrast value is greater than or equal to the allowable error threshold value and the third reference contrast value is smaller than the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedforward-feedback control mode and outputting the first road profile information;
when the second reference contrast value is smaller than the allowable error threshold value and the third reference contrast value is larger than or equal to the allowable error threshold value, switching the active suspension control mode of the following vehicle to a feedforward-feedback control mode and outputting the third road profile information;
and when the second reference contrast value and the third reference contrast value are both smaller than the allowable error threshold value, switching the active suspension control mode of the follower vehicle to a feedforward-feedback control mode, and outputting the first road profile information or the third road profile information.
8. An intelligent networked fleet active suspension control system, the system comprising:
the system comprises a construction module, a dynamic model generation module and a dynamic model analysis module, wherein the construction module is used for constructing an active suspension dynamic model and acquiring a model state space equation according to the active suspension dynamic model;
the obtaining module is used for carrying out navigation lane contour estimation on a current road by combining an estimator based on the model state space equation to obtain first road contour information, and obtaining second road contour information of the current road through a first pre-aiming sensor of a navigation vehicle, wherein the first road contour information and the second road contour information are respectively input into an active suspension control system of the navigation vehicle;
the calculation module is used for calculating a first reference contrast value of the first road profile information and the second road profile information and comparing the first reference contrast value with an allowable error threshold;
the first switching module is used for switching an active suspension control mode of the pilot vehicle into a feedback control mode when the first reference contrast value is larger than or equal to the allowable error threshold value, wherein the feedback control mode is realized by a feedback control adjusting system after a disturbance input in the previous period acts on the feedback control mode;
and the second switching module is used for switching the active suspension control mode of the pilot vehicle into a feedforward-feedback control mode when the first reference contrast value is smaller than the allowable error threshold, wherein the feedforward-feedback control mode is to switch in feedforward control before the suspension system receives the road profile input interference and does not generate action.
9. The intelligent networked fleet active suspension control system of claim 8, further comprising:
the system comprises an acquisition module, a tracking module and a tracking module, wherein the acquisition module is used for constructing a dynamic model of a following vehicle active suspension, acquiring a first model state space equation according to the dynamic model of the following vehicle active suspension, estimating a following vehicle road profile of a current road based on the first model state space equation and by combining a first estimator to obtain fourth road profile information, and acquiring third road profile information through a second pre-aiming sensor of a following vehicle, wherein the third road profile information and the fourth road profile information are respectively input into an active suspension control system of the following vehicle;
and the comparison module is used for comparing the second road profile information and the third road profile information with the fourth road profile information respectively and switching an active suspension control mode corresponding to the following vehicle according to a comparison result.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the smart internet fleet active suspension control method as recited in any one of claims 1 to 7.
CN202211224542.8A 2022-10-09 2022-10-09 Intelligent internet fleet active suspension control method and system and computer equipment Active CN115284809B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211224542.8A CN115284809B (en) 2022-10-09 2022-10-09 Intelligent internet fleet active suspension control method and system and computer equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211224542.8A CN115284809B (en) 2022-10-09 2022-10-09 Intelligent internet fleet active suspension control method and system and computer equipment

Publications (2)

Publication Number Publication Date
CN115284809A CN115284809A (en) 2022-11-04
CN115284809B true CN115284809B (en) 2023-01-24

Family

ID=83834525

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211224542.8A Active CN115284809B (en) 2022-10-09 2022-10-09 Intelligent internet fleet active suspension control method and system and computer equipment

Country Status (1)

Country Link
CN (1) CN115284809B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020126480A1 (en) * 2018-12-21 2020-06-25 Continental Automotive Gmbh Method for adapting a transmission function of an active running gear of a vehicle
CN111873744A (en) * 2020-07-15 2020-11-03 吉林大学 Active suspension pre-aiming control method based on camera sensor road surface information identification
CN113147308A (en) * 2021-03-30 2021-07-23 浙江工业大学 Suspension pre-aiming control method based on binocular vision technology and suspension control device
CN113752773A (en) * 2020-06-02 2021-12-07 丰田自动车株式会社 Vibration damping control device and vibration damping control method

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110154666B (en) * 2019-04-28 2021-12-21 西安理工大学 Vehicle suspension system self-adaptive reverse-thrust control method capable of realizing road condition prediction

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020126480A1 (en) * 2018-12-21 2020-06-25 Continental Automotive Gmbh Method for adapting a transmission function of an active running gear of a vehicle
CN113752773A (en) * 2020-06-02 2021-12-07 丰田自动车株式会社 Vibration damping control device and vibration damping control method
CN111873744A (en) * 2020-07-15 2020-11-03 吉林大学 Active suspension pre-aiming control method based on camera sensor road surface information identification
CN113147308A (en) * 2021-03-30 2021-07-23 浙江工业大学 Suspension pre-aiming control method based on binocular vision technology and suspension control device

Also Published As

Publication number Publication date
CN115284809A (en) 2022-11-04

Similar Documents

Publication Publication Date Title
CN109421630B (en) Controller architecture for monitoring health of autonomous vehicles
KR101741608B1 (en) Preceding vehicle selection apparatus
US20210171050A1 (en) System for evaluating vehicle performance
EP3712806A1 (en) Driving assistance apparatus
EP2694344A1 (en) Estimation of road inclination
CN107884002A (en) The state calculating device and computational methods of moving body, program and recording medium
KR102024986B1 (en) Apparatus and method for lane Keeping control
US8401784B2 (en) Course evaluation apparatus and course evaluation method
US11897511B2 (en) Multi-hypothesis object tracking for automated driving systems
JP2017138660A (en) Object detection method, object detection device and program
JP2019066302A (en) Object estimation device
US20190186962A1 (en) Quality of Service for a Vehicular Plug-and-Play Ecosystem
CN110550041B (en) Road adhesion coefficient estimation method based on cloud data sharing
KR101618501B1 (en) Method for ego-motion estimation of vehicle
CN114296095A (en) Method, device, vehicle and medium for extracting effective target of automatic driving vehicle
CN111959515B (en) Forward target selection method, device and system based on visual detection
CN115284809B (en) Intelligent internet fleet active suspension control method and system and computer equipment
CN113176562A (en) Multi-target tracking method and device, electronic equipment and readable storage medium
JP6555132B2 (en) Moving object detection device
JP2021135191A (en) Object detection device
CN107719373B (en) Method and system for estimating road friction
CN116022163A (en) Automatic driving vehicle scanning matching and radar attitude estimator based on super local subgraph
CN104517464A (en) Preceding vehicle selection apparatus
CN112069867B (en) Learning association of multi-objective tracking with multi-sensory data and missing modalities
CN115098821A (en) Trajectory reference curvature determination method, apparatus, device, medium, and program

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