CN113609586A - Joint identification method and system for lateral deflection rigidity and rotational inertia parameters - Google Patents
Joint identification method and system for lateral deflection rigidity and rotational inertia parameters Download PDFInfo
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Abstract
The application relates to a method and a system for jointly identifying lateral deflection rigidity and rotational inertia parameters, which are characterized by comprising the following steps of: establishing a vehicle transverse dynamic state space equation based on the vehicle dynamic state by taking a vehicle transverse axis as a control object; establishing an axle-side deflection rigidity identification model taking the axle-side deflection rigidity of the vehicle as a parameter to be estimated and/or a vehicle body rotational inertia identification model taking the vehicle body rotational inertia as a parameter to be estimated based on the vehicle transverse dynamics state space equation; using an iterative least square method with forgetting factors for the axle side deflection rigidity identification model to identify the axle side deflection rigidity of the vehicle on line; and acquiring the rotational inertia of the vehicle body based on the axle lateral deflection rigidity and the rotational inertia recognition model of the vehicle body. The present invention can solve the problems in the related art and can be applied to variable load vehicles such as commercial tractors.
Description
Technical Field
The invention relates to the technical field of automobile control, in particular to a method and a system for jointly identifying lateral deflection rigidity and rotational inertia parameters.
Background
At present, the vehicle transverse control technology is to analyze the stress state and the moment state of a vehicle, establish a dynamic model describing the motion state of the vehicle, and use the dynamic model as a controlled object for a motion control scheme of an intelligent vehicle. In a state equation of a transverse dynamic model of a vehicle, two state quantities of cornering stiffness and rotational inertia are important control parameters, whether data of the cornering stiffness and the rotational inertia of a vehicle body in the running process of the vehicle can be obtained quickly and accurately or not can be obtained quickly, and the effect of controlling the transverse motion of the intelligent vehicle is obviously influenced. Because no sensor for directly collecting the cornering stiffness and the rotational inertia data exists at present, a parameter identification method is generally adopted, and the two state quantities of the cornering stiffness and the rotational inertia of the vehicle body are identified by utilizing the collectable vehicle body data and a scientific conjecture method.
Some techniques collect steering wheel angle, yaw rate, lateral acceleration, and vehicle speed signals through a steering wheel angle pulse test, identify a transfer function of an equivalent two-degree-of-freedom vehicle model of a vehicle system offline using a least squares method, and identify moment of inertia and yaw stiffness in the transfer function using a nonlinear least squares method. However, the yaw stiffness and the rotational inertia are identified off line by using the transfer function of the vehicle model, the calculated amount is gradually increased along with the accumulation of the collected data, online measurement cannot be performed, and the real-time requirement cannot be met.
Some technologies establish a vehicle dynamics model, simplify the vehicle dynamics simplified model based on a linear lateral tire force model to obtain a vehicle dynamics simplified model, discretize the simplified model to obtain a tire lateral deflection rigidity recursion model, and adopt a least square method to identify front and rear wheel lateral deflection rigidity on line. However, the cornering stiffness of the tire is identified on line by using the tire cornering stiffness recurrence model, the moment of inertia of the vehicle body cannot be identified on line at the same time, and the identification object is the cornering stiffness of the tire of a constant-load vehicle, so that the method is not suitable for variable-load vehicles such as commercial tractors.
Disclosure of Invention
The embodiment of the invention provides a method and a system for jointly identifying lateral deflection rigidity and rotational inertia parameters, which are used for solving the problems in the related art and can be suitable for variable-load vehicles such as commercial tractors and the like.
On one hand, the embodiment of the invention provides a method for jointly identifying lateral deflection rigidity and rotational inertia parameters, which is characterized by comprising the following steps of:
establishing a vehicle transverse dynamic state space equation based on the vehicle dynamic state by taking a vehicle transverse axis as a control object;
establishing an axle-side deflection rigidity identification model taking the axle-side deflection rigidity of the vehicle as a parameter to be estimated and/or a vehicle body rotational inertia identification model taking the vehicle body rotational inertia as a parameter to be estimated based on the vehicle transverse dynamics state space equation;
using an iterative least square method with forgetting factors for the axle side deflection rigidity identification model to identify the axle side deflection rigidity of the vehicle on line;
and acquiring the rotational inertia of the vehicle body based on the axle lateral deflection rigidity and the rotational inertia recognition model of the vehicle body.
In some embodiments, the method for establishing the vehicle transverse dynamic state space equation based on the vehicle dynamic state by taking the vehicle transverse axis as the control object comprises the following steps:
under a vehicle body coordinate system determined by a right-hand rule, establishing a vehicle transverse motion equation to determine the relationship between the vehicle transverse linear acceleration, the vehicle yaw angular velocity and the vehicle transverse linear velocity and the transverse force borne by a front/rear transverse shaft of the vehicle body;
establishing a moment balance equation of the vehicle body rotating around the z axis to determine the relationship between the yaw angular acceleration of the vehicle, the rotational inertia of the vehicle body and the transverse force borne by the front/rear axle of the vehicle body;
and establishing a vehicle transverse dynamic state space equation according to the vehicle transverse motion equation and the moment balance equation of the vehicle body rotating around the z axis.
In some embodiments, establishing a vehicle lateral motion equation to determine vehicle lateral linear acceleration, vehicle yaw rate, and a relationship between vehicle lateral linear velocity and lateral force experienced by a front/rear cross-vehicle body axis comprises the steps of:
determining a vehicle transverse motion equation according to a first formula;
the first formula is:
wherein m is the mass of the vehicle body,
for transverse linear acceleration of the vehicle, VxAs is the longitudinal speed of the vehicle,
yaw rate of vehicle body, FyfTransverse forces acting on the front axle of the vehicle body, FyrThe transverse force borne by the rear axle of the vehicle body.
In some embodiments, establishing a moment balance equation of the vehicle body rotating around the z-axis to determine the relationship between the vehicle yaw acceleration, the vehicle body moment of inertia and the lateral force applied to the front/rear transverse axis of the vehicle body comprises the following steps:
determining a moment balance equation of the vehicle body rotating around the z axis according to a second formula;
the second formula is:
wherein, IzAs the moment of inertia of the vehicle body,
for yaw angular acceleration of the vehicle body, /)fIs the distance from the front axle of the vehicle body to the center of mass,/rThe distance from the rear axle of the vehicle body to the center of mass.
In some embodiments, establishing a vehicle lateral dynamic state space equation according to the vehicle lateral motion equation and the moment balance equation of the vehicle body rotating around the z-axis comprises the following steps:
defining the relation between the transverse force borne by the front axle and the rear axle of the vehicle body and the lateral deflection rigidity of the axle according to a third formula;
the third formula is: fyf＝Cαf(δ-θVf)，Fyr＝Cαr(-θVr)，
Wherein, CαfPositive cornering stiffness for the front axle of a vehicle, CαrPositive cornering stiffness of the rear axle of the vehicle, delta being the angle between the direction of the front wheels and the X-axis of the body coordinate system, thetaVfIs the angle between the rotational direction of the front wheel and the X-axis of the coordinate system of the vehicle body, thetaVrIs the included angle between the speed direction of the rear wheel and the X axis of the coordinate system of the vehicle body;
and respectively substituting the third formula into the first formula and the second formula to determine a vehicle transverse dynamic state space equation.
In some embodiments, establishing a vehicle lateral dynamic state space equation according to the vehicle lateral motion equation and the moment balance equation of the vehicle body rotating around the z-axis comprises the following steps:
determining vehicle lateral/longitudinal velocity and θ according to a fourth formulaVf、θVrThe relationship between;
the fourth formula is:
substituting the fourth formula into a third formula to determine the vehicle lateral dynamics state space equation;
the vehicle transverse dynamic state space equation is as follows:
In some embodiments, establishing an axle lateral deflection stiffness recognition model using the axle lateral deflection stiffness of the vehicle as a parameter to be estimated based on the vehicle transverse dynamics state space equation comprises the following steps:
substituting the third formula and the fourth formula into the vehicle transverse dynamic state space equation to determine an axle lateral deflection stiffness identification model;
the axle lateral deflection rigidity identification model is expressed as follows:
establishing a vehicle body rotational inertia recognition model taking the vehicle body rotational inertia as a parameter to be estimated based on the vehicle transverse dynamics state space equation, and comprising the following steps of:
substituting the second formula, the third formula and the fourth formula into the vehicle transverse dynamic state space equation to determine a vehicle body rotational inertia identification model;
the vehicle body moment of inertia recognition model is expressed as:
in some embodiments, the iterative least square method with forgetting factor is used for the axle lateral deflection rigidity identification model to identify the axle lateral deflection rigidity of the vehicle on line, and the method comprises the following steps:
building a linear system comprising m inputs and n outputs
Wherein
For the predicted output value, phi, of the linear systemT(t) theta is a state space equation of the linear system, and theta represents a parameter to be identified;
solving the system output of the linear system according to a fifth formula;
the fifth formula is:
m and n are integers greater than 0, respectively, and are used to represent the parameters theta and theta to be identified
λ (t) is a forgetting factor, ΛtIn the form of a diagonal matrix,
is a predicted value of cornering stiffness at time t,
is a predicted value of cornering stiffness at time (t-1), R (t) is a system output value at time t, phiT(t) is the system input value at time t, and L (t), P (t-1) and P (t) are all intermediate matrixes;
identifying a model and the linear system from the yaw stiffness to [ C ]αf Cαr]TAs a parameter theta to be identified, to
Is composed of
To be provided with
Is phiT(t) solving the linear system output R (t) to obtain a result of identifying the axle cornering stiffness of the vehicle online.
In some embodiments, obtaining the rotational inertia of the vehicle body based on the yaw stiffness and the rotational inertia recognition model of the vehicle body comprises:
identifying a model and the linear system from the rotational inertia of the vehicle body to
As a parameter theta to be identified, to
Is composed of
To be provided with
Is phiT(t) solving the linear system output R (t) to obtain a result of obtaining the rotational inertia of the vehicle body by the identification model.
In another aspect, a system for joint identification of yaw stiffness and moment of inertia parameters is provided, wherein the system comprises a memory, a processor, and a computer program stored on the memory and executable by the processor, wherein the computer program is configured to be executed by the processor to implement the method according to any one of claims 1 to 9.
The embodiment provides a dynamic model (a vehicle transverse dynamic state space equation) established by taking a vehicle transverse axis as a control object, and a vehicle body coordinate system (not a wheel coordinate system) can be used for modeling by considering the dynamic state of the whole vehicle; and the lateral deflection rigidity of the transverse shaft of the vehicle is used as an identification object to establish a corresponding identification model, so that more accurate state identification and vehicle control can be realized for vehicle types (such as commercial tractors) with variable vehicle body length and variable vehicle load. Meanwhile, the online identification is carried out by using an iteration method, so that the real-time performance of the identification result is enhanced.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a joint identification method for lateral stiffness and rotational inertia parameters according to an embodiment of the present invention;
FIG. 2 is a schematic view of a coordinate system of a vehicle body according to an embodiment of the present invention;
fig. 3 is a combined identification system for lateral stiffness and rotational inertia parameters of a commercial tractor 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 clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a joint identification method for yaw stiffness and moment of inertia parameters, including:
s100: establishing a vehicle transverse dynamic state space equation based on the vehicle dynamic state by taking a vehicle transverse axis as a control object;
s200: establishing an axle-side deflection rigidity identification model taking the axle-side deflection rigidity of the vehicle as a parameter to be estimated and/or a vehicle body rotational inertia identification model taking the vehicle body rotational inertia as a parameter to be estimated based on the vehicle transverse dynamics state space equation;
s300: using an iterative least square method with forgetting factors for the axle side deflection rigidity identification model to identify the axle side deflection rigidity of the vehicle on line;
s400: and acquiring the rotational inertia of the vehicle body based on the axle lateral deflection rigidity and the rotational inertia recognition model of the vehicle body.
The embodiment provides a dynamic model (a vehicle transverse dynamic state space equation) established by taking a vehicle transverse axis as a control object, and a vehicle body coordinate system (not a wheel coordinate system) can be used for modeling by considering the dynamic state of the whole vehicle; and the lateral deflection rigidity of the transverse shaft of the vehicle is used as an identification object to establish a corresponding identification model, so that more accurate state identification and vehicle control can be realized for vehicle types (such as commercial tractors) with variable vehicle body length and variable vehicle load. Meanwhile, the online identification is carried out by using an iteration method, so that the real-time performance of the identification result is enhanced.
In some embodiments, step S100 comprises:
s110: under a vehicle body coordinate system determined by a right-hand rule, establishing a vehicle transverse motion equation to determine the relationship between the vehicle transverse linear acceleration, the vehicle yaw angular velocity and the vehicle transverse linear velocity and the transverse force borne by a front/rear transverse shaft of the vehicle body;
s120: establishing a moment balance equation of the rotation of the vehicle body around the z axis to determine the relationship between the yaw angular acceleration of the vehicle, the rotational inertia of the vehicle body and the transverse force borne by the front/rear transverse axis of the vehicle body;
s130: and establishing a vehicle transverse dynamic state space equation according to the vehicle transverse motion equation and the moment balance equation of the vehicle body rotating around the z axis.
When the vehicle body coordinate system is established, it is assumed that the vehicle is a rigid body, the traveling plane is horizontal, and the vehicle steering mode is front wheel steering. The vehicle has a movement in both the longitudinal and transverse directions in the plane of travel, wherein the transverse movement can be decomposed into transverse sliding and transverse swinging. The lateral motion state of the vehicle can be described in terms of the lateral displacement of the center of mass of the vehicle body and the yaw angle.
In some embodiments, step S110 includes S111: determining a vehicle transverse motion equation according to a first formula; the first formula is:
wherein m is the mass of the vehicle body,
for transverse linear acceleration of the vehicle, VxAs is the longitudinal speed of the vehicle,
yaw rate of vehicle body, FyfTransverse forces acting on the front axle of the vehicle body, FyrThe transverse force borne by the rear axle of the vehicle body.
It should be noted that the first formula is derived according to newton's second law, and the stress balance equation of the vehicle is first obtained as follows: ma isy＝Fyf+FyrWherein a isyIs the vehicle lateral acceleration; then obtaining the relation between the lateral acceleration of the vehicle and the lateral linear acceleration and the yaw angular acceleration of the vehicle body
The first formula can be derived.
In the present embodiment, the lateral linear acceleration of the vehicle
And vehicle longitudinal speed VxThe yaw velocity of the vehicle body CAN be directly acquired from the acquisition unit in real time on line through the CAN network of the vehicle body
The real-time acquisition can be realized through an Inertial Measurement Unit (IMU).
In some embodiments, step S120 includes S121: determining a moment balance equation of the vehicle body rotating around the z axis according to a second formula; the second formula is:
wherein, IzAs the moment of inertia of the vehicle body,
for yaw angular acceleration of the vehicle body, /)fIs the distance from the front axle of the vehicle body to the center of mass,/rThe distance from the rear axle of the vehicle body to the center of mass.
In the present embodiment, the yaw angular acceleration of the vehicle body
Vehicle body yaw velocity obtained through real-time acquisition
The differential of the time t is obtained, and the accuracy of the signal is improved.
As shown in fig. 2, in some embodiments, step S120 includes step S122 of defining a relationship between lateral forces acting on front and rear axles of a vehicle body and lateral offset stiffness of the axles according to a third formula;
the third formula is: fyf＝Cαf(δ-θVf)，Fyr＝Cαr(-θVr)，
Wherein, CαfPositive cornering stiffness for the front axle of a vehicle, CαrThe positive cornering stiffness of the rear axle of the vehicle is shown as delta, which is the angle between the direction of the front wheels and the X-axis of the body coordinate system (i.e. the wheel angle), thetaVfIs the angle between the rotational direction of the front wheel and the X-axis of the coordinate system of the vehicle body, thetaVrIs the included angle between the speed direction of the rear wheel and the X axis of the coordinate system of the vehicle body;
step S130 includes S131: and respectively substituting the third formula into the first formula and the second formula to determine a vehicle transverse dynamic state space equation.
In the present embodiment, the lateral force applied to the front/rear lateral axle of the vehicle body is considered to be generated by the lateral sideslip of the axle-belt tires, and is in direct proportion to the number of the axle-belt tires. Under the working condition of a small slip angle, the magnitude of the cornering force of a single tire is in a direct proportional relation with the slip angle. The relationship between the lateral force applied to the front/rear transverse axle of the vehicle body and the slip angle of the tire is as follows:
Fyf＝nfknf∝f＝kf∝f，Fyr＝nrknr∝r＝kr∝r(ii) a Wherein n isf、nrIndicates the number of front and rear axle tires, knf、knrRepresenting the cornering stiffness, k, of a single tyre for the front and rear axlesf、krRepresents the lateral deflection stiffness of the front and rear axes, ocf、∝rIndicating the slip angles of the front and rear axles. Due to kf、krNegative values, for ease of calculation, convention: cαf＝-kf，Cαr＝-kr，Cαf、CαrNamely the positive cornering stiffness of the front and rear axles. Slip angle is defined as the angle between the tire direction and the wheel speed direction, i.e.. alphaf＝δ-θVf，∝r＝-θVr。
In some embodiments, step S120 includes S122: determining vehicle lateral/longitudinal velocity and θ according to a fourth formulaVf、θVrThe relationship between;
the fourth formula is:
step S130 includes S132: substituting the fourth formula into a third formula to determine the vehicle lateral dynamics state space equation;
the vehicle transverse dynamic state space equation is as follows:
In this embodiment, the transverse speeds of the front and rear axles when the front and rear bodies of the vehicle rotate around the z-axis are respectively:
longitudinal and transverse speeds of front and rear axles and thetaVf、θVrThe relationship between them is:
considering thetaVf、θVrThe value is very small, and the obtained approximate relation is as follows:
in some embodiments, the establishing of the identification model of the axle deflection stiffness with the axle deflection stiffness of the vehicle as the parameter to be estimated in S200 includes the steps of: substituting the third formula and the fourth formula into the vehicle transverse dynamic state space equation to obtain:
finishing to obtain:
therefore, the identification model of the lateral deflection stiffness of the shaft can be determined as follows:
in S200, a vehicle body rotational inertia recognition model with vehicle body rotational inertia as a parameter to be estimated is established, and the method comprises the following steps: substituting the second formula, the third formula and the fourth formula into the vehicle transverse dynamic state space equation to determine a vehicle body rotational inertia identification model;
the vehicle body moment of inertia recognition model is expressed as:
in some embodiments, step S300 includes the steps of:
s310: building a linear system comprising m inputs and n outputs
Wherein
For the predicted output value, phi, of the linear systemT(t) θ is the state of the linear systemA space equation, theta represents a parameter to be identified;
s320: solving the system output of the linear system according to a fifth formula;
the fifth formula is:
m and n are integers greater than 0, respectively, and are used to represent the parameters theta and theta to be identified
λ (t) is a forgetting factor, ΛtIn the form of a diagonal matrix,
is a predicted value of cornering stiffness at time t,
is a predicted value of cornering stiffness at time (t-1), R (t) is a system output value at time t, phiT(t) is the system input value at time t, and L (t), P (t-1) and P (t) are all intermediate matrixes;
identifying a model and the linear system from the yaw stiffness to [ C ]αf Cαr]TAs a parameter theta to be identified, to
Is composed of
To be provided with
Is phiT(t) solving the linear system output R (t) to obtain a result of identifying the axle cornering stiffness of the vehicle online.
In some embodiments, step S400 includes the steps of:
identifying a model and the linear system from the rotational inertia of the vehicle body to
As a parameter theta to be identified, to
Is composed of
To be provided with
Is phiT(t) solving the linear system output R (t) to obtain a result of obtaining the rotational inertia of the vehicle body by the identification model.
In this embodiment, an iterative least square method with forgetting factors is used in this embodiment, and each parameter identified from the identification model of the lateral offset stiffness is input to the linear system to identify the lateral offset stiffness, and meanwhile, the axial offset stiffness is identified by using
Recognizing lateral stiffness, and use
The rotational inertia is calculated, and two observation output quantities are used for respectively identifying results, thereby avoiding the situation that
The problem of inaccurate calculation caused by parameter accumulation is solved.
The embodiment of the present invention further provides a system for joint identification of yaw stiffness and rotational inertia parameters, where the system includes a memory, a processor, and a computer program stored in the memory and executable by the processor, where the computer program is configured to be executed by the processor to implement the method according to any one of the foregoing embodiments.
As shown in FIG. 3, in one embodiment, the invention relates to a commercial tractor cornering stiffness and moment inertia parameter joint identification system, which comprises a sensor and a sensorA Micro Controller Unit (MCU), an Inertia Measurement Unit (IMU) and a forward-looking all-in-one machine unit which are connected with a CAN bus network of the vehicle body. The Inertial Measurement Unit (IMU) can acquire a yaw rate signal of the vehicle
And a vehicle acceleration signal ay(ii) a The forward-looking all-in-one machine unit is used for acquiring a transverse displacement signal y; the CAN bus network of the vehicle body also obtains a system switch signal and a vehicle longitudinal speed signal V through an upper computer controller and a whole vehicle systemxSteering wheel angle signal deltaW(proportional relationship δ to wheel rotation angle δW19.5 x δ) and lateral linear acceleration
The MCU is used for processing various parameters collected from the IMU, the forward-looking all-in-one machine unit and the CAN bus network of the vehicle body, including yaw rate signals
Differentiating the time t to obtain the yaw angular acceleration
Differentiating the transverse displacement signal y by the time t to obtain the transverse linear velocity
The MCU is also used for carrying out online identification on the lateral deviation stiffness identification model by using an iterative least square method with a forgetting factor so as to obtain the lateral deviation stiffness of the front/rear axle of the vehicle, substituting the lateral deviation stiffness of the front/rear axle of the vehicle obtained by identification into the rotational inertia identification model of the vehicle body, and carrying out online identification by using the iterative least square method with the forgetting factor again so as to obtain the rotational inertia of the vehicle body.
In the embodiment, all parameters for systematically identifying the yaw stiffness and the rotational inertia of the front/rear axle of the vehicle CAN be obtained through the vehicle body CAN bus network and the intelligent vehicle sensor (the inertial measurement unit IMU and the front-view all-in-one machine unit), additional equipment does not need to be additionally arranged, and the deployment is easy.
The system includes three states: an on state, a fault state, and an off state. The starting state comprises a standby state and an activation state, and the upper computer controller controls the system to be started and closed. After the vehicle is electrified and self-checking is finished, the system detects whether data of the IMU, the front-view all-in-one machine and the vehicle body CAN bus network are normally transmitted or not, and if the data are normally transmitted, the system enters an open standby state. After the system enters a standby state, the longitudinal speed of the vehicle body is continuously detected, after the longitudinal speed reaches a preset threshold value, the vehicle enters an activation state, and the MCU starts to identify the lateral deflection rigidity of the front shaft and the rear shaft and the rotational inertia of the vehicle body on line. And after receiving the fault signal, the system enters a fault state and processes and stores fault information. And the system enters a closing state after receiving a closing instruction sent by the upper computer control system.
The embodiment of the invention has the following beneficial effects:
1) the vehicle dynamics modeling is carried out on the basis of a vehicle body coordinate system by taking a vehicle transverse shaft as a control object, the dynamic state of the whole vehicle (the motion states of a front shaft and a rear shaft of the vehicle) can be more accurately described, and the model expression is more concise and intuitive; meanwhile, the cornering stiffness of the transverse shaft of the vehicle is taken as a recognition result, the method is suitable for a commercial tractor, the condition that the cornering stiffness and the rotational inertia of the vehicle body of the commercial tractor (the vehicle type with variable vehicle body length and vehicle body load) are changed along with the change of the load and the vehicle body length is considered, and the recognition result is more accurate.
2) On-line acquisition of transverse linear acceleration signals by using vehicle body CAN network
Real-time acquisition of yaw rate using Inertial Measurement Unit (IMU)
And is arranged at
On the basis, a multi-step long mean filtering differential derivation method is adopted to calculate the acceleration of the yaw angleDegree of rotation
The accuracy is improved from the signal acquisition source to the calculation result as a whole.
3) Use of
Recognizing lateral stiffness, and use
And the rotational inertia is calculated, and the two observation output quantities are used for respectively identifying results, so that parameter coupling is avoided, and the problem of inaccurate calculation caused by parameter accumulation is solved.
4) All parameters for system identification CAN be obtained through a vehicle body CAN bus network and intelligent vehicle sensors (an inertial measurement unit IMU and a forward-looking all-in-one machine unit), additional equipment does not need to be additionally arranged, and the system is easy to deploy.
In the description of the present invention, it should be noted that the terms "upper", "lower", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and operate, and thus, should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
It is to be noted that, in the present invention, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A joint identification method for lateral deflection rigidity and rotational inertia parameters is characterized by comprising the following steps:
establishing a vehicle transverse dynamic state space equation based on the vehicle dynamic state by taking a vehicle transverse axis as a control object;
establishing an axle-side deflection rigidity identification model taking the axle-side deflection rigidity of the vehicle as a parameter to be estimated and/or a vehicle body rotational inertia identification model taking the vehicle body rotational inertia as a parameter to be estimated based on the vehicle transverse dynamics state space equation;
using an iterative least square method with forgetting factors for the axle side deflection rigidity identification model to identify the axle side deflection rigidity of the vehicle on line;
and acquiring the rotational inertia of the vehicle body based on the axle lateral deflection rigidity and the rotational inertia recognition model of the vehicle body.
2. The method for jointly identifying the lateral deflection stiffness and the moment of inertia parameters as claimed in claim 1, wherein a vehicle lateral dynamic state space equation based on the vehicle dynamic state is established by taking a vehicle lateral axis as a control object, and comprises the following steps:
under a vehicle body coordinate system determined by a right-hand rule, establishing a vehicle transverse motion equation to determine the relationship between the vehicle transverse linear acceleration, the vehicle yaw angular velocity and the vehicle transverse linear velocity and the transverse force borne by a front/rear transverse shaft of the vehicle body;
establishing a moment balance equation of the vehicle body rotating around the z axis to determine the relationship between the yaw angular acceleration of the vehicle, the rotational inertia of the vehicle body and the transverse force borne by the front/rear axle of the vehicle body;
and establishing a vehicle transverse dynamic state space equation according to the vehicle transverse motion equation and the moment balance equation of the vehicle body rotating around the z axis.
3. The method for jointly identifying cornering stiffness and moment of inertia parameters according to claim 2, wherein a vehicle lateral motion equation is established to determine the vehicle lateral linear acceleration, the vehicle yaw rate and the relationship between the vehicle lateral linear velocity and the lateral force applied to the front/rear transverse axis of the vehicle body, and the method comprises the following steps:
determining a vehicle transverse motion equation according to a first formula;
the first formula is:
wherein m is the mass of the vehicle body,
for transverse linear acceleration of the vehicle, VxAs is the longitudinal speed of the vehicle,
yaw rate of vehicle body, FyfTransverse forces acting on the front axle of the vehicle body, FyrThe transverse force borne by the rear axle of the vehicle body.
4. The method for jointly identifying cornering stiffness and moment of inertia parameters according to claim 3, wherein a moment balance equation of a vehicle body rotating around a z-axis is established to determine a relationship among a yaw angular acceleration of the vehicle, a moment of inertia of the vehicle body and a lateral force borne by a front/rear transverse axis of the vehicle body, and the method comprises the following steps:
determining a moment balance equation of the vehicle body rotating around the z axis according to a second formula;
the second formula is:
wherein, IzAs the moment of inertia of the vehicle body,
for yaw angular acceleration of the vehicle body, /)fIs the distance from the front axle of the vehicle body to the center of mass,/rThe distance from the rear axle of the vehicle body to the center of mass.
5. The method for jointly identifying cornering stiffness and moment of inertia parameters according to claim 4, wherein a vehicle transverse dynamic state space equation is established according to the vehicle transverse motion equation and a moment balance equation of the vehicle body rotating around the z-axis, and the method comprises the following steps:
defining the relation between the transverse force borne by the front axle and the rear axle of the vehicle body and the lateral deflection rigidity of the axle according to a third formula;
the third formula is: fyf＝Cαf(δ-θVf)，Fyr＝Cαr(-θVr)，
Wherein, CαfPositive cornering stiffness for the front axle of a vehicle, CαrPositive cornering stiffness of the rear axle of the vehicle, delta being the angle between the direction of the front wheels and the X-axis of the body coordinate system, thetaVfIs the angle between the rotational direction of the front wheel and the X-axis of the coordinate system of the vehicle body, thetaVrIs the included angle between the speed direction of the rear wheel and the X axis of the coordinate system of the vehicle body;
and respectively substituting the third formula into the first formula and the second formula to determine a vehicle transverse dynamic state space equation.
6. The method for jointly identifying cornering stiffness and moment of inertia parameters according to claim 5, wherein a vehicle transverse dynamic state space equation is established according to the vehicle transverse motion equation and a moment balance equation of the vehicle body rotating around the z-axis, and the method comprises the following steps:
determining vehicle lateral/longitudinal velocity and θ according to a fourth formulaVf、θVrThe relationship between;
the fourth formula is:
substituting the fourth formula into a third formula to determine the vehicle lateral dynamics state space equation;
the vehicle transverse dynamic state space equation is as follows:
7. The method of claim 6, wherein the joint identification of yaw stiffness and moment of inertia parameters,
establishing an axle-side deflection stiffness recognition model taking the axle-side deflection stiffness of the vehicle as a parameter to be estimated based on the vehicle transverse dynamics state space equation, and comprising the following steps of:
substituting the third formula and the fourth formula into the vehicle transverse dynamic state space equation to determine an axle lateral deflection stiffness identification model;
the axle lateral deflection rigidity identification model is expressed as follows:
establishing a vehicle body rotational inertia recognition model taking the vehicle body rotational inertia as a parameter to be estimated based on the vehicle transverse dynamics state space equation, and comprising the following steps of:
substituting the second formula, the third formula and the fourth formula into the vehicle transverse dynamic state space equation to determine a vehicle body rotational inertia identification model;
the vehicle body moment of inertia recognition model is expressed as:
8. the method of claim 7, wherein the joint identification of yaw stiffness and moment of inertia parameters,
the method for identifying the lateral axle deflection stiffness of the vehicle on line by using the iterative least square method with forgetting factors for the lateral axle deflection stiffness identification model comprises the following steps:
building a linear system comprising m inputs and n outputs
Wherein
For the predicted output value, phi, of the linear systemT(t) theta is a state space equation of the linear system, and theta represents a parameter to be identified;
solving the system output of the linear system according to a fifth formula;
the fifth formula is:
m and n are integers greater than 0, respectively, and are used to represent the parameters theta and theta to be identified
λ (t) is a forgetting factor, ΛtIn the form of a diagonal matrix,
is a predicted value of cornering stiffness at time t,
is a predicted value of cornering stiffness at time (t-1), R (t) is a system output value at time t, phiT(t) is the system input value at time t, and L (t), P (t-1) and P (t) are all intermediate matrixes;
identifying a model and the linear system from the yaw stiffness to [ C ]αf Cαr]TAs a parameter theta to be identified, to
Is composed of
To be provided with
Is phiT(t) solving the linear system output R (t) to obtain a result of identifying the axle cornering stiffness of the vehicle online.
9. The method for jointly identifying cornering stiffness and moment of inertia parameters according to claim 8, wherein a body moment of inertia is obtained based on the axle cornering stiffness and the body moment of inertia identification model, comprising the steps of:
identifying a model and the linear system from the rotational inertia of the vehicle body to
As a parameter theta to be identified, to
Is composed of
To be provided with
Is phiT(t) solving the linear system output R (t) to obtain a result of obtaining the rotational inertia of the vehicle body by the identification model.
10. A system for joint identification of cornering stiffness and moment of inertia parameters, the system comprising a memory, a processor and a computer program stored on the memory and executable by the processor, wherein the computer program is for execution by the processor to implement the method according to any of claims 1-9.
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