Abstract:
A method of controlling vehicle stability includes the steps of obtaining a measured yaw rate from the vehicle, generating a predicted yaw rate based on the measured yaw rate, calculating a first error signal based on a difference between the measured yaw rate and a desired yaw rate, calculating a second error signal based on a difference between the predicted yaw rate and the desired yaw rate, and sending a selected one of the first and second error signals to a yaw controller to conduct stability control. The predicted yaw rate can be generated by sending the measured yaw rate through a lead filter.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of and claims the benefit of PCT Application No. PCT/US2013/031469, filed on Mar. 14, 2013, which claims the benefit of U.S. Provisional Application Ser. No. 61/662,553, filed Jun. 21, 2012, which applications are fully incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present teachings relate to vehicle stability control, and more particularly to active vehicle stability control using predictive methodologies. 
     BACKGROUND 
     Vehicle stability systems may engage anti-lock braking systems (ABS) and/or electronically-controlled limited-slip differentials (ELSDs) to improve vehicle traction and stability. For example, when a vehicle attempts to accelerate or climb on a split-mu, low-high friction surface, the ABS and the ELSD may be controlled to send more driving torque to the driven wheel so the vehicle can maintain longitudinal motion, sending more traction torque to the higher friction wheel. However, at higher vehicle speeds, yaw stability must be carefully controlled, particularly near the vehicle&#39;s stability limit, to prevent undesired yaw motion so the vehicle does not deviate laterally from the driver&#39;s intended direction. 
     Generally, yaw control in the stability system can be conducted by comparing a desired vehicle yaw rate with a measured vehicle yaw rate obtained from an on-board Inertia Measurement Unit (IMU) sensor. The desired yaw rate can be calculated in real time using a vehicle model calibrated with the desired vehicle handling, characteristics. When the measured yaw rate differs from the desired yaw rate, a yaw controller is triggered to correct the yaw rate and reduce the difference between the measured and desired values. 
     A fast response time is desirable to achieve proper vehicle yaw control. However, actuator and sensor delay can significantly delay corrections to an input in the yaw controller and therefore delay engagement and disengagement of the ABS and/or the ELSD for stability control. This delay can reduce the overall effectiveness of the vehicle yaw control system. Thus, it is important to minimize delays in both engaging and disengaging the vehicle stability system. 
     There is a desire for a yaw control that has a fast response time to minimize response time delay in a vehicle stability system. 
     SUMMARY 
     One aspect of the present teachings is directed to a method of controlling vehicle stability. The method includes the steps of obtaining a measured yaw rate from the vehicle, generating a predicted yaw rate based on the measured yaw rate, calculating a first error signal based on a difference between the measured yaw rate and a desired yaw rate, calculating a second error signal based on a difference between the predicted yaw rate and the desired yaw rate, and sending the greater of the first and second error signal to a yaw controller to conduct stability control. 
     Another aspect of the present teachings is directed to a method of controlling stability of a vehicle. The method includes obtaining a measured yaw rate from the vehicle, generating a predicted yaw rate based on the measured yaw rate, wherein the predicted yaw rate is obtained by sending the measured yaw rate through a lead filter, calculating a first error signal based on a difference between the measured yaw rate and a desired yaw rate, calculating a second error signal based on a difference between the predicted yaw rate and the desired yaw rate, sending the greater of a saturated value of the first and second error signal to a yaw controller, which generates a yaw command, and sending the yaw command to at least one of an anti-lock braking system and an electronic limited slip differential to conduct stability control. 
     Another aspect of the teachings is directed to a vehicle stability control system using the above methods. 
     Various aspects of the present teachings will become apparent to those skilled in the art from the following detailed description of the embodiments, when road in light of the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the invention will now be described, by way of example, with reference to the accompanying drawings, wherein: 
         FIG. 1  is a schematic diagram of a vehicle stability system incorporating a yaw control according to an aspect of the teachings; 
         FIG. 2  is a block diagram illustrating a yaw control strategy according to an aspect of the teachings; 
         FIG. 3  is a block diagram illustrating a yaw control strategy according to another aspect of the teachings; 
         FIG. 4  is an examples of test results using a yaw control strategy according to an aspect of the teachings; 
         FIG. 5  is an expanded example of test results using a yaw control strategy according to an aspect of the teachings; 
         FIG. 6  is an example illustrating a predicted yaw rate and a measured yaw rate. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail with respect to embodiments of the present teachings, examples of which are described herein and illustrated in the accompanying drawings. While concepts will be described in conjunction with embodiments, it will be understood that the invention is not intended to limit the specific disclosures associated with the embodiments. On the contrary, the invention is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the invention as defined by the appended claims. 
       FIG. 1  is a schematic diagram of a vehicle  10  that can incorporate a vehicle stability control system according, to one aspect of the present teachings. The vehicle  10  may include an engine  11 , an anti-lock brake system (ABS)  12  that controls braking to wheels  13 , and an electronically limited slip differential (ELSD)  14 . Although  FIG. 1  shows the ELSD  14  disposed in the rear axle, the ELSD  14  may be placed in the front axle or in both the front and rear axles without departing from the scope of the teachings. An inertia measurement unit (IMU) sensor  15  monitors the yaw rate of the vehicle  10  and sends it to an electronic control unit (ECU)  16  having a yaw controller  18 . The ECU  16  can use the measured yaw rate in a yaw control strategy so that the yaw controller  18  in the ECU  16  can output a yaw command signal to vehicle  10  components, such as but not limited to the ABS  12  and the ELSD  14 , for stability control as described in greater detail below. 
       FIG. 2  is a block diagram illustrating a yaw control strategy  20  according to one aspect of the present teachings. Generally, the strategy  20  can provide a yaw command to one or more components (e.g., an ABS or an ELSD in a vehicle  10  to control engagement and disengagement of a stability control system in the vehicle  10 . The vehicle  10  outputs a measured yaw rate r MEAS . Note that the terms “measured yaw rate” and “feedback” are used interchangeably in the present description, with the term “feedback” referring more particularly to the measured yaw rate r MEAS  after yaw control has been conducted. The measured yaw rate r MEAS  is sent to a first comparator  26  and a lead filter  28 . 
     The first comparator  26  compares the measured yaw rate r MEAS  with a model of a desired yaw rate r DES    30 . The desired yaw rate r DES  can be approximated and characterized by the following equation: 
     
       
         
           
             
               
                 
                   
                     r 
                     DES 
                   
                   = 
                   
                     
                       
                         V 
                         x 
                       
                       ⁢ 
                       ρ 
                     
                     
                       L 
                       + 
                       
                         
                           
                             k 
                             as 
                           
                           ⁢ 
                           
                             V 
                             x 
                             2 
                           
                         
                         g 
                       
                     
                   
                 
               
               
                 
                   eq 
                   . 
                   
                       
                   
                   ⁢ 
                   
                     ( 
                     1 
                     ) 
                   
                 
               
             
           
         
       
     
     where V x  is the vehicle speed, ρ is the vehicle steer angle, L is the wheelbase length, k us  is the vehicle understeer gradient, and g is the gravitational constant. The first comparator  26  outputs the difference between the measured yaw rate r MEAS  and the desired yaw rate r DES  as a first error signal r error1 . 
     The lead filter  28  is included in the yaw control strategy  20  to predict a vehicle yaw rate before receiving actual yaw feedback (i.e., a change in the measured yaw rate r MEAS ) from the vehicle  10 . The output of the lead filter  28  will have a negative time shift and lead the input. The measured yaw rate r MEAS  is sent through the lead filter  28 , and the lead filter  28  outputs a predicted yaw rate r PRED . The predicted yaw rate r PRED  is the lead filter&#39;s response to the measured yaw rate r MEAS . The lead filter is characterized by the following transfer function G(s): 
                     G   ⁡     (   s   )       =         Y   ⁡     (   s   )         X   ⁡     (   s   )         =     K   ⁢       (     s   +   a     )       (     s   +   b     )                   eq   .           ⁢     (   2   )                 
where X(s) is the input signal (i.e., the measured yaw rate r MEAS ), Y(s) is the output signal, K is the filter gain, −a is the filter zero, and −b is the filter pole, with b being greater than a. In one aspect of the teachings, a, b, and K may be chosen so that the output of the lead filter  28  has a magnitude of 0 db (i.e., the same magnitude as the input) and a phase shift in the time domain equal to a desired prediction time.
 
     The predicted yaw rate r PRED  output by the lead filter  28  and the desired yaw rate r DES  output by the model  30  may be sent to a second comparator  32 . The second comparator  32  outputs the difference between the predicted yaw rate r PRED  and the desired yaw rate r DES    30  as a second error signal r Error2 . 
     The first and second error signals r Error1 , r Error2  are then each sent to a multiplier  34 ,  36  where the first error signal r Error1  is multiplied by the sign of the measured yaw rate r MEAS , and the second error signal r Error2  is multiplied by the sign of r PRED . 
     In the aspect of the present teachings shown in  FIG. 2 , the control strategy  20  designed to activate when the vehicle is oversteering. To do this, the first and second error signals r Error1 , r Error2  are sent to saturation functions  38   a ,  38   b  so that only positive error signals are sent to the yaw controller  18 . As shown in  FIG. 2 , the saturation functions  38   a ,  38   b  allow the error signals r Error1 , r Error2  to pass through if they are positive and block them if they are negative. The two error signals r Error1 , r Error2  are then sent through a third comparator  42 , which outputs the maximum of the two error signals. The maximum error signal is then passed through a deadhand filter  44 , which blocks small error signals from being output to the yaw controller  18 . The deadband filter  44  prevents unwanted engagements of the stability control in the vehicle  10  when the yaw error is low. Since only positive error signals r Error1 , r Error2  reach the yaw controller  18  (because the saturation functions  38   a ,  38   b  prevent negative error signals from passing through), the control strategy  20  in  FIG. 2  activates only during oversteer conditions. 
     A variation of the control strategy  20  is shown in  FIG. 3 . This control strategy  20  can activate during both oversteer and understeer conditions. For oversteer conditions, the control strategy  20  in  FIG. 3  works the same way as the control strategy  20  in  FIG. 2 , with the saturation functions  38   a ,  38   b  allowing only positive error signals to pass through and the third comparator  42  outputting the maximum of the two error signals. For understeer conditions, the two error signals r Error1 , r Error2  can also be sent through additional saturation functions  45   a ,  45   b  that allow the error signals r Error , r Error2  to pass through if they are negative and block them if they are positive. The negative error signals r Error1 , r Error2  are then sent to a fourth comparator  46  that outputs the minimum of the two error signals. Two deadband filters  44   a ,  44   b  receive the outputs of the comparators  42 ,  46  to prevent unwanted engagements of the stability control strategy  20  when the yaw error is low. In this variation, both positive and negative signals reach the law controller  18 , so the control strategy  20  activates during both oversteer and understeer conditions if the error is large enough to pass through either of the deadband filters  44   a ,  44   b.    
     In both embodiments described above, the yaw controller  18  responds to the error signal output from the deadband filter  44  by outputting a yaw command to the vehicle  10 . In one aspect of the teachings, the yaw controller  18  can be implemented through a set of cascading proportional-integral-derivative controllers (PIDs). In one example, a first PID generates a clutch torque command in response to the yaw error. The clutch torque command may then be converted to a desired clutch pressure using a model tuned for the vehicle&#39;s  10  particular application. The desired clutch pressure can then be compared to an actual clutch pressure, and a difference between the desired and actual clutch pressures may be used to generate a command (e.g., a pulse width modulated (PWM) voltage command) for a control valve, motor, or pump of a vehicle  10  clutch to build clutch pressure for clutch engagement. For example, the PWM command may be proportional to a control current sent to the valve, motor, or pump. If a ABS system is used for stability control, a similar process may be used to generate a brake torque command in place of the clutch torque command. Regardless of how the yaw controller  18  output is used by the vehicle  10 , the yaw command output by the yaw controller  18  is sent to components in the vehicle  10  (e.g., clutches, differentials, braking systems, etc.) that can be operated to stabilize the vehicle  10 . 
     By using the larger of the two error signals, engagement of the stability control in the vehicle  10  will be triggered faster due to the negative phase shill of the lead filter. Since the original error signal lags the output of the lead filter  28 , yaw control terminates when the first yaw rate error r Error1  (which is calculated from the measured yaw rate r MEAS ) drops below the deadband filter  44  threshold. More particularly, estimating, the yaw rate r DES , shifting the measured yaw rate r MEAS  backwards in time, calculating error signals based on both the predicted yaw rate and the measured yaw rate, and operating the yaw controller  18  based only on the extremes of the error signals (either a maximum or a minimum error signal) causes the yaw controller  18  to react to the predicted yaw rate before it even receives information regarding the measured yaw rate, thereby providing fast stability control. Also, as the predicted yaw rate approaches the measured yaw rate, the time shill of the measured yaw rate will cause the second error signal r Error2  to decay faster than the first error signal r Error1  and thereby cause the yaw controller  18  to react to r Error1 . The control strategy  20  therefore reduces the engagement time while maintaining the original control termination point. By reducing the engagement time, the overall effectiveness of the stability controller is improved. By utilizing the lead filter feedback in combination with real time feedback, the stability system engagement time can be greatly reduced. 
       FIG. 4  illustrates sample test results obtained during a double lane change test using the yaw control strategy  20  described above. For comparison, the test results show the yaw rate (in degrees per second) and yaw command (in percent duty cycle of the PWM command) The graphs show test results without any active stability control, with stability control but without a lead filter, and with stability control having an activated lead filter.  FIG. 5  illustrates the same results shown in  FIG. 4  within the 7.5 second to 9.5 second time range to show the differences between the test results in more detail. 
     For the illustrated sample tests, the lead filter  28  was tuned to predict the vehicle yaw rate 100 ms in advance of the measured yaw rate. This directly correlates to a 100 ms reduction in engagement time. In the test results, a yaw control strategy  20  using a lead filter  28  can provide a 17% improvement in peak yaw damping when compared to a normal feedback strategy (a 10.7 degrees per second reduction with a lead filter  28  vs. 9.1 degrees per second reduction without a lead filter  28 ). The control strategy  20  described above therefore reduces the yaw rate more quickly and to a greater degree than currently known strategies, making it more effective in maintaining, vehicle stability. 
       FIG. 6  illustrates one example of a predicted yaw rate output by the lead filter  28  compared with an actual measured yaw rate. As can be seen in  FIG. 6 , the predicted yaw rate output by the lead filter  28  is very close to the actual measured yaw rate. 
     It will be appreciated that the above teachings are merely exemplary in nature and is not intended to limit the present teachings, their application or uses. While specific examples have been described in the specification and illustrated in the drawings, it will be understood by those of ordinary skill in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present teachings as defined in the claims. Furthermore, the mixing and matching of features, elements and/or functions between various examples is expressly contemplated herein so that one of ordinary skill in the art would appreciate from this disclosure that features, elements and/or functions of one example may be incorporated into another example as appropriate, unless described otherwise, above. Moreover, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from the essential scope thereof. Therefore, it is intended that the present teachings not be limited to the particular examples illustrated by the drawings and described in the specification as the best mode presently contemplated for carrying out the teachings of the present disclosure, but that the scope of the present disclosure will include any embodiments falling within the foregoing description and the appended claims.