SYSTEM AND METHOD FOR MAINTAINING THE STABILITY OF A MOTOR VEHICLE

Abstract

A method (100) for maintaining the stability of a motor vehicle (10) with a first axle (16-1), a second axle (16-2) and a steering actuator (20) configured to steer the first axle (16-1) through a steering angle (θ), wherein the method (100) comprises: Determine, via an electronic control (26), the position and course of the motor vehicle (10) in relation to the road surface; Determining a current swim angle (α) of the second axis (16-2); Setting, via the electronic control (26), a maximum float angle (α) max ) of the second axle (16-2) via using a coefficient of friction (µ) at an interface between the motor vehicle (10) and the road surface; Predictions, via the electronic control (26), when the maximum buoyancy angle (α) max) of the second axis (16-2) is exceeded, using the localization and course of the motor vehicle (10) and the determined current sideslip angle (α) of the second axis as inputs into a linear computation model; Updating, via the electronic control (26), the linear computational model using the prediction of when the maximum float angle (α) max ) the second axis is exceeded in order to determine an impending instability of the motor vehicle (10); and Correcting, via the electronic control (26), the impending instability using the updated linear calculation model (48) and the maximum float angle (α) max ) of the second axis (16-2) by instructing the steering actuator (20) to change the steering angle (θ), characterized by the fact that the motor vehicle (10) is configured to operate in an autonomous mode controlled by the electronic control unit (26); and Determining the location and course of the motor vehicle (10) includes determining a desired path and discrete waypoints for the motor vehicle (10) via the electronic control (26) by receiving data from a global positioning satellite (GPS), where, with respect to the vehicle's course, the first axle (16-1) is a leading axle and the second axle (16-2) is a trailing axle, and the instability of the motor vehicle (10) is an oversteering condition that ends in a skid, wherein determining the current sideslip angle (α) of the second axis (16-2) includes receiving data via the electronic control (26) indicating a dynamic state of the motor vehicle (10) from sensors (24A) attached to the vehicle, including a yaw rate sensor for detecting a yaw rate (ψ̇) of the motor vehicle and at least one of the wheel sensors, the GPS and the accelerometer(s) for detecting the longitudinal speed (Vx) and the lateral speed (Vy) of the motor vehicle (10), wherein the linear computational model is embedded in a model predictive control (MPC) algorithm configured to determine the impending instability of the motor vehicle (10) by repeatedly solving an optimal finite horizon open-loop control equation, imposing constraints on the steering actuator and the maximum slip angle (α) max ) of the second axis, which further includes detecting the steering angle (θ) of the steering actuator via a steering position sensor, wherein the steering actuator (20) is designed as an electric power steering system, where the maximum swimming angle (α max ) according to the relationship α max = µ * α is determined, and where µ is the coefficient of friction at the interface between the motor vehicle (10) and the road surface, where: If µ < 0.3, the maximum swimming angle (α) is max ) in a range of 1-2 degrees; and If µ > 0.3, the maximum swimming angle (α) is max ) in a range of 3-4 degrees, where, if µ is unknown, the maximum swimming angle (α max ) is set to 1 degree.

Description

INTRODUCTION

[0001] The present disclosure relates to a system and method for maintaining the stability of a motor vehicle.

[0002] Autonomous vehicles have sensors that detect vehicle operation and the vehicle's surroundings, as well as a processing unit that controls all aspects of vehicle operation. Semi-autonomous vehicles operate in a similar manner but may require some driver input, monitoring, and / or control. Autonomous and semi-autonomous vehicles generally use a vehicle navigation system integrated with the vehicle's controller to determine the vehicle's position and the vehicle's direction to a selected waypoint.

[0003] Typically, a vehicle navigation system uses a GPS satellite to obtain its positioning data, which is then correlated with the vehicle's position relative to a surrounding geographic area. Based on the GPS satellite signal, if directions to a specific waypoint are needed, a route to such a destination can be calculated, thereby determining a vehicle path. In particular, the vehicle sensors and the computing device can work together to identify intermediate waypoints and maneuver the vehicle between such waypoints to keep the vehicle on the selected path.

[0004] Maneuvering a motor vehicle, especially at higher speeds, depends on the vehicle's traction, which is generally a function of the coefficient of friction at the interface between the vehicle and the road surface. Similar to operator-driven vehicles, autonomous and semi-autonomous vehicles may experience a loss of traction at one or more wheels while following a predetermined path, negatively impacting the vehicle's stability and control. While in operator-driven vehicles, the driver's driving demands may play a role in such a loss of traction, in autonomous and semi-autonomous vehicles, the loss of traction is primarily due to road conditions.

[0005] Jakob Philipp Bechtloff: Estimating the Sideslip Angle and Driving Dynamics Parameters to Improve Model-Based Vehicle Dynamics Control. Dissertation, 2018, ISBN 978-3-18-380912-7 [online] deals with the development of a method for estimating the non-measurable motion variables of center of gravity velocity and sideslip angle. For this purpose, only the sensors of the Electronic Stability Control are used. To enable a model-based control system or a guidance model to react to changes in lateral dynamic behavior, the most important vehicle dynamics parameters, such as the slip stiffness and the maximum friction coefficient, are also estimated during driving. For this purpose, vehicle dynamics models are developed that accurately model all required situations, such as driving in banked curves and driving at the physical limit with high and low friction coefficients.

[0006] DE 198 51 978 A1 describes a method for controlling the lateral dynamics of a vehicle with front-axle steering, whereby the yaw rate is controlled depending on deviations between the measured actual yaw rate values ​​and the continuously generated target values. The actual values ​​are tracked by adjusting the wheel steering angles and / or by adapting the wheel braking forces. For this purpose, a first target value for the yaw rate is determined using a linear vehicle model that reflects the desired dynamic behavior of the vehicle. This first target value corresponds to the driver's command, which is input via a control element. Under the additional condition that the sideslip angle of the non-steered rear wheels must not exceed a certain, limited value, a further target value for the yaw rate is calculated.The smaller of these two target values ​​is ultimately used as the decisive target value for the control in order to prevent the vehicle from skidding with high reliability.

[0007] DE 10 2020 201 516 A1 describes a system for controlling the steering of a vehicle. It includes an electric steering controller that influences the vehicle's steering based on at least one specified state variable. A state observer derives this state variable based on the vehicle's dynamics. An LQR (Linear Quadratic Regulator) controller is also integrated, which generates a state feedback based on the state variable derived by the state observer. This state feedback is then used to control the vehicle's steering.

[0008] DE 10 2018 203 182 B4 describes a method and a device for generating a yaw moment for controlling the driving dynamics of a vehicle, wherein a target yaw moment of the vehicle is determined from a target yaw rate predetermined by a predetermined steering angle so that the vehicle can pass through the predetermined steering angle at the current vehicle speed, wherein the target yaw moment is divided into a steering yaw moment generated by the steering, a roll yaw moment and a drive yaw moment, the roll yaw moment is divided into individual roll yaw moments of the individual wheels, which are variably adjustable. DESCRIPTION

[0009] The object of the invention is to provide an improvement in the driving stability and control of a motor vehicle, in particular in autonomous driving mode, by predicting and avoiding instabilities such as those that may occur during oversteering or skidding.

[0010] This object is achieved by the subject matter according to claim 1.

[0011] A method for maintaining the stability of a motor vehicle having a first axle, a second axle, and a steering actuator configured to steer the first axle via a steering angle (θ), comprises determining the location and heading of the motor vehicle relative to a road surface via an electronic controller. The method also includes determining a current sideslip angle (α) of the second axle. The method further includes setting a maximum sideslip angle (α max ) of the second axle via the electronic control system using a friction coefficient (µ) at an interface between the motor vehicle and the road surface. The method also includes predicting via the electronic control system when the maximum sideslip angle (α max) of the second axle is to be or would be exceeded, using the location and heading of the motor vehicle and the determined current sideslip angle (α) of the second axle as inputs to a linear calculation model. The method further comprises updating the linear calculation model via the electronic control using the prediction of when the maximum sideslip angle (α max ) of the second axle is exceeded in order to determine an impending instability of the motor vehicle. In addition, the method includes the correction of the impending instability via the electronic control using the updated linear calculation model and the maximum sideslip angle (α max ) of the second axle by instructing the steering actuator to change the steering angle (θ).

[0012] The motor vehicle may be configured to operate in an autonomous mode controlled by the electronic controller. Determining the location and heading of the motor vehicle may include determining a desired path and discrete waypoints for the motor vehicle via the electronic controller by receiving data from a global positioning satellite (GPS) and data from vehicle sensors such as radar and lidar.

[0013] With respect to the vehicle's direction of travel, the first axle may be a leading axle and the second axle a trailing axle. In such an embodiment, the instability of the motor vehicle may be an oversteer, culminating in a skid.

[0014] According to the method, determining the current sideslip angle (α) of the second axle may include receiving, via the electronic controller, data indicative of a dynamic state of the motor vehicle from sensors mounted on the vehicle, including a yaw rate sensor for detecting a yaw rate (ψ̇) of the motor vehicle and at least one of the wheel sensors, the GPS and the accelerometer(s) for detecting the longitudinal velocity (V x ) and the lateral velocity (V y ) of the motor vehicle.

[0015] The linear calculation model can be embedded in a Model Predictive Control (MPC) algorithm. According to the method, the MPC algorithm can be configured to determine the impending instability of the motor vehicle by repeatedly solving an optimal open-loop, finite-horizon control equation, using constraints on the steering actuator, such as limits on the steering speed and angle that vary with the vehicle speed, and the maximum slip angle (α max ) of the second axis.

[0016] According to the method, the steering angle (θ) of the steering actuator can be detected via a steering position sensor.

[0017] The steering actuator can be designed as an electric power steering system.

[0018] The maximum sideslip angle (α max ) can be calculated according to the relationship α max= µ * α. In such an embodiment, µ is the friction coefficient at the interface between the motor vehicle and the road surface.

[0019] If µ < 0.3, the maximum sideslip angle (α max ) can be in a range of 1-2 degrees. If µ > 0.3, the maximum sideslip angle (α max ) in a range of 3-4 degrees.

[0020] If µ is unknown, the maximum sideslip angle (α max ) should be set to 1 degree.

[0021] A motor vehicle having an electronic control unit configured to carry out the above method is also disclosed.

[0022] The above features and advantages, as well as other features and advantages of the present disclosure, will be readily apparent from the following detailed description of the embodiment(s) and the best mode(s) for carrying out the described disclosure, taken in conjunction with the accompanying drawings and the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a schematic plan view of a motor vehicle having first and second axles traversing a geographical area; the vehicle utilizes a global positioning satellite (GPS) and vehicle sensors that communicate with an electronic control unit configured to maintain vehicle stability as the vehicle traverses a selected path. Fig. Figure 2 is a schematic partial plan view of the vehicle suffering a loss of stability caused by a sideslip angle of the second axle relative to that shown in Fig. 1 shown selected path according to the present disclosure. Fig. 3 is a diagram of a lateral force on the second axis as a function of the Fig. 1 and Fig. 2 illustrated float angle of the second axle according to the present disclosure. Fig. 4 is a flowchart of a method for maintaining the stability of the Fig. 1-2 shown motor vehicle and in accordance with the Fig. 3 shows the diagram of the lateral force as a function of the sideslip angle of the second axle. DETAILED DESCRIPTION

[0023] Those of ordinary skill in the art will recognize that terms such as "top," "bottom," "upward," "downward," "upper," "downward," "left," "right," etc., are used descriptively for the figures and do not represent limitations on the scope of the disclosure as defined by the appended claims. Furthermore, the teachings herein may be described in terms of functional and / or logical block components and / or various processing steps. It should be understood that such block components may consist of any number of hardware, software, and / or firmware components configured to perform the specified functions.

[0024] With reference to the figures, in which like numerals indicate like parts which are related to the drawings, like reference numbers referring to like components, Fig. 1 is a schematic view of a motor vehicle 10 positioned relative to a road surface 12. The vehicle 10 may comprise some type of movable platform, such as, but not limited to, a car, truck, van, etc. As shown in Fig. 1, the vehicle 10 includes a vehicle body 14 defining four body sides—a first body end or front end 14-1, an opposite second body end or rear end 14-2, a first lateral or left body side 14-3, and a second lateral or right body side 14-4. Although not specifically shown, the vehicle body 14 may also define an upper body portion, which may include a vehicle roof, an underbody portion, and a passenger compartment. The left body side 14-3 and the right body side 14-4 are generally parallel to each other and arranged with respect to a virtual longitudinal body axis X of the motor vehicle 10, and span the distance between the front end 14-1 and the rear end 14-2.

[0025] The motor vehicle 10 also includes a first axle 16-1 and a second axle 16-2. With respect to the direction of travel of the motor vehicle 10, the first axle 16-1 may be a leading axle and the second axle 16-2 may be a trailing axle. As illustrated, the first axle 16-1 includes a first set of road wheels 18-1, while the second axle 16-2 includes a second set of road wheels 18-2 (e.g., individual left and right wheels on each axle). Each of the wheels 18-1, 18-2 has tires configured to make notional contact with the road surface 12. Although two axles, the first and second axles 16-1, 16-2, are expressly shown, nothing precludes the motor vehicle 10 from having additional axles. The motor vehicle 10 also includes a steering actuator 20 configured to steer the first axle 16-1 via a steering angle (θ). The steering actuator 20 may be embodied as an electric power steering system.The motor vehicle 10 additionally comprises a drive train 22 with a power source 22A, which is designed to transmit a drive torque to the first axle 16-1 and / or the second axle 16-2.

[0026] As in Fig. 1, the motor vehicle 10 includes at least one sensor 24A and an electronic control unit 26 that cooperate to control, guide, and maneuver the vehicle 10 at least partially in an autonomous mode in certain situations. As such, the vehicle 10 may be referred to as a semi-autonomous or fully autonomous vehicle. To enable efficient and reliable vehicle control, the electronic control unit 26 may be in operative communication with the steering actuator 20, which is embodied as an electric power steering system. The sensors 24A of the motor vehicle 10 are capable of sensing the road surface 12 and monitoring a surrounding geographic area as well as traffic conditions in the vicinity of the host vehicle.

[0027] The sensors 24A of the vehicle 10 may include, among other things, at least one LIDAR (Light Detection and Ranging) sensor, a radar, and a camera arranged around the vehicle 10 to detect boundary indicators, such as boundary conditions, of the road surface 12. The nature of the sensors 24A, their arrangement on the vehicle 10, and their operation for detecting and / or sensing the boundary indicators of the road surface 12 and for monitoring the surrounding geographical area and traffic conditions are known to those skilled in the art, are not relevant to the teachings of this disclosure, and therefore will not be described in detail here. The vehicle 10 may additionally include sensors 24B mounted on the vehicle's powertrain 22, such as a yaw rate sensor, an accelerometer, wheel speed sensors, a longitudinal speed sensor, and a lateral speed sensor.

[0028] The electronic controller 26 communicates with the sensors 24A of the vehicle 10 to receive their respective measurement data related to the detection or sensing of the road surface 12 and the monitoring of the surrounding geographical area and traffic conditions. The electronic controller 26 may alternatively be referred to as a control module, control unit, control device, control unit of the vehicle 10, computer, etc. The electronic controller 26 may include a computer and / or processor 28, as well as software, hardware, memory, algorithms, connections (e.g., to sensors 24A and 24B), etc., for managing and controlling the operation of the vehicle 10. As such, a method described below and generally in Fig. 4, may be embodied as a program or algorithm operable on the electronic controller 26. It should be appreciated that the electronic controller 26 may include a device capable of analyzing data from the sensors 24A and 24B, comparing data, making the decisions necessary to control the operation of the vehicle 10, and performing the tasks necessary to control the operation of the vehicle 10.

[0029] The electronic controller 26 may be embodied as one or more digital computers or host machines, each including one or more processors 28, read-only memory (ROM), random access memory (RAM), electrically programmable read-only memory (EPROM), optical drives, magnetic drives, etc., a high-speed clock, analog-to-digital (A / D) circuitry, digital-to-analog (D / A) circuitry, and input / output (I / O) circuitry, I / O devices and communication interfaces, as well as signal conditioning and buffer electronics. The computer-readable memory may comprise a non-volatile / tangible medium involved in providing data or computer-readable instructions. The memory may be non-volatile or volatile. Examples of non-volatile media include optical or magnetic hard drives and other persistent storage.An example of volatile media is dynamic random-access memory (DRAM), which can be used as main memory. Other examples of storage include floppy disks, flexible disks or hard disks, magnetic tape or other magnetic media, CD-ROMs, DVDs, and / or other optical media, as well as other possible storage devices such as flash memory.

[0030] The electronic controller 26 includes a tangible, non-transitory memory 30 in which computer-executable instructions, including one or more algorithms, for controlling the operation of the motor vehicle 10 are stored. The algorithm(s) in question may, in particular, include a Model Predictive Control (MPC) algorithm 32 for maintaining the stability of the motor vehicle 10, which is described in detail below. The processor 28 of the electronic control unit 26 is configured to execute the algorithm 32. The algorithm 32 implements a method for maintaining the stability of the vehicle 10 as the vehicle travels the road surface 12 along a desired or selected path, including various curves and bends.In particular, the instability of the motor vehicle 10, especially when negotiating a curve, can be defined as an oversteer condition that leads to the vehicle skidding and possibly spinning.

[0031] The motor vehicle 10 also includes a vehicle navigation system 34, which may be part of the integrated vehicle control system or an additional device used to determine the direction of travel in the vehicle. The vehicle navigation system 34 may be operatively connected to a Global Positioning Satellite (GPS) 36. The vehicle navigation system 34, in conjunction with the GPS 36 and the aforementioned sensors 24A, may be used to automate the vehicle 10. The vehicle navigation system 34 uses a satellite navigation device (not shown) to receive its position data from the GPS 36, which is then correlated with the vehicle's position relative to the surrounding geographic area. Based on this information, if directions to a specific waypoint are needed, a route plan to such a destination can be calculated. Current traffic information may be used to adjust the route.The current position of a vehicle 10 can be calculated using dead reckoning by using a previously determined position and extrapolating that position over elapsed time and heading based on known or estimated speeds. Data from sensors 24B mounted on the driveline of the vehicle 22, such as yaw rate sensors, accelerometers, and speed sensors, as well as vehicle-mounted radar and optical devices, can be used for increased reliability and to avoid GPS 36 signal loss and / or multipath interference due to urban canyons or tunnels.

[0032] The electronic control 26 is additionally configured, ie programmed, to determine the localization 38 (current position in the XY plane, in Fig. 1), a desired or intended path 40 and the course 42 of the motor vehicle 10 on the road surface 12 are determined via the navigation system 34, which receives data from the GPS 36. The desired path 40 may include discrete waypoints, such as those shown in Fig. 1. As already mentioned, the motor vehicle 10 may be configured to operate in an autonomous mode controlled by the electronic controller 26. In such a mode, the electronic controller 26 may also receive data from the vehicle sensors 24B to guide the vehicle along the desired path 40, for example, by regulating the steering actuator 20. The electronic controller 26 may additionally be programmed to detect and monitor the steering angle (θ) of the steering actuator 20 along the desired path 40, for example, while cornering. In particular, the electronic controller 26 may be programmed to determine the steering angle (θ) by receiving and processing data signals from a steering position sensor 44 (in Fig. 1), which is connected to the steering actuator 20.

[0033] The electronic control 26 is also configured to determine a current slip angle (α) of the second axle 16-2, for example by detecting data indicative of a dynamic state of the motor vehicle 10 (in Fig. 2). The slip angle (α) of the second axle 16-2 indicates how much the road wheels 18-2 have slipped in a direction generally perpendicular to the desired path 40. In particular, the electronic controller 26 may be programmed to determine the current slip angle (α) of the second axle 16-2, which may occur when the vehicle 10 negotiates a curve on the desired path 40. Determining the current slip angle (α) of the second axle 16-2 may be performed using data indicative of the dynamic state of the motor vehicle 10 and received from suitable sensors 24B mounted on the vehicle's powertrain 22. For example, the data in question may be collected via suitable sensors 24B and transmitted to the electronic controller 26, e.g.A yaw rate sensor can be used to detect the yaw rate ψ̇, while wheel speed sensors and / or accelerometers can be used to detect the longitudinal velocity V. x and the lateral velocity V y The GPS 36 can also be used to measure the longitudinal velocity V x and the lateral velocity V y to detect or assist in detecting. Furthermore, roll and pitch rate sensors may also be used among the sensors 24B to assist in determining the sideslip angle (α) of the second axle 16-2.

[0034] The electronic control 26 is also configured to provide a maximum permissible sideslip angle (α max) of the second axle 16-2 using a friction coefficient (µ) at the interface between the motor vehicle 10, ie the wheels 18-1, 18-2, and the road surface 12. In particular, the electronic control 26 can be programmed to determine the maximum sideslip angle (α max ) according to the following relationship: αmax=μ*α

[0035] In the relevant relationship 46, µ is the coefficient of friction at the interface between the motor vehicle 10 and the road surface 12. The value of the slip angle (α) is generally linked to the specific tire characteristics of the vehicle in question and represents the point at which the lateral force on an axle saturates and the corresponding tire begins to slip. For example, on a dry road surface, the value of α before the vehicle begins to slide is usually around 8 degrees. If µ < 0.3, e.g. on a wet road surface, the maximum slip angle (α max ) can be adjusted in a range of 1 to 2 degrees. If µ > 0.3, e.g. on dry roads, the maximum sideslip angle (α max ) can be set in a range of 3 to 4 degrees. If µ is not known, the maximum sideslip angle (α max ) can be set to 1 degree.

[0036] The electronic control 26 is also configured to predict when the maximum sideslip angle (α max ) of the second axle 16-2 is exceeded or would be exceeded by using the location 38 and heading 42 of the vehicle 10 and the determined current sideslip angle (α) of the second axle as inputs to a linear calculation model 48 embedded in the algorithm 32. The electronic controller 26 is also configured to optimize the linear calculation model 48 using the prediction of when the maximum sideslip angle (α max ) of the second axle 16-2 is exceeded, to determine the impending instability of the motor vehicle 10. The electronic controller 26 is further configured to determine the impending instability using the updated linear calculation model 48 and the maximum sideslip angle (α max) of the second axle 16-2 by commanding the steering actuator 20 to change the steering angle (θ).

[0037] If the vehicle loses grip or traction on the second axle 16-2, e.g., due to an unexpected encounter with a low-friction area (such as a puddle or a patch of gravel or ice), the force on the second axle 16-2 decreases rapidly while the force on the first axle 16-1 increases (see Fig. ). As a result, a torque M is generated on the vehicle body 14 around the vehicle's center of gravity C g , so that the vehicle 10 is prone to skidding. By instructing the steering actuator 20 to change the steering angle (θ), the force on the first axle 16-1 is to be reduced and the force on the second axle 16-2 is to be increased, thereby stabilizing the vehicle 10. The electronic controller 26 can be configured to provide a desired steering angle (θ T) of the steering actuator 20 and compares the target steering angle with the detected steering angle (θ). If the target steering angle (θ T ), for example, greater than the detected steering angle (θ), the electronic control 26 can instruct the steering actuator 20 to adjust the steering angle (θ) to the target steering angle (θ T ). The desired reduction in the steering angle (θ) would thereby limit the sideslip angle (α) of the second axle 16-2 and prevent an impending instability of the vehicle 10 on the desired path 40, for example when negotiating a curve.

[0038] As in Fig. 1, the linear computational model 48 may be part of the aforementioned MPC (Model Predictive Control) algorithm 32. As part of the MPC algorithm 32, the linear computational model 48 may be configured to determine the impending instability of the motor vehicle 10 by repeatedly solving a model-based equation for optimal finite-horizon, open-loop control using constraints on the steering actuator 20. The constraints on the steering actuator 20 may include limits for the steering speed and the steering angle that are dependent on the longitudinal speed V x and the lateral velocity V y of the vehicle 10 and the maximum sideslip angle (α max) of the second axle 16-2 may be variable. In particular, the MPC algorithm 32 can be used to find an optimal control sequence over a future horizon in a limited number of steps. At each sampling time, the vehicle state can be estimated to minimize a performance index subject to the predictive model and the constraints on the steering actuator 20. Quadratic programming (QP) can then be used to solve a resulting mathematical optimization problem comprising a multivariate quadratic function subject to linear constraints applied to the steering actuator 20. The slip angle (α) of the second axle 16-2 is defined as the initial condition of the optimization problem, i.e., the maximum slip angle (α max ) to avoid instability of the vehicle 10.

[0039] The linear calculation model 48 may use data of a lateral force (Sr) on the second axle 16-2 as a function of the toe angle (α) of the second axle, which is shown in a diagram 50 in Fig. 3 to predict when the maximum toe angle (α max ) of the second axle would be exceeded. In particular, the toe angle (α) of the second axle 16-2 can be expressed as follows: α=(−Vy+lr*ψ) / Vx

[0040] In expression 52, lr (in Fig. shown) a distance from the vehicle's center of gravity C g to the second axis 16-2.

[0041] In addition, the lateral force (Sr) on the second axis 16-2 can be expressed as follows: Sr=f(α)=+ / −Cr*α

[0042] In expression 54, Cr is the cornering stiffness of the vehicle, which is defined as the slope of the linear region 56 in Fig. In general, the slip angle of a vehicle axle is a linear combination of the vehicle's lateral velocity and the yaw rate. As shown in Fig. However, as shown, the lateral force (Sr) is a nonlinear function that depends on the sideslip angle (α) and the road conditions, i.e., the friction coefficient µ. In general, vehicle instability occurs when the lateral force (Sr) exceeds the friction coefficient (µ) at the interface between the motor vehicle 10 and the road surface 12.

[0043] Due to the nonlinearity of the lateral force (Sr), a simple linear MPC prediction model is unable to calculate output limits. In order for the linear calculation model 48 to effectively calculate output limits, the MPC algorithm 32 is first adapted to effectively predict vehicle spin—when the maximum sideslip angle (α max) of the second axle 16-2 is exceeded in order to determine an impending instability of the motor vehicle 10. Then, the linear calculation model 48 is updated to predict when the maximum sideslip angle (α max ) of the second axle 16-2 is exceeded, to determine the impending instability of the motor vehicle 10. In other words, the updated linear calculation model 48 is used to determine the impending instability by using initial conditions, ie the maximum sideslip angle (α max) of the second axle 16-2. Specifically, in the prediction, after calculating the lateral force (Sr) via Expression 54, the cornering stiffness (Cr) of the vehicle is set to zero so that the MPC model can describe a spinning vehicle. The MPC model then uses a maximum value for the lateral force (Sr) as input to limit the vehicle's skidding speed to a controllable level.

[0044] The electronic control 26 is further configured to detect the impending instability using the updated linear calculation model 48 and the maximum sideslip angle (α max) of the second axle 16-2 by commanding the steering actuator 20 to change, e.g., decrease, the steering angle (θ). The changed steering angle (θ) serves to correct the impending instability of the motor vehicle 10, i.e., for example, the electronic controller 26 can command the steering actuator 20 to steer the first axle 16-1 with a target steering angle (θ T ) if the detected sideslip angle (α) of the second axle is greater than the maximum sideslip angle (α max ) of the second axle 16-2, in order to counteract and limit the instability of the motor vehicle 10 during the curve. The electronic control 26 can therefore also be programmed to determine the desired steering angle (θ T) with the detected steering angle (θ) of the steering actuator 20 and determines a required incremental steering angle change (Δθ) to maintain the stability and balance of the motor vehicle 10. Thus, the impending instability of the motor vehicle 10 can be proactively limited by controlling the steering angle (θ) in order to keep the vehicle on its intended path 40.

[0045] A method 100 for maintaining the stability of a motor vehicle, such as that described in the Fig. 1-3 described vehicle 10, is in Fig. 4 and is explained in detail below. The method 100 begins at frame 102 by determining, via the electronic controller 26, that the vehicle 10 is moving. The electronic controller 26 may detect the motion of the vehicle 10 via the GPS 36 and / or the vehicle sensors 24A, 24B. At frame 102, the method may additionally include detecting the steering angle (θ) of the steering actuator 20 via the steering position sensor 44. As previously mentioned, the motor vehicle 10 may be configured to operate in an autonomous or semi-autonomous mode controlled by the electronic controller 26. Following frame 102, the method proceeds to frame 104.

[0046] At frame 104, the method includes determining the location 38 and heading 42 of the motor vehicle 10 relative to the road surface 12 via the electronic controller 26. Determining the location 38 and heading 42 of the motor vehicle 10 may include determining the desired path 40 and discrete waypoints, such as points A, B, and C, via the navigation system 34 using data from the GPS 36 and the vehicle sensors 24A. After frame 104, the method proceeds to frame 106. At frame 106, the method includes determining the current slip angle (α) of the second axle 16-2, e.g., during a turn along the desired path 40.Determining the current slip angle (α) of the second axle 16-2 may include receiving, via the electronic controller 26, data indicative of the dynamic state of the motor vehicle 10 from sensors 24B, including a yaw rate sensor for sensing the yaw rate (ψ̇) of the vehicle, wheel speed sensors, the GPS 36, and accelerometer(s) for sensing the longitudinal velocity (V. x ) and the lateral velocity (V y ) of the vehicle. From Figure 106, the procedure continues to Figure 108.

[0047] In frame 108, the method includes setting the maximum sideslip angle (α max ) of the second axle 16-2 via the electronic control 26 using the friction coefficient (µ) at the interface between the second set of vehicle wheels 18-2 and the road surface 12. As shown in the Fig. 1-3, the maximum sideslip angle (α max ) according to the relationship α max= µ * 8 degrees. If µ < 0.3, the maximum sideslip angle (α max ) in the range of 1 to 2 degrees, while at µ > 0.3 the maximum sideslip angle (α max ) can be set in the range of 3 to 4 degrees. If µ is not known, the maximum sideslip angle (α max ) can also be set to 1 degree. After Figure 108, the procedure continues with Figure 110.

[0048] In frame 110, the method includes predicting via the electronic control 26 when the maximum sideslip angle (α max ) of the second axle 16-2 is exceeded, wherein the location 38 and the course 42 of the motor vehicle 10 and the determined current sideslip angle (α) of the second axle 16-2 are used as inputs for the linear calculation model 48. As described above with respect to the Fig.1-3, the linear computational model 48 may be embedded in the MPC algorithm 32, which is configured to determine the impending instability of the motor vehicle 10. In particular, the MPC algorithm 32 is configured to repeatedly solve the open-loop, finite-horizon optimal control equation, with constraints on the steering actuator 20 and the maximum slip angle (α max ) of the second axis 16-2. After frame 110, the method proceeds to frame 112.

[0049] In frame 112, the method includes updating the linear calculation model 48 via the electronic control 26 using the prediction of when the maximum sideslip angle (α max) of the second axle 16-2 is exceeded to determine the impending instability of the vehicle 10. From frame 112, the method proceeds to frame 114, where the method initiates the correction of the impending instability via the electronic controller 26 using the updated linear calculation model 48 and the maximum sideslip angle (α max ) of the second axle 16-2 by commanding the steering actuator 20 to change the steering angle (θ). The method may return from frame 114 to frame 106 to further determine the current slip angle (α) of the second axle 16-2 along the desired path 40. Alternatively, the method may conclude at frame 116 with the motor vehicle 10 reaching its desired destination without losing stability.

[0050] The detailed description and the drawings or figures are supporting and descriptive of the disclosure, but the scope of the disclosure is defined solely by the claims. While some of the best modes and other embodiments for carrying out the claimed disclosure have been described in detail, various alternative designs and embodiments exist for carrying out the disclosure defined in the appended claims. Moreover, the embodiments illustrated in the drawings or the features of various embodiments recited in this description are not necessarily intended to be independent embodiments.Rather, it is possible that each of the features described in one of the embodiments may be combined with one or more other desired features of other embodiments, resulting in other embodiments not described in words or by reference to the drawings. Accordingly, such other embodiments fall within the scope of the appended claims.

Claims

[1] A method (100) for maintaining the stability of a motor vehicle (10) having a first axle (16-1), a second axle (16-2), and a steering actuator (20) configured to steer the first axle (16-1) via a steering angle (θ), the method (100) comprising: Determining, via an electronic control (26), the position and course of the motor vehicle (10) with respect to the road surface; Determining a current float angle (α) of the second axis (16-2); Setting, via the electronic control (26), a maximum float angle (α max ) of the second axle (16-2) using a friction coefficient (µ) at an interface between the motor vehicle (10) and the road surface; Predictions, via the electronic control (26), when the maximum sideslip angle (α max) of the second axle (16-2) is exceeded, wherein the location and course of the motor vehicle (10) and the determined current sideslip angle (α) of the second axle are used as inputs to a linear calculation model; Updating, via the electronic control (26), the linear calculation model using the prediction of when the maximum sideslip angle (α max ) of the second axle is exceeded in order to determine an impending instability of the motor vehicle (10); and Correct, via the electronic control (26), the impending instability using the updated linear calculation model (48) and the maximum sideslip angle (α max ) of the second axle (16-2) by instructing the steering actuator (20) to change the steering angle (θ), characterized by , that the motor vehicle (10) is configured to operate in an autonomous mode controlled by the electronic controller (26); and determining the location and course of the motor vehicle (10) comprises determining a desired path and discrete waypoints for the motor vehicle (10) via the electronic controller (26) by receiving data from a global positioning satellite (GPS), wherein, with respect to the vehicle's course, the first axle (16-1) is a leading axle and the second axle (16-2) is a trailing axle, and the instability of the motor vehicle (10) is an oversteering condition which ends in a skid, wherein determining the current slip angle (α) of the second axle (16-2) includes receiving data via the electronic controller (26) indicating a dynamic state of the motor vehicle (10) from sensors (24A) mounted on the vehicle, including a yaw rate sensor for detecting a yaw rate (ψ̇) of the motor vehicle and at least one of the wheel sensors, the GPS and the accelerometer(s) for detecting the longitudinal speed (Vx) and the lateral speed (Vy) of the motor vehicle (10), wherein the linear calculation model is embedded in a model predictive control (MPC) algorithm configured to determine the impending instability of the motor vehicle (10) by repeatedly solving an optimal finite horizon open loop control equation, with constraints on the steering actuator and the maximum slip angle (α max ) of the second axis, which further comprises detecting the steering angle (θ) of the steering actuator via a steering position sensor, wherein the steering actuator (20) is designed as an electric power steering, where the maximum sideslip angle (α max ) according to the relationship α max = µ * α is determined, and where µ is the friction coefficient at the interface between the motor vehicle (10) and the road surface, where: if µ < 0.3, the maximum sideslip angle (α max ) in a range of 1-2 degrees; and if µ > 0.3, the maximum sideslip angle (α max ) in a range of 3-4 degrees, where, if µ is unknown, the maximum sideslip angle (α max ) is set to 1 degree.

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