GB2440451A

GB2440451A - Longitudinal velocity determination for an automotivevehicle

Info

Publication number: GB2440451A
Application number: GB0715104A
Authority: GB
Inventors: Li Xu; Eric E Tseng
Original assignee: Ford Global Technologies LLC
Current assignee: Ford Global Technologies LLC
Priority date: 2005-06-10
Filing date: 2006-06-07
Publication date: 2008-01-30
Anticipated expiration: 2026-06-07
Also published as: GB2440451B; GB0715104D0

Abstract

A method of controlling a system of a vehicle comprising: generating a plurality of vehicle speed signals 20 and generating vehicle speed in response to the plurality of wheel speed signals; generating a lateral acceleration signal 32; generating a yaw rate signal 28; generating longitudinal acceleration signal 36; determining a stability index 62 in response to the yaw rate signal 28; determining a reference longitudinal velocity 58 in response to the longitudinal acceleration signal 36, the yaw rate signal 28, a pitch attitude and vehicle speed from the plurality of wheel speed sensors 20; determining a second longitudinal velocity in response to the longitudinal acceleration signal 36, the yaw rate signal 28, a lateral velocity, a pitch attitude; determining an output longitudinal velocity from either the reference longitudinal velocity, the second longitudinal velocity in response to the stability index 62; and controlling a vehicle system 46, 48, 50, 52, 54 in response to the output longitudinal velocity.

Description

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LONGITUDINAL VELOCITY DETERMINATION FOR AN AUTOMOTIVE

VEHICLE

The present invention relates generally to a control apparatus for controlling a system of an automotive vehicle in response to sensed dynamic behaviour, and more specifically, to a method and apparatus for controlling the system of the vehicle by determining the longitudinal velocity of the vehicle.

In recent years, various vehicle yaw stability control systems that prevent vehicles from spinning out and drifting using differential braking have been developed. Lateral velocity (or side slip angle) is one of the most important vehicle dynamic variables for these systems and is also crucial for many other chassis control functions. In critical dynamic situations, lateral velocity is necessary to detect and then control an unstable vehicle which may have normal yaw rates. Also in these situations, the longitudinal vehicle velocity cannot be accurately measured by wheel speed because of excessive wheel slip. Hence a successful vehicle dynamics control must involve an accurate determination of the vehicle lateral and longitudinal velocities. Although it is possible to measure vehicle velocities directly by using dedicated measuring devices such as optical sensor and GPS, there are practical issues such as cost, accuracy and reliability that prevent the use of such devices on production vehicles.

The vehicle state estimation algorithms implemented on a production vehicle for vehicle dynamic control purposes are normally based on dead reckoning sensors only, such as wheel/steering encoders and inertia sensors which are utilized to predict the high frequency behavior of the vehicle. The vehicle state estimates may be obtained from a physical vehicle model, or via integration of the inertial sensor signals, or a combination of both. The estimation accuracy, however, can be very crude for a lot of manoeuvres/road conditions, which in turn severely limits the control performance. One reason is that the vehicle model is only effective in the linear region. Another, perhaps more important, reason is that there is simply not enough inertia information. In order to accurately estimate vehicle states in all operating modes, a full six-degree-of-freedom inertial measurement unit (IMU) may be used. A typical IMU consists of three accelerometers and three gyroscopes mounted in a set of three orthogonal axes. The IMU measures the acceleration and the rotation rate of the vehicle in all three dimensions at a high sampling rate, typically at frequencies higher than 100 Hz. From this information, the velocity of the vehicle may be derived via mathematical integration. Vehicle position and heading are generally not observable without external information.

Recent progress in the development of Micro-Electro Mechanical Systems (MEMS) has made it possible to put IMU on production vehicles because of their small size, low cost and ruggedness. The reduction in size and cost, especially cost, however, has also led to a drop in accuracy of the inertial unit as a whole. The predominant error sources in the inertial sensors, whether they are gyros or accelerometers, are bias, scale factors and random walk.

These errors are added up via mathematical integration, and may lead to large drifts in the attitude and velocity estimates, unless external absolute sensors are used to constantly bound the errors.

In practice, all inertia sensing systems are aided in some way by low frequency external sensors, such as global positioning system (GPS), Doppler radar, or star trackers to name a few. Due to the increasing popularity and decreasing cost of GPS, a lot of effort has been devoted to the development of GPS aided inertial systems for vehicle control purpose. While fairly good estimation accuracy may be attained in open sky environment using this approach, the performance deteriorates when the satellite signals bounce off of reflective surfaces such as tall buildings and other structures in the "urban canyon." In the worst case, when fewer than three or four satellites can be "seen" (i.e., driving through a tunnel), the GPS provides no information to bound the errors associated with high frequency inertia sensors. Another disadvantage is that GPS devices are not at all common and/or cost effective on current production vehicles.

Therefore, there is a significant need for a low-cost device that provides accurate and robust estimate of the vehicle lateral velocity and longitudinal velocity.

It is the primary objective of the present invention to provide a methodology of estimating the vehicle longitudinal velocity and lateral velocity of the vehicle. The proposed methodology may use the following sensors: (i) a low-cost strapdown IMU sensor cluster, (ii) a steering wheel angle sensor, (iii) and wheel speed sensors. The method utilizes the kinematic relationship among sensor signals, a bicycle model, and the nonholonomic constraints for a vehicle moving on a surface. The vehicle velocity estimates are obtained via a fusion of the data from all the sensors.

It is another objective of the present invention to provide a technique of qualifying different sensor signals so that they may be fused to accurately estimate the vehicle velocities. A number of criteria are proposed for identifying cases that are not suitable for using one sensor signal but suitable for using others. As a result, the proposed sensing algorithm is robust to sensor bias and noise, vehicle manoeuvres, vehicle parameter variation, road disturbances and the friction coefficient between the tires and the road.

It is yet another objective of the present invention to optimize the vehicle performance for ride, safety and fuel economy by providing an accurate estimate of the vehicle velocity. Even in future vehicle models equipped with standard GPS devices, the proposed methodology is able to help achieve desired performance when sky-obstruction/GPS faults occur.

With a view to achieving these and other objectives, the present invention provides a method of controlling a system of a vehicle comprising: generating a plurality of vehicle speed signals and generating vehicle speed in response to the plurality of wheel speed signals; generating a lateral acceleration signal; generating a yaw rate signal; generating a longitudinal acceleration signal; determining a stability index in response to the yaw rate signal; determining a reference longitudinal velocity in response to the longitudinal acceleration signal, the yaw rate signal, a pitch attitude and vehicle speed from the plurality of wheel speed sensors; determining a second longitudinal velocity in response to the longitudinal acceleration signal, the yaw rate signal, a lateral velocity, a pitch attitude; determining an output longitudinal velocity from either the reference longitudinal velocity, the second longitudinal velocity in response to the stability index; and controlling a vehicle system in response to the output longitudinal velocity.

Other advantages and features of the present invention will become apparent when viewed in light of the detailed description of the preferred embodiment when taken in conjunction with the attached drawings and appended claims.

Figure 1 is a diagrammatic view of a vehicle with variable vectors and coordinator frames according to the present invention.

Figure 2 is a block diagram of a stability system according to the present invention.

Figure 3 is a block diagrammatic view of reference velocity generation according to the present invention.

In the following figures the same reference numerals will be used to identify the same components.

The present invention may be used in conjunction with a dynamic control system of a vehicle such as a rollover control system or a yaw stability control system. However, the present invention may also be used with a deployment device such as an airbag or roll bar. The present invention will be discussed below in terms of preferred embodiments relating to an automotive vehicle moving in a three-dimensional road terrain.

Referring to Figure 1, an automotive vehicle 10 with a safety system of the present invention is illustrated with the various forces and moments thereon during a rollover condition, spinning out condition, and ploughing condition.

Vehicle 10 has front right and front left tires 12a and 12b and rear right tires l3a and left rear tires 13b, respectively. The vehicle 10 may also have a number of different types of front steering systems 14a and rear steering systems l4b including having each of the front and rear wheels configured with a respective controllable actuator, the front and rear wheels having a conventional type system in which both of the front wheels are controlled together and both of the rear wheels are controlled together, a system having conventional front steering and independently controllable rear steering for each of the wheels or vice versa. Generally, the vehicle has a weight represented as Mg at the centre of gravity of the vehicle, where g=9.8m/s2 and M is the total mass of the vehicle.

As mentioned above, the system may also be used with active/semi-active suspension systems, anti-roll bar or other safety devices deployed or activated upon sensing predetermined dynamic conditions of the vehicle.

The sensing system 16 is coupled to a control system 18. The sensing system 16 may use a standard yaw stability control sensor set (including lateral accelerometer, yaw rate sensor, steering angle sensor and wheel speed sensor) together with a roll rate sensor, a pitch rate sensor, and a longitudinal accelerometer. The various sensors will be further described below. The wheel speed sensors 20 are mounted at each corner of the vehicle, and the rest of the sensors of sensing system 16 are preferably mounted directly on the centre of gravity of the vehicle body, along the directions x, y and z shown in Figure 1. It should be noted that less than four wheel sensors may be used such as a three channel system. As those skilled in the art will recognize, the frame from b1, b2 and b is called a body frame 22, whose origin is located at the centre of gravity of the car body, with the b7 corresponding to the x axis pointing forward, b2 corresponding to the y axis pointing off the driving side (to the left), and the b corresponding to the z axis pointing upward. The angular rates of the car body are denoted about their respective axes as w for the roll rate, w, for the pitch rate and a, for the yaw rate.

The present invention calculations preferably take place in an inertial frame 24 that may be derived from the body frame 22 as described below.

The angular rate sensors and the accelerometers are mounted on the vehicle car body along the body frame directions b1, b2 and b3, which are the x-v-z axes of the vehicle's sprung mass.

The longitudinal acceleration sensor is mounted on the car body located at the centre of gravity, with its sensing direction along by-axis, whose output is denoted as a. The lateral acceleration sensor is mounted on the car body located at the centre of gravity, with its sensing direction along b,-axis, whose output is denoted as as,.

The other frame used in the following discussion includes the road frame, as depicted in Figure 1. The road frame system ijr,r3 is fixed on the driven road surface, where the i axis is along the average road normal direction computed from the normal directions of the four tire/road contact patches.

Referring now to Figure 2, a stability control system 18 such as a yaw or roll system is illustrated in further detail having a controller 26 used for receiving information from a number of sensors which may include speed sensors 20, a yaw rate sensor 28, a lateral acceleration sensor 32, a roll rate sensor 34, a vertical acceleration sensor 35, a longitudinal acceleration sensor 36, a pitch rate sensor 37 and steering angle position sensor 38. Sensors 28-38 may be part of an integrated measurement unit 40 or 1W).

In the preferred embodiment the sensors are located at the centre of gravity of the vehicle. Those skilled in the art will recognize that the sensor may also be located off the centre of gravity and translated equivalently thereto.

Lateral acceleration, roll orientation and speed may be obtained using a global positioning system (GPS) . Based upon inputs from the sensors, controller 26 may control a safety device 44. Depending on the desired sensitivity of the system and various other factors, not all the sensors 28-38 may be used in a commercial embodiment. Safety device 44 is part of a vehicle subsystem control. Safety device 44 may control a passive safety device 46 such as an airbag or a steering actuator 48, a braking actuator 50 at one or more of the wheels 12a, 12b, 13a, l3b of the vehicle. Engine intervention 52 may act to reduce engine power to provide a safety function. Also, other vehicle components such as a suspension control 54 may be used to adjust the suspension to prevent rollover.

Roll rate sensor 34 and pitch rate sensor 37 may sense the roll condition of the vehicle based on sensing the height of one or more points on the vehicle relative to the road surface. Sensors that may be used to achieve this include a radar-based proximity sensor, a laser-based proximity sensor and a sonar-based proximity sensor.

Roll rate sensor 34 and pitch rate sensor 37 may also sense the roll condition based on sensing the linear or rotational relative displacement or displacement velocity of one or more of the suspension chassis components which may include a linear height or travel sensor, a rotary height or travel sensor, a wheel speed sensor used to look for a change in velocity, a steering wheel position sensor, a steering wheel velocity sensor and a driver heading command input from an electronic component that may include steer by wire using a hand wheel or joy stick.

The roll condition may also be sensed by sensing the force or torque associated with the loading condition of one or more suspension or chassis components including a pressure transducer in an act of air suspension, a shock absorber sensor such as a load cell, a strain gauge, the steering system absolute or relative motor load, the steering system pressure of the hydraulic lines, a tire lateral force sensor or sensors, a longitudinal tire force sensor, a vertical tire force sensor or a tire sidewall torsion sensor.

The roll condition of the vehicle may also be established by one or more of the following translational or rotational positions, velocities or accelerations of the vehicle including a roll gyro, the roll rate sensor 34, the yaw rate sensor 28, the lateral acceleration sensor 32, a vertical acceleration sensor, a vehicle longitudinal acceleration sensor, lateral or vertical speed sensor including a wheel-based speed sensor, a radar-based speed sensor, a sonar-based speed sensor, a laser-based speed sensor or an optical-based speed sensor.

Steering control 48 may control the position of the front right wheel actuator, the front left wheel actuator, the rear left wheel actuator, and the right rear wheel actuator. Although as described above, two or more of the actuators may be simultaneously controlled. For example, in a rack-and-pinion system, the two wheels coupled thereto are simultaneously controlled. Based on the inputs from sensors 28 through 38, controller 26 determines a roll condition and controls the steering position of the wheels.

Speed sensor 20 may be one of a variety of speed sensors known to those skilled in the art. For example, a suitable speed sensor may include a sensor at every wheel or several of Lhe wheels that is averaged by controller 26.

Preferably, the controller translates the wheel speeds into the speed of the vehicle. Yaw rate, steering angle, wheel speed and possibly a slip angle estimate at each wheel may be translated back to the speed of the vehicle at the centre of gravity. Various other algorithms are known to those skilled in the art. For example, if speed is determined -10 -while speeding up or braking around a corner, the lowest or highest wheel speed may not be used because of its error.

Also, a transmission sensor may be used to determine vehicle speed.

Controller 26 may include a reference velocity generator 58 and a kinematic model velocity generator 60.

Each of the reference velocity generator 58 and the kinematic model velocity generator 60 generate a lateral velocity and a longitudinal velocity. A stability index generator 62 is used by the controller to generate an output corresponding to the lateral velocity and longitudinal velocity of the vehicle as selected using the stability index of the vehicle. That is, the stability index of the vehicle is used to choose the desired lateral and longitudinal velocity from either generator 58 or generator 60. While these functions are provided by controller 26, several controllers may be used to provide the same functions. One, several, or all of the safety devices in the vehicle may use lateral and longitudinal velocity determined by the reference velocity generator and the kinematic model velocity generator 60.

Referring now to Figure 3, a method for determining the lateral and longitudinal velocities is illustrated. In step the various sensors are read. In the following description it is assumed that the sensor (IMU) in this embodiment is placed at the centre of gravity and there is no misalignment with respect to the vehicle body frame.

Using the kinematic relationship between the sensors (IMU output) and the rates of changes of the Euler angles, and assuming that the rate of rotation of the earth is negligible, the state equations in step 82 for vehicle motion may be written as (L) +(w, sin9 +o. *cos9).tan9,, (1) -11 -OyWycosG_(&sin9, (2) G=(wsin91+(o,.cos9).sec91,, (3) =a1 +w v, -wi, *v +g*sin9, , (4) , =a-(o v1 +w v -gsin9.cos91., (5) =a +&, *v -w v, -g*cos9, *cos9,, (6) in which v=[v,v,,v]' represent velocities, (O=[(O,W,,(O]T represent angular velocities, a=[ar,ai,,a]T represent accelerations, all in body frame; 9=[9,9,,9J1 represent the three Euler angles, roll, pitch and yaw, respectively; g is the gravitational constant which is assumed to be known.

Equations (l)-(6) are the fundamental equations that govern the 3-D motion of the vehicle.

For vehicle dynamic control purpose, the Euler yaw angle 0, (or the heading) is not required. As can be seen, the yaw angle 9. does not find its way into the above equations except Equation (3) . Furthermore, since the vehicle is constrained to move on a surface, the vertical velocity v. is normally very small and may be neglected.

Thus the estimation determination is based on the following reduced kinematic equations: 9r =w +(w, *sjn9 +w, *cos9)*tant9,, (7) = CO, cos9 -sin 9, (8) =a+(o. .v+g.sinO, (9) a. -w. v -g sin 9 cos. (1 0) -12 -The vehicle roll angle (9.) and pitch angle (9,) estimates may be obtained via the technique in U.S. patent application 10/752,741, filed January 7, 2004, which is incorporated by reference herein and is assumed to be known here. Theoretically, the vehicle velocities may be computed via mathematical integration of equations (9) and (10) However, in practice, direct integration intends to drift due to sensor bias and inevitable numerical errors.

Absolute sensors such as GPS may be needed to constantly eliminate errors due to gyro integration. It is known to those skilled in the art that Kalman filters provide a way to fuse IMU signals and absolute sensor signals. However, probabilistic information regarding the measurement and process noises is normally required.

As will be seen in this embodiment, the present invention proposes a vehicle velocity estimation method that utilizes the measured accelerations, yaw rate, wheel speed and steering wheel angle to correct acceleration integration. In other words, the IMU/wheel speed/steering wheel angle sensors are used to provide information which is normally provided by absolute sensors such as GPS.

OBTAIN REFERENCE VELOCITIES

In step 84 the vehicle speed is determined from the wheel speed sensors. It is well known to those skilled in the art that wheel speed sensors can provide fairly accurate information about the vehicle speed, especially when wheel slip ratio is small: v = vehicle speed from wheel speed sensors (11) Based on v, and a kinematics model, a vehicle lateral velocity estimate may also be obtained when vehicle yaw rate is not zero. In this invention, the vehicle attitude -13 -estimates (8, O) are used in the kinematics model to reduce the estimation errors. The attitude estimates may be obtained via the technique proposed in U.S. patent application 10/752,741.

When the vehicle has a nonzero yaw rate, i.e., k;I>s, the reference velocities are obtained via the following observer in step 86: ce, (k) = a1Jk) + &(k). V,r(f(k) + g sin 9(k) + k1(v, Vtref)l (12) Vi,rf(k) = aç(k) -w., (k) Vrej*(k) -g -sin 9 (k)cos9 (k) + -Vxrei) (13) where k represents the sampling instance, *r('[ represents reference signals, s represents measured quantities, s is a positive design parameter called a stability index near zero which sets a threshold for yawing of the vehicle, a is a positive design parameter, and the observer gains k1 and k2 are defined as k1 = 2afro, , k, (a2 -I)a. . (1 4) By integration or numerical equivalent the reference lateral and longitudinal velocities may be determined in step 88. The velocity values may be obtained by using any numerical integration schemes, such as the trapezoidal method: (k)+. (k-I) Vret*(k) = Virij (k -I) + re/ 2 u4, (15) .(k)+i' (k-I) v,. (k) = VI,r(,i (k -1) + rr/ 2 vre/ , (1 6) where T is the sampling period. When w.>e and v represents the true vehicle speed, the reference velocities Vrct and Vl,r(,t exponentially converge to the actual longitudinal and lateral velocities, respectively. The -14 -convergence rate may be adjusted by the observer gains k1 and k,.

OBTAIN VELOCITY ESTIMATES BASED ON REFERENCES AND

KINEMATICS MODEL

Note that accuracy of Vrc,i and Vr(j relies on the magnitude of (0 and the accuracy of v. When () is very small, i.e., vehicle slides sideways (laterally), (12) and (13) basically provide no information about the vehicle velocities. As a result, VTref and Vi,ri can no longer converge. When v, is not accurate, i.e., during ABS braking, Vrei and V,rei will converge to wrong values.

Therefore, a second observer which is a discrete-time nonlinear observer in step 90 is proposed to provide additional velocity information when magnitude of w is small and v, is not reliable: Jk) a(k)+(o.(k).,(k)+g.sin(k)+A, (17) (18) where i represents computed quantities, and the adjustment A and, are determined in step 92 as = K (t) . (v -, ( 1 9) = K,(t) (V1 -, (20) in which K and K11, are non-negative tunable observer gains. In step 94 the longitudinal and lateral velocity of the vehicle is determined using the second observer. The velocity values may be obtained by using any numerical integration schemes, such as the trapezoidal method: -15 -(21) 2 (22) It can be seen that when K =K1, =0, the above scheme is equivalent to pure integration. When K1, >0 and K,1 >0, the estimates and), exponentially converge to their references Vrii and respectively. The convergence rate and final accuracy may be adjusted by the observer gains.

The above scheme uses the second observer in Equations (17)-(18) to blend the IMU accelerometer signals with the reference signals. Observer gains K, and K, are determined based on vehicle status. When w>e and v is reliable, the reference signals are normally very accurate and the observer gains may be increased as a rule. In such cases, the reference signals are trusted more and the integrations are trusted less. On the other hand, as v, becomes unreliable or w.j decreases, the reference signals normally are not trustworthy. The tunable observer gains should be reduced so that the estimates rely more on the integrations.

Thus, using the stability index s the more reliable lateral velocity and longitudinal velocity is chosen for an output from the reference velocities and the velocities from the second observer.

There are many variables that may be used to determine the observer gains, i.e., drive torque, steering wheel rate (,), desired yaw rate (&d), measured yaw rate (), desired lateral acceleration (a1), measured lateral acceleration (a1), wheel slip (2), driver brake request, ABS-in-cycle flag, TCS-in-cycle flag, etc. The observer gains can be scheduled by certain fuzzy logics, or in general, by any appropriate functions of these variables, i.e., K(t)=j(S11,2,...), (22) -16 -K11(t)=f(S,2,...). (23)

RESET LATERAL VELOCITY ESTIMATE USING LINEAR TIRE SLIP

ANGLE

In step 96 the linear slip angle of the rear axle is determined. The linear slip angle of rear axle (ar) provides good information about the phase of the lateral velocity, and it may be used to reset the lateral velocity estimate to further improve the estimation accuracy and robustness. ar is obtained from the bicycle model: r =Ma,, -(24) 1 L

-R (25) M g

where F1,. represents the lateral tire force, M is the vehicle mass, L is the yaw moment of inertia, L is the wheel base, and R,,,, is the rear cornering compliance. , and V,,ret are reset to zero using the following logic in step 98: If ar(k)=O, then,(k)=() and V,,r,(k)=O* (26) In step 100, one or all of the safety systems may be controlled using the lateral velocity, the longitudinal velocity or both. iL)

Claims

-17 -Claims 1. A method of controlling a system of a vehicle

comprising: generating a plurality of vehicle speed signals and generating vehicle speed in response to the plurality of wheel speed signals; generating a lateral acceleration signal; generating a yaw rate signal; generating a longitudinal acceleration signal; determining a stability index in response to the yaw rate signal; determining a reference longitudinal velocity in response to the longitudinal acceleration signal, the yaw rate signal, a pitch attitude and vehicle speed from the plurality of wheel speed sensors; determining a second longitudinal velocity in response to the longitudinal acceleration signal, the yaw rate signal, a lateral velocity, a pitch attitude; determining an output longitudinal velocity from either the reference longitudinal velocity, the second longitudinal velocity in response to the stability index; and controlling a vehicle system in response to the output longitudinal velocity.

2. A method as claimed in claim 1, wherein determining an output longitudinal velocity comprises determining the output longitudinal velocity in response to the first observer, second observer, the stability index and the wheel speed from wheel speed sensor.

3. A method as claimed in claim 1 or 2, wherein determining an output lateral velocity and an output longitudinal velocity comprises determining the output lateral velocity and the output longitudinal velocity in -18 -response to the first observer when the yaw rate is above the stability index.

4. A method as claimed in claims 1 to 3, wherein determining an output lateral velocity and an output longitudinal velocity comprises determining the output lateral velocity and the output longitudinal velocity in response to the first observer when the yaw rate is above the stability index and the wheel speed from wheel speed sensor is reliable.

5. A method as claimed in claims 1 to 4, wherein determining an output lateral velocity and an output longitudinal velocity comprises determining the output lateral velocity and the output longitudinal velocity in response to the second observer when the yaw rate is below the stability index or the wheel speed from wheel speed sensor is unreliable.