System and method for driver-centric automation

Abstract

A method for steering control includes: receiving at least one steering input value; receiving at least one vehicle speed value; and determining a vehicle sideslip angle and a yaw rate based on the at least one steering input value and the at least one vehicle speed value. The method also includes generating an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value. The method further includes determining a final steering control value based on the initial steering control value and the at least one steering input value; and selectively controlling at least one aspect of the vehicle steering system based on the final steering control value.

Description

Technical Field

[0001] The present disclosure relates to vehicle steering and, more particularly, to systems and methods for providing driver-centric steering automation. Background Art

[0002] Vehicles (such as cars, trucks, sport utility vehicles, crossovers, minivans, boats, aircraft, all-terrain vehicles, recreational vehicles, or other suitable forms of transportation) typically include various systems, such as steering systems and / or other suitable systems (e.g., such as braking systems, propulsion systems, etc.). The steering system may include an electric power steering (EPS) system, a steer-by-wire (SbW) steering system, a hydraulic steering system, or other suitable steering systems. Such systems of a vehicle generally control various aspects of vehicle steering (e.g., including providing steering assistance to an operator of the vehicle, controlling a steering wheel of the vehicle, etc.), vehicle propulsion, vehicle braking, etc. Summary of the Invention

[0003] The present disclosure generally relates to steering systems.

[0004] One aspect of the disclosed embodiments includes a method for steering control. The method includes: receiving at least one steering input value; receiving at least one vehicle speed value; and determining a vehicle sideslip angle and a yaw rate based on the at least one steering input value and the at least one vehicle speed value. The method further includes: generating an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value. The method further includes: determining a final steering control value based on the initial steering control value and the at least one steering input value; and selectively controlling at least one aspect of a vehicle steering system based on the final steering control value.

[0005] Another aspect of the disclosed embodiments includes a system for steering control. 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 steering input value; receive at least one vehicle speed value; determine a vehicle sideslip angle and a yaw rate based on the at least one steering input value and the at least one vehicle speed value; generate an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value; determine a final steering control value based on the initial steering control value and the at least one steering input value; and selectively control at least one aspect of a vehicle steering system based on the final steering control value.

[0006] Another aspect of the disclosed embodiments includes an apparatus for steering control. The apparatus includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive at least one steering input value; receive at least one vehicle speed value; determine a vehicle sideslip angle and a yaw rate based on the at least one steering input value and the at least one vehicle speed value and using a non-linear bicycle model; generate a reference yaw rate value based on the at least one steering input value, the at least one vehicle speed value, and a tire-road friction coefficient; generate an initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate value; determine a final steering control value based on the initial steering control value and the at least one steering input value; and selectively control at least one aspect of a vehicle steering system based on the final steering control value.

[0007] These and other aspects of the disclosure are disclosed in the following detailed description of the embodiments, the appended claims, and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It should be emphasized that, in accordance with common practice, the various features of the drawings are not drawn to scale. Instead, the dimensions of the various features are arbitrarily enlarged or reduced for clarity.

[0009] Figure 1 A vehicle is generally shown in accordance with the principles of the present disclosure.

[0010] Figure 2 A controller is generally shown in accordance with the principles of the present disclosure.

[0011] Figure 3 Vehicle dynamics are generally shown in accordance with the principles of the present disclosure.

[0012] Figure 4 is a flowchart generally showing a steering control method in accordance with the principles of the present disclosure.

[0013] Figure 5 is a flowchart generally showing another steering control method in accordance with the principles of the present disclosure. DETAILED DESCRIPTION

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

[0015] As described, a vehicle (such as a sedan, truck, sport utility vehicle, crossover vehicle, minivan, ship, aircraft, all-terrain vehicle, recreational vehicle, or other suitable form of transportation) typically includes various systems, such as a steering system and / or other suitable systems (e.g., such as a braking system, a propulsion system, etc.). The steering system can include an electric power steering (EPS) system, a steer-by-wire (SbW) steering system, a hydraulic steering system, or other suitable steering systems. Such systems of a vehicle generally control various aspects of the vehicle's steering (e.g., including providing steering assistance to an operator of the vehicle, controlling a steering wheel of the vehicle, etc.), vehicle propulsion, vehicle braking, etc.

[0016] Due to the ability to handle input saturation and state constraints in a dynamic environment, model predictive control (MPC) (e.g., which can include control techniques for controlling a process given a set of constraints) is increasingly used for vehicle stability control. However, such systems do not provide vehicle-level stability control.

[0017] Accordingly, systems and methods (such as the systems and methods described herein) configured to improve vehicle maneuverability and stability via an active steering assistance system in adverse conditions may be desirable. Yaw rate is generally an important metric for improving vehicle stability. Thus, the systems and methods described herein can be configured to use active steering assistance control of a vehicle to achieve stability control, which can be elucidated using two important objectives: improved maneuverability and stability; and smooth steering (e.g., or control actions that allow the steering force applied by a driver to allow the driver and / or passengers to feel relatively comfortable).

[0018] In some embodiments, the systems and methods described herein can be configured to use a steering system that includes a stability control system configured to ensure safe driving of a vehicle based on the operation of a driver of the associated vehicle. Such stability control can be challenging due to complex real-world conditions (such as wind disturbances, tire pressure imbalance, tire wear, etc.). Many vehicle accidents occur each year due to untimely and inaccurate application of control inputs.

[0019] A steering assistance control system can be used to improve vehicle maneuverability and stability, which can help a driver stabilize the vehicle and provide collision avoidance based on vehicle state information (e.g., vehicle state information can include yaw rate, lateral acceleration, and vehicle sideslip angle).

[0020] In some embodiments, the systems and methods described herein may be configured to use Model Predictive Control (MPC) or other suitable control methods for several systems. The systems and methods described herein may be configured to use a model subsystem that includes an expected value of a vehicle yaw rate, which may be calculated based on a steering angle, a vehicle speed, and a tire-road friction coefficient. The systems and methods described herein may be configured to use driver inputs, such as a steering angle and speed, and a road friction coefficient (e.g., provided by a vehicle simulation mechanism). The systems and methods described herein may be configured to use a non-linear bicycle model with a steering angle, speed, and road friction coefficient to calculate vehicle states and parameters, such as front and rear lateral forces, lateral acceleration, yaw rate, vehicle sideslip angle, etc. The non-linear bicycle model may include an infinite loop for calculating vehicle states based on driver inputs.

[0021] In the non-linear bicycle model, the Fiala tire model may be used to calculate the front and rear axle lateral forces according to the following formula:

[0022]

[0023] In some embodiments, the systems and methods described herein may be configured to estimate a slip angle (α) based on a non-linear bicycle model after adjusting a tire model of a vehicle for use in a simulation to find a cornering stiffness (C α ). The systems and methods described herein may be configured to provide a vehicle sideslip angle and a yaw rate, together with a reference yaw rate generated at a reference generator block, to the MPC, where the reference yaw rate may be described according to the following formula:

[0024]

[0025] K_us = 0 (understeer gradient) ----->

[0026]

[0027] In some embodiments, the systems and methods described herein may be configured to use a state space model generally shown in Figure 3 , which is described according to the following formula:

[0028]

[0029]

[0030]

[0031] The state equations of the system are discretized by the Euler method:

[0032]

[0033] The discrete form can be written in an incremental form, where the static error is eliminated by using an incremental mode. The systems and methods described herein can be configured to use a state - space augmented model, which can be described by the following equation:

[0034] x k+1 = Ax k + Bu k = Ax k + Bu k - Bu k-1 + Bu k-1 = Ax k + B(u k - u k-1 ) + Bu k-1 = Ax k + BΔu k + Bu k-1

[0035]

[0036]

[0037] In some embodiments, the systems and methods described herein can be configured to minimize the deviation of the yaw rate and the rate of change of the control action and / or to establish a yaw - rate reference model using vehicle simulation software. The yaw - rate reference model can be constrained by at least the hard constraints of the actuator (e.g., the steering system), which can be described by the following equation:

[0038] |δf| < δ max

[0039] |Δδ f | < |Δδ max |

[0040] where other constraints can be described by the following equation:

[0041]

[0042] The reference for tracking can be described by the following equation:

[0043]

[0044] In some embodiments, the systems and methods described herein may be configured to improve vehicle stability using active front steering (e.g., in an SbW steering system or other suitable steering system). The systems and methods described herein may be configured to use existing sensors (e.g., such as a steering angle sensor and / or a speed sensor) without using a yaw rate sensor and / or a lateral acceleration sensor. The systems and methods described herein may be configured to use an MPC method and / or any other suitable method, such as sliding mode control, fuzzy logic sliding mode control, adaptive control, etc.

[0045] In some embodiments, the systems and methods described herein may be configured to provide vehicle steering control. For example, the systems and methods described herein may be configured to receive at least one steering input value. The at least one steering input value may correspond to driver input provided at the steering wheel of the steering system. The systems and methods described herein may be configured to receive at least one vehicle speed value.

[0046] The systems and methods described herein may be configured to determine a vehicle sideslip angle and a yaw rate based on at least one steering input value and at least one vehicle speed value. For example, the systems and methods described herein may be configured to use a nonlinear bicycle model, which may be a Fiala tire model and / or other suitable models.

[0047] The systems and methods described herein may be configured to generate a reference yaw rate value based on at least one steering input value, at least one vehicle speed value, and a tire-road friction coefficient. The systems and methods described herein may be configured to generate an initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate value. The systems and methods described herein may be configured to use MPC, sliding mode control, fuzzy logic sliding mode control, adaptive control, and / or any suitable control method or technique to generate the initial steering control value.

[0048] The systems and methods described herein may be configured to determine a final steering control value based on the initial steering control value and at least one steering input value. The systems and methods described herein may be configured to selectively control at least one aspect of the vehicle steering system based on the final steering control value.

[0049] Figure 1Generally shown is a vehicle 10 in accordance with the principles of the present disclosure. The vehicle 10 can include any suitable vehicle, such as a sedan, a truck, a sport utility vehicle, a minivan, a crossover, any other passenger vehicle, any suitable commercial vehicle, or any other suitable vehicle. Although the vehicle 10 is shown as a passenger vehicle with wheels and for use on roads, the principles of the present disclosure can be applied to other vehicles, such as airplanes, boats, trains, drones, or other suitable vehicles.

[0050] The vehicle 10 includes a vehicle body 12 and a hood 14. A passenger compartment 18 is at least partially defined by the vehicle body 12. Another portion of the vehicle body 12 defines an engine compartment 20. The hood 14 is movably attached to a portion of the vehicle body 12 such that when the hood 14 is in a first position or an open position, the hood 14 provides access to the engine compartment 20, and when the hood 14 is in a second position or a closed position, the hood 14 covers the engine compartment 20. In some embodiments, the engine compartment 20 can be disposed at the rear of the vehicle 10, rather than as generally shown.

[0051] The passenger compartment 18 can be disposed behind the engine compartment 20, but in embodiments where the engine compartment 20 is disposed at the rear of the vehicle 10, the passenger compartment can be disposed in front of the engine compartment 20. The vehicle 10 can include any suitable propulsion system, including: an internal combustion engine, one or more electric motors (e.g., for an electric vehicle), one or more fuel cells, a hybrid (e.g., for 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.

[0052] In some embodiments, the vehicle 10 may include a petrol or gasoline fuel engine, such as a spark ignition engine. In some embodiments, the vehicle 10 may include a diesel fuel engine, such as a compression ignition engine. The engine compartment 20 houses and / or encloses at least some components of the propulsion system of the vehicle 10. Additionally or alternatively, propulsion controllers (such as an accelerator actuator (e.g., an accelerator pedal), a brake actuator (e.g., a brake pedal), a steering wheel, and other such components) are provided in the passenger compartment 18 of the vehicle 10. The propulsion controllers may be actuated or controlled by an operator of the vehicle 10, and the propulsion controllers may be directly connected to the corresponding components of the propulsion system, such as a throttle, brakes, vehicle axles, a vehicle transmission, etc. In some embodiments, the propulsion controllers may transmit signals to a vehicle computer (e.g., via drive-by-wire), and the vehicle computer may in turn control the corresponding propulsion components of the propulsion system. Thus, in some embodiments, the vehicle 10 may be an autonomous vehicle.

[0053] In some embodiments, the vehicle 10 includes a transmission that communicates with a crankshaft via a flywheel or a clutch or a fluid 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 a hybrid vehicle, the vehicle 10 may include one or more pistons that operate in concert with the crankshaft to generate a force that is transmitted through the transmission to one or more axles that cause the wheels 22 to rotate. When the vehicle 10 includes one or more electric motors, a vehicle battery and / or a fuel cell supply energy to the electric motors to cause the wheels 22 to rotate.

[0054] The vehicle 10 may include an automatic vehicle propulsion system, such as cruise control, adaptive cruise control, automatic brake control, other automatic vehicle propulsion systems, or combinations thereof. The vehicle 10 may be an autonomous or semi-autonomous vehicle, or other suitable type of vehicle. The vehicle 10 may include more or fewer features than those generally shown and / or disclosed herein.

[0055] 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, etc.), and a local interconnect network component (LIN) 32. Vehicle 10 may use the CAN bus 26, MOST 28, FlexRay component 30, LIN 32, other suitable networks or communication systems, or combinations thereof to transfer various information from sensors, such as inside or outside the vehicle, to various processors or controllers, such as inside or outside the vehicle. Vehicle 10 may include more or fewer features than those generally shown and / or disclosed herein.

[0056] In some embodiments, vehicle 10 may include a steering system, such as an EPS system, a steer-by-wire steering system (e.g., which may include one or more controllers or communicate with the one or more controllers, and the one or more controllers control components of the steering system without using a mechanical connection between the steering wheel of vehicle 10 and the wheels 22), a hydraulic steering system (e.g., which may include a magnetic actuator incorporated into a valve assembly of the hydraulic steering system), or other suitable steering systems.

[0057] The 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 roadwheel positions, other suitable inputs or information, or combinations thereof.

[0058] Additionally or alternatively, the inputs may include steering wheel torque, steering wheel angle, motor speed, vehicle speed, estimated electric motor torque command, other suitable inputs, or combinations thereof. The steering system may be configured to provide a steering function and / or control to vehicle 10. For example, the steering system may generate assist torque based on various inputs. The steering system may be configured to use the assist torque to selectively control a motor of the steering system to provide steering assistance to an operator of vehicle 10.

[0059] In some embodiments, vehicle 10 may include a controller, such as Figure 2Controller 100 illustratively generally. Controller 100 can include any suitable controller, such as an electronic control unit or other suitable controller. Controller 100 can be configured to control various functions of, for example, a steering system and / or various functions of vehicle 10. Controller 100 can include processor 102 and memory 104. Processor 102 can include any suitable processor, such as the processors described herein. Additionally or alternatively, as a supplement or alternative to processor 102, controller 100 can include any suitable number of processors. Memory 104 can 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 memory 104. In some embodiments, memory 104 can include flash memory, semiconductor (solid-state) memory, etc. Memory 104 can include random access memory (RAM), read-only memory (ROM), or a combination thereof. Memory 104 can include instructions that, when executed by processor 102, enable processor 102 to at least control various aspects of vehicle 10.

[0060] Controller 100 can receive one or more signals from various measurement devices or sensors 106 that indicate sensed or measured characteristics of vehicle 10. Sensors 106 can include any suitable sensors, measurement devices, and / or other suitable mechanisms. For example, sensors 106 can 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 can indicate steering wheel torque, steering wheel angle, motor speed, vehicle speed, other suitable information, or combinations thereof.

[0061] In some embodiments, controller 100 can be configured to provide vehicle steering control. For example, controller 100 can receive at least one steering input value. The at least one steering input value can correspond to driver input provided at the steering wheel of the steering system. Controller 100 can receive at least one vehicle speed value.

[0062] Controller 100 can determine a vehicle sideslip angle and yaw rate based on the at least one steering input value and the at least one vehicle speed value. For example, controller 100 can use a non-linear bicycle model, which can be a Fiala tire model and / or other suitable models.

[0063] The controller 100 can generate a reference yaw rate value based on at least one steering input value, at least one vehicle speed value, and a tire-road friction coefficient. The controller 100 can generate an initial steering control value based on a vehicle sideslip angle, a yaw rate, and the reference yaw rate value. The controller 100 can use MPC, sliding mode control, fuzzy logic sliding mode control, adaptive control, and / or any suitable control method or technique to generate the initial steering control value.

[0064] The controller 100 can determine a final steering control value based on the initial steering control value and at least one steering input value. The controller 100 can selectively control at least one aspect of the vehicle steering system and / or any suitable aspect of the vehicle 10 based on the final steering control value.

[0065] In some embodiments, the controller 100 can execute the methods described herein. However, the methods described herein as executed by the controller 100 are not meant to be limiting, and any type of software executed on a controller or processor can execute the methods described herein without departing from the scope of the present disclosure. For example, a controller (such as a processor executing software within a computing device) can execute the methods described herein.

[0066] Figure 4 A steering control method 200 in accordance with the principles of the present disclosure is generally shown. As described herein and shown in the figures, δ d refers to a driver steering input, v x refers to a vehicle speed, β refers to a vehicle sideslip angle, r refers to a yaw rate, r ref refers to a reference yaw rate, μ refers to a tire-road friction coefficient, δ c refers to a controller steering output (e.g., the controller steering output can be referred to herein as an initial steering control output or value), and δ refers to a final steering signal (e.g., the final steering signal can be referred to herein as a final steering control output or final steering value), which can be sent to the steering system of the vehicle 10.

[0067] At 202, the method 200 includes receiving driving inputs such as a steering input and a vehicle speed. For example, the controller 100 can receive driver inputs.

[0068] At 204, method 200 provides driver input to a non-linear bicycle model (e.g., a non-linear bicycle model using a Fiala tire model and / or other suitable models). The non-linear tire model may also receive the tire-road friction coefficient. The non-linear bicycle model may generate a vehicle sideslip angle and yaw rate based on the driver input and / or the tire-road friction coefficient. For example, controller 100 may use the non-linear bicycle model based on the driver input and / or the tire-road friction coefficient to generate a vehicle sideslip angle and yaw rate.

[0069] At 206, method 200 uses a yaw rate reference generator to generate a reference yaw rate based on the driver input and / or the tire-road friction coefficient. For example, controller 100 may use the yaw rate reference generator to generate a reference yaw rate based on the driver input and / or the tire-road friction coefficient.

[0070] At 208, method 200 uses a control technique (e.g., a control technique such as those described herein, including but not limited to MPC and / or any other suitable control method or technique) to determine a controller steering output based on the vehicle sideslip angle, yaw rate, and reference yaw rate. For example, controller 100 may use the control technique to determine a controller steering output based on the vehicle sideslip angle, yaw rate, and reference yaw rate.

[0071] At 210, method 200 generates a final steering signal based on the driver steering input and the controller steering output. For example, controller 100 may add the driver steering input and the controller steering output to generate a final steering signal. Additionally or alternatively, controller 100 may determine the final steering signal based on any suitable use of the driver steering input and the controller steering output.

[0072] At 212, method 200 provides the final steering signal to a steering system control mechanism of the steering system. For example, controller 100 may provide the final steering signal to a steering controller, which may then use the final steering signal to control at least one aspect of the steering control and / or vehicle control of vehicle 10, and / or controller 100 may use the final steering signal to control at least one aspect of the steering control and / or vehicle control of vehicle 10.

[0073] At 214, method 200 uses a tire-road friction coefficient estimation algorithm and / or any other suitable technique to estimate the tire-road friction coefficient. For example, controller 100 may use the tire-road friction coefficient estimation algorithm and / or any other suitable technique to estimate the tire-road friction coefficient.

[0074] Figure 5is a flowchart generally showing another steering control method 300 in accordance with the principles of the present disclosure. At 302, method 300 receives at least one steering input value. For example, controller 100 may receive at least one steering input value.

[0075] At 304, method 300 receives at least one vehicle speed value. For example, controller 100 may receive at least one vehicle speed value.

[0076] At 306, method 300 determines a vehicle sideslip angle and a yaw rate based on at least one steering input value and at least one vehicle speed value. For example, controller 100 may determine a vehicle sideslip angle and a yaw rate based on at least one steering input value and at least one vehicle speed value.

[0077] At 308, method 300 generates an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value. For example, controller 100 may generate an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value.

[0078] At 310, method 300 determines a final steering control value based on the initial steering control value and at least one steering input value. For example, controller 100 may determine a final steering control value based on the initial steering control value and at least one steering input value.

[0079] At 312, method 300 selectively controls at least one aspect of the vehicle steering system based on the final steering control value. For example, controller 100 may selectively control at least one aspect of the vehicle steering system of vehicle 10 and / or any other suitable aspect of vehicle 10 based on the final steering control value.

[0080] In some embodiments, a method for steering control includes: receiving at least one steering input value; receiving at least one vehicle speed value; and determining a vehicle sideslip angle and a yaw rate based on at least one steering input value and at least one vehicle speed value. The method further includes: generating an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value. The method further includes: determining a final steering control value based on the initial steering control value and at least one steering input value; and selectively controlling at least one aspect of the vehicle steering system based on the final steering control value.

[0081] In some embodiments, a vehicle steering system includes an electric power steering system. In some embodiments, a vehicle steering system includes a steer-by-wire steering system. In some embodiments, at least one steering input value corresponds to a steering wheel of the vehicle steering system. In some embodiments, based on at least one steering input value and at least one vehicle speed value, determining a vehicle sideslip angle and a yaw rate includes: using a non-linear bicycle model. In some embodiments, the non-linear bicycle model includes a Fiala tire model. In some embodiments, the method further includes: generating a reference yaw rate value based on at least one steering input value, at least one vehicle speed value, and a tire-road friction coefficient. In some embodiments, generating an initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate includes: using model predictive control. In some embodiments, generating an initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate includes: using sliding mode control. In some embodiments, generating an initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate includes: using fuzzy logic sliding mode control. In some embodiments, generating an initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate includes: using adaptive control.

[0082] In some embodiments, a system for steering control includes a processor and a memory. The memory includes instructions that, when executed by the processor, enable the processor to: receive at least one steering input value; receive at least one vehicle speed value; determine a vehicle sideslip angle and a yaw rate based on at least one steering input value and at least one vehicle speed value; generate an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value; determine a final steering control value based on the initial steering control value and at least one steering input value; and, selectively control at least one aspect of the vehicle steering system based on the final steering control value.

[0083] In some embodiments, the instruction further enables the processor to: determine a vehicle sideslip angle and a yaw rate using a non-linear bicycle model based on at least one steering input value and at least one vehicle speed value. In some embodiments, the non-linear bicycle model includes a Fiala tire model. In some embodiments, the instruction further enables the processor to: generate a reference yaw rate value based on at least one steering input value, at least one vehicle speed value, and a tire-road friction coefficient. In some embodiments, the instruction further enables the processor to: generate an initial steering control value using model predictive control based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate. In some embodiments, the instruction further enables the processor to: generate an initial steering control value using sliding mode control based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate. In some embodiments, the instruction further enables the processor to: generate an initial steering control value using fuzzy logic sliding mode control based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate. In some embodiments, the instruction further enables the processor to: generate an initial steering control value using adaptive control based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate.

[0084] In some embodiments, an apparatus for steering control includes a processor and a memory. The memory includes instructions that, when executed by the processor, enable the processor to: receive at least one steering input value; receive at least one vehicle speed value; determine a vehicle sideslip angle and a yaw rate based on at least one steering input value and at least one vehicle speed value and using a non-linear bicycle model; generate a reference yaw rate value based on at least one steering input value, at least one vehicle speed value, and a tire-road friction coefficient; generate an initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate value; determine a final steering control value based on the initial steering control value and at least one steering input value; and, based on the final steering control value, selectively control at least one aspect of a vehicle steering system.

[0085] The foregoing discussion is intended to illustrate the principles of the present disclosure and various embodiments. Once the foregoing disclosure is fully understood, many variations and modifications will become apparent to those of ordinary skill in the art. The appended claims are intended to be construed to cover all such variations and modifications.

[0086] As used herein, the term "example" is used to mean an example, instance, or illustration. Any aspect or design described herein as an "example" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Instead, the use of the term "example" is intended to present concepts in a concrete fashion. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X includes A or B" is intended to mean any of the natural inclusive permutations. That is, if X includes A, X includes B, or X includes both A and B, then "X includes A or B" is satisfied under any of the foregoing instances. Additionally, as used in this application and the appended claims, the articles "a" and "an" shall generally be construed to mean "one or more" unless otherwise specified or clear from the context that they refer to the singular form. Furthermore, the use of the term "an embodiment" or "one embodiment" throughout the specification is not necessarily intended to refer to the same embodiment or implementation unless so described specifically.

[0087] Embodiments of the systems, algorithms, methods, instructions, etc. described herein can be implemented in hardware, software, or any combination thereof. The hardware can include, for example, a computer, an intellectual property (IP) core, an application specific integrated circuit (ASIC), a programmable logic array, an optical processor, a programmable logic controller, microcode, a microcontroller, a server, a microprocessor, a digital signal processor, or any other suitable circuitry. In the claims, the term "processor" should be understood to cover any of the foregoing hardware individually or in combination. The terms "signal" and "data" may be used interchangeably.

[0088] As used herein, the term module can include a packaged functional hardware unit that is designed to be used 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 an independent 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, gates, and other types of hardware or combinations thereof. In other embodiments, a module can include a memory that stores instructions that can be executed by a controller to implement the features of the module.

[0089] In addition, in one aspect, for example, a general-purpose computer or a general-purpose processor having a computer program can be used to implement the systems described herein, and the computer program, when executed, implements any one of the various methods, algorithms, and / or instructions described herein. Additionally or alternatively, for example, a special-purpose computer / processor can be utilized, which can include other hardware for implementing any one of the methods, algorithms, or instructions described herein.

[0090] Furthermore, all or a portion of an embodiment of the present disclosure can take the form of a computer program product accessible from, for example, a computer-usable medium or a computer-readable medium. A computer-usable medium or a computer-readable medium can be any device that can tangibly contain, store, communicate, or transport a program for use by or in connection with any processor. The medium can be, for example, an electronic, magnetic, optical, electromagnetic, or semiconductor device. Other suitable media are also available.

[0091] The above-described embodiments, implementations, and aspects have been described to enable an easy understanding of the present disclosure and do not limit the present disclosure. On the contrary, the present disclosure is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, and the scope should be given the broadest interpretation so as to cover all such modifications and equivalent structures permitted under the law.

Claims

1. A method for steering control, the method comprising: Receiving at least one steering input value; Receiving at least one vehicle speed value; Determining a vehicle sideslip angle and a yaw rate based on the at least one steering input value and the at least one vehicle speed value; Generating an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value; Determining a final steering control value based on the initial steering control value and the at least one steering input value; And Selectively controlling at least one aspect of a vehicle steering system based on the final steering control value.

2. The method according to claim 1, wherein The vehicle steering system includes an electric power steering system.

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

4. The method according to claim 1, wherein, The at least one steering input value corresponds to a steering wheel of the vehicle steering system.

5. The method according to claim 1, wherein Determining the vehicle sideslip angle and the yaw rate based on the at least one steering input value and the at least one vehicle speed value includes: using a non-linear bicycle model.

6. The method according to claim 5, wherein The non-linear bicycle model includes a Fiala tire model.

7. The method according to claim 1 further comprises: Generating the reference yaw rate value based on the at least one steering input value, the at least one vehicle speed value, and a tire-road friction coefficient.

8. The method according to claim 1, wherein Generating the initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate includes: using model predictive control.

9. The method according to claim 1, wherein Generating the initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate includes: using sliding mode control.

10. The method according to claim 1, wherein, Generating the initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate includes: using fuzzy logic sliding mode control.

11. The method according to claim 1, wherein, Generating the initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate includes: using adaptive control.

12. A system for steering control, the system comprising: A processor; And A memory including instructions that, when executed by the processor, cause the processor to be able to: Receive at least one steering input value; Receive at least one vehicle speed value; Determine a vehicle sideslip angle and a yaw rate based on the at least one steering input value and the at least one vehicle speed value; Generate an initial steering control value based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate value; Determine a final steering control value based on the initial steering control value and the at least one steering input value; And Selectively control at least one aspect of a vehicle steering system based on the final steering control value.

13. The system according to claim 12, wherein, The instructions further cause the processor to be able to: determine the vehicle sideslip angle and the yaw rate using a non-linear bicycle model based on the at least one steering input value and the at least one vehicle speed value.

14. The system according to claim 13, wherein The non-linear bicycle model includes a Fiala tire model.

15. The system according to claim 12, wherein The instructions further cause the processor to be able to: generate the reference yaw rate value based on the at least one steering input value, the at least one vehicle speed value, and a tire-road friction coefficient.

16. The system according to claim 12, wherein, The instructions further enable the processor to: generate the initial steering control value using model predictive control based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate.

17. The system according to claim 12, wherein, The instructions further enable the processor to: generate the initial steering control value using sliding mode control based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate.

18. The system according to claim 12, wherein The instructions further enable the processor to: generate the initial steering control value using fuzzy logic sliding mode control based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate.

19. The system according to claim 12, wherein, The instructions further enable the processor to: generate the initial steering control value using adaptive control based on the vehicle sideslip angle, the yaw rate, and a reference yaw rate.

20. An apparatus for steering control, the apparatus comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to: receive at least one steering input value; receive at least one vehicle speed value; determine a vehicle sideslip angle and a yaw rate based on the at least one steering input value and the at least one vehicle speed value and using a non-linear bicycle model; generate a reference yaw rate value based on the at least one steering input value, the at least one vehicle speed value, and a tire-road friction coefficient; generate an initial steering control value based on the vehicle sideslip angle, the yaw rate, and the reference yaw rate value; determine a final steering control value based on the initial steering control value and the at least one steering input value; and selectively control at least one aspect of a vehicle steering system based on the final steering control value.

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