System and method for rack position offset learning

Abstract

A method for rack position offset learning includes receiving at least one vehicle characteristic value, and determining whether a vehicle associated with the at least one vehicle characteristic value is traveling in a straight line based on the at least one vehicle characteristic value. The method further includes calculating a straight-line distance traveled by the vehicle within the predetermined time period based on the vehicle speed and the length of the predetermined time period. The method further includes determining whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold, activating a counter, and in response to a counter value of the counter being greater than or equal to a counter threshold, determining a rack position offset value based on a rack position of a rack of a steering system of the vehicle.

Description

Technical Field

[0001] This disclosure relates to steering systems, and more particularly to systems and methods for learning rack position offsets in steering systems. Background Technology

[0002] Vehicles (such as cars, trucks, SUVs, crossovers, minivans, ships, aircraft, all-terrain vehicles, recreational vehicles, or other suitable forms of transportation) typically include various systems, such as steering systems (which may include electric power steering (EPS), steer-by-wire (SbW), hydraulic steering, or other suitable steering systems) and / or other suitable systems (e.g., braking systems, propulsion systems, etc.). These systems of a vehicle typically control various aspects of the vehicle's steering (e.g., including providing steering assistance to the operator, controlling the vehicle's steerable wheels, etc.), propulsion, braking, etc. Summary of the Invention

[0003] This disclosure generally relates to steering systems.

[0004] One aspect of the disclosed embodiments includes a system for learning rack position offset. The system includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive at least one vehicle characteristic value; determine, based on the at least one vehicle characteristic value, whether a vehicle associated with the at least one vehicle characteristic value is traveling in a straight line; in response to determining that the vehicle is traveling in a straight line, calculate a straight-line distance traveled by the vehicle within a predetermined time period based on the vehicle speed and the length of a predetermined time period; determine whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold; in response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, start a counter; and in response to the counter value being greater than or equal to the counter threshold, determine a rack position offset value based on the rack position of the rack of the vehicle's steering system.

[0005] Another aspect of the disclosed embodiments includes a method for learning rack position offset. The method includes: receiving at least one vehicle characteristic value, and determining, based on the at least one vehicle characteristic value, whether a vehicle associated with the at least one vehicle characteristic value is traveling in a straight line. The method further includes: in response to determining that the vehicle is traveling in a straight line, calculating a straight-line distance traveled by the vehicle within the predetermined time period based on the vehicle speed and the length of the predetermined time period. The method further includes: determining whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold; in response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, activating a counter; and in response to the counter value being greater than or equal to the counter threshold, determining a rack position offset value based on the rack position of the rack of the vehicle's steering system.

[0006] Another aspect of the disclosed embodiments includes an apparatus for learning rack position offset. The apparatus includes a controller configured to: receive at least one vehicle characteristic value; determine, based on the at least one vehicle characteristic value, whether a vehicle associated with the at least one vehicle characteristic value is traveling in a straight line; in response to determining that the vehicle is traveling in a straight line, calculate a straight-line distance traveled by the vehicle within a predetermined time period based on the vehicle speed and the length of a predetermined time period; determine whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold; in response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, activate a counter; in response to the counter value being greater than or equal to the counter threshold, determine a rack position offset value based on the rack position of the rack of the vehicle's steering system; and adjust the rack position based on the rack position offset value.

[0007] These and other aspects of this disclosure are disclosed in the following detailed description of the embodiments, the appended claims and the accompanying drawings. Attached Figure Description

[0008] This disclosure is best understood by reading in conjunction with the accompanying drawings and through the following detailed description. It should be emphasized that, by convention, the various features in the drawings are not drawn to scale. Instead, for clarity, the dimensions of the various features have been arbitrarily enlarged or reduced.

[0009] Figure 1 A vehicle based on the principles of this disclosure is shown in general.

[0010] Figure 2 A controller based on the principles of this disclosure is shown in general.

[0011] Figure 3A and Figure 3B A block diagram of rack position offset learning based on the principles of this disclosure is shown in general.

[0012] Figure 4 This is a flowchart that generally illustrates a rack position offset learning method based on the principles of this disclosure.

[0013] Figure 5 This is a flowchart that generally illustrates an alternative rack position offset learning method based on the principles of this disclosure. Detailed Implementation

[0014] The following discussion pertains to various embodiments of this disclosure. While one or more of these embodiments may be preferred, the disclosed embodiments should not be construed as or otherwise used to limit the scope of this disclosure, including the claims. Furthermore, those skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is intended only as an illustrative discussion of that embodiment and not to imply that the scope of this disclosure, including the claims, is limited to that embodiment.

[0015] As described above, vehicles (such as automobiles, trucks, SUVs, crossovers, minivans, ships, aircraft, all-terrain vehicles, recreational vehicles, or other suitable forms of transportation) typically include various systems, such as steering systems (which may include EPS systems, SbW steering systems, hydraulic steering systems, or other suitable steering systems) and / or other suitable systems (e.g., braking systems, propulsion systems, etc.). These systems of a vehicle typically control various aspects of the vehicle's steering (e.g., including providing steering assistance to the vehicle's operator, controlling the vehicle's steerable wheels, etc.), propulsion, braking, etc.

[0016] In a SbW steering system, there is no mechanical connection between the wheels and the steering wheel. The wheel actuators (RWA) and steering wheel actuators (HWA) are separate mechanical systems that operate independently. To ensure the vehicle travels along a straight trajectory, a steering wheel angle offset is used in conventional EPS steering systems. However, for SbW steering systems, it may be necessary to find and / or know the absolute zero rack position (e.g., offset) to synchronize with the zero steering wheel angle.

[0017] Due to the separate structure of the SbW steering system, inputs used to estimate rack position offset in the EPS steering system (e.g., steering wheel angle, steering wheel speed, steering wheel torque) may not be useful for estimating rack position offset in the SbW steering system.

[0018] Therefore, systems and methods configured to provide improved rack position offset learning, as described in this paper, may be desirable. References Figure 3A and Figure 3B The diagram illustrates the differences between the conventional EPS steering system and the SbW steering system. For example, as... Figure 3AAs shown, the SbW steering system inputs used to learn rack position offset include rack position, rack speed, rack force, vehicle speed, and vehicle yaw rate. Alternatively, as... Figure 3B As shown, the EPS steering system inputs used to learn rack position offset include steering wheel angle, steering wheel speed, steering wheel torque, vehicle speed, and vehicle yaw rate.

[0019] In some embodiments, such as Figure 3A As generally illustrated, the system and method described in this paper can be configured to perform first-level conditional judgments based on one or more of rack position, rack speed, rack force, vehicle speed, and vehicle yaw rate. The system and method described in this paper can also be configured to calculate straight-line distance based on an enable sign and vehicle speed.

[0020] The systems and methods described herein can be configured to perform second-level conditional judgments based on distance. The systems and methods described herein can be configured to initiate learning based on a learning flag and calculate the learning time. The systems and methods described herein can be configured to enable learning value output judgments based on rack force and learning time. The systems and methods described herein can be configured to perform learning based on an output flag, a learning flag, and rack position (e.g., using a low-pass filter).

[0021] In some embodiments, the systems and methods described herein can be configured to provide rack position offset learning. In some embodiments, the systems and methods described herein can be configured to accurately enter offset learning when learning is required and ensure the accuracy of offset learning. In some embodiments, the systems and methods described herein can be configured to perform decision conditions based on rack force, rack position, rack speed, vehicle speed, and yaw rate.

[0022] In some embodiments, the systems and methods described herein can be configured to ensure that a vehicle travels straight without using steering wheel angle offset. In some embodiments, the systems and methods described herein can be configured to determine an absolute 0 rack position (e.g., offset) and synchronize it to a 0 steering wheel angle. In some embodiments, the systems and methods described herein can be configured to use rack-related variables, such as rack position, rack speed, and rack force, instead of steering wheel-related signals (e.g., steering wheel angle, steering wheel speed, and steering wheel torque). In some embodiments, the systems and methods described herein can be configured to provide rack position offset learning for SbW-RWA.

[0023] In some embodiments, the systems and methods described herein can be configured to provide rack position offset learning. The systems and methods described herein can be configured to receive at least one vehicle characteristic value. The at least one vehicle characteristic may include at least one of rack position, vehicle speed, rack speed, rack force, vehicle yaw rate, or other suitable vehicle characteristic values. The systems and methods described herein can be configured to determine, based on the at least one vehicle characteristic value, whether the vehicle associated with the at least one vehicle characteristic value is traveling in a straight line.

[0024] The system and method described herein can be configured to calculate the straight-line distance traveled by a vehicle within a predetermined time period based on the vehicle's speed and the length of the predetermined time period in response to determining that the vehicle is traveling in a straight line.

[0025] The system and method described herein can be configured to determine whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold. The system and method described herein can be configured to start a counter in response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold.

[0026] The system and method described herein can be configured to determine a rack position offset value based on the rack position of the rack of a vehicle's steering system in response to a counter value being greater than or equal to a counter threshold. The rack position can correspond to the zero position of the rack or other suitable positions of the rack. The steering system can include an SbW steering system or other suitable steering systems.

[0027] In some embodiments, the systems and methods described herein can be configured to use at least one low-pass filter to determine a rack position offset value based on the rack position of the rack of a vehicle's steering system. The systems and methods described herein can also be configured to use a low-pass filter to limit the rack position offset value.

[0028] The systems and methods described herein can be configured to adjust the rack position based on rack position offset values. For example, the systems and methods described herein can be configured to synchronize the rack to the zero position of the steering wheel angle or other suitable positions.

[0029] Figure 1 A vehicle 10 according to the principles of this disclosure is generally shown. Vehicle 10 may include any suitable vehicle, such as a car, truck, SUV, minivan, crossover, any other passenger vehicle, any suitable commercial vehicle, or any other suitable vehicle. Although vehicle 10 is illustrated as a wheeled passenger vehicle used on a road, the principles of this disclosure can be applied to other vehicles, such as airplanes, ships, trains, drones, or other suitable vehicles.

[0030] Vehicle 10 includes a body 12 and a hood 14. A passenger compartment 18 is defined at least partially by the body 12. Another portion of the body 12 defines an engine compartment 20. The hood 14 is movably attached to a portion of the body 12 such that when the hood 14 is in a first position or open position, the hood 14 provides access to the engine compartment 20, and when the hood 14 is in a second position or closed position, the hood 14 covers the engine compartment 20. In some embodiments, the engine compartment 20 may be located at the rear of the vehicle 10 (as opposed to what is typically shown).

[0031] The passenger compartment 18 may be located behind the engine compartment 20, but in embodiments where the engine compartment 20 is located at the rear of the vehicle 10, the passenger compartment 18 may be located in front of the engine compartment 20. The vehicle 10 may include any suitable propulsion system, including: an internal combustion engine; one or more electric motors (e.g., an electric vehicle); one or more fuel cells; a hybrid (e.g., a hybrid vehicle) propulsion system including a combination of an internal combustion engine and one or more electric motors; and / or any other suitable propulsion system.

[0032] In some embodiments, vehicle 10 may include a gasoline engine or a gasoline-fueled engine, such as a spark-ignition engine. In some embodiments, vehicle 10 may include a diesel-fueled engine, such as a compression-ignition engine. Engine compartment 20 houses and / or surrounds at least some components of the propulsion system of vehicle 10. Additionally or alternatively, propulsion control devices (e.g., accelerator actuators (e.g., accelerator pedal), brake actuators (e.g., brake pedal), steering wheel, and other such components) are disposed in passenger compartment 18 of vehicle 10. The propulsion control devices may be actuated or controlled by the operator of vehicle 10 and may be directly connected to corresponding components of the propulsion system, such as throttle, brakes, axles, vehicle transmission, etc. In some embodiments, the propulsion control devices may transmit signals to a vehicle computer (e.g., drive-by-wire), which in turn may control the corresponding propulsion components of the propulsion system. Thus, in some embodiments, vehicle 10 may be an autonomous vehicle.

[0033] In some embodiments, vehicle 10 includes a transmission communicated with a crankshaft via a flywheel, clutch, or hydraulic coupling. In some embodiments, the transmission includes a manual transmission. In some embodiments, the transmission includes an automatic transmission. In the case of an internal combustion engine or hybrid vehicle, vehicle 10 may include one or more pistons that cooperate with the crankshaft to generate force, which is transmitted via the transmission to one or more axles, causing wheels 22 to rotate. When vehicle 10 includes one or more electric motors, a vehicle battery and / or fuel cell provides energy to the electric motors to rotate the wheels 22.

[0034] Vehicle 10 may include an automated vehicle propulsion system, such as cruise control, adaptive cruise control, automatic braking control, other automated vehicle propulsion systems, or combinations thereof. Vehicle 10 may be an automated or semi-automated vehicle, or other suitable type of vehicle. Vehicle 10 may include additional or fewer features compared to those generally shown and / or disclosed herein.

[0035] In some embodiments, vehicle 10 may include an Ethernet component 24, a controller area network (CAN) bus 26, a media-oriented system transport component (MOST) 28, a FlexRay component 30 (e.g., a brake-by-wire system), and a local interconnect component (LIN) 32. Vehicle 10 may use the CAN bus 26, MOST 28, FlexRay component 30, LIN 32, other suitable network or communication systems, or combinations thereof, to transmit various information from sensors, such as those inside or outside the vehicle, to various processors or controllers, such as those inside or outside the vehicle. Vehicle 10 may include additional or fewer features compared to those generally shown and / or disclosed herein.

[0036] In some embodiments, the vehicle 10 may include a steering system, such as an EPS system, a steer-by-wire system (e.g., which may include or be connected to one or more controllers that control components of the steering system without using a mechanical connection between the steering wheel and the wheels 22 of the vehicle 10), a hydraulic steering system (e.g., which may include a magnetic actuator incorporated into a valve assembly of a hydraulic steering system), or other suitable steering systems.

[0037] A steering system may include an open-loop feedback control system or mechanism, a closed-loop feedback control system or mechanism, or a combination thereof. The steering system may be configured to receive various inputs, including but not limited to: steering wheel position, input torque, one or more wheel positions, other suitable inputs or information, or a combination thereof.

[0038] Additionally or alternatively, inputs may include steering wheel torque, steering wheel angle, motor speed, vehicle speed, estimated motor torque command, other suitable inputs, or combinations thereof. The steering system may be configured to provide steering functionality and / or control to the vehicle 10. For example, the steering system may generate auxiliary torque based on various inputs. The steering system may be configured to use the auxiliary torque to selectively control the motor of the steering system to provide steering assistance to the operator of the vehicle 10.

[0039] In some embodiments, the vehicle 10 may include a controller, such as controller 100, as... Figure 2 The controller 100 may include any suitable controller, such as an electronic control unit or other suitable controller. For example, the controller 100 may be configured to control various functions of the steering system and / or various functions of the vehicle 10. The controller 100 may include a processor 102 and a memory 104. The processor 102 may include any suitable processor, such as those described herein. Additionally or alternatively, the controller 100 may include any suitable number of processors other than or excluding the processor 102. The memory 104 may include a single disk or multiple disks (e.g., a hard disk drive) and includes a storage management module that manages one or more partitions within the memory 104. In some embodiments, the memory 104 may include flash memory, semiconductor (solid-state) memory, etc. The memory 104 may include random access memory (RAM), read-only memory (ROM), or a combination thereof. The memory 104 may include instructions that, when executed by the processor 102, cause the processor 102 to control at least various aspects of the vehicle 10.

[0040] The controller 100 may receive one or more signals from various measuring devices or sensors 106, which indicate sensed or measured characteristics of the vehicle 10. Sensors 106 may include any suitable sensors, measuring devices, and / or other suitable mechanisms. For example, sensors 106 may include one or more torque sensors or devices, one or more steering wheel position sensors or devices, one or more motor position sensors or devices, one or more position sensors or devices, one or more radar sensors or devices, one or more lidar sensors or devices, one or more sonar sensors or devices, one or more image capture sensors or devices, other suitable sensors or devices, or combinations thereof. One or more signals may indicate steering wheel torque, steering wheel angle, motor speed, vehicle speed, other suitable information, or combinations thereof.

[0041] In some embodiments, controller 100 may be configured to provide rack position offset learning. For example, controller 100 may receive at least one vehicle characteristic value of vehicle 10. The at least one vehicle characteristic may include at least one of rack position, vehicle speed, rack speed of the rack, rack force of the rack, vehicle yaw rate, or other suitable vehicle characteristic value. Controller 100 may determine, based on the at least one vehicle characteristic value, whether vehicle 10 is traveling in a straight line (e.g., along a straight trajectory) or substantially in a straight line.

[0042] The controller 100 can, in response to determining that the vehicle 10 is traveling in a straight line, calculate the straight-line distance traveled by the vehicle 10 within the predetermined time period based on the vehicle speed and the length of the predetermined time period.

[0043] The controller 100 can determine whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold. In response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, the controller 100 can start a counter.

[0044] The controller 100 can determine a rack position offset value based on the rack position of the rack of the steering system of the vehicle 10 in response to a counter value being greater than or equal to a counter threshold. The rack position can correspond to the zero position of the rack or other suitable positions of the rack.

[0045] In some embodiments, the controller 100 may use at least one low-pass filter to determine a rack position offset value based on the rack position of the rack of the vehicle's steering system. The controller 100 may use the low-pass filter to limit the rack position offset value.

[0046] The controller 100 can adjust the position of the rack based on the rack position offset value. For example, the controller 100 can synchronize the rack to the zero position of the steering wheel angle or other suitable positions.

[0047] In some embodiments, controller 100 may perform the methods described herein. However, the methods performed by controller 100 as described herein are not intended to be limiting, and any type of software executing on the controller or processor may perform the methods described herein without departing from the scope of this disclosure. For example, a controller (such as a processor executing software within a computing device) may perform the methods described herein.

[0048] Figure 4 This is a flowchart generally illustrating a rack position offset learning method 300 according to the principles of this disclosure. Method 300 begins at 302.

[0049] At point 304, method 300 performs a first-level condition determination. For example, method 300 predicts whether at least one condition is met. If the first-level condition is not met, method 300 does not initiate the rack position offset learning function. Alternatively, if the first-level condition is met, method 300 initiates the rack position learning offset function. The first-level condition can be determined based on any suitable combination of rack position, rack speed, rack force, vehicle speed, and vehicle yaw rate. Method 300 can set a flag in response to the satisfaction of the first-level condition (e.g., setting the flag in the associated memory of controller 100).

[0050] At point 306, method 300 calculates the straight-line distance. For example, in response to the setting of a sign (e.g., meeting a first-level condition), method 300 determines that the vehicle is traveling in a straight line or substantially in a straight line. Then, method 300 calculates the straight-line distance of vehicle 10 based on the vehicle's speed and travel time.

[0051] At 308, method 300 determines whether the distance is satisfied and sets an offset learning flag. For example, after a predetermined period of time (e.g., three minutes or other suitable period of time), method 300 compares the distance with a calibration value to determine whether the distance is long enough to set the offset learning flag.

[0052] At 310, method 300 initiates offset learning and calculates the learning time. For example, method 300 can use a low-pass filter-low-pass filter (LPF-LPF filter) to limit the offset value. Method 300 can determine the counter accumulation.

[0053] At 312, method 300 determines whether the learning time (e.g., counter accumulation) is satisfied and whether the rack force is within range. For example, if the counter accumulation value is greater than or equal to the time threshold and the rack force is less than the rack force threshold, method 300 sets an output flag.

[0054] At position 314, method 300 outputs the offset learned value. For example, method 300 can output LPF-LPF filter values ​​and limit the final offset value.

[0055] Figure 5 This is a flowchart generally illustrating an alternative rack position offset learning method 400 according to the principles of this disclosure. At 402, method 400 receives at least one vehicle characteristic value.

[0056] At 404, method 400 determines, based on the at least one vehicle characteristic value, whether the vehicle associated with the at least one vehicle characteristic value is traveling in a straight line.

[0057] At 406, in response to determining that the vehicle is traveling in a straight line, method 400 calculates the straight-line distance traveled by the vehicle within the predetermined time period based on the vehicle speed and the length of the predetermined time period.

[0058] At 408, method 400 determines whether the calculated straight-line distance is greater than or equal to the straight-line distance threshold.

[0059] At 410, method 400 starts a counter in response to determining that the calculated straight-line distance is greater than or equal to a straight-line distance threshold.

[0060] At 412, method 400 determines a rack position offset value based on the rack position of the vehicle's steering system rack in response to a counter value being greater than or equal to a counter threshold.

[0061] In some embodiments, a system for learning rack position offset includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive at least one vehicle characteristic value; determine, based on the at least one vehicle characteristic value, whether a vehicle associated with the at least one vehicle characteristic value is traveling in a straight line; in response to determining that the vehicle is traveling in a straight line, calculate a straight-line distance traveled by the vehicle within a predetermined time period based on the vehicle speed and the length of a predetermined time period; determine whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold; in response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, start a counter; and in response to the counter value being greater than or equal to the counter threshold, determine a rack position offset value based on the rack position of the rack of the vehicle's steering system.

[0062] In some embodiments, the rack position corresponds to the zero position of the rack. In some embodiments, the instructions further instruct the processor to: adjust the rack position based on a rack position offset value. In some embodiments, adjusting the rack position includes: synchronizing the rack to the zero position of the steering wheel angle. In some embodiments, at least one vehicle characteristic includes at least the rack position. In some embodiments, at least one vehicle characteristic includes at least the vehicle speed. In some embodiments, at least one vehicle characteristic includes at least the rack speed, the rack force, and the vehicle yaw rate. In some embodiments, the steering system includes a steer-by-wire system. In some embodiments, the instructions further instruct the processor to: use at least one low-pass filter to determine a rack position offset value based on the rack position of the rack of the vehicle's steering system. In some embodiments, the instructions further instruct the processor to: use a low-pass filter to limit the rack position offset value.

[0063] In some embodiments, a method for learning rack position offset includes: receiving at least one vehicle characteristic value, and determining, based on the at least one vehicle characteristic value, whether a vehicle associated with the at least one vehicle characteristic value is traveling in a straight line. The method further includes: in response to determining that the vehicle is traveling in a straight line, calculating a straight-line distance traveled by the vehicle within a predetermined time period based on the vehicle speed and the length of a predetermined time period. The method further includes: determining whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold; in response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, activating a counter; and in response to the counter value being greater than or equal to the counter threshold, determining a rack position offset value based on the rack position of the rack of the vehicle's steering system.

[0064] In some embodiments, the rack position corresponds to the zero position of the rack. In some embodiments, the method further includes: adjusting the rack position based on a rack position offset value. In some embodiments, adjusting the rack position includes: synchronizing the rack to the zero position of the steering wheel angle. In some embodiments, at least one vehicle characteristic includes at least the rack position. In some embodiments, at least one vehicle characteristic includes at least the vehicle speed. In some embodiments, at least one vehicle characteristic includes at least the rack speed, the rack force, and the vehicle yaw rate. In some embodiments, determining the rack position offset value based on the rack position of the vehicle's steering system includes using at least one low-pass filter. In some embodiments, the method further includes: using a low-pass filter to limit the rack position offset value.

[0065] In some embodiments, an apparatus for learning rack position offset includes a controller configured to: receive at least one vehicle characteristic value; determine, based on the at least one vehicle characteristic value, whether a vehicle associated with the at least one vehicle characteristic value is traveling in a straight line; in response to determining that the vehicle is traveling in a straight line, calculate a straight-line distance traveled by the vehicle within a predetermined time period based on the vehicle speed and the length of a predetermined time period; determine whether the calculated straight-line distance is greater than or equal to a straight-line distance threshold; in response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, activate a counter; in response to the counter value being greater than or equal to the counter threshold, determine a rack position offset value based on the rack position of the rack of the vehicle's steering system; and adjust the rack position based on the rack position offset value.

[0066] The foregoing discussion is intended to illustrate the principles and various embodiments of this disclosure. Once the foregoing disclosure is fully understood, many variations and modifications will become apparent to those skilled in the art. The appended claims are intended to be construed as covering all such variations and modifications.

[0067] The word “example” is used herein to mean used as an example, illustration, or description. Any aspect or design described herein as an “example” is not necessarily to be construed as being more preferred or advantageous than other aspects or designs. Rather, the use of the word “example” is intended to present a concept in a specific manner. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise stated or clearly apparent from the context, “X comprises A or B” is intended to mean any natural inclusion. That is, if X comprises A; X comprises B; or X comprises both A and B, then “X comprises A or B” is satisfied in any of the foregoing cases. Additionally, the article “a / an” used in this application and the appended claims should generally be interpreted as meaning “one or more” unless otherwise stated or clearly apparent from the context to the singular form. Furthermore, unless so described, the use of the terms “implementation” or “an embodiment” throughout the document is not intended to refer to the same embodiment or implementation.

[0068] The systems, algorithms, methods, and instructions described herein can be implemented in hardware, software, or any combination thereof. Hardware may include, for example, a computer, intellectual property (IP) core, application-specific integrated circuit (ASIC), programmable logic array, optical processor, programmable logic controller, microcode, microcontroller, server, microprocessor, digital signal processor, or any other suitable circuit. In the claims, the term "processor" should be understood to include any of the foregoing hardware, individually or in combination. The terms "signal" and "data" are used interchangeably.

[0069] As used herein, the term "module" can include a packaged functional hardware unit designed for use with other components, a set of instructions executable by a controller (e.g., a processor executing software or firmware), processing circuitry configured to perform a specific function, and a self-contained hardware or software component that interfaces with a larger system. For example, a module can include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), circuitry, digital logic circuitry, analog circuitry, a combination of discrete circuitry, gate circuits, and other types of hardware, or combinations thereof. In other embodiments, a module can include a memory storing instructions executable by a controller to implement the features of the module.

[0070] Furthermore, in one respect, for example, the system described herein can be implemented using a general-purpose computer or general-purpose processor with a computer program that, when executed, implements any of the corresponding methods, algorithms, and / or instructions described herein. Additionally or alternatively, for example, a special-purpose computer / processor may be utilized, which may contain additional hardware for implementing any of the methods, algorithms, or instructions described herein.

[0071] Furthermore, all or part of the embodiments of this disclosure may take the form of a computer program product accessible from, for example, a computer-usable or computer-readable medium. A computer-usable or computer-readable medium may be, for example, any means capable of tangibly containing, storing, conveying, or transmitting a program for use by or in conjunction with any processor. The medium may be, for example, an electrical, magnetic, optical, electromagnetic, or semiconductor device. Other suitable media may also be used.

[0072] The above embodiments, implementations, and aspects have been described to allow for easy understanding of this disclosure and do not limit it. Rather, this disclosure is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, which should be interpreted in the broadest possible sense to cover all such modifications and equivalent structures permitted by law.

Claims

1. A system for learning rack position offset, the system comprising: processor; as well as The memory includes instructions that, when executed by the processor, cause the processor to: Receives a value for at least one vehicle characteristic; Based on the value of the at least one vehicle characteristic, determine whether the vehicle associated with the value of the at least one vehicle characteristic is traveling in a straight line; In response to determining that the vehicle is traveling in a straight line, the straight-line distance traveled by the vehicle during the predetermined time period is calculated based on the vehicle speed and the length of the predetermined time period; Determine whether the calculated straight-line distance is greater than or equal to the straight-line distance threshold; In response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, a counter is started; as well as In response to the counter value being greater than or equal to the counter threshold, a rack position offset value is determined based on the rack position of the steering system rack of the vehicle.

2. The system according to claim 1, wherein, The rack position corresponds to the zero position of the rack.

3. The system according to claim 2, wherein, The instruction also causes the processor to adjust the position of the rack based on the rack position offset value.

4. The system according to claim 3, wherein, Adjusting the position of the rack includes synchronizing the rack to the zero position of the steering wheel angle.

5. The system according to claim 1, wherein, The at least one vehicle characteristic includes at least the rack position.

6. The system according to claim 1, wherein, The at least one characteristic of the vehicle includes, at least, the speed of the vehicle.

7. The system according to claim 1, wherein, The at least one vehicle characteristic includes at least the rack speed of the rack, the rack force of the rack, and the yaw rate of the vehicle.

8. The system according to claim 1, wherein, The steering system includes a steer-by-wire system.

9. The system according to claim 1, wherein, The instructions also instruct the processor to: use at least one low-pass filter to determine the rack position offset value based on the rack position of the rack of the vehicle's steering system.

10. The system according to claim 9, wherein, The instruction also causes the processor to use a low-pass filter to limit the rack position offset value.

11. A method for learning rack position offset, the method comprising: Receives a value for at least one vehicle characteristic; Based on the value of the at least one vehicle characteristic, determine whether the vehicle associated with the value of the at least one vehicle characteristic is traveling in a straight line; In response to determining that the vehicle is traveling in a straight line, the straight-line distance traveled by the vehicle during the predetermined time period is calculated based on the vehicle speed and the length of the predetermined time period; Determine whether the calculated straight-line distance is greater than or equal to the straight-line distance threshold; In response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, a counter is started; as well as In response to the counter value being greater than or equal to the counter threshold, a rack position offset value is determined based on the rack position of the steering system rack of the vehicle.

12. The method according to claim 11, wherein, The rack position corresponds to the zero position of the rack.

13. The method of claim 12, further comprising: The position of the rack is adjusted based on the rack position offset value.

14. The method according to claim 13, wherein, Adjusting the position of the rack includes synchronizing the rack to the zero position of the steering wheel angle.

15. The method according to claim 11, wherein, The at least one vehicle characteristic includes at least the rack position.

16. The method according to claim 11, wherein, The at least one characteristic of the vehicle includes, at least, the speed of the vehicle.

17. The method according to claim 11, wherein, The at least one vehicle characteristic includes at least the rack speed of the rack, the rack force of the rack, and the yaw rate of the vehicle.

18. The method according to claim 11, wherein, Determining the rack position offset value based on the rack position of the steering system of the vehicle includes using at least one low-pass filter.

19. The method of claim 18, further comprising: A low-pass filter is used to limit the rack position offset value.

20. An apparatus for learning rack position offset, the apparatus comprising: The controller is configured as follows: Receives a value for at least one vehicle characteristic; Based on the value of the at least one vehicle characteristic, determine whether the vehicle associated with the value of the at least one vehicle characteristic is traveling in a straight line; In response to determining that the vehicle is traveling in a straight line, the straight-line distance traveled by the vehicle during the predetermined time period is calculated based on the vehicle speed and the length of the predetermined time period; Determine whether the calculated straight-line distance is greater than or equal to the straight-line distance threshold; In response to determining that the calculated straight-line distance is greater than or equal to the straight-line distance threshold, a counter is started; In response to the counter value being greater than or equal to the counter threshold, a rack position offset value is determined based on the rack position of the rack of the vehicle's steering system. as well as The position of the rack is adjusted based on the rack position offset value.

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