Patent Publication Number: US-11027776-B2

Title: System and method for adapting parameters used in target slip estimation

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to estimating a target slip for mobile platforms, and more particularly relates to systems and methods for generating adapted parameters used in estimating the target slip based on real-time surface conditions. 
     To maximize vehicle tracking performance and to minimize braking distance, knowledge of maximum tire forces (before tire saturation) is required. Often the values of the actual tire forces are not known, so they are estimated. 
     One of the tire forces that is important for vehicle stability control is target slip or target grip. Estimating a target slip is often performed in a target slip estimator module, and performed using an algorithm. Currently, most target slip estimation algorithms employ models that do not adapt to dynamically changing driving conditions, such as, real-time changes in road surface conditions. This limitation is a technological problem that can result in inferior target slip estimations, which leads to inferior target slip estimator module performance. 
     Accordingly, it is desirable to provide improved systems and methods for tire slip limit estimation. The following disclosure provides technological solutions to this problem, in addition to addressing related issues. Furthermore, other desirable features and characteristics of the system and method will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the preceding background. 
     SUMMARY 
     In accordance with a first embodiment, a system for generating adapted tuning parameters for use by a target slip estimator module in a vehicle is provided. The system includes: a road surface detection module for providing a road surface condition, S n , range of friction, mu, and a confidence level, Ci, between 0 and 1; a sensor system for providing sensor system data; and a controller module in operative communication with the road surface detection module and the sensor system, and programmed to: receive the S n , range of mu, and Ci; receive the sensor system data; determine, as a function of S n , range of mu, and Ci, initial estimator values including an estimated initial frictional force, {circumflex over (Θ)}(0) and an initial projected range of signal bounds, (P u  and P l ); determine an initial gain P 0  as a function of the sensor system data; generate a set of adapted tuning parameters for an estimation method employed by the target slip estimator module, the set of adapted tuning parameters being a function of {circumflex over (Θ)}(0), P 0 , and P u  and P l ; and output the set of adapted tuning parameters. 
     In an embodiment, the controller module is further configured to: compare Ci to a preprogrammed confidence threshold Ct; determine that Ci is high when it is &gt;=Ct; and determine that Ci is low when it is &lt;Ct. 
     In an embodiment, the controller module is further configured to determine, as a function of S n , range of mu, and Ci, an initial forgetting factor, β 0 , for a recursive least squares estimation method. 
     In an embodiment, the controller module is further configured to generate an adapted parameter β by adapting β 0  as a function of Ci. 
     In an embodiment, S n  is one of N road conditions, S, and the system further includes: a database for storing data comprising, for each combination of S n  and Ci: respective initial estimator values; and wherein the controller module determines the {circumflex over (Θ)}(0), β 0 , and P u  and P l  by referencing the data using S n  and Ci. 
     In an embodiment, the controller module generates adaptive parameter β by widening β 0 , as a function of Ci when Ci is low. 
     In an embodiment, the controller module is further programmed to widen the estimated initial frictional force, {circumflex over (Θ)}(0), as a function of Ci when Ci is low. 
     In an embodiment, the controller module is further programmed to widen the projection range of signal bounds, P u  and P l , to plus or minus Δp, as a function of the estimated frictional force {circumflex over (Θ)}(0) when Ci is low. 
     In accordance with another embodiment, a method for generating adapted tuning parameters for a target slip estimator module for a vehicle is provided. The method including: at a controller module, receiving, from a road surface detection module, a road surface condition, S n , range of friction, mu, and a confidence level, Ci that is between 0 and 1; receiving sensor system data from a sensor system; determining, as a function of S n , range of mu, and Ci, initial estimator values including an estimated initial frictional force, {circumflex over (Θ)}(0), and an initial projected range of signal bounds, (P u  and P l ); determining an initial gain P 0  as a function of the sensor system data; generating a set of adapted tuning parameters for an estimation method employed by the target slip estimator module, the set of adapted tuning parameters being a function of {circumflex over (Θ)}(0), P 0 , and P u  and P l ; and outputting the set of adapted tuning parameters. 
     In an embodiment, S n  is one of N road conditions, S, and further including: storing, in a database, previously developed data comprising, for each combination of S n  and Ci: respective initial estimator values; and wherein determining the {circumflex over (Θ)}(0), and P u  and P l  comprises referencing the previously developed data using S n  and Ci. 
     In an embodiment, further including: comparing Ci to a preprogrammed confidence threshold Ct; determining that Ci is high when it is &gt;=Ct; and determining that Ci is low when it is &lt;Ct. 
     In an embodiment, further including widening the estimated initial frictional force, {circumflex over (Θ)}(0), as a function of Ci when Ci is low. 
     In an embodiment, further including generating Δp by widening the projection range of signal bounds, P u  and P l , as a function of the estimated frictional force when Ci is low. 
     In an embodiment, wherein the estimation method employed by the target slip estimator module is recursive least squares, and further including: determining an initial forgetting factor, β 0 ; and generating an adapted tuning parameter, β, by widening β 0 , as a function of Ci. 
     In an embodiment, further including determining the β 0  by referencing the data using S n  and Ci. 
     Also provided is a vehicle, including: a road surface detection module for providing a road surface condition, S n , range of friction, mu, and a confidence level, Ci, between 0 and 1; a sensor system for providing sensor system data; and a controller module in operative communication with the road surface detection module and the sensor system, and programmed to: receive the S n , range of mu, and Ci; receive the sensor system data; determine, as a function of S n , range of mu, and Ci, initial estimator values including an estimated initial frictional force, {circumflex over (Θ)}(0), and an initial projected range of signal bounds, (P u  and P l ); determine an initial gain P 0  as a function of the sensor system data; generate a set of adapted tuning parameters for an estimation method employed by the target slip estimator module, the set of adapted tuning parameters being a function of {circumflex over (Θ)}(0), P 0 , and P u  and P l ; and output the set of adapted tuning parameters. 
     In an embodiment, the controller module is further configured to: compare Ci to a preprogrammed confidence threshold Ct; determine that Ci is high when it is &gt;=Ct; and determine that Ci is low when it is &lt;Ct. 
     In an embodiment, wherein S n  is one of N road conditions, S, and further comprising: a database for storing previously developed data comprising, for each combination of S n  and Ci: respective initial estimator values; and wherein the controller module is further programmed to determine the {circumflex over (Θ)}(0), and P u  and P l  by referencing the previously developed data using S n  and Ci. 
     In an embodiment, the controller module further: determines, as a function of S n , range of mu, and Ci, an initial forgetting factor, β 0 ; and generates and adapted tuning parameter, β, by widening β 0 , when Ci is low. 
     In an embodiment, the controller module is further programmed to determine the β 0  by referencing the data in the database using S n  and Ci. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
       The exemplary embodiments will hereinafter be described in conjunction with the following drawing figures, wherein like numerals denote like elements, and wherein: 
         FIG. 1  is a schematic diagram illustrating a vehicle that includes an adapted parameter system for a target slip estimator, in accordance with various embodiments; 
         FIG. 2  is a data flow diagram for an adapted parameter system, in accordance with an exemplary embodiment; and 
         FIG. 3  is a process flow chart depicting an example method for parameter adaptation for a target slip estimator module in a vehicle, in accordance with various embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, summary, or the following detailed description. 
     Embodiments of the present disclosure may be described herein in terms of functional and/or logical block components and various processing steps. It should be appreciated that such block components may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. 
     As used herein, the term “module” refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), a field-programmable gate-array (FPGA), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the functionality attributed to the module. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and that the systems described herein are merely exemplary embodiments of the present disclosure. 
     For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning models, radar, lidar, image analysis, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent example functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the present disclosure. 
       FIG. 1  depicts an example mobile platform. The example mobile platform is a vehicle  100  that is capable of movement and carrying passengers from one location to another. The vehicle  100  is depicted in the illustrated embodiment as a passenger car, but other vehicle types, including motorcycles, taxi cabs, vehicle fleets, buses, sedans, wagons, trucks, sport utility vehicles, other automobiles, recreational vehicles (RVs), locomotives, and other vehicles may also be used. As is generally understood, the vehicle  100  may embody a body, chassis, and wheels, each of which are rotationally coupled to the chassis near a respective corner of the body. The vehicle  100  is depicted with four wheels  20 , but the number of wheels  20  may vary in other embodiments. The vehicle  100  may be autonomous or semi-autonomous. 
     The example vehicle  100  includes a number of communicatively coupled components. Non-limiting examples of the communicatively coupled components include a sensor system  108 , a road surface detection module  110 , a target slip estimator module  106 , at least one data storage device, a database  112 , the proposed controller module  104 , and drive systems  114 . The collective functional block “drive systems”  114  generally includes known vehicle systems for vehicle operation, such as, a propulsion system, a transmission system, a steering system, actuators for the wheels, and a brake system, and generates a variety of signals, including vehicle speed and vehicle acceleration. The drive systems  114  and other on-board systems provide signals to the target slip estimator module  106  from which the target slip estimator module  106  generates, as an output, a target slip estimation. On-board systems, such as the drive systems  114 , receive the target slip estimation and utilize it to control various aspects of vehicle  100  operation, for example, by controlling traction devices associated with the wheels and brakes. 
     The vehicle  100  includes a system for adapting parameters used in target slip estimation based on road surface conditions, shown generally as “system”  102 . As described in greater detail below, the system  102  adaptively, in real-time, and responsive to detected surface conditions, adapts parameters that the target slip estimator module  106  uses in generating the target slip estimation. More specifically, the system  102  generates, as an output, adapted parameters for use by the target slip estimator module  106  to generate the target slip estimation. A controller module  104 , described in more detail below, may perform the processing steps of operations attributed to the system  102 . The functions and operations of each of these components are described in more detail below. 
     The sensor system  108  includes one or more sensing devices  109   a - 109   n  that sense observable conditions of the exterior environment (such as surface condition, precipitation, light level, distance to objects, and the like) and/or the interior environment of the vehicle  100  (such as the state of one or more occupants) and generate sensor data relating thereto. The sensing devices  109   a - 109   n  might include, but are not limited to, radars (e.g., long-range, medium-range-short range), lidars, global positioning systems (GPS), optical cameras (e.g., forward facing, 360-degree, rear-facing, side-facing, stereo, etc.), thermal (e.g., infrared) cameras, ultrasonic sensors, odometry sensors (e.g., encoders) and/or other sensors that might be utilized in connection with systems and methods in accordance with the present subject matter. Non-limiting examples of characteristics that may be embodied in the sensor system data include amount of precipitation, unevenness of the surface, presence of ice, presence of water, panoramic view, amount of light, etc. 
     To provide background information, the operations of the road surface detection module  110  are described. The road surface detection module  110  operates in real-time to receive, from the sensor system  108 , sensor system data, and analyze the characteristics that may be embodied in the sensor system data to identify a road surface condition, S n , from among N predefined road surface conditions, S. The road surface detection module  110  then generally associates the surface condition type, S n  with conditions, such as, a range for road coefficient of friction, mu; in various embodiments, this may be based on an access to a pre-programmed lookup table (see, for example, Table 1) to. In various embodiments, the confidence level is a fractional number between 0 and 1. Outputs from the road surface detection module  110  are: S n , range of mu, and Ci. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 example lookup table in road surface detection module. 
               
            
           
           
               
               
               
            
               
                   
                 Surface Condition 
                 Conditions, e.g., Range for 
               
               
                   
                 Type, S n   
                 road coefficient of friction, mu 
               
               
                   
                   
               
               
                   
                 Dry 
                 0.2-0.3 
               
               
                   
                 Wet 
                  0.9-0.12 
               
               
                   
                 Snow 
                 0.02-0.3  
               
               
                   
                   
               
            
           
         
       
     
     In a simplified example of road surface detection module  110  operation, a detected (i.e., sensed) amount of precipitation and detected amount of water present may be characteristics embodied in the sensor system data that define a “wet” surface condition S n . With reference to Table 1, the road surface detection module  110  may output “wet,” the range 0.4-0.7, and a confidence level, Ci. As may be appreciated, the outputs S n , range of mu, and Ci may vary in real-time, reflective of the detected surface condition and confidence level. 
     As used herein, the term “controller module” refers to any means for facilitating communications and/or interaction between the components of the system  102  and performing additional processes, tasks and/or functions to support operation of the system  102 , as described herein. Depending on the embodiment, the controller module  104  may be implemented or realized with a general purpose processor (shared, dedicated, or group) controller, microprocessor, or microcontroller, and memory that executes one or more software or firmware programs; a content addressable memory; a digital signal processor; an application specific integrated circuit (ASIC), a field programmable gate array (FPGA); any suitable programmable logic device; combinational logic circuit including discrete gates or transistor logic; discrete hardware components and memory devices; and/or any combination thereof, designed to perform the functions described herein. 
     In various embodiments, the controller module  104  is realized as an enhanced computer system, comprising a memory  204  for storage of instructions, algorithms, and/or programs, such as adapted parameter program  210 , a processor  202  to execute the program  210 , and input/output interface (I/O)  206 . The computer readable storage device or media, memory  204 , and database  112 , may each include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or nonvolatile memory that may be used to store various operating variables while the processor  202  is powered down. The computer-readable storage device or memory  204  may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller module  104  in controlling the vehicle  100 . In various embodiments, controller module  104  is configured to implement the system  102 . 
     Controller module  104  may employ a computer readable storage media, such as the database  112 , to store and maintain, for each combination of S n  and Ci: a set of initial tuning parameters for use by the target slip estimator module  106 . The initial tuning parameters may include, an estimated initial frictional force, {circumflex over (Θ)}(0), an initial gain, P 0 , and an initial projected range of signal bounds, (P u ) and (P l ). In embodiments for use with a target slip estimator module  106  that employs a recursive least squares (RLS) estimation method, the controller  104  may store and maintain an initial forgetting factor, β 0 . The values for the initial tuning parameters are developed prior to installation and operation of the system  104 , based on data gathering during vehicle performance testing across the suite of N road surface conditions S. In an exemplary embodiment, the initial estimator values may be stored as a lookup table. 
     Information in the memory  204  and/or database  112  may be organized and/or imported from an external source during an initialization or installment operation in a method; it may also be programmed via a user input device. Within the controller module  104 , the input/output interface (I/O)  206  enables intra-controller module communication, as well as communications between the controller module and other components in operable communication with the controller module  104 . The input/output interface (I/O)  206  may include one or more network interfaces and can be implemented using any suitable method and apparatus. In various embodiments, the input/output interface (I/O)  206  supports communication with technicians, and/or one or more storage interfaces for direct connection to storage apparatuses, such as the database  112 . In embodiments as shown in  FIG. 2 , during operation of the controller module  104 , the processor  202  loads and executes one or more algorithms, instructions, and rules embodied as program  210 , and, as such, controls the general operation of the system  102 . 
     The controller module  104  is programmed to receive the sensor data from the sensor system  108 , and S n , range of mu, and Ci (collectively labeled inputs  117 ) from the road surface detection module  110 . The controller module  104  is programmed to generate adapted tuning parameters (also referred to as estimator values herein) as a function of the sensor system data, the adapted tuning parameters being adapted specifically for the estimation method employed by the target slip estimator module  106 , as described below. Depending on the embodiment of the controller module  104 , it may perform operations in accordance with an algorithm for adapted parameters, perform operations in accordance with state machine logic, perform operations in accordance with logic in a programmable logic array, or the like. 
     The target slip estimator module  106  is operable to generate slip limit estimations, λ, using an estimation method that operates on received parameters. When the provided system  104  is employed, the adapted tuning parameters generated by the controller module  104  are sent to the target slip estimator module  106  to thereby serve as the parameters used in the estimation method. Since the adapted tuning parameters are adapted in response to real-time road surface conditions, this enables the target slip estimator module  106  to generate faster, more accurate, and more reliable slip limit estimations, λ, than conventional target slip estimators. Although shown as a separate functional block, in various embodiments, the target slip estimator module  106  is integrated with the system  102 . 
     While the exemplary embodiment of  FIG. 1  is described in the context of a controller module  104  embodied as fully functioning enhanced computer system, those skilled in the art will recognize that the mechanisms of the present disclosure are capable of being distributed as a program product including program  210 . Such a program product may comprise an arrangement of instructions organized as multiple interdependent program code modules, each to achieve a separate process, arranged to manage data flow through the system  102  (see,  FIG. 2 ). The program code modules may each comprise an ordered listing of executable instructions for implementing logical functions for the processes performed by the system  102 . The instructions, when executed by a processor, cause the processor to receive and process signals, and perform logic, calculations, methods and/or algorithms as described herein for automatically and in real-time generating adapted parameters for use by the target slip estimator module  106 . 
     Once developed, the program code modules constituting program product may be stored and distributed individually, or together, using one or more types of non-transitory computer-readable signal bearing media may be used to store and distribute the instructions, such as a non-transitory computer readable medium. Such a program product may take a variety of forms, and the present disclosure applies equally regardless of the type of computer-readable signal bearing media used to carry out the distribution. Examples of signal bearing media include: recordable media such as floppy disks, hard drives, memory cards and optical disks, and transmission media such as digital and analog communication links. It will be appreciated that cloud-based storage and/or other techniques may also be utilized as memory and as program product time-based viewing of clearance requests in certain embodiments. 
     Turning now to  FIG. 2  a data flow diagram  200  illustrates an exemplary arrangement for the processes performed by the system  102 . Process  202  uses data from the road surface detection module  110  and previously developed data to generate a set of adapted tuning parameters, functions of {circumflex over (Θ)}(0), P 0 , and P u  and P l , as described hereinbelow; the initial estimator values are near a target slip estimation. 
     Process  204  uses received sensor system data to determine an initial estimator value for gain, P 0 . At process  204 , on-board and real-time sensor data is received from the sensor system  108 ; this data (z) includes signal and noise. An initial estimator value for gain, P 0  is determined as a function of the sensor system data (z). Process  212  generates an adapted gain, tuning P 0  as a function Ci. 
     The initial estimator values from process  204  may flow to tuning processes  206 ,  208 , and  210 , to thereby generate components of the set of adapted tuning parameters. 
     In various embodiments, process  208  tunes the initial projected range of signal bounds, (P u ) and (P l ) to generate therefrom an adapted tuning parameter ΔP, also referred to as the adapted projection range. The projection range has an upper bound and a lower bound. To determine the adapted projection range, the system  102  processes S n  and Ci to setup initial estimations of signal bounds, (P u ) and (P l ), near a target slip. The system  102  adaptively generates, in real-time, an estimated initial projected range of signal bounds around the target slip. The estimations are constrained by the initial conditions (e.g., the ranges of mu associated with the S n ). 
     The various initial projected ranges around the target slip are shown in Equation 1, below: 
     
       
         
           
             
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     In some embodiments, process  210  tunes the initial forgetting factor, β 0 , generating adapted tuning parameter β. At process  210 , the forgetting factor β is calculated as a function of Ci. 
     The controller module  104  compares Ci to a preprogrammed confidence threshold Ct. In various embodiments, the confidence level is considered high when Ci is greater than or equal to the preprogrammed confidence threshold Ct. In an embodiment, Ct is 70% or 0.70. 
     In various embodiments, when Ci is greater than or equal to Ct (i.e., high confidence in the detected road surface condition, S n ), the system  102  utilizes the initial estimator values as the set of adapted tuning parameters to output via process  214 . For example, for a value of S n  with Ci greater than or equal to Ct, the adapted tuning parameters for S n  may include: the estimated initial frictional force, {circumflex over (Θ)}(0), the initial projected range of signal bounds, (P u ) and (P l ), and initial forgetting factor, β 0 . 
     When Ci is less than Ct, (i.e., lower confidence in the detected road surface condition, S n ) the controller module  104  adjusts/adapts the initial estimator values to generate the set of adapted tuning parameters for S n  to output via process  214 . For example, generating the set of adapted tuning parameters for a S n  with lower confidence (Ci&lt;Ct), may include the following processing steps: 
     (1) Widening the range of the estimated initial frictional force, {circumflex over (Θ)}(0), ( FIG. 2 , process  206 ) as shown in Equation 2, below:
 
−Δ+{circumflex over (θ)} g ≤{circumflex over (θ)}(0)≤Δ+{circumflex over (θ)} g  
 
     where Δ and {circumflex over (θ)} g  are previously programmed initial guess ranges that the controller  104  selects as a function of the confidence level Ci: 
     (2) Widening the projection range of signal bounds, (P u ) and (P l ), ( FIG. 2, 208 ) as shown in Equation 3, below:
 
−Δ p +{circumflex over (θ)} l ( t )≤{circumflex over (θ)}( t )≤{circumflex over (θ)} u ( t )+Δ p  
 
     where −Δp is equal to P l , and Δp is equal to P u  and where {circumflex over (Θ)} l (t) and {circumflex over (Θ)} u (t) are the widened estimated initial frictional force values, as a function of time, from equation 2 above, and 
     (3) In embodiments that generate adapted tuning parameters for a RLS estimation method, adaptive parameter β is increased over the initial estimator value β 0 , as shown in Equation 3, ( FIG. 2, 210 ) below:
 
Δ f +β o ≤β
 
     where Δ f  s a pre-programmed range of forgetting factors determined by the controller  104  as a function of the confidence level Ci. 
     Process  214  receives the generated (i.e., tuned) data from processes  206 ,  208 ,  210 , and  212 , and makes it available as data output for the target slip estimator  106 . Each of the processes  202 - 214  may be performed by the system  102 , in accordance with steps of a method, described in more detail below. The outputs from the controller module  104  are the set of the adapted tuning parameters, functions of {circumflex over (Θ)}(0), P 0 , and P u  and P l , and which are for use by a target slip estimation method employed by the target slip estimator module  106 . 
     Turning now to  FIG. 3 , the system  102  described above may be implemented by a processor-executable method for adapting parameters used in target slip estimation, shown generally as method  300 . For illustrative purposes, the following description of method  300  may refer to elements mentioned above in connection with  FIGS. 1-2 . In practice, portions of method  300  may be performed by different components of the described system. It should be appreciated that method  300  may include any number of additional or alternative operations and tasks, the tasks shown in  FIG. 3  need not be performed in the illustrated order, and method  300  may be incorporated into a more comprehensive procedure or method having additional functionality not described in detail herein. Moreover, one or more of the tasks shown in  FIG. 3  could be omitted from an embodiment of the method  300  as long as the intended overall functionality remains intact. 
     As may be appreciated, prior to the method  300  starting, the controller module  104  is initialized. When implemented using enhanced computer systems (e.g.,  FIG. 2 ), initialization may comprise uploading, installing, or updating the instructions constituting the program  210  for execution by the processor  202 . 
     At operation  302 , a surface type, S n , range of mu, and C i  are received by the controller module  104 . At  304 , the previously developed data, from vehicle performance testing across the suite of N road surface conditions S, is referenced. As mentioned above, the database  112  may be used to store this previously developed data. At  304 , responsive to referencing the previously developed data using the received S n  and Ci, the controller module  104  determines initial estimator values including: an estimated initial frictional force, {circumflex over (Θ)}(0), an initial projected range of signal bounds, (P u ) and (P l ), and, in some embodiments, an initial forgetting factor, β 0 . 
     At operation  306 , the sensor system data is received, and at operation  308 , an initial estimation value for gain, P0, is determined as a function of the sensor system data. 
     In operation  312 , the controller module  104  utilizes the processes described in connection with  FIG. 2  to generate the set of adapted tuning parameters based on the initial estimator values. In doing so, the controller module  104  must first ascertain what estimation method the target slip estimator module  106  is going to use. In some embodiments, this may be a formal operational step  310 . In other embodiments, the estimation method used by the target slip estimator module  106  may be preprogrammed into the instructions of the controller module  104 . As mentioned, the adapted tuning parameters may include any combination of two or more of:
         an estimated initial frictional force, {circumflex over (Θ)}(0) is from the range of mu   an estimated initial gain, P 0      an initial projection range of signal bounds, (P u ) and (P l ), and   an initial forgetting factor, β 0 , is from the previously developed data.       

     As may be appreciated from the above discussion, the controller module  104  is said to generate adapted tuning parameters because it determines how and when to adapt the initial estimator values from the previously developed data for use by the target slip estimator module  106 . 
     In some embodiments, the target slip estimator module  106  may be included in the system  102 . As stated, the adapted parameters that are functions of {circumflex over (Θ)}(0), P 0 , and P u  and P l  are received by the target slip estimator module  106 . Target slip estimator modules  106  may employ various slip estimation methods, so the particular set of adapted tuning parameters generated by the controller  104  may vary by embodiment. Recall, as mentioned, in various embodiments, the target slip estimator module  106  employs a recursive least squares analysis (RLS). The slip limit estimation, λ, is generated by a target slip estimator module  106  that employs the RLS method of slip estimation. The RLS method may be employed in accordance with Equation 5, below: 
     
       
         
           
             
               
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     The adaptation gain (or the initial condition of P) may be determined by the controller  104  as a function of the confidence level Ci; if the confidence level is low, a high value of the adaptation gain is used, and when the confidence level is high, a low value is used for the adaptation gain. As can be seen in the above equations, the forgetting factor β affects how fast accumulated data is refreshed (P), and it also keeps the adaptation gains, high. 
     As mentioned above, the functional improvement in target slip estimation that is achieved by using the adapted parameters generated by the system  102  is a faster, more accurate, and more reliable slip limit, λ, estimation. Each of the adapted parameters described above contributes an aspect of improvement to target slip estimation. 
     The slip limit, lambda, λ, estimations are generally demarked on the Y axis and the time is demarked on the X axis. In a first example, the system  102  quickly (generally under a half second) adapts to toggling around a λ value of 0.3 ( 502 ); this provides a fast adaptation to the slip limit λ of 0.3. In the first example, the λ values swing from about 0.34 on the upper limit to about 0.24 on the lower limit. 
     The functional improvement provided by the projected upper and lower bounds generated by using the system  102  is: they constrain the swing in the slip limit: In a second example, with the slip limit, lambda {circumflex over (Θ)}(0)=0.3, the upper bound is 0.31 and the lower bound is 0.28; this is more constrained than the first example. Note, in the second example, the lower bound may be, for example, −Δp+{circumflex over (Θ)} l (t)=0.02, and the upper bound is {circumflex over (Θ)} u (t)+Δp=0.1. Also note an upper forgetting factor value is 5 (in  FIG. 4 b   ) and a lower forgetting factor value is 0.98 (in  FIG. 4 c   ), therefore, the Δ f  is 5−0.98. 
     The functional improvement provided by utilizing gain, generated by using the system  102 , is that, by utilizing adaptation gain, noise attenuation is added. 
     Thus, the system  102  generates adaptive parameter values reflective of real-time road surface conditions, for use in the target slip estimator module  106 . Accordingly, the real-time parameter value adaptation provided by the system  102  provides a functional improvement over conventional approaches to target slip estimation. 
     While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. Various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.