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 drift angle of the vehicle Technical field of the invention

[0001] The present invention relates to the field of systems embedded on board vehicles for managing their operation while driving. The invention relates in particular to a method for managing, by a computer device embedded on board 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 applies to motor vehicles such as land motor vehicles, in particular cars. State of the prior art

[0002] It is known that the drift angle of a vehicle directly determines the stability of the vehicle while driving, in other words its road holding. Therefore, when we want to develop efficient and safe driving assistance systems, it is necessary to be able to take into account this crucial parameter, the drift angle. However, until now, the drift angle of a vehicle has generally been determined by equipping the vehicle with advanced detection means, for example several GPS antennas, wheel speed sensors and / or an inertial unit. However, various uncertainties affect the estimation of the drift angle, in particular the noise of the sensor measurements. To remedy this, manufacturers have been led to develop estimation methods for estimating the state variables of a system on the basis of measurements obtained using the sensors.However, nonlinear systems are subject to uncertainties, disturbances, and measurement noise, which makes the problem of state estimation challenging. State estimation methods based on sliding mode control have been widely studied in the scientific literature. These methods are robust to uncertainties and disturbances, but they may suffer from chattering phenomena, which can lead to performance degradation. On the other hand, conventional observation methods, such as the Kalman filter or its variants, provide 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 more precisely the drift angle of a vehicle. automobile while driving. More specifically, the invention aims to provide a method and a system that solve the problem of estimating the state(s) of time-varying and / or non-linear dynamic systems that are subject to uncertainties, disturbances and measurement noise. By this means, the invention aims to contribute to the provision of more efficient and safer motor vehicle driver assistance systems.

[0004] To achieve these objectives, the invention relates, according to a first aspect, to a method for managing, by a computer device on board a motor vehicle, the operation of at least one chassis actuator of the vehicle, the method comprising the steps of: i. obtaining data characterizing the dynamics of movement of the vehicle; ii. feeding a determination module of said device with the data characterizing the dynamics of movement 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 dynamics of movement of the vehicle which establishes a first state defined by a type model “super-twisting” and a second state defined by a sliding mode observer model; and iii. manage the operation of said actuator based on data characterizing the drift angle of the vehicle.

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

[0006] According to another variant, the data characterizing the movement 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 angle at the wheels 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 dynamics of movement 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 least one processor, and a data storage medium configured to implement a method as described above.

[0010] According to a third aspect, the invention relates to a computer program comprising program code instructions for executing the steps of a method 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 has a device as described above. Brief description of the figures

[0013] Other characteristics and advantages of the invention will appear on examining the detailed description below, and the appended 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 method according to the invention. Detailed description of the invention

[0017] [Fig.l] schematically illustrates a motor vehicle 1 according to the invention. This comprises 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 below. When implementing the method, the device 100 according to the invention firstly obtains data characterizing the dynamics of movement of the vehicle, by interacting to do this by means of a wired communication network of the vehicle (e.g. CAN, Ethernet) - materialized by the two-way arrows - with, for example, a steering wheel angle sensor 2 (or front wheel angle sensor), a yaw sensor 3 or an inertial unit 4 arranged in the vehicle.And it is from these data alone that the device 100 according to the invention advantageously determines data characterizing the drift angle of the vehicle, according to which it manages the operation of at least one chassis actuator 5 of the vehicle. This is how the provision of more efficient and safer driving assistance systems for motor vehicles is made possible.

[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 which comprises at least one information processing unit 101, comprising one or more processors, a support for data storage 102, on which is recorded in particular a program which comprises program code instructions for the execution of the steps of the method according to the invention described later, and an input and output interface 103 allowing the reception and transmission of data. Advantageously, the device 100 according to the invention also comprises a determination module 104, which, as will be seen in more detail below, operates on the basis of a modeling of the movement dynamics of the vehicle which, 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 hosted on an independent computer and it interacts via its input and output interface 103 and by means of a wired communication network of the vehicle (e.g. CAN, Ethernet) - shown in [Fig.l] by the two-way arrows - with the steering wheel angle sensor 2, the yaw sensor 3 and the inertial unit 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 driving 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 connection with [Fig.3].

[0021] [Fig. 3] illustrates by means of a flowchart the steps of the method according to the invention. According to a first step 301 of the method according to the invention, the device 100 according to the invention obtains data characterizing the dynamics of movement 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 wheel angle sensor 2, the yaw sensor 3 and / or the inertial 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 method contains 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] Then, according to a second step 302 of the method according to the invention, the device 100 according to the invention feeds its determination module 104 with the data obtained during the previous step 301 in order to obtain data characterizing a drift angle of the vehicle. As will be seen, the determination module 104 advantageously operates on the basis of a modeling of the movement dynamics of the vehicle which establishes a first state defined by a “super-torsion” type model and a second state defined by a sliding mode observer model. Indeed, sliding mode observers are characterized by their robustness with respect to a category of disturbances and by the finite-time convergence of the sliding surface towards zero. Finite-time convergence is an advantageous attribute for sliding mode estimators, because it allows 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 broken down into two parts by means of two equations which define the generalized dynamics of a time-varying system, namely: x ] = ax ] + a^t). x2 + b-^.u (1) x2 = a3 ( t ) xj + a4 ( t ) jc2 + b£t).u (2) where Xj, x^ e are the states, ue is the control input, VG X. is the measured output. The time-varying nonlinear functions a^ / ) ( / =1, ...4), bj(t) (j = 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 modeling such that: iro (3) ^ = 6^(7)..^ + (72(0-0 + 0(0-^ + ^10 -O 2 +0p x2 = a3(t)jcï + a4(t).x2 + b2(t).u + l3:i'\ d\ 0 d=^*l2hign(S)dr with [ x] — |x|0zgn(x) and S — xl-xi = e}

[0025] The gains of the estimator / and l3 > 0 must all be positive. They are determined by solving a set of linear matrix inequalities (LMI).

[0026] The dynamics of estimation errors are defined as: 1 r Q (y) èï = a1(t)£1 + a-1(t)£2-L'*[ q] 2 -Zj [ ej dr “o

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

[0028] The error (the sliding surface) and its first derivative converge to zero in finite time, i.e. x2 ) “* ( X], X^ ) • Which gives the following relation: “2(0-e2 = 4*î[ ^11 From which we obtain ■ Jr 1, <12) = eiJ dT = d We therefore obtain e2 = 0.

[0029] Now considering that (4) is subtracted from (12), we have: e2 = a4(f).e2-4*[ ^2! ° ^3)

[0030] Here we rely on the fact that = 0 and on the result of ( 12 ). The design of the observer is based on the theory of Lyapunov stability, considering a candidate Lyapunov function such that: (14) V = e2% (15) V = e2 * (“4(0 £2 - h *$ign ( e2 ) ) (16) Finally V = - )<0 (17) When L > Ia* ( t) Je-, 11 ■ Therefore, (g — 0 converges in finite time.

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

[0032] Furthermore, this modeling on the basis of which the determination module 104 operates advantageously makes it possible to estimate the drift angle of different types of vehicles, including passenger cars, trucks and buses, in different driving conditions, in particular in a straight line, in bends and during emergency maneuvers.

[0033] According to a first embodiment, the determination module 104 uses a bicycle model. We therefore have: And on the other hand, A (21)

[0034] According to a second embodiment, the determination module 104 uses a kinematic model. We therefore have: «V = Vy.( - V') = ( ) ay = VA, (0 + tp) = (Vv + yxvx) (24) (25) where ax and ay are the longitudinal and transverse accelerations.

[0035] The state representation of the model of the system to be observed is then written: y=cMi o][[;]. (27) with the vehicle speed, ax the acceleration on the longitudinal axis, v>' the lateral speed of the vehicle, ay the lateral acceleration and V' or} the yaw rate.

[0036] The kinematic model does not explicitly involve the lateral forces of the trains, which reflects a strong assumption on the model of the tire chosen during an analysis of the dynamic behavior of a vehicle. The strength of this model lies in its use of easily accessible quantities, provided by sensors present on all vehicles, in particular inertial units, wheel speed sensors and GPS.

[0037] We thus obtain: a / t) = 0, a2(0 ~ V' (28) a3(f) = - ÿy a4(t) = 0 (29)

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] ^1(0= U 0], Also, we have: AAAA 1^ = ^, Vy=X2 Xj = VY, X2 ~ Vv and e2 = x2-X2 = eVy = vy - Oy ^) = [0 1] «y J And er = x}-x}- eVx = vx-vx The drift angle observer using the kinematic model is therefore written: Â ■ - ro = + + erl 2 +12] [ ^.1 (r)dr b laughed 0 “ V'L+ ay + / 3 al t \r)dr d=~--— u ip With [ P-\x\Psign(x) (30) (31) (32) (33) (34) (35) (36) (37) The sliding mode dynamics of the velocity error is obtained using the relationship below ft = ÿeVy + ax - Zj * [ eVx ] 2 - Z2J[ eVx ] 0dr (38) The trajectory of the error ei and its first derivative Zq converge towards the origin in finite time. This allows us to deduce tpeVv = J °dr “0 It follows: Çl[ «iJ n(r)dr ey = ~---7----- (39) (40) This correction term is used in the observer's equation (Eq. (35)), knowing that dd 0 eVy= - igeVx-i3 \ (41)

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

[0045] Equation (41) therefore becomes è,,= - / 3*f 0 («) when / 3>0, —> = = a (44)

[0046] We can thus deduce the value of the state vector, i.e. the longitudinal and lateral speeds. Once the two components of the speed (the longitudinal speed and the lateral speed) have been estimated, the estimate of the drift angle is obtained by (small angle approximation): fi -arctan(~^ (45)

[0047] Tests have demonstrated the effectiveness of these models implemented by the determination module 104 to estimate the longitudinal and lateral speeds \ v> and the drift angle of the vehicle, with a maximum error between the measurement and the estimation of the order of 0.5 m / s. It has also been observed that these models are more efficient than those based on other unrelated filters (UKF: unscented Kalman filter and the PF: particle filter). In addition, these models are also easier to implement. The advantage of these models (observer by sliding mode) making it possible to know the drift angle in real time is reflected in the fact 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 compensates for the dynamics of the system.

[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 as a function of 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 the device according to the invention described above, a solution is provided for being able to determine more precisely the drift angle of a motor vehicle during driving. Thanks to these means, the invention thus advantageously contributes to the provision of more efficient and safer motor vehicle driving assistance systems in all types of vehicles.

[0050] All variables used in the foregoing description are identified below for clarity. ôf Angle at the front axle wheels Sr Angle at the rear axle wheels VBr / Yaw rate achieved by the front steering system ^Brr Yaw rate achieved by the rear steering system wTV Yaw rate achieved by torque vectoring m Vehicle mass / Vehicle wheelbase h Height of the center of gravity at the point of contact of the wheels with the ground g Gravitational constant gjg2 Corrective adaptive gain SMC Yaw rate corresponding to a given trajectory ax Line acceleration of the vehicle ay Transverse acceleration of the vehicle Csf Longitudinal rigidity of the front axle c ^sr Longitudinal rigidity of the rear axle ^dyn Dynamic radius of the wheel Jtôt J Jtôt2 Total inertia of the front axle / Total inertia of the rear axle J eqt / Jeq^ Equivalent inertia of the front axle / Equivalent inertia of the rear axle JT AVATAR Inertia of the front axle / Inertia of rear axle JmAVl JmAK Front machine inertia / Machine inertiarear Moment of inertia of the vehicle w ' meas Yaw acceleration measured by the inertial unit WJ ! w des' ^meas Desired yaw rate / Measured yaw rate a^lf Distance between the center of mass and the front axle a 2 ** lr Distance between the center of mass and the rear axle Front wheel drift stiffness Car Rear wheel drift stiffness K <-> ç ^sat ^max Maximum slip Vx Longitudinal vehicle speed Ky Transverse vehicle speed Vmoy Average inter-axle speed vARext Speed ​​of the outside wheel of the rear axle JJJy Longitudinal jerk / Lateral jerk s Laplace variable fi Vehicle drift angle h Front axle drift angle Pr Rear axle drift angle «f Front axle tire drift angle a,.Rear axle tire drift angle Wf Vehicle half-track ^theo Theoretical inter-axle slip deviation A ^measured Measured inter-axle slip deviation AV target Target inter-axle speed deviation A SpA / RA A Starget Target inter-axle slip deviation ^FA^RA Front axle speed / Rear axle speed a Axle mass distribution coefficient . KN / PA k Electric machine transmission ratio T en Electric machine torque Si Wheel slip mfa!Mra Front axle mass / Rear axle mass FFA ^RA ^xfront and Fxrear Front axle traction force / Rear axle traction force Tcu Curative torque Oi Wheel angular velocity GVFATt Transfer function between front torque and front axle speed GVRATt Transfer function between rear torque and rear axle speed

Claims

Claims

1. Method for managing, by a computer device (100) on board 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 movement dynamics of the vehicle; ii. feeding a determination module (104) of said device with the data characterizing the movement 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 movement 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 as a function of 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. Method according to claim 2, characterized in that the data characterizing the dynamics of movement 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 angle at the wheels of the front axle.

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

5. Method according to claim, characterized in that the data characterizing the dynamics of movement 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 actuator chassis (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. A computer program comprising program code instructions for executing the steps of a method according to any one of claims 1 to 5 when said program is executed by at least one processor.

8. Support usable in a computer, characterized in that a program according to claim 7 is recorded therein.

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

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