JP6475013B2 - Vehicle control apparatus and vehicle control method - Google Patents

Description

  The present invention relates to a vehicle control device and a vehicle control method.

  Conventionally, for example, in Patent Document 1 below, in an electric vehicle driven by an in-wheel motor, a target yaw rate calculated based on a vehicle steering angle, a vehicle speed, and a lateral acceleration of the vehicle, and a yaw angular velocity detection means are detected. A configuration is disclosed in which a target yaw moment is calculated based on a deviation from the yaw rate of the vehicle and the braking / driving force output is controlled so that the target yaw moment is realized.

  In Patent Document 2 below, rudder capable of counter steer control, which is control for steering the rudder angle of the front wheel of a vehicle in a direction opposite to the turning direction when it is determined that there is a risk of rollover. A configuration is described in which an angle control unit and a driving force control unit capable of controlling acceleration of a vehicle when it is determined that there is a risk of rollover.

JP 2009-143310 A JP 2008-265560 A

  However, in the method described in Patent Document 1, the target yaw moment cannot be calculated properly when the running condition such as the friction coefficient of the road surface changes, and the braking / driving force output is controlled according to the running condition. It is difficult to do. For example, when the road surface friction coefficient is low (low μ), such as in rainy weather or on snowy roads, the target yaw moment may be excessive, causing the vehicle to become unstable or difficult to control. The

  Further, in the method described in the above-mentioned Patent Document 2, the values pre-weaved in the ECU are used as the rollover threshold value and the stability factor, and braking is performed by a hydraulic brake, so that the vehicle behavior is stabilized according to the traveling state. There is a problem that it is not possible to quickly switch between the control to perform and the control to place importance on the turning performance.

  In addition, when the yaw moment is applied by braking the wheel by braking and providing a driving force difference between the inner and outer wheels, the braking force is delayed due to a delay in the response of the hydraulic system generated when driving the brake booster, master cylinder, etc. Control intervention and yaw moment application direction switching processing are also delayed, leading to a vehicle behavior unintended by the driver, resulting in a deterioration in ride comfort.

  In addition, in motor-driven yaw moment control, response delay due to mechanical elements is reduced compared to brake braking, but when the difference between the control target yaw moment and the actual vehicle yaw moment is increased on a low μ road surface, the motor torque is always maintained. Therefore, the vehicle turns more than expected by the driver and causes a sense of incongruity. If the difference between the target yaw moment and the actual yaw moment is overshooting or hunting, the vehicle will vibrate more than the yaw moment control by brake braking, and the yaw moment fed back to the controller will also vibrate accordingly. However, there is a problem that the ride comfort deteriorates.

  Accordingly, the present invention has been made in view of the above problems, and an object of the present invention is to optimally control the behavior of the vehicle by calculating an optimal target yaw rate according to the driving conditions. It is an object of the present invention to provide a new and improved vehicle control apparatus and vehicle control method capable of performing the above.

In order to solve the above-described problem, according to an aspect of the present invention, target yaw rate calculation that calculates a target yaw rate from a vehicle model that defines a relationship between a target yaw rate of a vehicle, a vehicle speed, and a steering angle by a target stability factor. A road surface condition parameter acquisition unit that acquires a parameter that represents a road surface condition, a correction processing unit that corrects the target stability factor based on the parameter that represents the road surface condition, and a first yaw rate obtained from a vehicle model with a yaw rate acquisition unit that acquires the second yaw rate obtained from the yaw rate sensor, a parameter indicating the road surface condition, the control device is provided for the difference der Ru vehicle between the first yaw rate and the second yaw rate Is done.

  The correction processing unit corrects the target stability factor so that the target yaw rate decreases as the absolute value of the difference between the first yaw rate and the second yaw rate increases. Also good.

  Further, as a yaw rate generated by the vehicle, a feedback yaw rate acquisition unit that acquires a feedback yaw rate for comparison with the target yaw rate, and a first control target moment based on a difference between the target yaw rate and the feedback yaw rate. A first control target moment calculating unit for calculating, wherein the first control target moment calculating unit calculates the first control target moment by adding a steady damping control moment and a transient inertia compensation moment; The damping control moment and the inertia compensation moment may be corrected based on the difference between the first yaw rate and the second yaw rate.

  In addition, the first control target moment calculation unit may reduce the damping control moment or the inertia compensation moment so that the absolute value of the difference between the first yaw rate and the second yaw rate increases. The control moment and the inertia compensation moment may be corrected.

  Further, the feedback yaw rate acquisition unit increases the distribution of the first yaw rate when the difference is small, and increases the first yaw rate when the difference is large, based on the difference between the first yaw rate and the second yaw rate. The yaw rate of 2 may be increased and the feedback yaw rate may be calculated from the first and second yaw rates.

  A second control target moment calculating unit that calculates a second control target moment that is a target value of the vehicle turning control from the Ackermann geometry based on the steering angle and the vehicle speed; and the first control target based on the vehicle speed. The control target moment is calculated by weighting the moment and the second control target moment, and the control target moment is calculated by increasing the distribution of the first control target moment with respect to the second control target moment as the vehicle speed increases. And a control target moment calculation unit.

  In addition, a motor control unit that individually controls motors that drive the left and right wheels so that the feedback yaw rate becomes the target yaw rate based on the control target moment may be provided.

  Further, the parameter representing the road surface condition may be a difference between the actual rotational speed of the motor driving the vehicle and the target rotational speed.

  The parameter representing the road surface condition may be an estimated value of a road surface friction coefficient estimated by an in-vehicle camera.

In order to solve the above problem, according to another aspect of the present invention, the target yaw rate is calculated from a vehicle model in which a relationship between a target yaw rate of the vehicle, a vehicle speed, and a steering angle is defined by a target stability factor. A step of obtaining a parameter representing a road surface condition; a step of correcting the target stability factor based on the parameter representing the road surface condition; a first yaw rate obtained from a vehicle model; and a second obtained from a yaw rate sensor and a step of obtaining a yaw rate of the parameter representing the road surface condition, the control method of the differential der Ru vehicle between the first yaw rate and the second yaw rate is provided.

  As described above, according to the present invention, there is provided a vehicle control device and a vehicle control method capable of optimally controlling the behavior of the vehicle by calculating an optimum target yaw rate according to the running condition. be able to.

It is a schematic diagram which shows the vehicle which concerns on this embodiment. It is a mimetic diagram showing turning control which vehicles concerning this embodiment perform. It is a schematic diagram which shows the structure of a control apparatus. It is a flowchart which shows the basic process performed by this embodiment. It is a schematic diagram for demonstrating the process performed with a control apparatus. It is a schematic diagram which shows the gain map at the time of a feedback yaw rate calculating part calculating weighting gain (kappa). It is a characteristic view which shows the map which calculates target stability factor SfTgt. It is a flowchart which shows the process which calculates target stability factor SfTgt. It is a characteristic view showing a map for calculating a damping moment correction gain GainForMgDamp. It is a flowchart which shows the process which calculates the damping moment correction gain GainForMgDamp. It is a flowchart which shows the process which calculates damping moment correction gain GainForMgDamp when (gamma) _diff> = 0. It is a flowchart which shows the process which calculates damping moment correction gain GainForMgDamp in the case of (gamma) _diff <0. It is a characteristic view showing a map for calculating an inertia compensation moment correction gain GainForMgTrans. It is a flowchart which shows the process which calculates the inertia compensation moment correction gain GainForMgTrans. It is a flowchart which shows the process which calculates an inertia compensation moment correction gain GainForMgTrans when (gamma) _diff> = 0. It is a flowchart which shows the process which calculates an inertia compensation moment correction gain GainForMgTrans when (gamma) _diff <0. It is a schematic diagram which shows the map used when a 2nd control target moment calculating part calculates 2nd control target moment Mg2. It is a characteristic view showing a map for calculating a vehicle speed variable gain GainVx according to the vehicle speed V. It is a flowchart which shows the process which calculates vehicle speed variable gain GainVx. It is a characteristic view for demonstrating the effect by the correction process of the target stability factor SfTg which concerns on this embodiment. It is a characteristic view for demonstrating the effect by the correction process of the target stability factor SfTg which concerns on this embodiment. It is a characteristic view for demonstrating the effect at the time of correct | amending the control target moment (Combined GainForMgDamp and GainForMgTrans) which concern on this embodiment. It is a characteristic view for demonstrating the effect at the time of correct | amending the control target moment (Combined GainForMgDamp and GainForMgTrans) which concern on this embodiment. It is a schematic diagram which shows the structure of the control apparatus which concerns on 2nd Embodiment. It is a schematic diagram which shows the structure of the control apparatus 204 which concerns on 3rd Embodiment. It is a characteristic view which shows the map which calculates target stability factor SfTgt. It is a flowchart which shows the process which calculates target stability factor SfTgt. It is a characteristic view showing a map for calculating a damping moment correction gain GainForMgDamp. It is a flowchart which shows the process which calculates the damping moment correction gain GainForMgDamp. It is a characteristic view showing a map for calculating an inertia compensation moment correction gain GainForMgTrans. It is a flowchart which shows the process which calculates the inertia compensation moment correction gain GainForMgTrans.

  Exemplary embodiments of the present invention will be described below in detail with reference to the accompanying drawings. In addition, in this specification and drawing, about the component which has the substantially same function structure, duplication description is abbreviate | omitted by attaching | subjecting the same code | symbol.

<1. First Embodiment>
First, with reference to FIG. 1, the structure of the vehicle 1000 which concerns on each embodiment of this invention is demonstrated. FIG. 1 is a schematic diagram showing a vehicle 1000 according to the present embodiment. As shown in FIG. 1, a vehicle 1000 includes driving force generators (motors) 108, 110, 112, driving front wheels 100 and 102, rear wheels 104 and 106, front wheels 100 and 102, and rear wheels 104 and 106. 114, wheel speed sensors 116, 118, 120, 122 for detecting the respective wheel speeds of the front wheels 100, 102 and the rear wheels 104, 106, a steering wheel 124, a steering angle sensor 130, a power steering mechanism 140, a yaw rate sensor 150, an acceleration It has a sensor 160, an external recognition unit 170, and a control device (controller) 200.

  The vehicle 1000 according to the present embodiment is provided with motors 108, 110, 112, and 114 for driving the front wheels 100 and 102 and the rear wheels 104 and 106, respectively. Therefore, the driving torque can be controlled by each of the front wheels 100 and 102 and the rear wheels 104 and 106. Accordingly, the yaw rate can be generated by the torque vectoring control by driving each of the front wheels 100 and 102 and the rear wheels 104 and 106 independently of the yaw rate generation by the steering of the front wheels 100 and 102. In particular, in this embodiment, the yaw rate is generated independently of the steering system by controlling the torque of the rear wheels 104 and 106 individually. The driving torque of the rear wheels 104 and 106 is controlled by controlling the motors 112 and 114 corresponding to the rear wheels 104 and 106 based on a command from the control device 200.

  The power steering mechanism 140 controls the steering angle of the front wheels 100 and 102 by torque control or angle control according to the operation of the steering wheel 124 by the driver. The steering angle sensor 130 detects the steering angle θh input by the driver operating the steering wheel 124. The yaw rate sensor 150 detects the actual yaw rate γ of the vehicle 1000. Wheel speed sensors 116, 118, 120 and 122 detect vehicle speed V of vehicle 1000.

  Note that the present embodiment is not limited to this embodiment, and the motors 108 and 102 for driving the front wheels 100 and 102 are not provided, and only the rear wheels 104 and 106 are independently driven by the motors 112 and 114. The generated vehicle may be used. Further, the present embodiment is not limited to torque vectoring based on driving force control, and can also be realized in a 4WS system for controlling the steering angle of the rear wheels.

  FIG. 2 is a schematic diagram illustrating turning control performed by the vehicle 1000 according to the present embodiment, and is a schematic diagram illustrating turning control (steering control) by steering. In the turning control by the steering, the turning of the vehicle 1000 is supported by generating a driving force difference between the rear wheels 104 and 106 in accordance with the operation of the steering wheel 124 by the driver. In the example shown in FIG. 2, the vehicle 1000 is turning to the left by the steering of the driver (driver). Further, due to the difference in driving force between the rear wheels 104 and 106, a forward driving force is generated on the right rear wheel 106, and the driving force on the left rear wheel 104 is suppressed with respect to the right rear wheel 106 or backwards. By generating a driving force, a driving force difference is generated on the left and right, and a moment is generated in a direction that supports a counterclockwise turn.

  In the present embodiment, in vehicle motion control, the vehicle turning characteristics are estimated from the difference between the yaw rate model value obtained from the vehicle model and the actual yaw rate obtained from the yaw rate sensor. When the difference is small, the tire grip performance is sufficiently ensured. The control target yaw rate corresponding to the situation is calculated.

  When the difference between the yaw rate model value and the actual yaw rate is large, it is determined that the tire grip performance is difficult to secure and the vehicle response limit is easily reached (low μ), and the control target yaw rate is calculated according to the situation. A “target stability factor” having a function is calculated.

  In addition, it has a function to calculate a coefficient for correcting the control target moment used in steering stability control according to the turning characteristics of the vehicle. The coefficient for correcting the steady term and the coefficient for correcting the transient term By changing it accordingly, both turning performance and stability performance during turning are achieved. Details will be described below.

  FIG. 3 is a schematic diagram illustrating the configuration of the control device 200. The control device 200 includes an in-vehicle sensor 210, a steering control unit 220, a control target moment calculation unit 250, a second control target moment calculation unit 270, and a motor required torque calculation unit 280. A correction processing unit 222, a vehicle model 224, a filter processing unit 226, a subtraction unit 228, a control target yaw rate calculation unit 230, a feedback yaw rate calculation unit 232, a subtraction unit 233, and a first control target moment calculation unit 234 are provided.

  The control target moment calculation unit 250 includes a vehicle speed variable gain calculation unit 252 and an arbitration unit 254.

  FIG. 4 is a flowchart showing basic processing performed in the present embodiment. First. In step S10, a difference (yaw rate deviation) γ_diff between the yaw rate model value γ_clc obtained from the vehicle model 224 and the yaw rate γ_fil obtained from the actual yaw rate γ detected by the yaw rate sensor 150 is calculated. In the next step S12, a target stability factor SfTg is calculated. In the next step S14, a control target yaw rate γ_Tgt is calculated based on the target stability factor SfTgt. In the next step S16, γ_clc and γ_fil are apportioned based on γ_diff to calculate a feedback yaw rate γ_F / B. In the next step S17, the first control target moment Mg1 is calculated based on the difference between the control target yaw rate γ_Tgt and the feedback yaw rate γ_F / B. In the next step S18, the second control target moment Mg2 is calculated based on the Ackermann geometry. In the next step S19, a control target moment MgTgt is calculated based on the first control target moment Mg1 and the second control target moment Mg2.

  FIG. 5 is a schematic diagram for explaining processing performed in the control device 200. FIG. 5 shows the components of the control device 200 in the same manner as FIG. 3 and shows the processing performed by each component in detail. Below, the process performed with the control apparatus 200 is demonstrated based on FIGS. 3-5. The in-vehicle sensor 210 includes the steering angle sensor 130, the yaw rate sensor 150, the acceleration sensor 160, and the wheel speed sensors 116, 118, 120, and 122 described above.

  The control target yaw rate calculation unit 230 calculates the operation control target yaw rate γ_Tgt from the following equation (1) representing a general two-wheel model. The steering control target yaw rate γ_Tgt corresponds to the vehicle yaw rate γ in the equation (1) of the plane two-wheel model, and is calculated by substituting each value for the right side of the equation (1).

In the equations (1) to (3), variables and constants are as follows.
<Variable>
γ: vehicle yaw rate V: vehicle speed δ: tire steering angle θh: steering wheel steering angle <constant>
l: vehicle wheel base l _f : distance from vehicle center of gravity to center of front wheel l _r : distance from vehicle center of gravity to center of rear wheel m: vehicle weight k _f : cornering power (front)
k _r : Cornering power (rear)
Gh: Conversion gain from steering wheel steering angle to tire steering angle (steering gear ratio)

  The target yaw rate for control γ_Tgt (γ on the left side of equation (1)) is calculated from equation (1) using the vehicle speed V and the tire steering angle δ as variables. Since the tire steering angle δ in equation (1) cannot be directly sensed, the tire steering angle conversion processing unit 230a of the control target yaw rate calculation unit 230 multiplies the steering wheel steering angle θh by the conversion gain Gh from equation (2). The steering angle δ is calculated. The tire steering angle δ may be calculated based on a steering motion model. A steering gear ratio is used as the conversion gain Gh. The target stability factor SfTgt in the formula (1) is generally calculated from the formula (3) as a constant A that represents the characteristics of the vehicle. In the present embodiment, the correction processing unit 222 corrects the target stability factor SfTgt. To do. The γ_Tgt calculation unit 230b of the control target yaw rate calculation unit 230 calculates the control target yaw rate γ_Tgt from equation (1) using the tire steering angle δ calculated by the tire steering angle conversion processing unit 230a. The control target yaw rate γ_Tgt is input to the subtraction unit 233.

  On the other hand, the vehicle model 224 and the filter processing unit 226 obtain the yaw rate generated by the vehicle 1000 by calculation or actual measurement. The vehicle model 224 calculates a yaw rate model value γ_clc from the following expression representing a vehicle model (a plane two-wheel model) for calculating the vehicle yaw rate. Specifically, the yaw rate model value is obtained by substituting the vehicle speed V and the steering angle θh of the steering wheel into the following expressions (4) and (5) and solving the expressions (4) and (5) simultaneously. γ_clc (γ in the equations (4) and (5)) is calculated. Since equation (1) can be derived from equations (4) and (5), vehicle model 224 is similar to control target yaw rate calculation unit 230 based on steering angle θh and vehicle speed V. The yaw rate model value γ_clc may be calculated from the equation (1) of the vehicle model by a technique.

  In equations (4) to (5), I is the yaw inertia of the vehicle, and β is the side slip angle of the vehicle.

  The yaw rate model value γ_clc is input to the feedback yaw rate calculation unit 232. The filter processing unit 226 performs filter processing to remove noise from the actual yaw rate γ detected by the yaw rate sensor 150, and inputs the yaw rate γ_fil obtained as a result of the filter processing to the feedback yaw rate calculation unit 232.

  The subtraction unit 228 subtracts the yaw rate γ_fil from the yaw rate model value γ_clc to obtain a difference γ_diff between the yaw rate model value γ_clc and the yaw rate γ_fil. The difference γ_diff is input to the feedback yaw rate calculation unit 232. Here, since the difference γ_diff corresponds to a parameter representing the road surface condition, the subtracting unit 228 corresponds to a component that acquires a parameter representing the road surface condition.

As described above, the yaw rate model value γ_clc, the yaw rate γ_fil, and the difference γ_diff are input to the feedback yaw rate calculation unit 232. The feedback yaw rate calculation unit 232 calculates a weighting gain κ that changes according to the difference γ_diff based on the difference γ_diff between the yaw rate model value γ_clc and the actual yaw rate γ_fil. Then, the feedback yaw rate calculation unit 232 weights the yaw rate model value γ_clc and the yaw rate γ_fil with the weighting gain κ based on the following equation (6) to calculate the feedback yaw rate γ_F / B. The calculated feedback yaw rate γ_F / B is input to the subtraction unit 233.
γ_F / B = κ × γ_clc + (1−κ) × γ_fil (6)

  FIG. 6 is a schematic diagram illustrating a gain map when the feedback yaw rate calculation unit 232 calculates the weighting gain κ. As shown in FIG. 6, the value of the weighting gain κ varies between 0 and 1 according to the reliability of the vehicle model 224. A difference (deviation) γ_diff between the yaw rate model value γ_clc and the yaw rate γ_fil is used as an index for improving the reliability of the vehicle model 224. As shown in FIG. 6, the gain map is set so that the value of the weighting gain κ increases as the absolute value of the difference γ_diff decreases. The feedback yaw rate calculation unit 232 performs the map process of FIG. 6 on the difference γ_diff and calculates a weighting gain κ corresponding to the reliability of the vehicle model 224.

  In FIG. 6, TH1_P is a threshold value (+ side) for switching the weighting gain κ, TH2_P is a switching threshold value (+ side) for the weighting gain κ, TH1_M is a switching threshold value (−side) for the weighting gain κ, TH2_M represents a switching threshold value (− side) of the weighting gain κ. The threshold value relationship on the + side is TH1_P <TH2_P, and the threshold value relationship on the − side is TH1_M> TH2_M.

  In FIG. 6, TH1_P is a threshold value (+ side) for switching the weighting gain κ, TH2_P is a switching threshold value (+ side) for the weighting gain κ, TH1_M is a switching threshold value (−side) for the weighting gain κ, TH2_M represents a switching threshold value (− side) of the weighting gain κ. The threshold value relationship on the + side is TH1_P <TH2_P, and the threshold value relationship on the − side is TH1_M> TH2_M.

  A region A1 of the gain map shown in FIG. 6 is a region where the difference γ_diff approaches 0, a region where the S / N ratio is small, and a region where the tire characteristics are linear (dry road surface), and is calculated from the vehicle model 216. The yaw rate model value γ_clc is highly reliable. Therefore, with the weighting gain κ = 1, the feedback yaw rate γ_F / B is calculated from the equation (7) with the distribution of the yaw rate model value γ_clc as 100%. Thereby, the influence of the noise of the yaw rate sensor 150 included in the yaw rate γ_fil can be suppressed, and the sensor noise can be excluded from the feedback yaw rate γ_F / B. Therefore, it is possible to suppress the vibration of the vehicle 1000 and improve the riding comfort.

  In particular, in the driving support control, the amount by which the vehicle 1000 turns based on the estimated travel path from the straight traveling state before the vehicle 1000 enters the corner is foresight controlled. Therefore, not only when the vehicle 1000 is turning but also when the vehicle 1000 is traveling straight, it is possible to move the vehicle 1000 straight without causing vibrations by eliminating the influence of sensor noise.

  As described above, the region where the reliability of the yaw rate model value γ_clc is high can be designated from the difference γ_diff and the traveling state. As shown in FIG. 6, when the vehicle is traveling on a dry road surface (high μ) and the turning amount is small (such as turning at a low curvature), the difference γ_diff is weighted so that the weighting gain κ becomes 1. Associating the gain κ is assumed as an example of coefficient setting by a map. The planar two-wheel model described above assumes a region where the relationship between tire slip angle and lateral acceleration (tire cornering characteristics) is linear. In the region where the cornering characteristics of the tire are nonlinear, the yaw rate and lateral acceleration of the actual vehicle become nonlinear with respect to the steering angle and slip angle, and the two-wheel model and the yaw rate sensed by the actual vehicle deviate. For this reason, if a model that takes into account the nonlinearity of the tire is used, control based on the yaw rate becomes complicated. However, according to the present embodiment, the reliability of the yaw rate model value γ_clc is easily determined based on the difference γ_diff. Is possible.

  Further, the gain map area A2 shown in FIG. 6 is an area where the difference γ_diff is large, which corresponds to a wet road surface, a snow road, or a turn with a high G, and the limit of tire slipping. It is an area. In this region, the reliability of the yaw rate model value γ_clc calculated from the vehicle model 216 becomes low, and the difference γ_diff becomes larger. For this reason, the feedback yaw rate γ_F / B is calculated with the weighting gain κ = 0 and the distribution of the yaw rate γ_fil as 100% from the equation (7). Thus, feedback accuracy is ensured based on the yaw rate γ_fil, and the yaw rate feedback control reflecting the behavior of the actual vehicle is performed. Therefore, the turning of the vehicle 1000 can be optimally controlled based on the yaw rate γ_fil. In addition, since the tire is slipping, even if the signal of the yaw rate sensor 150 is affected by noise, the driver does not feel it as vibration of the vehicle 1000, and a decrease in riding comfort can be suppressed. For the setting of the low μ region A2 shown in FIG. 6, the region where the weighting gain κ = 0 may be determined from the design requirements, and the steering stability performance and the ride when the vehicle 1000 actually travels on the low μ road surface. You may decide experimentally from comfort etc.

  Further, the gain map area A3 shown in FIG. 6 is an area (nonlinear area) in which a transition from a linear area to a limit area is performed, and the yaw rate model value γ_clc is considered in consideration of tire characteristics of the actual vehicle 1000 as necessary. And the distribution of the yaw rate γ_fil (weighting gain κ) are linearly changed. In the transition from the region A1 (high μ region) to the region A2 (low μ region) or in the region transitioning from the region A2 (low μ region) to the region A1 (high μ region), the torque associated with the sudden change of the weighting gain κ In order to suppress fluctuations and fluctuations in the yaw rate, the weighting gain κ is calculated by linear interpolation.

  A gain map region A4 shown in FIG. 6 corresponds to a case where the yaw rate γ_fil is larger than the yaw rate model value γ_clc. For example, when an incorrect parameter is input to the vehicle model 216 and the yaw rate model value γ_clc is erroneously calculated, control can be performed using the yaw rate γ_fil based on the map of the region A4. Further, according to the map of the region A4, even when the yaw rate model value γ_clc is temporarily smaller than the yaw rate γ_fil due to the phase delay of the yaw rate γ_fil accompanying the filter processing, the control using the yaw rate γ_fil is performed. It can be carried out. It should be noted that the range of the weighting gain κ is not limited to 0 to 1, but the configuration of the present invention can also be changed so that an arbitrary value can be taken as long as the range is established as vehicle control. Enter into a possible category.

  The correction processing unit 222 corrects the target stability factor SfTgt when the control target yaw rate calculation unit 230 calculates the control target yaw rate γ_Tgt from the equations (1) to (3). FIG. 7 is a characteristic diagram showing a map for calculating the target stability factor SfTgt. FIG. 8 is a flowchart showing a process for calculating the target stability factor SfTgt. In the process of FIG. 8, first, in step S10, it is determined whether γ_diff ≧ 0. If γ_diff ≧ 0, the process proceeds to step S12, and it is determined whether γ_diff ≦ TH1_P. If γ_diff ≦ TH1_P, the process proceeds to step S14, where SfTgt = Sf1.

  If γ_diff> TH1_P in step S12, the process proceeds to step S16 to determine whether γ_diff ≧ TH2_P. If γ_diff ≧ TH2_P, the process proceeds to step S18, and SfTgt = Sf2.

  If γ_diff <TH2_P in step S16, the process proceeds to step S20, and SfTgt is calculated from the following equation (7).

  If γ_diff <0 in step S10, the process proceeds to step S22 to determine whether γ_diff ≧ TH1_M. If γ_diff ≧ TH1_M, the process proceeds to step S24, where SfTgt = Sf1. If γ_diff <TH1_M in step S22, the process proceeds to step S26 to determine whether γ_diff ≦ TH2_M. If γ_diff ≦ TH2_M, the process proceeds to step S28, and SfTgt = Sf2.

  If γ_diff> TH2_M in step S26, the process proceeds to step S30, and SfTgt is calculated from the following equation (8).

  According to the process of FIG. 8, the target stability factor (SfTgt) used for estimating the state quantity of the vehicle 1000 is the yaw rate obtained by filtering the yaw rate model value γ_clc (the yaw rate reference value) and the detection value of the yaw rate sensor 142. It is variable according to the degree of deviation from γ_fil (γ_diff). When the absolute value of the difference γ_diff is smaller than the absolute value of the threshold value (TH1_M, TH1_P) set on the side where the difference between the reference value and the detected value is small, the high μ region where the vehicle behavior is stable, or The target stability factor “Sf1 (focus on turning ability)” for determining the normal region and promoting the rotation (yaw motion) of the vehicle 1000 is set. Further, when the absolute value of the difference γ_diff is larger than a predetermined threshold (TH2_M, TH2_P), it is determined to be a low μ region or a limit region, and excessive rotation (yaw mobility) of the vehicle 1000 is suppressed, A target stability factor “Sf2 (emphasis on stability)” is set to ensure stable performance during turning. When the absolute value of the difference γ_diff is between the threshold values TH1_P and TH2_P or between the threshold values TH1_M and TH2_M, “Sf1” and “Sf2” are set according to the value of the difference γ_diff. The target stability factor “SfTgt” is set by interpolation.

  Thus, by correcting the target stability factor SfTgt by estimating the turning characteristic of the vehicle based on the difference γ_diff, the value of the control target yaw rate γ_Tgt increases as the road surface friction coefficient increases, and the vehicle turning is promoted. Is done. Further, the value of the control target yaw rate γ_Tgt becomes smaller as the road surface friction coefficient becomes lower μ, and the vehicle turning is suppressed. Therefore, the optimal control target yaw rate γ_Tgt according to the road surface condition at that time can be calculated.

The subtraction unit 233 receives the control target yaw rate γ_Tgt from the control target yaw rate calculation unit 230 and receives the feedback yaw rate γ_F / B from the feedback yaw rate calculation unit 232. The subtractor 233 subtracts the feedback yaw rate γ_F / B from the control target yaw rate γ_Tgt to obtain a difference (yaw rate correction amount) Δγ_Tgt between γ_Tgt and γ_F / B. That is, the difference Δγ_Tgt is calculated from the following equation (9).
Δγ_Tgt = γ_Tgt−γ_F / B (9)
The difference Δγ_Tgt is output to the first control target moment calculator 234.

  The first control target moment calculator 234 calculates a moment for correcting the vehicle behavior based on the difference Δγ_Tgt. The first control target moment calculation unit 234 calculates the control target moment using the difference Δγ_Tgt, and corrects the control target moment with the adjustment gain, thereby ensuring stability at low μ and high stability at high μ. The steering stability performance is controlled from the viewpoints of both steady behavior and transient behavior when turning the vehicle while making the turning support control compatible. Therefore, the first control target moment calculation unit 234 includes a damping control moment calculation unit (steady term calculation unit) 234a that calculates a “target damping moment MgDampTgt” that is a parameter for converging the yaw rate of the vehicle 1000, and the vehicle 1000. And an inertia compensation moment calculation unit (transient term calculation unit) 234b for calculating “target inertia compensation moment MgTransTgt” which is a parameter for compensating the yaw inertia of the motor.

A damping control moment calculation unit (steady term calculation unit) 234a multiplies Δγ_Tgt by a coefficient D1 applied to the yaw rate in a formula in which a well-known plane two-wheel model (yaw motion) is arranged with respect to the yaw moment. The basic amount MgDampBasis of “target damping moment MgDampTgt” that improves the convergence performance of is calculated. Here, the coefficient D1 is equivalent to (5) hanging on the γ in equation ^{_{2 / V (l f 2 K}} f -l r 2 K r). At this time, the damping control moment calculation unit (steady term calculation unit) 234a multiplies the basic amount MgDampBasis of the target damping moment by a gain set to reflect the road surface condition according to the difference γ_diff of the yaw rate. Then, the basic amount MgDampBasis is corrected to calculate “target damping moment MgDampTgt”. With the “target damping moment MgDampTgt”, the vehicle behavior can be stabilized particularly with respect to the steady behavior of the vehicle 1000.

  More specifically, the damping control moment calculator 234a has a function of calculating a gain GainForMgDamp for correcting the target damping moment and varying the gain GainForMgDamp according to the yaw rate difference γ_diff. In addition, when it is determined that the damping control moment calculation unit 234a is transitioning to the low μ region or the limit region based on the difference γ_diff, the gain is gradually changed and the gain is controlled in the low μ region or the limit region. When it is determined that the vehicle has reached, in order to place importance on the stability performance of the vehicle, the gain is set to the negative side, a reverse moment is applied during the turn, and control for suppressing excessive rotation of the vehicle 1000 is performed.

  For this reason, the damping control moment calculation unit 234a first calculates the basic amount MgDampBasis by multiplying Δγ_Tgt by the coefficient D1. Then, a target damping moment MgDampTgt is calculated by multiplying the calculated basic amount MgDampBase and the damping moment correction gain GainForMgDamp that changes according to the difference γ_diff.

  FIG. 9 is a characteristic diagram showing a map for calculating the damping moment correction gain GainForMgDamp. FIG. 10 is a flowchart showing a process for calculating the damping moment correction gain GainForMgDamp. In the process of FIG. 10, first, in step S40, it is determined whether or not the difference γ_diff ≧ 0. If the difference γ_diff ≧ 0, the process proceeds to step S42, and the damping moment is based on the value of γ_diff that is 0 or more. A correction gain GainForMgDamp is calculated. If the difference γ_diff <0, the process proceeds to step S44, and the damping moment correction gain GainForMgDamp is calculated based on the value of γ_diff which is a value smaller than 0.

  FIG. 11 is a flowchart showing a process for calculating the damping moment correction gain GainForMgDamp when γ_diff ≧ 0. In the case of γ_diff ≧ 0, the damping moment correction gain GainForMgDamp is calculated according to the characteristics of the region of γ_diff ≧ 0 in the map shown in FIG. First, in step S50, it is determined whether or not γ_diff ≦ TH1_P. If γ_diff ≦ TH1_P, the process proceeds to step S52 to set GainForMgDamp = MAX_GAIN. On the other hand, if γ_diff> TH1_P in step S50, the process proceeds to step S54, and it is determined whether γ_diff ≦ TH1_P. If γ_diff ≦ TH1_P, the process proceeds to step S56, and the damping moment correction gain is calculated from the following equation (10). GainForMgDamp is calculated.

  If γ_diff> TH1_P in step S54, the process proceeds to step S58 to determine whether γ_diff ≦ TH3_P. If γ_diff ≦ TH3_P, the process proceeds to step S60 to set GainForMgDamp = MID_GAIN.

  If γ_diff> TH3_P in step S58, the process proceeds to step S62, where it is determined whether γ_diff ≦ TH4_P. If γ_diff ≦ TH4_P, the process proceeds to step S64 and the damping moment correction gain is calculated from the following equation (11). GainForMgDamp is calculated.

  If γ_diff> TH4_P in step S62, the process proceeds to step S66 to set GainForMgDamp = MIN_GAIN.

  FIG. 12 is a flowchart showing processing for calculating the damping moment correction gain GainForMgDamp when γ_diff <0. In the case of γ_diff <0, the damping moment correction gain GainForMgDamp is calculated according to the characteristics of the region of γ_diff <0 in the map shown in FIG. First, in step S70, it is determined whether or not γ_diff ≧ TH1_M. If γ_diff ≧ TH1_M, the process proceeds to step S72 to set GainForMgDamp = MAX_GAIN. On the other hand, if γ_diff <TH1_M in step S70, the process proceeds to step S74, and it is determined whether γ_diff ≧ TH2_M. If γ_diff ≧ TH2_M, the process proceeds to step S76, and the damping moment correction gain is calculated from the following equation (12). GainForMgDamp is calculated.

  If γ_diff <TH2_M in step S74, the process proceeds to step S78 to determine whether γ_diff ≧ TH3_M. If γ_diff ≧ TH3_M, the process proceeds to step S80 to set GainForMgDamp = MID_GAIN.

  If γ_diff <TH3_M in step S78, the process proceeds to step S82, where it is determined whether γ_diff ≧ TH4_M. If γ_diff ≧ TH4_M, the process proceeds to step S84 and the damping moment correction gain is calculated from the following equation (13). GainForMgDamp is calculated.

  If γ_diff <TH4_M in step S62, the process proceeds to step S86 to set GainForMgDamp = MIN_GAIN.

  Through the above processing, the damping moment correction gain GainForMgDamp is calculated according to the map of FIG. The damping control moment calculator 234a determines the degree of stability of the vehicle behavior with respect to steering, such as high μ and low μ, according to the magnitude of γ_diff, and achieves both turning performance and convergence performance of the vehicle with respect to steering. In (range γ_diff is from TH1_M to TH1_P), the damping moment correction gain GainForMgDamp is calculated so that the correction moment accompanying the turning is output 100%. In the middle μ range (γ_diff is in the range from TH2_P to TH3_P, or in the range from TH2_M to TH3_M), the damping moment correction gain GainForMgDamp is calculated so that the correction moment accompanying turning is zero. Also, in the low μ region (a range where γ_diff is equal to or greater than TH4_P or a range equal to or less than TH4_M), a damping moment correction gain GainForMgDamp is calculated so as to give a reverse moment with turning. Further, the correction gain GainForMgDamp is calculated so as to change the output linearly in a region where the mode transitions to each mode of the high μ region, the medium μ region, and the low μ region.

  In this embodiment, in order to improve the convergence performance of the vehicle 1000 with respect to steering, 0 or a positive or negative value is set in the damping moment correction gain GainForMgDamp. However, if the vehicle control is in a range, a positive value is set. Arbitrary constants can be set, including the case of only.

  The inertia compensation moment calculation unit (transient term calculation unit) 234b multiplies the differential value of Δγ_Tgt by a coefficient T1 applied to the yaw acceleration in an equation in which a well-known plane two-wheel model (yaw motion) is arranged with respect to the yaw moment. Thus, the basic amount MgTransBasis of the “target inertia compensation moment MgTransTgt” for compensating the yaw inertia at the time of turning of the vehicle is calculated. Here, the coefficient T1 corresponds to I (yaw inertia of the vehicle) applied to dγ / dt in the equation (5). At this time, the inertia compensation moment calculation unit (transient term calculation unit) 234b is a gain set to reflect the road surface condition according to the difference γ_diff of the yaw rate with respect to the basic amount MgTransBasis of the “target inertia compensation moment MgTransTgt”. To correct the basic quantity MgTransBasis and calculate “target inertia compensation moment MgTransTgt”. As a result, the vehicle behavior can be stabilized with respect to the instantaneous behavior of the vehicle 1000 by the “target inertia compensation moment MgTransTgt”.

  More specifically, the inertia compensation moment calculating unit 234b has a function of calculating a gain GainForMgTrans for correcting the inertia compensation moment and varying the gain GainForMgTrans according to γ_diff, similarly to the damping control moment calculating unit 234a. In addition, the inertia compensation moment calculation unit 234b determines the degree of stability of the vehicle behavior with respect to steering such as a high μ range and a low μ range based on γ_diff, and also has a function of limiting the output of the inertia compensation moment in the low μ range. The correction gain GainForMgTrans is calculated. If it is determined that the transition is being made to the low μ region or the limit region, the gain is gradually reduced, while if it is determined that the low μ region or the limit region has been reached, the yaw inertia is reduced. In order to reduce the compensation moment to 0 or maintain the turning performance, the output of the yaw inertia compensation moment is reduced, and control is performed to maintain the function for temporarily adjusting the behavior of the vehicle even at low μ.

  Therefore, the inertia compensation moment calculator 234b first calculates a basic amount MgTransBasis by multiplying Δγ_Tgt by the coefficient T1. Then, the target inertia compensation moment MgTransTgt is calculated by multiplying the calculated basic amount MgTransBasis by the inertia compensation moment correction gain GainForMgTrans that changes according to the difference γ_diff.

  FIG. 13 is a characteristic diagram showing a map for calculating the inertia compensation moment correction gain GainForMgTrans. FIG. 14 is a flowchart showing a process for calculating the inertia compensation moment correction gain GainForMgTrans. In the process of FIG. 14, first, in step S90, it is determined whether or not the difference γ_diff ≧ 0. If the difference γ_diff ≧ 0, the process proceeds to step S92, and inertia compensation is performed based on the value of γ_diff that is 0 or more. A moment correction gain GainForMgTrans is calculated. If the difference γ_diff <0, the process proceeds to step S94, and the inertia compensation moment correction gain GainForMgTrans is calculated based on the value of γ_diff, which is a value smaller than 0.

  FIG. 15 is a flowchart showing processing for calculating the inertia compensation moment correction gain GainForMgTrans when γ_diff ≧ 0. When γ_diff ≧ 0, the inertia compensation moment correction gain GainForMgTrans is calculated according to the characteristics of the region where γ_diff ≧ 0 in the map shown in FIG. First, in step S100, it is determined whether or not γ_diff ≦ TH1_P. If γ_diff ≦ TH1_P, the process proceeds to step S102, and GainForMgTrans = MAX_GAIN. If γ_diff> TH1_P, the process proceeds to step S104 to determine whether or not γ_diff ≧ TH2_P. If γ_diff ≧ TH2_P, the process proceeds to step S106, and GainForMgTrans = MIN_GAIN.

  If γ_diff <TH2_P in step S104, the process proceeds to step S108, and an inertia compensation moment correction gain GainForMgTrans is calculated from the following equation (14).

  FIG. 16 is a flowchart showing a process for calculating the inertia compensation moment correction gain GainForMgTrans when γ_diff <0. In the case of γ_diff <0, the inertia compensation moment correction gain GainForMgTrans is calculated according to the characteristics of the region of γ_diff <0 in the map shown in FIG. First, in step S110, it is determined whether or not γ_diff ≧ TH1_M. If γ_diff ≧ TH1_M, the process proceeds to step S112 to set GainForMgTrans = MAX_GAIN. If γ_diff <TH1_M, the process proceeds to step S114 to determine whether γ_diff ≦ TH2_M. If γ_diff ≦ TH2_M, the process proceeds to step S116, and GainForMgTrans = MIN_GAIN.

  If γ_diff ≦ TH2_M in step S114, the process proceeds to step S118, and an inertia compensation moment correction gain GainForMgTrans is calculated from the following equation (15).

  Through the above processing, the inertia compensation moment correction gain GainForMgTrans is calculated according to the map of FIG.

  As shown in FIGS. 9 and 13, when the difference γ_diff is larger than TH2_P, the damping moment correction gain GainForMgDamp becomes 0, and the turning support is not performed in the steady term. Further, when the difference γ_diff is larger than TH3_P, the damping moment correction gain GainForMgDamp becomes a negative value, and depending on the setting of the inertia compensation moment correction gain GainForMgTrans at that time, the target damping moment MgDampTgt and the target inertia compensation moment MgTransTgt are added. The value is negative. Therefore, the first control target moment Mg1 can be generated in a direction to suppress the turning.

  In this embodiment, in order to emphasize the response performance of the vehicle 1000 with respect to steering, the gain is instructed only with a positive value. However, if the vehicle control is in a range, 0 or a negative value is included. Arbitrary constants can be set.

  When the target damping moment MgDampTgt and the target inertia compensation moment MgTransTgt are calculated as described above, the addition unit 234c of the first control target yaw rate calculation unit 234 adds the target damping moment MgDampTgt and the target inertia compensation moment MgTransTgt. Then, the first control target moment Mg1 used in the steering control is calculated. The first control target moment Mg1 is input to the control target moment calculator 250.

  On the other hand, the second control target moment calculation unit 270 assumes the Ackerman region and inputs the steering angle θh and the vehicle speed V to the model simulating the geometric relationship of the vehicle, thereby obtaining the second control target moment Mg2. calculate. In the low vehicle speed range, the centrifugal force accompanying the turning of the vehicle does not work or only a very small amount of centrifugal force is generated, so the influence of the cornering force can be ignored. The second control target moment calculation unit 270 calculates the vehicle motion using a so-called Ackermann steering geometry based on the geometric relationship in order to compensate for the driving force control in the low speed range. The second control target moment calculator 270 calculates the second control target moment Mg2 from the steering angle θh and the vehicle speed V based on the following equation (16). The function f (V, θH) in the equation (16) may be a calculation formula derived from the geometric relationship of the vehicle being turned, or may be a map reflecting experimental results. good.

  FIG. 17 is a schematic diagram showing a map used when the second control target moment calculator 270 calculates the second control target moment Mg2. As shown in FIG. 17, the second control target moment Mg2 is set to a larger value as the steering angle θh is larger. Further, the second control target moment Mg2 is set to a different value according to the vehicle speed pattern corresponding to the vehicle speed V even if the steering angle θh is the same. For example, the second control target moment Mg2 is set to a larger value as the vehicle speed V increases. Note that the map shown in FIG. 17 is merely an example, and as long as the second control target moment Mg2 is calculated based on the steering angle θh and the vehicle speed V, any map such as a map based on a simulation, a map based on an experimental value, etc. It may be a simple map. As described above, in the Ackerman control model assuming the low vehicle speed range, the second control target moment Mg2 used in the feedforward control is calculated from the map based on the steering angle θh and the vehicle speed V.

  In the map shown in FIG. 17, it is assumed that the steering angle θh is the horizontal axis, the second control target moment Mg2 is the vertical axis, and the vehicle speed pattern is switched according to the vehicle speed V. For the map for calculating the second control target moment Mg2, a map constant may be designated from the geometrical relationship between the steering angle θh and the skid angle, or a map constant adapted through actual vehicle evaluation may be designated.

  The control target moment calculation unit 250 includes a vehicle speed variable gain calculation unit 252 and an arbitration unit 254. The control target moment calculation unit 250 receives the first control target moment Mg1 from the operation control unit 220. The control target moment calculator 250 receives the second control target moment Mg2 from the second control target yaw rate calculator 270.

  As described above, in the low vehicle speed range, the centrifugal force accompanying the turning of the vehicle does not work or only a small amount of centrifugal force is generated, so the influence of the cornering force can be ignored and the geometric relationship Based on this, the vehicle motion can be calculated by the so-called Ackermann steering geometry.

  On the other hand, the centrifugal force accompanying the turn increases as the vehicle speed increases. In this situation, when the control in the low vehicle speed range is applied to the steering control, an error between the behavior of the actual vehicle 1000 and the control target is expanded, and the driver may feel uncomfortable with respect to the vehicle behavior. In other words, the control of the Ackermann area is based on the premise that the vehicle 1000 travels at a low speed and no centrifugal force is applied during the turn, so that the turning situation of the vehicle in the middle / high speed range (the situation in which the centrifugal force is applied to the vehicle) is determined. It cannot be simulated.

  For this reason, in the present embodiment, according to the vehicle speed V, the behavior of the actual vehicle 1000 and the control target are controlled by smoothly switching between the control in the Ackermann region at the low vehicle speed region and the control control in the medium and high speed region. I try to suppress the error.

  The control target moment calculation unit 250 apportions the first control target moment Mg1 and the second control target moment Mg2 based on the vehicle speed V in order to achieve both the steering control in the medium and high speed range and the turning control in the low speed range. The control target moment MgTgt is calculated. Therefore, the vehicle speed variable gain calculation unit 252 calculates the vehicle speed variable gain GainVx according to the vehicle speed V. FIG. 18 is a characteristic diagram showing a map for calculating the vehicle speed variable gain GainVx according to the vehicle speed V. FIG. 19 is a flowchart showing a process for calculating the vehicle speed variable gain GainVx. As shown in FIG. 18, the vehicle speed variable gain GainVx is 0 when the vehicle speed is Vx_1 or less. Further, the vehicle speed variable gain GainVx increases as the vehicle speed V increases when the vehicle speed V is higher than Vx_1, and becomes 1 when the vehicle speed V is equal to or higher than Vx_2. In the present embodiment, the vehicle speed variable gain GainVx is calculated by linear interpolation between the maximum value and the minimum value of the vehicle speed variable gain GainVx. The range of the vehicle speed variable gain GainVx may be changed so that an arbitrary value can be taken as long as the range is established as vehicle control.

  In the process of FIG. 19, first, in step S120, it is determined whether or not V ≦ Vx_1. If V ≦ Vx_1, the process proceeds to step S122 to set GainVx = 0. On the other hand, if V> Vx_1, the process proceeds to step S124, and it is determined whether V ≧ Vx_2. If V ≧ Vx_2, the process proceeds to step S126, and GainVx = 1 is set.

  If V <Vx_2 in step S124, the process proceeds to step S128, and the vehicle speed variable gain GainVx is calculated from the following equation (17).

  With the above processing, the vehicle speed variable gain GainVx is calculated according to the map of FIG.

Based on the following equation (18), the arbitrating unit 254 apportions the first control target moment Mg1 and the second control target moment Mg2 based on the vehicle speed variable gain GainVx, and calculates the control target moment MgTgt.
MgTgt = GainVx * Mg1 + (1-GainVx) * Mg2 (18)

  The control target moment MgTgt calculated by the control target moment calculation unit 250 as described above is input to the motor required torque calculation unit 280. The motor request torque calculator 280 calculates the motor request torque based on the control target moment MgTgt.

  Next, the vehicle behavior when the control of the present embodiment is performed will be described based on FIGS. 20 and 21 are characteristic diagrams for explaining the effect of the correction process of the target stability factor SfTg according to the present embodiment obtained by simulation. The vehicle speed V is constant, and the steering angle θh is ramped. The vehicle behavior is shown when the road surface μ suddenly changes from high μ to low μ at time t1 when the steering input manipulated in the shape is applied to the vehicle 1000. More specifically, FIGS. 20 and 21 show changes in the steering angle θh, the actual yaw rate γ, the difference γ_diff, the road surface friction coefficient μ, the target stability factor Sftgt, the roll angle φ, and the roll rate φ_dot. Here, FIG. 20 shows yaw rate feedback control in the case where correction processing of the target stability factor SfTg is not performed for comparison, and FIG. 21 shows correction processing of the target stability factor SfTg by the control of this embodiment. The case where it went is shown.

  As shown in FIG. 20, when the target stability factor SfTg is not corrected, γ_diff increases at the moment when the road surface friction coefficient μ changes. As the γ_diff increases, the turning ratio (actual yaw rate γ) and the roll angle φ increase. Therefore, it can be understood that the inclination (roll angle φ) of the vehicle body increases and the behavior becomes unstable despite the situation where the road surface is slippery.

  On the other hand, as shown in FIG. 21, when the target stability factor SfTg is corrected by the control of the present embodiment, the actual yaw rate γ and the steady-state gain change of the difference γ_diff are smaller than those in FIG. In addition, since the roll rate φ_dot is also generated on the roll return side, the amount of change in the steady gain of the roll angle φ is also reduced. Therefore, it can be seen that the vehicle behavior is stabilized by changing the target stability factor SfTgt.

  FIGS. 22 and 23 are characteristics for explaining the effect when the control target moment (a combination of the damping moment correction gain GainForMgDamp and the inertia compensation moment correction gain GainForMgTrans) according to the present embodiment obtained by simulation is corrected. FIG. 6 shows vehicle behavior when a lane change is performed at time t2 on a road surface having a low road surface friction coefficient such as a wet road or a snowy road. The input steering amount is “dry road surface” (high μ) in a state where the driving force control accompanying the turning support is not applied, and a lateral acceleration of 0.5 [G] is generated by steady steering. Shall. More specifically, FIGS. 22 and 23 show changes in the actual yaw rate γ, difference γ_diff, damping moment correction gain GainForMgDamp, inertia compensation moment correction gain GainForMgTrans, roll angle φ, and roll rate φ_dot. Here, FIG. 22 shows a case where the control target moment correction according to the present embodiment is not performed for comparison, and FIG. 23 shows a case where the control target moment correction according to the present embodiment is performed.

  As shown in FIG. 22, when the control target moment correction according to the present embodiment is not performed, the actual yaw rate γ and the difference γ_diff vibrate after the lane change. Further, the roll rate φ_dot also vibrates in conjunction with the change in yaw movement (vibration of the actual yaw rate γ), which gives the driver a sense of incongruity.

  On the other hand, as shown in FIG. 23, when the control target moment correction is performed by the control of the present embodiment, the damping moment correction gain GainForMgDamp is set to the negative side, and the control for reducing the gain of the inertia compensation moment correction gain GainForMgTrans is performed. Therefore, the difference γ_diff after the end of the lane change and the amplitude of the actual yaw rate γ are clearly reduced as compared with the control in FIG. For this reason, the vibration of the roll rate φ_dot decreases in conjunction with the change in the yaw motion (reduction in the amplitude of the actual yaw rate γ), and the change becomes gentle. Therefore, it is possible to prevent the driver from feeling uncomfortable.

  As described above, according to the first embodiment, the reliability of the yaw rate model value γ_clc is determined according to the difference γ_diff between the yaw rate model value γ_clc and the sensor value γ_fil, and the target value increases as the absolute value of the difference γ_diff increases. The target stability factor SfTgt is corrected so that the value of the stability factor SfTgt increases. As a result, under the situation where the road surface friction coefficient μ is decreased, the value of the target stability factor SfTgt increases, the target yaw rate γ_Tgt can be decreased, and the behavior of the vehicle 1000 can be stabilized.

  In addition, since the damping moment correction gain GainForMgDamp and the inertia compensation moment correction gain GainForMgTrans are decreased in accordance with the increase in the absolute value of the difference γ_diff, the first control target moment is reduced under a situation where the road surface friction coefficient μ is decreased. The value of Mg1 can be reduced. Therefore, the turning support of the vehicle 1000 can be optimally controlled, and the behavior of the vehicle 1000 can be stabilized.

<2. Second Embodiment>
Next, a second embodiment of the present invention will be described. In the second embodiment, the motors 108, 110, 112 of the front wheels 100, 102 and the rear wheels 104, 106 are used as indices for varying the target stability factor SfTg, the damping moment correction gain GainForMgDamp, and the inertia compensation moment correction gain GainForMgTrans. , 114 and the deviation of the actual rotational speed of each of the front wheels 100, 102 and the rear wheels 104, 106 corresponding to the motors 108, 110, 112, 114, of the wheel having the maximum absolute value. Deviation ΔMotRev is used. As the road surface condition is lower μ, the front wheels 100 and 102 and the rear wheels 104 and 106 idle and the deviation ΔMotRev increases, so that the deviation ΔMotRev reflects the road surface state in the same way as γ_diff. Therefore, the deviation ΔMotRev can be used as an index for changing the target stability factor SfTg, the damping moment correction gain GainForMgDamp, and the inertia compensation moment correction gain GainForMgTrans.

  FIG. 24 is a schematic diagram illustrating a configuration of the control device 202 according to the second embodiment. The basic configuration of the control device 202 according to the second embodiment is the same as the configuration of the control device 200 according to the first embodiment, but includes a rotation speed difference detection unit 290 that detects the deviation ΔMotRev. The correction processing unit 222 calculates the target stability factor SfTgt based on the deviation ΔMotRev. The calculation of the target stability factor SfTgt based on the deviation ΔMotRev is performed in the same manner as the calculation of the target stability factor SfTgt based on the difference γ_diff in the first embodiment, and the target is obtained from the map with the horizontal axis in FIG. 7 as the deviation ΔMotRev. A stability factor SfTgt is calculated. Here, since the deviation ΔMotRev corresponds to a parameter that represents the road surface condition, the rotation speed difference detection unit 290 corresponds to a component that acquires a parameter that represents the road surface condition.

  Further, the damping control moment calculation unit (steady term calculation unit) 234a and the inertia compensation moment calculation unit (transient term calculation unit) 234b calculate the target damping moment MgDampTgt and the target inertia compensation moment MgTransTgt based on the deviation ΔMotRev, respectively. Calculation of the target damping moment MgDampTgt and the target inertia compensation moment MgTransTgt based on the deviation ΔMotRev is performed in the same manner as the calculation of the target damping moment MgDampTgt and the target inertia compensation moment MgTransTgt based on the difference γ_diff in the first embodiment. 9. A target damping moment MgDampTgt and a target inertia compensation moment MgTransTgt are calculated from a map in which the horizontal axis in FIG. 13 is the deviation ΔMotRev.

  In calculating ΔMotRev, an average value may be taken as the absolute value of the deviation between the target rotational speed of each motor of each wheel and the actual rotational speed of each wheel, and may be designated as ΔMotRev. Specify the deviation ΔMotRev in any combination that establishes vehicle control, such as selecting the motor with the maximum absolute value from the deviation between the target motor speed and the actual motor speed, or using the average absolute value as an index. May be. Further, control may be performed by using as a parameter a parameter that maximizes the slip ratio of each wheel obtained from the wheel speed and the vehicle speed.

  As described above, according to the second embodiment, the target stability factor SfTgt is corrected so that the value of the target stability factor SfTgt increases as the absolute value of the deviation ΔMotRev increases. As a result, under the situation where the road surface friction coefficient μ is decreased, the value of the target stability factor SfTgt increases, the target yaw rate γ_Tgt can be decreased, and the behavior of the vehicle 1000 can be stabilized.

  In addition, since the damping moment correction gain GainForMgDamp and the inertia compensation moment correction gain GainForMgTrans are decreased in accordance with the increase in the absolute value of the deviation ΔMotRev, the first control target moment is reduced under the condition that the road surface friction coefficient μ is decreased. The value of Mg1 can be reduced. Therefore, the turning support of the vehicle 1000 can be optimally controlled, and the behavior of the vehicle 1000 can be stabilized.

<3. Third Embodiment>
Next, a third embodiment of the present invention will be described. In the third embodiment, environment information μ_prev acquired from the external field recognition hand 170 is used as an index for changing the target stability factor SfTg, the damping moment correction gain GainForMgDamp, and the inertia compensation moment correction gain GainForMgTrans.

  The external recognition unit 170 includes a pair of left and right cameras having image sensors such as a CCD sensor and a CMOS sensor, and can capture an external environment outside the vehicle and recognize the external environment as image information. The external environment recognition unit 170 according to the present embodiment includes a color camera that can acquire color information as an example. The external recognition unit 170 can estimate the road surface friction coefficient μ based on a pair of captured left and right stereo images.

  Specifically, the external recognition unit 170 determines whether the road surface is wet or snowy based on the acquired environmental information, and estimates the road surface friction coefficient μ according to the determined road surface state. The estimated value μ_prev of the estimated road surface friction coefficient μ is output. Here, it is assumed that the value of the road surface friction coefficient μ decreases as the estimated value μ_prev increases. Since the estimated value μ_prev corresponds to a parameter representing the road surface condition, the external recognition unit 170 corresponds to a component that acquires a parameter representing the road surface condition.

  The following method can also be used as a method for estimating the road surface friction coefficient μ. For example, a technique using a vehicle motion model in which the behavior of a vehicle is modeled based on the vehicle motion theory is well known. In this method, based on the actual vehicle motion state (for example, slip angle), for example, the vehicle motion model motion state assuming a high μ road and the vehicle motion model motion state assuming a low μ road are obtained. By comparison, the current friction coefficient μ is estimated. Details of the method for estimating the friction coefficient μ are disclosed in, for example, Japanese Patent Application Laid-Open No. 2000-071968, and should be referred to if necessary. In addition to this, for example, as disclosed in JP 2003-237558 A, the friction coefficient μ may be estimated based on the speed difference between the two wheels 6 and the acceleration. Further, as disclosed in, for example, Japanese Patent Application Laid-Open No. 2002-127882, the coefficient of friction μ is taken into consideration in the motion state of the vehicle in consideration of the detection result obtained by detecting the road surface condition obtained from the external environment recognition unit 170. May be estimated. Thus, in the present embodiment, a method for estimating the friction coefficient based on the state of the vehicle can be widely used.

  As the road surface condition is lower μ, the estimated value μ_prev of the road surface friction coefficient μ becomes larger, and the estimated value μ_prev reflects the road surface condition in the same manner as γ_diff. Therefore, the estimated value (μ_prev) can be used as an index for changing the target stability factor SfTg, the damping moment correction gain GainForMgDamp, and the inertia compensation moment correction gain GainForMgTrans.

  FIG. 25 is a schematic diagram illustrating a configuration of a control device 204 according to the third embodiment. The basic configuration of the control device 204 according to the third embodiment is the same as the configuration of the control device 200 according to the first embodiment, but the correction processing unit 222 uses the target stability based on the estimated value μ_prev. The factor SfTgt is calculated.

  FIG. 26 is a characteristic diagram showing a map for calculating the target stability factor SfTgt. FIG. 27 is a flowchart showing a process for calculating the target stability factor SfTgt. In the process of FIG. 27, first, in step S130, it is determined whether or not μ_prev ≦ TH1, and if μ_prev ≦ TH1, the process proceeds to step S132 to set SfTgt = Sf1. On the other hand, if μ_prev> TH1 in step S130, the process proceeds to step S134, and it is determined whether μ_prev ≧ TH2, and if μ_prev ≧ TH2, the process proceeds to step S136 to set SfTgt = Sf2.

  If μ_prev <TH2 in step S134, the process proceeds to step S138, and SfTgt is calculated from the following equation (21).

  As described above, according to the processing of FIG. 27, the target stability factor SfTgt can be calculated according to the map of FIG.

  Further, the damping control moment calculation unit (steady term calculation unit) 234a and the inertia compensation moment calculation unit (transient term calculation unit) 234b obtain the target damping moment MgDampTgt and the target inertia compensation moment MgTransTgt based on the estimated value (μ_prev). Calculate each.

  FIG. 28 is a characteristic diagram showing a map for calculating the damping moment correction gain GainForMgDamp. FIG. 29 is a flowchart showing a process for calculating the damping moment correction gain GainForMgDamp. In the process of FIG. 29, first, in step S140, it is determined whether or not μ_prev ≧ TH1_P. If μ_prev ≧ TH1_P, the process proceeds to step S142 to set GainForMgDamp = MAX_GAIN. On the other hand, if μ_prev <TH1_P in step S140, the process proceeds to step S144, and it is determined whether μ_prev ≦ TH2_P. If μ_prev ≦ TH2_P, the process proceeds to step S146, and the damping moment correction gain is calculated from the following equation (22). GainForMgDamp is calculated.

  If μ_prev> TH2_P in step S144, the process proceeds to step S148 to determine whether μ_prev ≦ TH3_P. If μ_prev ≦ TH3_P, the process proceeds to step S150 to set GainForMgDamp = MID_GAIN.

  If μ_prev> TH3_P in step S148, the process proceeds to step S152 to determine whether μ_prev ≦ TH4_P. If μ_prev ≦ TH4_P, the process proceeds to step S154, and the damping moment correction gain is calculated from the following equation (23). GainForMgDamp is calculated.

  If μ_prev> TH4_P in step S152, the process proceeds to step S156, and GainForMgDamp = MIN_GAIN is set.

  As described above, according to the processing of FIG. 29, the damping moment correction gain GainForMgDamp can be calculated according to the map of FIG.

  FIG. 30 is a characteristic diagram showing a map for calculating the inertia compensation moment correction gain GainForMgTrans. FIG. 31 is a flowchart showing a process for calculating the inertia compensation moment correction gain GainForMgTrans. In the process of FIG. 31, first, in step S160, it is determined whether or not μ_prev ≦ TH1_P. If μ_prev ≦ TH1_P, the process proceeds to step S162, and GainForMgTrans = MAX_GAIN. If μ_prev> TH1_P, the process proceeds to step S164 to determine whether μ_prev ≧ TH2_P. If μ_prev ≧ TH2_P, the process proceeds to step S166 to set GainForMgTrans = MIN_GAIN.

  If μ_prev <TH2_P in step S164, the process proceeds to step S168, and the inertia compensation moment correction gain GainForMgTrans is calculated from the following equation (24).

  As described above, according to the processing of FIG. 31, the inertia compensation moment correction gain GainForMgTrans can be calculated according to the map of FIG.

  As described above, according to the third embodiment, the value of the target stability factor SfTgt increases as the estimated value μ_prev of the road surface friction coefficient μ increases (lower μ), that is, with respect to the steering input. The target stability factor SfTgt is corrected so as to be understeered. As a result, under the situation where the road surface friction coefficient μ is decreased, the value of the target stability factor SfTgt increases, the target yaw rate γ_Tgt can be decreased, and the behavior of the vehicle 1000 can be stabilized.

  Further, since the damping moment correction gain GainForMgDamp and the inertia compensation moment correction gain GainForMgTrans are decreased in accordance with the increase in the estimated value μ_prev, the first control target moment Mg1 is reduced under a situation where the road surface friction coefficient μ is decreased. The value can be lowered. Therefore, the turning support of the vehicle 1000 can be optimally controlled, and the behavior of the vehicle 1000 can be stabilized.

  The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention pertains can come up with various changes or modifications within the scope of the technical idea described in the claims. Of course, it is understood that these also belong to the technical scope of the present invention.

150 Yaw Rate Sensor 200 Control Device 222 Correction Processing Unit 228 Subtraction Unit 230 Control Target Yaw Rate Calculation Unit 232 Feedback Yaw Rate Calculation Unit 234 First Control Target Moment Calculation Unit 250 Control Target Moment Calculation Unit 270 Second Control Target Moment Calculation Unit 280 Motor Required Torque Calculation unit

Claims (10)

A target yaw rate calculation unit that calculates the target yaw rate from a vehicle model that defines the relationship between the target yaw rate of the vehicle, the vehicle speed, and the steering angle by a target stability factor;
A road surface condition parameter obtaining unit for obtaining a parameter representing the road surface condition;
Based on a parameter representing the road surface condition, a correction processing unit that corrects the target stability factor;
A yaw rate acquisition unit for acquiring a first yaw rate obtained from a vehicle model and a second yaw rate obtained from a yaw rate sensor;
Equipped with a,
Parameter indicating the road surface condition is characterized by a differential der Rukoto between the first yaw rate and the second yaw rate, the control device of the vehicle.   The correction processing unit corrects the target stability factor so that the target yaw rate decreases as the absolute value of the difference between the first yaw rate and the second yaw rate increases. The vehicle control device according to claim 1.   As a yaw rate generated by the vehicle, a feedback yaw rate acquisition unit that acquires a feedback yaw rate for comparison with the target yaw rate;
  A first control target moment calculator that calculates a first control target moment based on a difference between the target yaw rate and the feedback yaw rate;
  The first control target moment calculation unit calculates a first control target moment by adding a steady damping control moment and a transient inertia compensation moment, and calculates the first yaw rate and the second yaw rate. The vehicle control device according to claim 1, wherein the damping control moment and the inertia compensation moment are corrected based on a difference.   The first control target moment calculator calculates the damping control moment so that the damping control moment or the inertia compensation moment decreases as the absolute value of the difference between the first yaw rate and the second yaw rate increases. The vehicle control device according to claim 3, wherein the inertia compensation moment is corrected.   The feedback yaw rate acquisition unit increases the distribution of the first yaw rate when the difference is small based on the difference between the first yaw rate and the second yaw rate, and the second yaw rate when the difference is large. 5. The vehicle control device according to claim 3, wherein a distribution of a yaw rate is increased and the feedback yaw rate is calculated from the first and second yaw rates. 6.   A second control target moment calculating unit that calculates a second control target moment that is a target value of vehicle turning control from the Ackermann geometry based on the steering angle and the vehicle speed;
  Based on the vehicle speed, the control target moment is calculated by weighting the first control target moment and the second control target moment. The higher the vehicle speed, the more the first control target moment than the second control target moment. A control target moment calculator for calculating the control target moment by increasing the distribution;
  The vehicle control device according to claim 3, comprising:   The motor control part which controls individually the motor which drives each of a right-and-left wheel so that the feedback yaw rate may turn into the target yaw rate based on the control target moment, The motor control part which controls each separately is provided. Vehicle control device.   The vehicle control device according to claim 1, wherein the parameter representing the road surface condition is a difference between an actual rotational speed of a motor driving the vehicle and a target rotational speed.   The vehicle control device according to claim 1, wherein the parameter representing the road surface condition is an estimated value of a road surface friction coefficient estimated by an in-vehicle camera.   Calculating the target yaw rate from a vehicle model in which the relationship between the target yaw rate of the vehicle and the vehicle speed and steering angle is defined by a target stability factor;
  Obtaining parameters representing road surface conditions;
  Correcting the target stability factor based on a parameter representing the road surface condition;
  Obtaining a first yaw rate determined from a vehicle model and a second yaw rate determined from a yaw rate sensor;
  With
  The vehicle control method characterized in that the parameter representing the road surface condition is a difference between the first yaw rate and the second yaw rate.

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