JP3441572B2 - Vehicle tire / road surface friction state estimation device - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vehicle tire / road surface friction state estimation device suitable for use in a turning behavior control device for controlling the turning behavior of a vehicle such as a four-wheel steering system.

[0002]

2. Description of the Related Art Conventionally, there has been a four-wheel steering vehicle in which four wheels are steered actively in response to changes in running conditions. Some vehicles use a function of a steering angle and a vehicle speed, and are provided with means for estimating a friction coefficient between a tire and a road surface according to an output of a cylinder internal pressure sensor of a power steering device.

In the conventional four-wheel steering vehicle described above, when a standard yaw rate uniquely set according to one tire and one road surface condition is set, the tire and road surface conditions change significantly with respect to the set conditions ( For example, if the standard tires are replaced with high-performance tires or studless tires, or when driving on a dry road on rainy or snowy roads, it is difficult to realize the optimum response characteristics corresponding to those changes. Therefore, if various sensors capable of discriminating each state are added, the price will increase and the weight will increase.

[0004]

Instead of providing the various sensors described above, a yaw rate sensor used in a turning behavior control device for controlling the turning behavior of a vehicle such as a four-wheel steering control device is used. The frictional state between the tire and the road surface (for example, friction coefficient) can be indirectly estimated. However, the response characteristic of the vehicle is represented by a high-order (at least second-order or higher) transfer function even in the linear region, and changes depending on the vehicle speed. Therefore, the algorithm for identifying the response characteristic of the vehicle becomes complicated, and the capacity and calculation capacity of the ECU used for the four-wheel steering control must be greatly increased. Not only does the device soar, The transfer function does not directly represent the road surface state, and there is a problem that some calculation must be added.

[0005]

SUMMARY OF THE INVENTION In order to solve such a problem and to realize that the frictional state between a tire and a road surface can be obtained without significantly increasing the control capacity and the calculation capability, the present invention is implemented. That is, a yaw rate sensor that detects the yaw rate of the vehicle and outputs an actual yaw rate signal, and a standard yaw rate signal that will appear based on at least the steering angle of the steering wheel and the vehicle speed when traveling on a standard road surface with standard tires. An estimated yaw rate signal that is calculated and output based on an estimated parameter that estimates a variation due to a frictional state between the mounted tire and the traveling road surface from the standard yaw rate output unit that outputs the standard yaw rate signal and the actual yaw rate signal. A calculator, the standard yaw rate signal, the actual yaw rate signal, and the estimated yaw rate signal. It was assumed to have an estimation parameter identifying unit for estimating the friction state to identify the estimated parameters based on and. In particular, the standard yaw rate signal and the actual yaw rate signal is output to the estimated yaw rate output section and the estimated parameter identification section through a low-pass filter, or, a constant is obtained from the estimated friction state, The standard yaw rate signal is reduced in accordance with the constant, and a friction state constant setting unit that changes the decrease of the constant stepwise according to an identification parameter for identifying the estimated parameter is provided. It is preferable to control the steering device that decreases the standard yaw rate signal according to the constant and controls the turning angle of the wheel so that the reduced standard yaw rate signal matches the actual yaw rate signal.

As described above, the estimated parameter is obtained from the standard yaw rate signal and the actual yaw rate signal, the estimated yaw rate signal is output, and the estimated parameter is identified based on the standard yaw rate signal, the actual yaw rate signal, and the estimated yaw rate signal. The friction state can be estimated without providing a separate sensor and without having to obtain a high-order transfer function.

[0007]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below in detail based on specific examples shown in the accompanying drawings.

FIG. 1 schematically shows the overall structure of a front and rear wheel steering device for a vehicle to which the present invention is applied. A steering shaft 2 having a steering wheel 1 fixed at one end is mechanically connected to a steering rod 4 of a front wheel steering device 3. Both ends of the steered rod 4 are connected to respective knuckle arms 6 supporting the left and right front wheels 5 via tie rods 7, respectively.

A rear wheel steering device 8 arranged at the rear of the vehicle
The steering rod 9 extending in the vehicle width direction is driven by an electric motor 10 as an actuator. Both ends of the steered rod 9 are connected to knuckle arms 12 that support the left and right rear wheels 11 via tie rods 13, similarly to the steered rod 4 on the front wheel 5 side.

The front and rear steering devices 3 and 8 are equipped with steering angle sensors 14 and 15 in order to detect the positions of the steering rods 4 and 9 and measure the steering amounts of the wheels 5 and 11. Has been done.
A steering angle sensor 16 for detecting the steering amount of the steering wheel 1 is attached to the steering shaft 2. In addition to this, each wheel 5/11 has
A vehicle speed sensor 17 is provided for each vehicle,
A lateral acceleration sensor 18 and a yaw rate sensor 19 are provided.

Each of these sensors 14 to 19 is electrically connected to a computer unit 20 for driving and controlling the electric motor 10.

In this device, the steering wheel 1
When the driver steers the vehicle, the steering rod 4 of the front wheel steering device 3
Are mechanically driven to steer the front wheels 5. At the same time
The steering amount of the steering wheel 1 and the movement amount of the steered rod 4 are input to the computer unit 20 via the steering angle sensors 14 to 16, respectively. Then, the computer unit 20 determines the optimum turning amount of the rear wheels 11 based on the running conditions of the vehicle obtained based on the input values of the front wheel turning angle, the vehicle speed, the lateral acceleration, and the yaw rate, and drives the electric motor 10. The rear wheels 11 are steered.

Next, control in the computer unit 20 will be described with reference to FIGS. 2 and 3.

FIG. 2 is a modeled block diagram for obtaining the yaw rate in an actual vehicle. In the figure,
The same actual yaw rate signal γ as the yaw rate signal detected and output by the yaw rate sensor 19 when the vehicle is traveling.
Is obtained by the actual vehicle yaw rate response unit 21 based on the respective signals of the steering angle θ _h of the steering wheel 1 and the rear wheel steering angle δ _r.
Can be configured by the standard yaw rate output unit 22 and the actual traveling variable element 23, assuming that the yaw rate when traveling on the standard road surface with the standard tire mounted varies depending on the road surface condition and the tire difference. Modeled in this way,
The standard yaw rate signal u that will appear in the standard running state (standard tires / standard road surface) is calculated based on the signals of the steering angle θ _h of the steering wheel 1 and the rear wheel turning angle δ _r that changes depending on the vehicle speed. And the actual yaw rate signal γ is obtained based on the standard yaw rate signal u through the actual traveling variation element 23. Therefore, conversely, the standard yaw rate signal u and the actual yaw rate signal γ are obtained.
It can be seen from the above that the actual traveling variation element 23 can be obtained. Although seeking in the wheel steering angle [delta] _r after which varies by _h and the vehicle speed theta steering angle since the standard yaw rate output unit is a front and rear wheel steering system, the steering angle theta _h and vehicle speed in a vehicle steering only the front wheels You can ask for it only.

In the present invention, the frictional state between the road surface and the tire is determined based on the steering angle θ _{h, the} rear wheel turning angle δ _r, and the actual yaw rate signal γ detected by the yaw rate sensor 19. A modeled block diagram is shown in FIG. FIG. 3 shows a yaw rate identifying section 24 as means for identifying the actual vehicle yaw rate responding section 21 shown in FIG.
The yaw rate identifying section 24 includes a standard yaw rate output section 22 similar to the above, an estimated yaw rate calculation section 25 to which the standard yaw rate signal u output from the standard yaw rate output section 22 is input, and an estimated parameter. The identification unit 26 is included.

Next, the operation procedure of the yaw rate identifying section 24 will be described below with reference to the flow chart of FIG.
First, the steering angle θ _h is read in the first step ST1, the rear wheel turning angle δ _r is read in the next second step ST2, and the vehicle speed V is read in the next third step ST3. Then, in a fourth step ST4, the standard yaw rate u in a state where the standard tire travels on the standard road surface is calculated. The calculation method of this standard yaw rate u is shown below.

First, the two-wheel model transfer function

[0018]

[Equation 1] Than,

U = P _f (s) · θ _h + P _r (s) · δ _r (2)

Can be expressed as Where M _s is the vehicle weight, J _z is the yaw moment of inertia, N _f is the steering gear ratio, and L _f and L
_r is the distance from the front and rear wheels to the center of gravity, K _f and K _r are the cornering powers of the front and rear wheels, V is the vehicle speed, L =
L _f + L _r and S are Laplace operators.

When the equation (2) is discretized by a bilinear transformation,

U (nT) = B ₀ (V) · (θ _h (nT) + θ _h (n−1 · T) + B ₂ (V) · (θ _h ((n-1) · T) + C ₀ (V) ・ (S _r (nT) + δ _r ((n-1) ・ T) + C ₂ (V) ・ (δ _r ((n-1) ・ T)) + δ _r ((n -1) ・ T)) + A ₁ (V) ・ u ((n-1) ・ T) + A ₂ (V) ・ u ((n-2) ・ T)… (3)

Can be expressed as Where the parameters A ₁ , A ₂ ,
B ₀ , B ₂ , C ₀ and C ₂ are map parameters depending on the vehicle speed.

Then, the actual yaw rate signal γ is read in the fifth step ST5, and in the next sixth step ST6, the estimated value of the yaw rate fluctuation is calculated using the estimation parameter.

[0025]

[Outer 1]

The estimated yaw rate calculator 25 calculates Here, if the yaw rate fluctuation γ (nT) can be expressed by a linear expression,

[0027]   γ (nT) =-a ・ γ ((n-1) ・ T) + b ・ u ((n-1) ・ T) (4)

[0028] The parameters a and b are estimated by the weighted least squares method based on the equation error. The sequential identification model for equation (4) is

[0029]

[Equation 2]

Can be expressed as

In the seventh step ST7, it is determined whether or not the state is the identifiable state, and if it is the identifiable state, the process proceeds to the eighth step ST8. In the eighth step ST8, the estimated parameters

[0032]

[Outside 2]

The estimation parameter identification unit 26 calculates the identification gain of, and in the next ninth step ST9, the estimation parameter is calculated.

[0034]

[Outside 2]

The identification gain of is updated.

As the sequential identification logic performed in the eighth step ST8 and the ninth step ST9, in the above equation (5),

[0037]

[Equation 3]

Putting this, the identification error ε (nT) is

[0039]

[Equation 4]

It becomes When ε (nT) → 0

[0041]

[Outside 3]

An identification rule of the form of the following equation (8) is used (weighted least squares method).

[0043]

[Equation 5]

Here, ρ is a forgetting factor indicating how much importance is attached to the past value, and 0 <ρ = <1. Therefore, from equation (8),

[0045]

[Outside 4]

Is calculated.

Then, in the tenth step ST10 after passing through the ninth step ST9, processing for proceeding to another block to be switched is performed. If it is determined in the seventh step ST7 that the identification is not possible, the eleventh step S
Proceed to T11, in which case the estimated parameters

[0048]

[Outside 2]

The identification gain of is held, and the process proceeds to the tenth step ST10. In this way, the processing according to the block diagram shown in FIG.

[0050]

[Outside 2]

The parameters of the 4WS control compensator (standard yaw rate, etc.) are switched according to the value of.

In the above flow, the standard yaw rate u is set between the fourth step ST4 and the fifth step ST5.
It is advisable to perform the filter calculation with respect to (1) using the filter 27 shown by the imaginary line in FIG. The filter 27 is a low-pass filter (2) for avoiding a mismatch between the standard yaw rate u and the actual yaw rate γ in a high frequency region (fast steering).
The next Butterworth filter) is good.

The continuous-time domain transfer function of the low-pass filter is

[0054]

[Equation 6]

It becomes In Expression (9), ω _n = 2π
f _n and f _n are cutoff frequencies. Then, when the input is u and the output is u ′, and the equation (9) is discretized by the bilinear transformation,

U ′ (nT) = β · (u (nT) + 2u ((n-1) · T) + u ((n-2) · T) + α ₁ · u ′ ((n-1) · T) + Α ₂ · u ′ ((n-2) · T) (10)

It becomes The fifth step ST5 and the sixth step
Between step ST6 and the actual yaw rate γ, the filter calculation may be performed by the filter 28 shown by the imaginary line in FIG. The case of this actual yaw rate γ is similar to the above, and u is replaced by γ and u ′ is replaced by γ ′ in the equation (10).

In this way, the standard yaw rate changes according to the conditions of the tires and the road surface, so that the disturbance (cross wind
The split μ braking) suppression function is improved, and the yaw rate deviation is reduced in general driving, so 4WS
The rear wheel does not move unnecessarily, discomfort is eliminated, and control characteristics are improved.

Further, as an individual feature, it can be configured with an algorithm simpler than identifying all the vehicle models. Further, the identification algorithm portion can be configured by an algorithm that does not depend on the vehicle speed. Further, the parameters to be identified are the parameters corresponding to the gain ratio in the case of the 0th-order model and the gain ratio and the cutoff frequency in the case of the 1st-order model. The state can be determined directly. These effects can reduce the calculation load and ROM / RAM capacity required for the control device.

A vehicle response signal such as lateral G may be used instead of the yaw rate signal. In addition to 4WS control, for example, ABS (antilock brake) and TCS
It can be applied to each control of (traction control) and 4WD (four-wheel drive), and can also be applied to switching of control parameters of devices other than 4WS control and alarms.

Further, the identification logic of FIG. 3 is not particularly limited, and a least square method, a maximum likelihood method, a Kalman filter, or the like can be used. Also, the identification parameters of Fig. 2

[0062]

[Outside 2]

May be a vector.

By the way, the standard yaw rate output unit 2 of FIG.
In the situation where the accuracy of the model of No. 2 deteriorates, the identification accuracy of FIG. 2 also deteriorates. Therefore, when such a situation can be detected and determined in advance (when proceeding from the seventh step ST7 to the eleventh step ST11), the identification logic of FIG.

[0065]

[Outside 2]

Hold. Such cases include low speed, large steering angle, and high G range. In addition, since the identification accuracy deteriorates even in the straight traveling state (small steering angle), the same processing may be performed.

Further, when it is difficult to determine the deterioration of the identification accuracy, the deterioration of the identification accuracy is prevented by preprocessing the external signals necessary for the identification by using the filters 27 and 28 as described above. To do. A low pass filter may be used in the high frequency region, and a high pass filter may be used in the extremely low frequency region.

Next, an example of the road surface condition determination logic will be described below with reference to FIGS. 5 and 6. FIG. 5 shows a change in the standard yaw rate u when traveling on standard tires and a standard road surface by a solid line, and shows an actual yaw rate γ when the vehicle is actually traveling and in a traveling condition almost equal to the standard state. . Then, the determination is made in real time. If the standard yaw rate u is M and the actual yaw rate γ is Y1 at a certain timing, Y1 / M≈1 in the figure,
It can be determined that the road is dry.

FIG. 6 shows the actual yaw rate γ when traveling on a snow-covered road by an imaginary line. In this case, if the standard yaw rate u is M and the actual yaw rate γ is Y2 at a certain timing, Y2 / M << 1. Then, by dividing each ratio between the case of the standard traveling state and the case of the snow-covered road traveling state into a plurality of stages, various road surface conditions can be determined, and in the case of FIG. 6, it can be determined that the road is a snow-compacted road.

Further, the adaptive target yaw rate characteristic when the lane is changed is shown below with reference to FIGS. 7 and 8. In the figure, the case of traveling on a dry road (standard road surface) is shown by a solid line, and the case of traveling on a snow-covered road is shown by an imaginary line. When the ratio of the yaw rate value S during actual running to the yaw rate value D when running on a standard road surface is S / D = 1, it means that the vehicle is running on dry roads, and when S / D = 0. It becomes a snowy road.

Next, the road surface determination logic will be described below with reference to the block diagram of FIG. In FIG. 7, the steering angle θ
_h is input to the F / F (feed forward) compensator 31 and the standard yaw rate calculation unit (having the same function as the standard yaw rate output unit 22) 32, and the vehicle speed V is also the F / F compensator 31. And the standard yaw rate calculator 32, respectively. Also, the output δ _{FF of} the F / F compensator 31
Is input to the standard yaw rate calculator 32.

The output u of the standard yaw rate calculation unit 32 is input to the road surface state determination logic unit 33, the actual yaw rate γ is input to the road surface state determination logic unit 33, and the output u and the output u of the road surface state determination logic unit 33 are input.

[0073]

[Outside 2]

Is input to the target yaw rate calculation section 34 as a friction state constant setting section. The output of the target yaw rate calculator 34 is subtracted by the actual yaw rate γ by the comparator 35, and the result u ₀ is input to the F / B (feedback) compensator 36. The vehicle speed V is also input to the F / B compensator 36, and the result of adding the output δ _{FB of} the F / B compensator 36 and the output δ _{FF of the} F / F compensator 31 by the adder 37 is the rear wheel rolling. It is designed to be output as the steering angle δ _r .

In the block diagram of FIG. 7, the standard yaw rate calculation unit 32 and the road surface state determination logic unit 33 correspond to the yaw rate identification unit 24, and the target yaw rate u is calculated from the output value of the road surface state determination logic unit 33. The target yaw rate calculation unit 34 performs the calculation.
Refer to. The road surface state determination logic unit 33 determines a road surface state (for example, from a standard paved road to a snowy road) from the standard yaw rate u and the actual yaw rate γ, and an estimated gain according to the road surface state.

[0076]

[Outside 2]

The target yaw rate calculation unit 34 determines
Estimated gain from the map as shown in FIG.

[0078]

[Outside 2]

Based on the above, the estimation constant k is obtained. In addition, in FIG. 8, k = 1 corresponds to a standard paved road, and the minimum value of k corresponds to a snow-covered road.

Then, in the target yaw rate calculation section 34,
Using k obtained from the map shown in FIG. 8,

[0081]   u ← u × k (11)

As a result, the target yaw rate u is calculated.
In this way, active control can be performed using the road surface state determination logic.

When the lane is changed on a dry road, the adaptive target yaw rate changes as shown by the solid line in FIG. 9 to speed up the responsiveness in the initial stage and improve the turning performance in the lane to which the lane has been changed to quickly re-establish the lane. In order to achieve this yaw rate characteristic, the convergence is finally improved.
Steer the rear wheels as indicated by the solid line.

Next, in the lane change on the snow-covered road, the adaptive target yaw rate changes as shown by the imaginary line in FIG. 9, and the stability is improved in the initial stage to move smoothly to the lane change operation. In order to achieve this yaw rate characteristic, in order to realize this yaw rate characteristic, the rear wheels are made to move in a stable manner by ensuring the translational movement and stable lateral movement. Steer.

In this way, the road surface condition can be determined, and the adaptive target yaw rate can be switched according to the result of the road surface condition determination to obtain optimum steering stability characteristics under various conditions.

[0086]

As described above, according to the present invention, a standard tire (normal tire) is changed to a high-performance tire or a snow road tire, or a standard road surface (dry road) is changed to a gravel road or a snow road. It can respond to changes in the friction between the tires and the road surface, and can always obtain stable steering characteristics. For example, when the cornering force generated by the tire is small on a low μ road or the like, the actual yaw rate does not reach the standard (normative) yaw rate, so the steering amount is increased as it is. The friction state between them can be estimated, and control can be performed to prevent the steering from being turned to a transient state. Further, by changing the constant only when the frictional state changes significantly, it is possible to improve the reproducibility of the steering angle without following a small change, and the driver does not feel uncomfortable.

[Brief description of drawings]

FIG. 1 is a plan view schematically showing the overall configuration of a front and rear wheel steering device for a vehicle to which the present invention is applied.

FIG. 2 is a modeled block diagram for obtaining a real yaw rate signal γ.

FIG. 3 is a modeled block diagram for determining a frictional state between a road surface and a tire.

FIG. 4 is a flowchart of control according to the present invention.

FIG. 5 is a diagram showing an example of a dry road of a road surface condition determination logic.

FIG. 6 is a diagram showing an example of a snow-covered road of a road surface condition determination logic.

FIG. 7 is a block diagram using a road surface determination logic.

FIG. 8 is a diagram corresponding to the map used to perform the target yaw rate calculation.

FIG. 9 is a diagram showing an adaptive target yaw rate.

FIG. 10 is a diagram showing a rear wheel steering characteristic.

[Explanation of symbols]

1 steering wheel 2 steering shaft 3 front wheel steering device 4 Steering rod 5 front wheels 6 knuckle arms 7 Tie rod 8 Rear wheel steering device 9 Steering rod 10 electric motor 11 rear wheels 12 knuckle arms 13 Tie rod 14.15 Rudder angle sensor 16 Rudder angle sensor 17 vehicle speed sensor 18 Lateral acceleration sensor 19 Yaw rate sensor 20 computer units 21 Actual vehicle yaw rate response section 22 Actual driving fluctuation factors 23 Standard yaw rate output section 24 Yaw rate identification section 25 Estimated yaw rate calculator 26 Estimated Parameter Identification Unit 27 ・ 28 filter 31 F / F compensator 32 Standard yaw rate calculator 33 Road condition judgment logic 34 Target Yaw Rate Calculator 35 comparator 36 F / B compensator 37 adder

─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Manabu Iketani 1-4-1 Chuo, Wako-shi, Saitama Incorporated in Honda R & D Co., Ltd. (56) References JP 62-137276 (JP, A) JP Heihei 7-96848 (JP, A) JP 4-204349 (JP, A) JP 3-248966 (JP, A) JP 6-286630 (JP, A) (58) Fields investigated (Int. Cl. ⁷ , DB name) B62D 7/14 B62D 6/00 B60T 8/58

Claims (3)

(57) [Claims] 1. A yaw rate sensor that detects a yaw rate of a vehicle and outputs an actual yaw rate signal, and a standard yaw rate that is displayed based on at least a steering angle of a steering wheel and a vehicle speed in a state where a standard tire is traveling on a standard road surface. A standard yaw rate output unit that outputs a signal, and calculates and outputs an estimated yaw rate signal based on an estimated parameter that estimates the variation due to the frictional state between the mounted tire and the road surface from the standard yaw rate signal and the actual yaw rate signal. For a vehicle, comprising: an estimated yaw rate calculation unit; and an estimated parameter identification unit that identifies the estimated parameter based on the standard yaw rate signal, the actual yaw rate signal, and the estimated yaw rate signal to estimate the frictional state. Tire / road surface friction state estimation device. 2. The vehicle according to claim 1, wherein the standard yaw rate signal and the actual yaw rate signal are output to the estimated yaw rate output section and the estimated parameter identification section through a low-pass filter. For estimating the frictional state between tires and road surfaces. 3. A constant is obtained from the estimated friction state, and the standard yaw rate signal is reduced corresponding to the constant, and the reduction of the constant is stepwise according to an identification parameter for identifying the estimated parameter. A steering that controls the turning angle of the wheel so as to reduce the standard yaw rate signal according to the constant and to match the reduced standard yaw rate signal with the actual yaw rate signal. The vehicle tire / road surface frictional state estimation device according to claim 1 or 2, wherein the device is controlled.