Method and device for managing the operation of a chassis actuator of a motor vehicle as a function of the vehicle's drift angle

Abstract

The invention relates to a method for managing the operation of at least one chassis actuator (5) of a motor vehicle (1). The invention also relates to a device (100) implementing such a method and to a motor vehicle (1) comprising such a device. Figure for the abstract: 1

Description

Title of the invention: Method and device for managing the operation of a chassis actuator of a motor vehicle as a function of the vehicle's drift angle. Technical field of the invention

[0001] The present invention relates to the field of embedded systems in vehicles for managing their operation during driving. The invention relates in particular to a method for managing, by means of a computer device embedded in a motor vehicle, the operation of at least one chassis actuator of the vehicle. The invention also relates to a device implementing such a method. The invention is applicable to motor vehicles such as motor vehicles, in particular cars. Prior art

[0002] It is known that the drift angle of a vehicle directly determines its stability during driving, in other words, its road holding. Therefore, when developing efficient and safe driver assistance systems, it is essential to be able to take this crucial parameter of drift angle into account. Currently, however, the drift angle of a vehicle is generally determined by equipping the vehicle with advanced detection devices, such as multiple GPS antennas, wheel speed sensors, and / or an inertial measurement unit (IMU). However, various uncertainties affect the estimation of the drift angle, notably noise in the sensor measurements. To address this, manufacturers have developed estimation methods that allow them to estimate the state variables of a system based on measurements obtained from the sensors.However, nonlinear systems are subject to uncertainties, disturbances, and measurement noise, making state estimation difficult. State estimation methods based on sliding mode control have been extensively studied in the scientific literature. These methods are robust to uncertainties and disturbances, but they can suffer from chattering, which can lead to performance degradation. On the other hand, conventional observation methods, such as the Kalman filter or its variants, offer high accuracy in state estimation but may not be robust to uncertainties and disturbances. Summary of the invention

[0003] The invention aims to overcome these drawbacks. In particular, it aims to provide a solution for determining the drift angle of a vehicle more precisely. automobile during driving. More specifically, the invention aims to provide a method and system that solve the problem of estimating the state(s) of time-varying and / or nonlinear dynamic systems that are subject to uncertainties, disturbances, and measurement noise. Through this, the invention aims to contribute to the provision of more efficient and safer driver assistance systems for motor vehicles.

[0004] To achieve these objectives, the invention relates, according to a first aspect, to a method for managing, by means of a computer device embedded in a motor vehicle, the operation of at least one chassis actuator of the vehicle, the method comprising the steps of: i. obtain data characterizing the vehicle's motion dynamics; ii. to supply a determination module for said device with data characterizing the vehicle's motion dynamics in order to obtain data characterizing the vehicle's drift angle, said determination module operating on the basis of a model of the vehicle's motion dynamics which establishes a first state defined by a "super-twisting" type model and a second state defined by a sliding mode observer model; and iii. manage the operation of said actuator according to the data characterizing the drift angle of the vehicle.

[0005] According to one variant, the determination module can use a bicycle model for said modeling.

[0006] According to another variant, the data characterizing the motion dynamics of the vehicle may contain data characterizing the drift stiffness of the front wheels, data characterizing the drift stiffness of the rear wheels, data characterizing the mass of the vehicle, data characterizing the moment of inertia of the vehicle, data characterizing the longitudinal speed of the vehicle, data characterizing the yaw rate of the vehicle and data characterizing the wheel angle of the front axle.

[0007] According to yet another variant, the determination module can use a kinematic model for said modeling.

[0008] According to yet another variant, the data characterizing the motion dynamics of the vehicle may contain data characterizing the transverse speed of the vehicle, data characterizing the transverse acceleration of the vehicle and data characterizing the yaw rate of the vehicle.

[0009] According to a second aspect, the invention relates to a device for managing the operation of at least one chassis actuator of a motor vehicle, the device comprising at least one information processing unit, comprising at minus one processor, and a data storage medium configured to implement a process as described above.

[0010] According to a third aspect, the invention relates to a computer program comprising program code instructions for the execution of the steps of a process as described above when said program is executed by at least one processor.

[0011] According to a fourth aspect, the invention relates to a medium usable in a computer on which a program as described above is recorded.

[0012] According to a fifth aspect, the invention relates to a motor vehicle which incorporates a device as described above. Brief description of the figures

[0013] Other features and advantages of the invention will become apparent from an examination of the detailed description below, and the accompanying drawings, in which:

[0014] [Fig-1] is a schematic illustration of a motor vehicle according to the invention;

[0015] [Fig.2] is a functional diagram of a device according to the invention; and

[0016] [Fig.3] is a flowchart of the steps of a process according to the invention. Detailed description of the invention

[0017] Figure 1 schematically illustrates a motor vehicle 1 according to the invention. This includes a device 100 for managing the operation of at least one chassis actuator of a motor vehicle within the meaning of the present invention, as described below, which implements a method for managing the operation of at least one actuator of a motor vehicle within the meaning of the present invention, as described further below. During the implementation of the method, the device 100 according to the invention first obtains data characterizing the vehicle's motion dynamics by interacting, for this purpose, via a wired communication network of the vehicle (e.g., CAN, Ethernet) – represented by the double-headed arrows – with, for example, a steering angle sensor 2 (or front wheel angle sensor), a yaw sensor 3, or an inertial measurement unit 4 arranged in the vehicle.And it is from this data alone that the device 100 according to the invention advantageously determines data characterizing the drift angle of the vehicle, based on which it manages the operation of at least one chassis actuator 5 of the vehicle. This is how more efficient and safer driver assistance systems for motor vehicles are provided.

[0018] The device 100 for managing the operation of at least one chassis actuator of a motor vehicle according to the invention is illustrated in [Fig. 2]. It is fundamentally a computer device comprising at least one information processing unit 101, including one or more processors, a support a data storage unit 102, on which is stored a program comprising program code instructions for executing the steps of the process according to the invention described below, and an input and output interface 103 for receiving and transmitting data. Advantageously, the device 100 according to the invention also includes a determination module 104, which, as will be seen in more detail later, operates on the basis of a model of the vehicle's motion dynamics that, among other things, establishes a first state defined by a "super-twisting" type model and a second state defined by a sliding mode observer model.

[0019] Preferably, the device 100 according to the invention is housed on a separate computer and interacts via its input / output interface 103 and by means of a wired vehicle communication network (e.g., CAN, Ethernet) – represented in [Fig. 1] by the double-headed arrows – with the steering angle sensor 2, the yaw sensor 3, and the inertial measurement unit installed on board the vehicle 1 according to the invention. Alternatively, the device 100 according to the invention is an integral part of a computer of the driver assistance system (not shown) of the vehicle 1.

[0020] According to the invention, all the elements described above contribute to enabling the implementation, on board a motor vehicle, of a method for managing the operation of at least one chassis actuator of the vehicle, as described below in relation to [Fig.3].

[0021] Figure 3 illustrates by means of a flowchart the steps of the process according to the invention. According to a first step 301 of the process according to the invention, the device 100 according to the invention obtains data characterizing the motion dynamics of the vehicle, using for this purpose its data storage medium on which some of this data is recorded or by interacting with the steering angle sensor 2, the yaw sensor 3 and / or the inertial measurement unit 4 in order to obtain in real time another part of this data.More specifically, depending on one of the two embodiments described below, the data obtained during this first step 301 of the process contain data characterizing the drift stiffness of the front wheels, data characterizing the drift stiffness of the rear wheels, data characterizing the mass of the vehicle, data characterizing the moment of inertia of the vehicle, data characterizing the longitudinal speed of the vehicle, data characterizing the yaw rate of the vehicle, data characterizing the wheel angle of the front axle, data characterizing the transverse speed of the vehicle, data characterizing the transverse acceleration of the vehicle and / or data characterizing the yaw rate of the vehicle.

[0022] Next, according to a second step 302 of the process according to the invention, the device 100 according to the invention supplies its determination module 104 with the data obtained In the previous step 301, data characterizing the vehicle's drift angle were obtained. As we will see, the determination module 104 operates advantageously based on a model of the vehicle's motion dynamics, which establishes a first state defined by a "super-twisting" type model and a second state defined by a sliding mode observer model. Indeed, sliding mode observers are characterized by their robustness to a category of disturbances and by the finite-time convergence of the sliding surface to zero. Finite-time convergence is an advantageous attribute for sliding mode estimators because it allows for an accurate estimation of the unknown states of a physical system, which can be used for system control and / or monitoring purposes.

[0023] From a mathematical point of view, the estimation of states by the determination module 104 is decomposed into two parts by means of two equations which define the generalized dynamics of a time-variable system, namely: = a^t). x x + a / t). x2 + b^u ( 1 ) x2 = a3(t)xi+a4(t)^2+b2(4 u where Xp € P are the states, U € P is the control input, y GP is the measured output. Nonlinear functions that vary over time ajt) (ï= X ...4), ^-(0(7=1,2) are known.

[0024] Thus, with reference to the table mentioned at the end of the description which describes the variables mentioned here, the determination module 104 is based on a model such as: it n (3) x1= a t (+ Sj 2 + 4Jr Sj dT 0 x2= a3(t)-$i+ct4( 0^2 + -^2( O-^+4*rd\ 0 d = *i2 / si 9n ( S) dT o with [ xj |x| p sigrn(x) and S = x1-x1=e1

[0025] The gains of the estimator 1^, 1 and > 0 must all be positive. They are determined by solving a set of linear matrix inequalities (LMI).

[0026] The dynamics of the estimation errors are defined as follows: 1 * n (9) 6^0^(0-61+^2(0.¾-^^ 6j ^2^( elJ “T 0

[0027] This modeling ensures that the trajectory of the slip surface converges to zero over time as a function of at least one input and output signal measurement of the system (observability rule for slip mode estimators).

[0028] The error el (the sliding surface) and its first derivative O converge to zero in finite time, that is, (XpX^) —* (Xp X2) • Which gives the following relation: a2( O.e2 = / 2*J[ ej °di (11) From which we obtain 1 (12) = ej dr=d Therefore, we obtain e2 = 0.

[0029] Now considering that (4) is subtracted from (12), we have: ë2 = a4(t).e2-l3*\ e2\° <13)

[0030] This relies on the fact that = 0 and on the result of ( 12). The observer design is based on Lyapunov stability theory, considering a Lyapunov candidate function such that: V = |e22 (14> V=e2*ë2 (15) V=e2*(a4(t)^2d3*sign(e2)) (16) Finally V=-le2|*(l3-|a4(t).|e2||)<0 (17) When | Ct4 ( O • | 621 | m • Therefore, 6 j = 0 converges in finite time.

[0031] Thus, the state ^2 is estimated in finite time. Such convergence in finite time is difficult in time-varying systems (equations (1) and (2)) using any other known principle.

[0032] Moreover, this modeling, on the basis of which the determination module 104 operates, advantageously allows for the estimation of the drift angle of different types of vehicles, including passenger cars, trucks and buses, in

[0033] different driving conditions, including straight lines, curves and during emergency maneuvers. According to a first embodiment, the determination module 104 uses a bicycle model. Therefore: 4) = ^ (20) And on the other hand, II II (21) and u=Ôf (22) 0) tj II X n: *!> w II ■Ç II and e1=x1-xl=p-^=ep (23)

[0034] According to a second embodiment, the determination module 104 uses a kinematic model. Therefore: ax=vAv;-v) =(ÿx-Wv y ) ay = vx.{^+ y) = ( vy + y / .vj

[0035] (24) (25) where and &y are the longitudinal and transverse accelerations. The state representation of the model of the system to be observed is then written as: ri oi r^' OjL v yJ + lO lJL a yJ' y = Cx = [1 (26) (27)

[0036] with vx the vehicle speed, the acceleration on the longitudinal axis, vy the vehicle's lateral speed, &y the lateral acceleration and ou r the yaw rate. The kinematic model does not explicitly incorporate the lateral forces of the trains, reflecting a strong assumption about the tire model chosen during a dynamic vehicle behavior analysis. The strength of this model lies in its use of readily available quantities provided by sensors present on all vehicles, including inertial measurement units (IMUs), wheel speed sensors, and GPS.

[0037] This gives us: aa^t)=^ (28) a3(t)=-y, a4(t) = 0 (29) ^)=(1 0], b / t)=[0 1] (30)

[0038] Also, we have: Vx = Xp Vy = X2 (31) , [a*l (32) x2=vy and u= a e2 = x2-x2- evy-Vy-Vy and e1-x1-x1-eVx-vx-vx

[0039] The drift angle observer using the kinematic model is therefore written: (34) ^2= ^y= - ÿvx+ay+734 dJ 0 (35) (36) With [ x] p- Ixl^sïgrnCx) (37)

[0040] The sliding mode dynamics of the velocity error are obtained using the relation below . . 1 7 fr . 0 , êVï = tyeVv + ax - / i* [ eVï ] 2 - / J [ eVï ] dr o

[0041] The trajectory of the error ei and its first derivative èj converge to the origin in finite time. This allows us to deduce 7 rr i o. (39) = evJ dr 0

[0042] It follows that: ij[evJ\T)dT (40) 6Vy ¢ / d

[0043] This correction term 2 is used in the observer equation (Eq. (35)), knowing that é Vy = - " 4 * [ evy j ° (41)

[0044] Since the error ei and its first derivative converge to the origin in finite time, we have: ei=èi=0 (42)

[0045] Equation (41) therefore becomes éFy=-;3*[ ev,J0 (43) when I3 > 0, =* eVv = Vy - Vy = 0. (44)

[0046] The value of the state vector, i.e., the longitudinal and lateral velocities, can thus be deduced. Once the two velocity components (longitudinal velocity and lateral velocity) have been estimated, the drift angle fi is estimated by (small angle approximation): p = arctan(^) (45)

[0047] Tests have demonstrated the effectiveness of these models implemented by the 104 determination module for estimating the longitudinal and lateral velocities vy and the drift angle of the vehicle, with a maximum error between measurement and estimation on the order of 0.5 m / s. It has also been observed that these models perform better than those based on other unbound filters (UKF: fragrance-free Kalman filter and PF: particulate filter). Furthermore, these models are also easier to implement. The advantage of these models (sliding mode observer) for determining the drift angle in real time is that the performance of the observer and the accuracy of the estimation do not necessarily depend on the vehicle model used, because the dynamics of the sliding mode compensate for the system dynamics.

[0048] Finally, according to a third step 303 of the method according to the invention, the device 100 according to the invention manages the operation of the chassis actuator 4 according to the data characterizing the drift angle of the vehicle obtained during the implementation of the previous step of the method.

[0049] Thus, thanks to the method and device according to the invention described above, a solution is provided for determining more precisely the drift angle of a motor vehicle during driving. Thanks to these means, the invention This contributes advantageously to the provision of more efficient and safer driver assistance systems for motor vehicles in all types of vehicles.

[0050] For the sake of clarity, all variables used in the preceding description are identified below. Sf Angle at the front axle wheels ôr Angle at the rear axle wheels Yaw rate achieved by the front steering system WBrr Yaw rate achieved by the rear steering system ^TV Yaw rate achieved by the torque vectoring m Vehicle mass 1 Vehicle wheelbase h Height of the center of gravity at the point of wheel contact with the ground ff Gravitational constant gjg2 Adaptive compensation gain SMC ^traj Yaw rate corresponding to a given trajectory Vehicle line acceleration ay Vehicle lateral acceleration ^sf Longitudinal stiffness of the front axle c ^sr Longitudinal stiffness of the rear axle rdyn Dynamic radius of the wheel J tot JJ tot2 Total inertia of the front axle / Total inertia of the rear axle J eq J Jeq^ Equivalent inertia of the front axle / Equivalent inertia of the rear axle J / JJ TAV1 J TAR Inertia of Front axle / Rear axle inertia J 1J J mAVl -J mAR Machine inertiafront / Rear machine inertia Iz Vehicle moment of inertia ^meas Yaw acceleration measured by the inertial measurement unit / «F des1 meas Desired yaw rate / Measured yaw rate a^lf Distance between the center of mass and the front axle a2^lr Distance between the center of mass and the rear axle Caf Front wheel drift stiffness Car Rear wheel drift stiffness $ s at $ max Maximum slip Vx Longitudinal speed of the vehicle Vy Lateral speed of the vehicle Vmoy Average inter-axle speed ^ARext.Speed ​​of the outer wheel of the rear axle J xJ J y Longitudinal jerk / Lateral jerk s Laplace variable P Vehicle drift angle Front axle drift angle Pr Rear axle drift angle “f Front axle tire drift angle ar Rear axle tire drift angle Wf Vehicle half-track △ Stheo Theoretical inter-axle slip difference A Ç ^measured Measured inter-axle slip difference & target Target inter-axle speed difference △ & FAJ RA w △ S target Target inter-axle slip difference. vp V JJ4 Front axle speed / Rear axle speed a Front / Rear axle mass distribution coefficient k Electric machine transmission ratio T x en Electric machine torque Si Wheel slip MFAIM Front axle mass / Rear axle mass F FA &t FRA^ Fxfront& p -1 xrear Front axle traction force / Rear axle traction force T cil Corrective torque 0, Wheel angular velocity GVFATt Transfer function between front torque and front axle speed GV^Tt Transfer function between rear torque and rear axle speed

Claims

Demands

1. A method for managing, by means of a computer device (100) embedded in a motor vehicle (1), the operation of at least one chassis actuator (5) of the vehicle, characterized in that the method comprises the steps of: i. obtaining data characterizing the motion dynamics of the vehicle; ii. supplying a determination module (104) of said device with the data characterizing the motion dynamics of the vehicle in order to obtain data characterizing the drift angle of the vehicle, said determination module operating on the basis of a modeling of the motion dynamics of the vehicle which establishes a first state defined by a "super-twisting" type model and a second state defined by a sliding mode observer model; and iii. managing the operation of said actuator according to the data characterizing the drift angle of the vehicle.

2. Method according to claim 1, characterized in that the determination module uses a bicycle model for said modeling.

3. A method according to claim 2, characterized in that the data characterizing the motion dynamics of the vehicle contain data characterizing the drift stiffness of the front wheels, data characterizing the drift stiffness of the rear wheels, data characterizing the mass of the vehicle, data characterizing the moment of inertia of the vehicle, data characterizing the longitudinal speed of the vehicle, data characterizing the yaw rate of the vehicle and data characterizing the wheel angle of the front axle.

4. Method according to claim 1, characterized in that the determination module uses a kinematic model for said modeling.

5. A method according to claim 4, characterized in that the data characterizing the motion dynamics of the vehicle contain data characterizing the transverse speed of the vehicle, data characterizing the transverse acceleration of the vehicle and data characterizing the yaw rate of the vehicle.

6. Device (100) for managing the operation of at least one chassis actuator (5) of a motor vehicle (1), characterized in that the device comprises at least one information processing unit (101), comprising at least one processor, and a data storage medium (102) configured to implement a method according to any one of the preceding claims.

7. Computer program comprising program code instructions for carrying out the steps of a process according to any one of claims 1 to 5 when said program is executed by at least one processor.

8. A medium usable in a computer, characterized in that a program according to claim 7 is stored thereon.

9. Motor vehicle, characterized in that it incorporates a device (100) according to claim 6.

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