Patent Abstract:
The present invention determines a longitudinal wheel speed of an individual wheel from an angular rate signal from a wheel rotation sensor. Vehicle suspension information or operating characteristics are input to a suspension system mathematical model to determine instantaneous rolling radius, taking into account changes in tire rolling radius resulting from vertical motion of the road surface. Improved accuracy of wheel speed permits less severe filtering of wheel speeds in detecting wheel slip and/or modified speed and acceleration thresholds in slip control.

Full Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of PCT International Application No. PCT/US01/27659, filed Sep. 7, 2001 and U.S. patent application Ser. No. 09/658,539, filed Sep. 9, 2000. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates in general to improving the determination of wheel speed for electronically-controlled vehicular braking systems, and, more specifically, to using suspension system information and a suspension system model to determine instantaneous wheel rolling radius for improved wheel speed calculation. 
     Electronically-controlled active vehicular braking systems include anti-lock braking (ABS), traction control (TC), and yaw stability control (YSC) functions. In such braking systems, sensors deliver input signals to an electronic control unit (ECU). The ECU sends output signals to electrically activated devices to apply, hold, and dump (relieve) pressure at wheel brakes of a vehicle. Electrically activated valves and pumps are used to control fluid pressure at the wheel brakes. Such valves and pumps can be mounted in a hydraulic control unit (HCU). The valves typically include two-state (on/off or off/on) solenoid valves and proportional valves. 
     A basic function of these braking systems is to detect wheel slip (e.g., skidding or loss of traction) and actuate the brakes (or reduce torque from the engine) in a manner to reduce or control wheel slip. An individual wheel speed is measured and wheel slip is detected by 1) comparing the individual wheel speed to the overall speed of the vehicle, and/or 2) monitoring the rate of change in the measured wheel speed. An angular rotation sensor mounted at the wheel produces pulses at a frequency proportional to the velocity at which the wheel spins. Using a predetermined nominal rolling radius of the particular wheel/tire combination, prior art systems convert the angular velocity of the wheel into a longitudinal speed for the particular wheel. 
     During actual driving conditions, the instantaneous rolling radius at a particular wheel will vary from the predetermined nominal rolling radius due to various forces acting on the tire, such as road undulations and load variations. The changes in rolling radius introduce error or noise into prior art wheel speed determinations. In order to avoid false activations of the braking system, the wheel speed needs to be filtered to remove this noise and/or the activation thresholds desensitized. Therefore, performance could be improved if a more accurate measurement of individual instantaneous wheel speed could be obtained. 
     Electronically-controlled suspension systems typically include semi-active suspension systems and active suspension systems to provide active damping for a vehicle. In such suspension systems, sensors deliver input signals to an electronic control unit (ECU). The ECU sends output signals to electrically activated devices to control the damping rate of the vehicle. Such devices include actuators to control fluid flow and pressure. The actuators typically include electrically activated valves such as two-state (digital) valves and proportional valves. 
     SUMMARY OF THE INVENTION 
     The present invention employs information from a suspension sensor to determine an instantaneous rolling radius for a particular wheel to improve a wheel speed measurement for that wheel. 
     According to one aspect of the invention, a method is provided for determining longitudinal speed of a vehicle wheel for use in a vehicle slip control system. An instantaneous angular rate of a vehicle wheel is measured via an individual wheel speed sensor. At least one operating characteristic of a portion of a vehicle suspension system associated with the vehicle wheel is measured, the suspension operating characteristic at least in part representative of an instantaneous rolling radius of the vehicle wheel. An instantaneous rolling radius deviation corresponding to the vehicle wheel is determined in response to the suspension operating characteristic. A longitudinal wheel speed signal is generated in response to the instantaneous angular rate and the instantaneous rolling radius deviation. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a schematic drawing showing a wheel, tire, and suspension components together with a wheel angular rate sensor and the calculations to determine longitudinal speed. 
     FIG. 2 is a block diagram showing a prior art braking system. 
     FIG. 3 is a block diagram showing a preferred embodiment of the wheel speed determination of the present invention. 
     FIG. 4 is a block diagram showing another preferred embodiment of the wheel speed determination of the present invention. 
     FIG. 5 is a plot showing an unfiltered wheel speed signal and a comparison of filtered signals between the invention and the prior art. 
     FIG. 6 is a schematic diagram showing a suspension model used in a preferred embodiment. 
     FIG. 7 is a block diagram showing another preferred embodiment of the invention. 
     FIG. 8 is a schematic diagram of an integrated vehicular control system useful in practicing the present invention. 
     FIG. 9 is a schematic diagram of another embodiment of an integrated vehicular control system. 
     FIG. 10 is a schematic diagram of yet another embodiment of an integrated vehicular control system. 
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     Turning to FIG. 1. a wheel for one particular corner of a vehicle is shown. A wheel assembly  10  includes a rim  11  and a tire  12 . During smooth, steady state operation at specified conditions (such as specified tire pressure), tire  12  has a certain shape so that the rolling radius of wheel assembly  10  has a nominal radius R 0 . During dynamic driving conditions, however, forces on tire  12  cause it to deform or deflect from its nominal shape resulting in variability of rolling radius. An instantaneous rolling radius R i  varies from nominal radius R 0  by a radial deviation ΔR, which may be positive or negative. 
     Wheel assembly  10  is connected to a suspension system  13 . Associated with wheel assembly  10  are a strut  14  and a spring  15  connected to the vehicle by a strut retainer  16 . A shock absorber  17  is coupled between strut  14  and strut retainer  16  to provide damping. Suspension system  13  may preferably is include an electronic suspension control system (not shown) for adjusting suspension performance (e.g., damping characteristics) in response to various operating characteristics of the suspension, such as strut displacement. A suspension sensor  18  (such as a displacement sensor mounted to a fixed part of the vehicle body or frame) measures at least one such operating characteristic, and may be part of an active suspension control system or may be dedicated for use with the active braking control system. 
     A toothed-wheel  20  is mounted for rotation with wheel assembly  10 . A sensor  21  is mounted in a fixed location adjacent toothed-wheel  20  and may be a variable reluctance sensor or an optical sensor for generating an electrical pulse signal as known in the art. The resulting pulse signal has a pulse rate determined by the angular rotation rate of toothed-wheel  20 . The period between leading pulse edges corresponds to the difference between times t 1  and t 2 . The pulse frequency can be determined in a frequency calculator  22  as the inverse of the time difference t 2  minus t 1 , or by counting a number of pulse edges detected during a fixed time window, for example. The pulse repetition frequency is multiplied in a multiplier  23  by a conversion factor to produce a longitudinal speed of the wheel. The conversion factor is a constant that relates the angular rotation frequency to a desired speed format (e.g., meters per second, miles per hour, etc.) and assumes a nominal rolling radius R 0 . To the extent that the instantaneous rolling radius R i  varies from R 0 , the instantaneous longitudinal speed will be in error. 
     FIG. 2 shows further details of a prior art slip control system, such as an anti-lock brake (ABS) system, traction control (TC) system, or vehicle stability control (VSC) system, including a physical portion  25  and a control portion  26 . Physical portion  25  includes tire and suspension system  27  which is subjected to road interaction and other forces as the vehicle moves. Based on wheel rim size and nominal tire shape, the rolling radius for any particular vehicle wheel is approximately a nominal rolling radius R 0 . Tire deflection in response to the various forces acting on the tire causes a rolling radius deviation ΔR to be superimposed on the nominal value in a summation  28 . Wheel  29  then spins with an instantaneous rolling radius R i  in direct response to the vehicle speed v. Thus, even though the physical wheel  29  is spinning at a rate dependent upon instantaneous rolling radius R i , prior art systems determine wheel speed as though it depended on R 0 . 
     Control portion  26  includes an angular rotation sensor and calculation block  30  to produce a longitudinal wheel speed signal which is filtered in a lowpass filter  31 . The filtered longitudinal speed is input to a slip detection and correction controller  32 , a vehicle speed estimator  33 , and an acceleration estimator  34 . Estimators  33  and  34  have their outputs coupled to controller  32 . The output of slip detection and correction controller  32  is coupled to brake actuators  35  which are actuated as needed to limit wheel slip. 
     The variation in rolling radius results in an additive noise in the output of sensor  30 . To avoid false detection of wheel slip and false activation of brake actuators  35 , the sensor output is filtered in lowpass filter  31  to at least partially remove the noise. This technique is partially effective since tire deflections occur with some of their frequencies above the frequencies of interest for slip detection. A cutoff frequency of about 20 Hz is typical for the lowpass filter in the prior art. However, the lowpass filtering also reduces performance when a false detection is not occurring by increasing the time required to detect the slippage. 
     An improved system is shown in FIG. 3. A sensor and rate calculator  40  provides an instantaneous angular rate signal to one input of a multiplier  41 . 
     A suspension sensor  43  measures a suspension system operating characteristic and provides a signal to a suspension model  44 . The suspension operating characteristic is at least in part representative of an instantaneous rolling radius of the vehicle wheel. Based on the suspension model, an instantaneous rolling radius deviation ΔR is determined. ΔR is added to the nominal rolling radius R 0  in a summer  45  to produce an instantaneous rolling radius R i . If any particular units are desired for the longitudinal speed signal, a conversion factor can be applied in a conversion block  46 , otherwise, the instantaneous rolling radius signal R i  is applied directly to the second input of multiplier  41 . By multiplying the instantaneous angular rate signal and the instantaneous rolling radius, the output of multiplier  41  provides an improved longitudinal speed signal. The output of multiplier  41  is lowpass filtered in a filter  42  and then provided to the slip controller. 
     An alternative embodiment is shown in FIG. 4 as an add-on to existing systems. While the embodiment of FIG. 3 modifies the initial calculation of longitudinal wheel speed itself, for existing products it may be more convenient to instead apply a correction factor to an existing calculation of wheel speed based on the nominal rolling radius. Since existing products typically implement the filtering and speed calculations digitally in software, it may sometimes be desirable to retain existing software code and to insert a patch that applies a correction factor to the prior art speed signal. Thus, FIG. 4 shows that an angular rate signal from sensor/rate calculator  40  is applied to one input of a multiplier  47 . The nominal rolling radius R 0  is applied to the second input of multiplier  47 , and a speed signal including any error from variation in the instantaneous rolling radius is coupled to a first input of a multiplier  48 . Instantaneous rolling radius deviation ΔR from suspension model  44  is coupled to a correction factor calculator  49 . A correction factor is calculated as the ratio of the actual instantaneous rolling radius R i  (determined as the sum of nominal rolling radius R 0  and the instantaneous rolling radius deviation ΔR) to the nominal rolling radius R 0 . Thus, for decreases in instantaneous rolling radius (i.e., ΔR is negative), the correction factor is less than one and a properly reduced longitudinal speed determination is made. For increases in instantaneous rolling radius (i.e., ΔR is positive), the correction factor is greater than one and a properly increased longitudinal speed determination is made. Calculator  49  may include a summer for adding R 0  and ΔR followed by a gain equal to 1/R 0 , for example. The correction factor is coupled to a second input of multiplier  48  and a corrected longitudinal wheel is speed signal at the output of multiplier  48  is coupled to the slip controller through filter  42 . 
     As a result of the improved accuracy of the longitudinal wheel speed determination, the need for filtering of the speed signal to avoid false activations is greatly reduced. While prior art filters used a typical cutoff frequency of about 20 Hz, the present invention can avoid false activation from remaining sources of random signal error while raising the cutoff frequency to 50 Hz. FIG. 5 shows a raw, unfiltered signal  50  from the angular rate sensor during a deceleration. A filtered signal  51  resulting from the prior art cutoff frequency shows a significant time lag before the deceleration can be detected. In contrast, the higher value of the cutoff frequency for a filtered signal  52  as enabled by the present invention more quickly follows the deceleration. Since the deceleration is more quickly detected, overall performance of the slip control system is greatly improved. 
     The suspension system model for determining deviation of the rolling radius will be discussed with reference to FIG.  6 . Block  55  represents the sprung mass M s  of a vehicle and block  56  the unsprung mass M u . There is a spring constant K s  between block  55  and block  56  and a spring constant K t  between block  56  and the ground  57 . There is also a damning constant C d  between blocks  55  and  56 . The model further includes height Z s  of the sprung mass, height Z u  of the unsprung mass, and height Z r  above terrain. 
     In a preferred embodiment, the suspension system model is implemented using a Kalman filter. The Kalman filter is designed using the constant coefficient linear time-invariant plant model of FIG.  6 . This assumption potentially reduces the accuracy of the estimation to a small operating region about an equilibrium point. The range of the operating region is dependent on the nonlinear nature of the system. To increase the accuracy over a larger range of the operating region, the extended Kalman filter implementation could be used. However, such design would be computation intensive and not as well suited for RAM/ROM and loop time constrained systems. A Kalman filter is useful is reducing the effects of process disturbances and measurement noise on the estimation process. The fact that the plant model used in the preferred embodiment contains constant coefficients means that the robustness of the estimation is limited to a certain variance in the constants. Therefore, changes in the actual plant versus the initial model will cause error in the estimation process. The variance allowed in each of the model parameters can be defined based on the accuracy requirement of the estimation. 
     The design of the Kalman filter assumes the following state definitions: 
     
       
         
           x 
           1 
           =Z 
           s 
           −Z 
           u 
         
       
     
     
       
         
           x 
           2 
           ={dot over (Z)} 
           s 
         
       
     
     
       
         
           x 
           3 
           =Z 
           u 
           −Z 
           r 
         
       
     
     
       
         
           x 
           4 
           ={dot over (Z)} 
           u 
         
       
     
     The following state equations are derived from the free body diagram of FIG.  6 : 
       {dot over (x)}   1   =x   2   −x   4             x   .     2     =       1     M   s            (         -     K   s            x   1       +     F   d     +     0.4        K   s         )                              {dot over (x)}   3   =x   4   −{dot over (Z)}   r             x   .     4     =       1     M   u            (         K   s          x   1       -       K   t          x   3       -     F   d     +     (       0.075        K   t       -     0.4        K   s         )       )                               
     Assuming the input u=F d , the process disturbance v={dot over (Z)} r , and the acceleration offset 0.4 K s /M s  and (0.075 K t -0.4 K s )/M u , the following state space representation is defined:          x   .     =         [         0       1       0         -   1                 -     K   s         M   s           0       0       0           0       0       0       1               K   s       M   s           0           -     K   t         M   u           0         ]        x     +       [         0             1     M   s               0               -   1       M   u             ]        u     +       [         0           0             -   1             0         ]        v     +     [         0               0.4        K   s         M   u               0               (       0.075        K   t       -     0.4        K   s         )       M   u             ]                              y=[ 1 0 0 0] x+w   
     
       
         
           {dot over (x)}=Ax+Bu+Gv+Λ 
         
       
     
     
       
         
           y=Cx+w 
         
       
     
     
       
           x   0   =[x   s0   −x   u0  0  x   u0  0] 
       
     
     The process disturbance v(t) and the measurement noise w(t) are stationary, zero mean, Gaussian white processes with covariance kernels of 
     
       
           E{v ( t ) v   T (τ)}= V δ( t −τ) 
       
     
     
       
           E{w ( t ) w   T (τ)}= W δ( t −τ). 
       
     
     Note that V≧0 because it is a covariance matrix. Also assume that W&gt;0. This assumption states that the noise affects all the measured outputs of the system, i.e., there are “no clean measurements.” 
     Assume that the initial state has mean and covariance given by 
     Mean: 
     
       
           E{x ( t   0 )}= {overscore (x)}   0   
       
     
     Covariance: 
     
       
           E{[x ( t   0 )− {overscore (x)}   0   ][x ( t   0 )− {overscore (x)}   0 ] T }=Σ 0   
       
     
     and that v, w, and x(t 0 ) are mutually uncorrelated. 
     Consider {circumflex over (x)}(t) an estimate of the state of the system at time t≧t 0 . Define the estimation error: e(t)=x(t)−{circumflex over (x)}(t) and the mean square estimation error: E{e(t)e(t) T }. Suppose that the input and (measured) output of the system for all prior times: u(t), y(t), t≧t 0  are known. The problem is then to use this information to construct an estimate {circumflex over (x)}(t) such that the mean square estimation error is minimized. 
     It turns out that the estimate {circumflex over (x)}(t) that minimizes the mean square error may be generated as the state of a dynamical system with the structure of an observer with a time-varying gain, L(t). The assumption is made that the estimation process is done for an arbitrarily long time, i.e., that t 0 →−∞. This assumption allows a constant estimator gain, L, to be designed by solving the dual Algebraic Riccatti equation: 
     
       
         {overscore (Σ)} A   T   +A{overscore (Σ)}+V−{overscore (Σ)}C   T   W   −1   C{overscore (Σ)}   
       
     
     
       
         
           L={overscore (Σ)}C 
           T 
           W 
           −1 
         
       
     
     Assuming (A,C) is detectable, then the primary design step is to choose the appropriate V and W such that the estimator has the appropriate properties, e.g., accuracy, bandwidth, and noise rejection. There exist some tactics in the literature for approaching the selection of V and W. Using the design of L, the Kalman filter implementation becomes 
     
       
           {circumflex over ({dot over (x)})}=A{circumflex over (x)}+Bu+L ( y−C{circumflex over (x)} )+Λ 
       
     
     
       
         z=[0 0 1 0 ]x   
       
     
     where z is the state output of interest. 
     Using this filter design to model the suspension system, a filter output equal to instantaneous rolling radius deviation from nominal, ΔR, is obtained. 
     While the model shown employs sprung mass height or strut position as a measured input to derive ΔR, the model could alternatively be designed to employ other suspension system operating characteristics such as strut velocity, strut force, strut internal pressures, bushing deflections, hub acceleration, body acceleration, tire stress, tire deflection, road position, and suspension member strain. Derivation of such models is within the skill of one normally skilled in the art. 
     Further improvements to the slip control system are shown in FIG.  7 . As a result of having a more accurate longitudinal wheel speed, other ameliorative actions taken in system design to avoid false activations can be reduced. For example, activation thresholds were previously set to produce a slower response to slip due to the possibility of signal error. Since signal error is reduced with the present invention, the activation thresholds can be tightened up to improve performance. Thus, FIG. 7 shows a difference block  60  which compares the improved longitudinal wheel speed signal to a reference signal. The reference signal represents the expected longitudinal wheel speed based on a determination of the overall vehicle speed. The difference signal represents any slip that is occurring at the particular wheel. The difference signal is coupled to a threshold block  61 . When slip exceeds the thresholds, then an activation signal is sent to an activation and control algorithm block  62 . Other than a tightening or reduction of thresholds, threshold block  61  and algorithm block  62  perform in a conventional manner. 
     Known slip control systems also monitor individual wheel speed accelerations to detect slip. Due to the greater mass of the whole vehicle, vehicle acceleration is much more constrained than is wheel acceleration. Thus, a measured wheel acceleration outside the range of the overall vehicle is also an indication of wheel slip. Wheel and overall vehicle acceleration estimates are determined in an acceleration estimate block  63  and are coupled to a threshold block  64 . Since the acceleration calculations are more accurate using the present invention, the thresholds used in block  64  can be more aggressive that those of the prior art. 
     In yet another improvement, the present invention employs a suspension and vehicle model  65  with an additional output representative of the contact patch of the individual tire at any given moment. Contact patch is the footprint of the portion of the tire actually in contact with the road. This can be determined by model  65  in response to the instantaneous rolling radius, for example. The contact patch signal is coupled to activation and control algorithm  62  which modifies its operation (e.g., braking force) based on the likelihood of slip at various amounts of contact patch. 
     The suspension modeling of the present invention can be implemented within a control module for a slip control system, such as ABS, TC, or VSC, since these modules typically already contain the necessary hardware. However, when an active suspension control system is present which also performs modeling, it may be desirable to perform portions of the invention in a suspension module. Therefore, integration of a suspension control system with a braking control system is discussed below. 
     A first embodiment of an integrated vehicular control system is indicated generally at  100  in FIG.  8 . The control system  100  is particularly adapted to control fluid pressure in an electronically-controlled vehicular braking system and an electronically-controlled vehicular suspension system. The braking system can include anti-lock braking, traction control, and vehicle stability control functions. The suspension system can include active damping functions. 
     The control system  100  includes a first electronic control unit (ECU)  102 . The first ECU  102  includes a signal processor  104  and a braking algorithm  106 . Various sensors  108  strategically placed in a vehicle deliver input signals  110  to the signal processor  104 . Specifically, a lateral acceleration sensor  112  delivers an input signal  114  to the signal processor  104 . A longitudinal acceleration sensor  115  delivers an input signal  116  to the signal processor  104 . A steering wheel sensor  117  delivers an input signal  118  to the signal processor  104 . A yaw rate sensor  120  delivers an input signal  122  to the signal processor  104 . Depending upon the braking functions of the braking system, some of the above-listed sensors and their associated input signals may be deleted and others may be added. For example, a braking system that provides only ABS and TC functions may not require some of the above-listed sensors. 
     The signal processor  104  delivers transfer signals  124  to the braking algorithm  106 . The braking algorithm  106  delivers output signals  126  to a hydraulic control unit (HCU)  128 . The HCU  128  can include electromechanical components such as digital and/or proportional valves and pumps (not illustrated). The HCU  128  is hydraulically connected to wheel brakes and a source of brake fluid, neither of which is illustrated. 
     The control system  100  also includes a second ECU  130 . The second ECU  130  includes a signal processor  132  and a suspension algorithm  134 . Various sensors  135  strategically placed in a vehicle deliver input signals  136  to the signal processor  132 . Specifically, a suspension state sensor  137  delivers an input signal  138  to the signal processor  132 . A suspension displacement sensor  139  delivers an input signal  140  to the signal processor  132 . A relative velocity sensor  141  delivers an input signal  142  to the signal processor  132 . An unsprung mass acceleration sensor  143  delivers an input signal  144  to the signal processor  132 . Depending upon the performance requirements of the suspension system, some of the above-listed sensors may be deleted and others may be included. 
     The second signal processor  132  delivers transfer signals  145  to the suspension algorithm  134 . The first signal processor  104  delivers transfer signals  146  to the suspension algorithm  134 . The suspension algorithm  134  delivers output signals  148  to suspension actuators  150 , only one of which is illustrated. The actuators  150  are electrically controlled devices such as dampers that vary and control a damping rate of a vehicle. An actuator  150  can include electromechanical components such as digital and proportional valves. 
     Information from the vehicular braking system can be shared with the vehicular suspension system. For example, ECU  102  can direct information to ECU  130 . One example of transferred information from the braking system to the suspension system is the transfer signal  146  from signal processor  104  to suspension algorithm  134 . A second example of transferred information from the braking system to the suspension system is indicated by transfer signal  152 , wherein information from the braking algorithm  106  is directed to the suspension algorithm  134 . 
     Information from the suspension system can also be shared with the braking system. For example, ECU  130  can direct information to ECU  102 . One example of transferred information from the suspension system to the braking system is a transfer signal  154  to a load and load transfer detector  155 . Another example is a transfer signal  156  to a turning detector  157 . Yet another example is a transfer signal  158  for surface and mismatch tire detector  159 . 
     The control system  100  can be configured in various manners to share information from ECU  102  to ECU  130 , and vice versa. In one example, an ECU  102  for the braking system that receives inputs signals  114 ,  116 ,  118  and  122 , for lateral acceleration, longitudinal acceleration, steering wheel angle, and yaw rate, respectively, can transfer these input signals to ECU  130  for the suspension system. The signal processor  104  of ECU  102  can send transfer signal  146  to the suspension algorithm  134 . 
     In another example, if lateral acceleration and steering wheel angle signals  114  and  122  are not available to the braking system, a turning detector signal can be generated by ECU  130  and transmitted to ECU  102  to improve braking performance. If an electronically controlled suspension system is integrated with an electronically controlled ABS/TC braking system, turning of the vehicle can be detected by the suspension system, thereby generating a turning detector signal that is transmitted to a braking system that does not receive signals from lateral acceleration and steering wheel angle sensors. A turn detection signal to the braking system via ECU  102  can enhance braking performance, particularly during braking-in-turn and accelerating-in-turn. 
     A second embodiment of an integrated control system for controlling vehicular braking and suspension functions is indicated generally at  200  in FIG.  9 . Elements of control system  200  that are similar to elements of control system  100  are labeled with like reference numerals in the  200  series. 
     Control system  200  also includes an ABS/TC algorithm  206 A and a VSC algorithm  206 B in place of the braking algorithm  106  of control system  100 . Signal processors  204  and  232  may be placed separately from their respective algorithms  206 A,  206 B, and  230 , or they may be located in common ECU&#39;s (not illustrated in FIG.  9 ). Transfer signal  270  between ABS/TC algorithm  206 A and VSC algorithm  206 B is provided. Transfer signal  272  for load and load transfer is provided to the VSC algorithm  206 B. Transfer signal  273  from the signal processor  204  is provided to the VSC algorithm  206 B. Transfer signal  274  for the surface and mismatch tire detector is provided to the VSC algorithm  206 B. Transfer signal  275  is provided from the VSC algorithm  206 B to the suspension algorithm  234 . Output signal  276  is sent from the VSC algorithm  206 B to the HCU  228 . 
     Various calculations can be made for the suspension system. For example, relative velocity can be calculated from suspension displacement if it is not directly measured. A vehicle load and load transfer signal  154 ,  254  can also be calculated or enhanced from a lateral acceleration signal  114 , a longitudinal acceleration signal  118 , and a steering wheel angle signal  122  when these are available. 
     A load and load transfer signal  154 ,  254  is used by the braking algorithms to enhance braking torque proportioning and apply and dump pulse calculations. 
     A turning detector signal  156 ,  256  (roll moment distribution) can be used to optimize vehicle handling before VSC activation and enhance brake torque distribution calculation during VSC activation. 
     A road surface roughness and tire mismatching signal  158 ,  258  can be detected from suspension states and used by ABS/TC and VSC systems. 
     Braking/traction status information from the wheels can also be used to enhance braking algorithms by predicting pitch and roll motion in advance. 
     Suspension algorithms and braking algorithms can be embodied in separate ECU&#39;s  102  and  130  as illustrated in FIG.  8 . In other embodiments, the suspension and braking algorithms can be integrated into a single electronic control unit. 
     If steering wheel angle signal  122 ,  222  and/or a lateral acceleration signal  114 ,  214  are available, then split mu detection in ABS and TC algorithms (for stand alone ABS and TC systems) can be improved. 
     In other examples, ECU  102  can only receive information from ECU  130 . Thus, various input signals from the suspension system can be transferred to the braking system, but no signals are transferred from the braking system to the suspension system. 
     In yet other examples, ECU  130  can only receive information from ECU  102 . Thus, various input signals from the braking system can be transferred to the suspension system, but no signals are transferred from the suspension system to the braking system. 
     A third embodiment of an integrated control system for controlling vehicular braking and suspension functions is indicated generally at  300  in FIG.  10 . In control system  300 , a single ECU  302  receives inputs signals  304  from various sensors  306  strategically placed in a vehicle. A signal processor  308  may be incorporated in the ECU  302  that delivers transfer signals  310  to an algorithm  312 . The algorithm  312  delivers output signals  314  to a HCU  328  to provide a desired brake response. The algorithm  312  also delivers output signals  316  to actuators  350  to provide a desired suspension response. Control system  300  may be referred to as a totally integrated system for controlling vehicular braking and suspension.

Technology Classification (CPC): 6