Fuzzy control based rack force fusion estimation method for different working conditions of steer-by-wire

Abstract

The present application relates to the technical field of vehicle steering control, and particularly relates to a rack force fusion estimation method for different working conditions of steer-by-wire based on fuzzy control, which comprises the following steps: collecting road roughness, judging whether it is not less than a first threshold value, if yes, measuring a first rack force based on a rack force estimator of a vehicle-tire model, otherwise collecting an actual vehicle speed; judging whether the vehicle speed is less than a second threshold value, if yes, measuring a second rack force based on a rack force estimator of a steering system model, otherwise judging whether the vehicle speed is less than a third threshold value, if yes, measuring the first rack force, otherwise measuring a front wheel steering angle of the vehicle, and judging whether it is not greater than a fourth threshold value, if yes, measuring the first rack force, otherwise measuring the second rack force; adopting fuzzy control when the first and second rack forces are converted, so as to realize smooth connection of the rack force estimation value under different working conditions. Thus, the problem that the applicability of the rack force estimator under bumping road, different vehicle speeds and steering angles is not fully studied in the steer-by-wire system is solved.

Description

Technical Field

[0001] This invention relates to the field of vehicle steering control technology, and in particular to a method for fusion estimation of rack force in steer-by-wire under different operating conditions based on fuzzy control. Background Technology

[0002] With the development of intelligent vehicle technology, traditional steering systems are struggling to meet the requirements of intelligent vehicles, leading to widespread attention for steer-by-wire systems that decouple the steering wheel and steering wheels. In steer-by-wire systems, the steering column is eliminated, and there is no mechanical connection between the steering wheel and steering wheels; steering and road feel information are transmitted via electrical signals. Steer-by-wire systems not only improve response speed but also meet the steering control requirements of intelligent vehicles, driving the development of automobiles towards greater intelligence.

[0003] The steer-by-wire system consists of two modules: road feel simulation and steering execution. Its performance directly impacts active safety and driving experience. In traditional mechanical steering systems, vehicle motion information and tire force are directly transmitted to the driver via mechanical connections, helping the driver judge the vehicle's operating status and road conditions. For steer-by-wire systems, a road feel simulation device is needed to simulate road feel for the driver. The steering execution module receives steering signals from the steering wheel and controls the movement of the steering rack to steer. The output torque of the steering motor is controlled based on the resistance torque of the steering rack. However, the estimation effect of the actual rack force under different operating conditions has not been fully studied. For example, one related technology—a driver road feel feedback adjustment method for steering systems based on dynamic rack force—divides the total rack force into a sum of comfort rack force and dynamic rack force, calibrates the comfort rack force and dynamic rack force, and determines the target hand force at different vehicle speeds using the calibrated parameters. However, this method requires calibration for different road conditions, is labor-intensive, and cannot fully cover all application scenarios. Related Technology 2: A road feel simulation device and its control method for steer-by-wire. The device calculates two components of the rack force based on a gear and rack model and a vehicle model. It then performs a weighted calculation on the two rack force components to obtain an estimated rack force. The estimated rack force is combined with the vehicle's driving state to calculate the basic road feel feedback. However, this solution does not fully discuss rack force estimation methods under different working conditions, and cannot meet the estimation accuracy requirements for specific working conditions (such as low-speed parking). It also cannot quickly obtain the estimation method for specific working conditions and has low adaptability to complex working conditions.

[0004] Furthermore, tire-ground contact information has a significant impact on road feel simulation and steering execution. Effectively extracting the coupling force between the wheels and the ground under different operating conditions directly affects the driver's steering feel and steering control accuracy. The rack force, as the tire return torque, acts on the rack through the steering tie rod, reflecting the tire-ground contact information.

[0005] Currently, the two most common methods for estimating rack forces are rack force estimators based on steering models and rack force estimators based on vehicle and tire models. In a steering model-based rack force estimator, the sensed steering motor angular position, speed, and torque, as well as the steering column torque, are fed into an input observer to calculate the rack force. In a vehicle and tire model-based rack force estimator, the road profile and steering angle are input into a combined vehicle and tire model to calculate the rack force.

[0006] While rack force estimators based on steering system models generally perform well, they cannot estimate the rack force components caused by road surface conditions. This can lead to drivers not fully understanding vehicle dynamics, potentially causing danger, especially at medium to high speeds or on steep inclines. The observers cannot separate rack forces from friction forces in steering disturbances. Since rack forces contain high-order nonlinear terms, both linear and nonlinear disturbance observers exhibit estimation distortions when road conditions change rapidly. Although rack force estimators based on vehicle-tire models utilize a two-degree-of-freedom vehicle dynamics model and a tire model to estimate rack force components caused by road surface conditions, their estimation performance is poor when front wheel steering angles are large, and the overall model is relatively limited, with some parameters difficult to obtain. Summary of the Invention

[0007] This invention provides a fuzzy control-based method for estimating rack force under different operating conditions in steer-by-wire systems. This method addresses the problems of being unable to quickly and accurately estimate rack force under specific operating conditions using a single model, and the sudden changes in rack force values ​​caused by switching between different estimators.

[0008] The first aspect of the present invention provides a method for fusion estimation of rack force of steer-by-wire under different working conditions based on fuzzy control, including the following steps: collecting the unevenness of the current road surface profile based on preset sensors set on the outside of the tire;

[0009] The system determines whether the unevenness is greater than or equal to a first threshold. If the unevenness is greater than or equal to the first threshold, a rack force estimator based on a vehicle-tire model is used to measure the first rack force estimate of the vehicle, and the first rack force estimate is added to the road feel feedback design. Otherwise, the actual vehicle speed is collected by the preset sensor. The system then determines whether the actual vehicle speed is less than a second threshold. If the actual vehicle speed is less than the second threshold, a rack force estimator based on a steering system model is used to measure the second rack force estimate of the vehicle, and the second rack force estimate is added to the road feel feedback design. Otherwise, the system determines whether the actual vehicle speed is less than a third threshold. If the vehicle speed is less than the first threshold, the system determines whether the unevenness is greater than or equal to a first threshold. If a third threshold is set, the first rack force estimate is measured using the rack force estimator based on the vehicle-tire model, and the first rack force estimate is added to the road feel feedback design. Otherwise, the front wheel angle of the vehicle is measured using the preset sensor. It is then determined whether the front wheel angle is less than or equal to a fourth threshold. If the front wheel angle is less than or equal to the fourth threshold, the first rack force estimate is measured using the rack force estimator based on the vehicle-tire model, and the first rack force estimate is added to the road feel feedback design. Otherwise, the second rack force estimate of the vehicle is measured using the rack force estimator based on the steering system model, and the second rack force estimate is added to the road feel feedback design.

[0010] Optionally, it further includes: before incorporating the road feel feedback design, fusing the first rack force estimate and the second rack force estimate based on fuzzy control, so as to smoothly connect the first rack force estimate and the second rack force estimate under different working conditions.

[0011] Optionally, the fusion of the first rack force estimate and the second rack force estimate based on fuzzy control includes:

[0012] The preset vehicle speed and preset steering wheel angle are input into the fuzzy controller to obtain the fusion weights;

[0013] The rack force fusion formula is determined based on the fusion weights. The first rack force estimate and the second rack force estimate are then fused by weights to obtain the fused rack force estimate.

[0014] Optionally, the rack force fusion formula is:

[0015] F rack =k*F rack-steer +(1-k)*F rack-tire

[0016] Among them, F rack F is the estimated value of the rack force after fusion. rack-steerF is the estimated value of the second rack force. rack-tire is the estimated force of the first rack, and k is the fusion weight.

[0017] Optionally, the rack force estimator based on the vehicle-tire model is:

[0018] F rack-tire =i p *M zf

[0019] Among them, F rack-tire Let i be the estimated force of the first rack. p M is the ratio of tire torque to rack force transmission given to the vehicle's steering kinematics. zf This is the return torque of the front tires.

[0020] Optionally, the first rack force estimate includes a steering rack force component and a road surface rack force component.

[0021] Optionally, when the vehicle is traveling on a smooth road surface, the steering rack force component is obtained by rotating the front wheel angle; when the front wheel angle is zero and the vehicle is traveling on an uneven road surface, the road surface rack force component is obtained.

[0022] A second aspect of the present invention provides a fuzzy control-based steerable rack force fusion estimation device for different operating conditions, comprising:

[0023] The measurement module is used to collect the unevenness of the current road surface profile based on preset sensors set on the outside of the tire;

[0024] The unevenness comparison module is used to determine whether the unevenness is greater than or equal to a first threshold. If the unevenness is greater than or equal to the first threshold, a rack force estimator based on a vehicle-tire model is used to measure the first rack force estimate of the vehicle and the first rack force estimate is added to the road feel feedback design. Otherwise, the actual vehicle speed is collected by the preset sensor.

[0025] A speed comparison module is used to determine whether the actual vehicle speed is less than a second threshold. If the actual vehicle speed is less than the second threshold, a rack force estimator based on a steering system model is used to measure the estimated second rack force of the vehicle, and the estimated second rack force is added to the road feel feedback design. Otherwise, it is determined whether the actual vehicle speed is less than a third threshold. If the vehicle speed is less than the third threshold, a rack force estimator based on a vehicle-tire model is used to measure the estimated first rack force, and the estimated first rack force is added to the road feel feedback design. Otherwise, the front wheel steering angle of the vehicle is measured by the preset sensor.

[0026] The front wheel steering angle comparison module is used to determine whether the front wheel steering angle is less than or equal to a fourth threshold. If the front wheel steering angle is less than or equal to the fourth threshold, the first rack force estimate is measured using the rack force estimator based on the vehicle-tire model, and the first rack force estimate is added to the road feel feedback design. Otherwise, the second rack force estimate is measured using the rack force estimator based on the steering system model, and the second rack force estimate is added to the road feel feedback design.

[0027] A third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the fuzzy control-based steer-by-wire force fusion estimation method for different operating conditions as described in the above embodiments.

[0028] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described fuzzy control-based steer-by-wire force fusion estimation method for different operating conditions.

[0029] The fuzzy control-based method for fusion estimation of rack force under different operating conditions for steer-by-wire systems, as described in this invention, employs a rack force estimator based on a vehicle-tire model at high speeds and a rack force estimator based on a steering model at low speeds, especially at low speeds and large steering angles. It also considers road surface unevenness, prioritizing the use of a rack force estimator based on both vehicle and tire models on bumpy roads. A complex tire model is used to separate the steering rack force component from the road surface rack force component, enabling classified estimation of rack force under different operating conditions, particularly at different vehicle speeds, steering angles, and road surface smoothness. Furthermore, the fuzzy control-based design of rack force fusion weights avoids abrupt changes in rack force values ​​during transitions between different estimators, better adapting to the needs of actual operation.

[0030] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0031] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0032] Figure 1 This is a flowchart illustrating a fuzzy control-based method for estimating rack and pinion forces under different operating conditions for steer-by-wire, according to an embodiment of the present invention.

[0033] Figure 2This is a control logic diagram of a fuzzy control-based steerable rack force fusion estimation method under different operating conditions provided by an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram illustrating the calculation of rack force components by a rack force estimator based on a vehicle-tire model according to an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram illustrating the application of the rack force estimation value to the steer-by-wire system according to an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of a fuzzy control-based fusion weight controller according to an embodiment of the present invention;

[0037] Figure 6 This is a schematic diagram of the vehicle speed membership function provided in an embodiment of the present invention;

[0038] Figure 7 This is a schematic diagram of the steering wheel angle membership function provided in an embodiment of the present invention;

[0039] Figure 8 This is a schematic diagram of the weighted membership function provided in an embodiment of the present invention;

[0040] Figure 9 This is a schematic diagram of the fusion weights for rack force estimation provided in an embodiment of the present invention;

[0041] Figure 10 This is a block diagram of a fuzzy control-based steerable rack force fusion estimation device under different operating conditions according to an embodiment of the present invention.

[0042] Figure 11 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0043] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0044] The following describes, with reference to the accompanying drawings, a method for fusion estimation of rack and pinion forces for steer-by-wire under different operating conditions based on fuzzy control, according to an embodiment of the present invention.

[0045] Figure 1 This is a flowchart illustrating a fuzzy control-based method for estimating rack and pinion forces under different operating conditions for steer-by-wire, as provided in an embodiment of the present invention.

[0046] like Figure 1 As shown, the fuzzy control-based method for estimating the rack force fusion under different operating conditions for steer-by-wire includes the following steps:

[0047] In step S101, the unevenness of the current road surface profile is collected based on preset sensors set on the outside of the tire.

[0048] In step S102, it is determined whether the unevenness is greater than or equal to a first threshold. If the unevenness is greater than or equal to the first threshold, the first rack force estimate of the vehicle is measured using a rack force estimator based on the vehicle-tire model, and the first rack force estimate is added to the road feel feedback design. Otherwise, the actual vehicle speed is collected by a preset sensor.

[0049] It should be noted that by constructing a two-degree-of-freedom vehicle dynamics model and a linear tire model, the restoring torque of the front tire is solved, and then a rack force estimator based on the vehicle-tire model is constructed based on the restoring torque of the front tire.

[0050] The two-degree-of-freedom dynamic model of the vehicle is as follows:

[0051]

[0052] Where m is the vehicle mass. yaw rate is lateral velocity; u is longitudinal velocity; I is yaw inertia; w, Let l be the yaw rate and yaw acceleration. f l is the distance from the vehicle's center of gravity to the front axle. r F is the distance from the vehicle's center of gravity to the rear axle. yr F is the lateral force of the front tire. yr This refers to the lateral force of the rear tire.

[0053] The linear tire model is as follows:

[0054]

[0055]

[0056] Among them, C af For the front wheel lateral stiffness, a f Front wheel slip angle

[0057]

[0058] Among them, t p Inflation trajectory for the front tires, t p0 Let μ be the inflation trajectory of the front tire when the slip angle is zero, and μ be the coefficient of friction between the tire and the road surface.

[0059] The self-centering torque M of the front tire zf It can be given as:

[0060] M zf =-(t) p +t m )*F yf

[0061] Where tm represents the mechanical trajectory of the front wheel tire.

[0062] The rack force estimator calculated from the vehicle-tire model is as follows:

[0063] F rack-tire =i p *M zf

[0064] Among them, i p The ratio of tire torque to rack force given to the vehicle's steering kinematics

[0065] Therefore, the rack force estimate obtained from the vehicle-tire model is F. rack-tire At this point, the rack force can be divided into a rack force component F caused by the steering direction. R-steering and the rack force component F caused by the road surface profile R-road ,Right now:

[0066] F rack-tire =F R-steering +F R-road

[0067] like Figure 3 and 4 As shown, by driving the vehicle on a smooth surface and rotating the front wheels, the steering angle F is obtained. R-steering Setting the front wheel angle to zero, the vehicle travels on an uneven road surface, yielding F. R-road At this point, the rack force component can be used for road feel feedback and steering execution, allowing the driver to simultaneously monitor the vehicle's steering status and road conditions.

[0068] Vehicle-tire model-based estimators can be used to determine rack forces caused by steering angle, independent of rack forces caused by road contours. These rack force components can be used to compensate for the individual effects of steering angle and road contours on steering feel.

[0069] Specifically, such as Figure 2 As shown, the road surface profile is first determined by external tire sensors. When the road surface is smooth and the unevenness is less than h1, the vehicle speed and front wheel steering angle are determined in the next step. If the road surface unevenness is greater than or equal to h1, a rack force estimator based on the vehicle-tire model is directly used to measure the estimated value of the vehicle's first rack force. The steering rack force component and the road rack force component in the estimated value of the first rack force are separated and added to the road feel feedback design to enhance the driver's control over the road surface.

[0070] In step S103, it is determined whether the actual vehicle speed is less than a second threshold. If the actual vehicle speed is less than the second threshold, a rack force estimator based on a steering system model is used to measure the estimated value of the second rack force of the vehicle, and the estimated value of the second rack force is added to the road feel feedback design. Otherwise, it is determined whether the actual vehicle speed is less than a third threshold. If the vehicle speed is less than the third threshold, a rack force estimator based on a vehicle-tire model is used to measure the estimated value of the first rack force, and the estimated value of the first rack force is added to the road feel feedback design. Otherwise, the front wheel steering angle of the vehicle is measured by a preset sensor.

[0071] It should be noted that the rack force estimator based on the steering system model is as follows:

[0072]

[0073] Among them, M r G is the mass of the rack, G is the reduction ratio of the reducer, and r is the mass of the rack. p B is the pitch circle radius of the pinion. r It is the rack resistance, T m It is the output torque of the steering actuator motor; x r , Specifically, the rack's lateral displacement, velocity, and acceleration; F a Fr is the return torque; Fr is the friction experienced by the rack during its movement.

[0074] The steering return torque F a The friction F experienced by the rack during its movement f Combined into generalized rack force F r :

[0075] F r =F a +F f

[0076] Design an observer to estimate the generalized rack force.

[0077] First, define x1 = x r , Let F be the state variable of the system, and let F be the restoring torque of the system. a and frictional force F f As the generalized rack force F r The state-space description of the extended disturbance observer designed based on formula (*-*) is as follows:

[0078]

[0079] in, These are the state variables x1, x2, and F. rThe estimated values ​​are given by k1, k2, and k3, which are the high-gain coefficients in the perturbation observer. When the gain value is large, the observer can quickly converge to near the actual value.

[0080] There is a linear relationship between the front wheel steering angle and the rack displacement:

[0081] δ f =x1*i r

[0082] Where, δ f For the front wheel steering angle, i r The gear ratio is the rotation angle from the rack to the front wheel.

[0083] Therefore, the rack force estimate obtained from the steering system model is F. rack-steer .

[0084] In step S104, it is determined whether the front wheel steering angle is less than or equal to the fourth threshold. If the front wheel steering angle is less than or equal to the fourth threshold, the first rack force estimate is measured using a rack force estimator based on the vehicle-tire model, and the first rack force estimate is added to the road feel feedback design. Otherwise, the second rack force estimate is measured using a rack force estimator based on the steering system model, and the second rack force estimate is added to the road feel feedback design.

[0085] Specifically, such as Figure 2 As shown, when the road surface unevenness is less than h1 and the vehicle speed is less than v1, a rack force estimator based on the steering system model is used to measure the estimated value of the vehicle's second rack force, and this estimated value is incorporated into the road feel feedback design. In low-speed driving conditions, such as steering and parking, where the front wheel steering angle is relatively large, the rack force estimator based on the steering system model offers higher accuracy. Furthermore, low-speed driving is safer, and even in non-steering conditions, the rack force estimator based on the steering system model is sufficiently accurate. Therefore, when the vehicle speed is less than v1, the rack force estimator based on the steering system model is directly used to avoid frequent switching of estimation methods.

[0086] When the road surface unevenness is less than h1 and the vehicle is traveling at a moderate speed (greater than v1 and less than v2), a steering condition will occur, requiring further assessment of the front wheel steering angle. If the front wheel steering angle is large (greater than a1), a rack force estimator based on the steering system model is used to measure the second rack force estimate, which is then incorporated into the road feel feedback design. If the front wheel steering angle is less than a1, indicating a small steering angle, a rack force estimator based on a vehicle-tire model can be used to measure the first rack force estimate, which is then incorporated into the road feel feedback design.

[0087] When the road surface unevenness is less than h1 and the vehicle is traveling at high speed (v>v2), the vehicle's steering becomes more sensitive, and drivers tend to make small steering angles. This necessitates higher accuracy estimation performance, leading to the adoption of a vehicle-tire model-based rack force estimator. Furthermore, since road surface unevenness has a significant impact on vehicle driving safety at this point, the vehicle-tire model-based rack force estimator can separate the rack force component caused by road unevenness, transmitting it as road feel information to the driver. This allows the driver to understand the vehicle's driving status and avoid dangerous situations.

[0088] In some implementations, to avoid abrupt changes in rack force values ​​when switching between different estimators, a rack force fusion weight design based on fuzzy control is used to better adapt to the needs of actual operation. That is, before adding road feel feedback design, the first rack force estimate and the second rack force estimate are fused by designing different weight coefficients based on fuzzy control, so that the first rack force estimate and the second rack force estimate are smoothly connected under different working conditions.

[0089] Specifically, such as Figure 5 As shown, the preset vehicle speed and preset steering wheel angle are input into the fuzzy controller to obtain the fusion weight k. Based on the aforementioned steps, the universe of discourse and fuzzy set of each variable in the fuzzy control are determined. The rack force fusion formula is determined based on the fusion weight k. The first rack force estimate and the second rack force estimate are then fused using weights to obtain the fused rack force estimate. This design is for vehicles operating under normal driving conditions, with a speed range of 0-120 km / h and a steering wheel angle range of -180° to 180°. The weight k varies between 0 and 1.

[0090] The formula for rack force fusion is as follows:

[0091] F rack =k*F rack-steer +(1-k)*F rack-tire

[0092] In the formula, F rack F is the estimated value of the rack force after fusion. rack-steer F is the estimated value of the second rack force. rack-tire is the estimated force of the first rack, and k is the fusion weight.

[0093] It should be noted that fuzzy control is an intelligent control method that mimics human reasoning and decision-making processes. It is particularly suitable for complex nonlinear systems that are difficult to model precisely, and it features strong anti-interference capabilities and good robustness. Furthermore, to prevent frequent switching from affecting steering feel, fuzzy control is used to fuse the two estimates.

[0094] The rack force estimation is designed for vehicles under normal driving conditions, with a speed range of 0-120 km / h. The fuzzy universe of discourse is {0, 120}, and the fuzzy set is {NB, NM, NS, ZO, PS, PM, PB} = {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. Figure 6 As shown, the membership function chosen is the widely used and highly sensitive triangular membership function.

[0095] The steering wheel angle, which is easily obtained, is used to represent the front wheel angle. The steering wheel angle range is assumed to be -180° to 180°, the fuzzy universe of discourse is {-180, 180}, and the fuzzy set is {NB, NM, NS, ZO, PS, PM, PB} = {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. For example... Figure 7 As shown, the membership function is also chosen to be the triangular membership function.

[0096] Let the weight k vary between 0 and 1, its universe of discourse be {0, 1}, the fuzzy set be {NB, NS, 0, PS, PB}, and the fusion weight membership function be... Figure 8 As shown.

[0097] Based on the applicable operating conditions of the two estimators, a rack force estimator based on the steering model should be used at low speeds to achieve rapid estimation; while a rack force estimator based on the vehicle-tire model should be used at high speeds to improve estimation accuracy. The fuzzy rules designed accordingly are shown in Table 1.

[0098] Table 1 Fuzzy Control Rules

[0099]

[0100] In summary, the weights for estimating rack force on smooth road surfaces using fuzzy control design are fused together, such as... Figure 9 As shown, the rack force estimates obtained from the fuzzy rule table in Table 1 are fused into a weighted graph.

[0101] The obtained fuzzy control is used for rack force estimation fusion control. Vehicle speed and steering wheel angle are input into the fuzzy controller, which outputs corresponding fusion weights. These weights are then input into the rack force fusion formula to obtain the final output rack force value. Figure 9 It can be seen that the fusion weight changes gradually under the fuzzy control strategy.

[0102] The fuzzy control-based method for estimating the force fusion of the steering rack and pinion gears under different operating conditions, proposed according to embodiments of the present invention, has the following advantages:

[0103] This paper thoroughly studies the applicability of rack force estimators under bumpy roads, different vehicle speeds, and different steering angles, and provides a fuzzy control-based method for fusion estimation of rack force under different working conditions for steer-by-wire. It achieves rack force classification estimation under different working conditions, especially under different vehicle speeds, different steering angles, and different road surface smoothness. It incorporates driving habits to avoid frequent switching of estimation methods caused by high-frequency steering angles at low speeds, which would prevent the driver from fully grasping the vehicle's condition.

[0104] Compared to rack force estimators based on steering models, this embodiment of the invention pre-judges road conditions and transforms them into rack force estimators based on vehicle-tire models when the road surface is bumpy. This allows the driver to estimate the rack force components caused by the road surface, enabling the driver to fully grasp the vehicle's driving status, and making it less dangerous, especially at medium and high speeds or on steep slopes.

[0105] Compared to rack force estimators based on vehicle-tire models, this embodiment of the invention transforms the model into a rack force estimator based on a steering model at medium vehicle speeds and large steering angles, avoiding the degradation of model estimation performance at large steering angles. In particular, at low vehicle speeds, the rack force estimator based on the steering model is directly used, resulting in good estimation performance and avoiding the driver's inability to fully grasp the vehicle's condition due to frequent switching of estimation methods caused by high-frequency steering angles at low speeds.

[0106] Compared to the simple rack force fusion method, the embodiments of the present invention provide a detailed description of rack force classification estimation under different working conditions, especially under different vehicle speeds, different turning angles and different road surface smoothness. This method cannot quickly obtain the estimation method under specific working conditions and cannot meet the estimation accuracy requirements for specific working conditions, thus improving the adaptability to complex working conditions.

[0107] In the road feel simulation method, the embodiment of the present invention has better estimation performance under specific working conditions. By introducing the rack force estimation value into the road feel model, it is indirectly reflected by the rack force under simple working conditions, and directly reflected by the self-aligning torque of the tire under complex working conditions. The driver can fully grasp the coupling force information between the wheel and the ground to generate road feel, enhance the driver's judgment of road information, and thus help driving decision-making.

[0108] In the steering execution control method, the embodiments of the present invention can use the real-time estimated rack force for feedforward compensation, and can effectively estimate the rack force under complex working conditions to maintain tracking performance. When driving at high speeds or on bumpy roads, the rack force estimation value based on the vehicle-tire model is used to improve the accuracy of steering execution and enable the driver to fully grasp the road conditions. When driving at low speeds or with large turning angles, the rack force estimation value based on the steering system is used. By switching the estimation method under different working conditions, the speed and accuracy of steering execution can be effectively met.

[0109] To prevent frequent switching from affecting steering feel, fuzzy control is used to fuse the two estimates. The inputs are vehicle speed and steering wheel angle, and the output is the fusion weight. The fusion weight value is substituted into the rack force fusion formula to obtain the final rack force value.

[0110] Next, referring to the accompanying drawings, a fuzzy control-based steerable rack force fusion estimation device for different operating conditions based on an embodiment of the present invention is described.

[0111] Figure 10 This is a block diagram of a fuzzy control-based steerable rack force fusion estimation device for different operating conditions according to an embodiment of the present invention.

[0112] like Figure 10 As shown, the fuzzy control-based steerable rack force fusion estimation device 100 for different working conditions includes: a measurement module 101, an unevenness comparison module 102, a speed comparison module 103, and a front wheel angle comparison module 104.

[0113] The measurement module 101 is used to collect the unevenness of the current road surface profile based on preset sensors set on the exterior of the tire. The unevenness comparison module 102 is used to determine whether the unevenness is greater than or equal to a first threshold. If the unevenness is greater than or equal to the first threshold, a rack force estimator based on a vehicle-tire model is used to measure the first rack force estimate of the vehicle, and the first rack force estimate is added to the road feel feedback design. Otherwise, the actual vehicle speed is collected by a preset sensor. The speed comparison module 103 is used to determine whether the actual vehicle speed is less than a second threshold. If the actual vehicle speed is less than the second threshold, a rack force estimator based on a steering system model is used to measure the second rack force estimate of the vehicle, and the second rack force estimate is added to the road feel feedback design. Otherwise, the module determines whether the actual vehicle speed is less than a third threshold. If the vehicle speed is less than the third threshold, a rack force estimator based on a vehicle-tire model is used to measure the first rack force estimate, and the first rack force estimate is added to the road feel feedback design. Otherwise, the front wheel steering angle of the vehicle is measured by a preset sensor. The front wheel steering angle comparison module 104 is used to determine whether the front wheel steering angle is less than or equal to a fourth threshold. If the front wheel steering angle is less than or equal to the fourth threshold, a rack force estimator based on a vehicle-tire model is used to measure the first rack force estimate and the first rack force estimate is added to the road feel feedback design. Otherwise, a rack force estimator based on a steering system model is used to measure the second rack force estimate of the vehicle and the second rack force estimate is added to the road feel feedback design.

[0114] Optionally, it also includes:

[0115] The fusion module is used to fuse the first rack force estimate and the second rack force estimate based on fuzzy control before adding road feel feedback design, so as to smoothly connect the first rack force estimate and the second rack force estimate under different working conditions.

[0116] Optionally, the first rack force estimate and the second rack force estimate are fused based on fuzzy control, including:

[0117] The preset vehicle speed and preset steering wheel angle are input into the fuzzy controller to obtain the fusion weights;

[0118] The rack force fusion formula is determined based on the fusion weights. The first rack force estimate and the second rack force estimate are fused by weights to obtain the fused rack force estimate.

[0119] Optionally, the rack force fusion formula is:

[0120] F rack =k*F rack-steer +(1-k)*F rack-tire

[0121] Among them, F rack F is the estimated value of the rack force after fusion. rack-steer F is the estimated value of the second rack force. rack-tire is the estimated force of the first rack, and k is the fusion weight.

[0122] Optionally, the rack force estimator based on the vehicle-tire model is:

[0123] F rack-tire =i p *M zf

[0124] Among them, F rack-tire Let i be the estimated force of the first rack. p M is the ratio of tire torque to rack force transmission given to the vehicle's steering kinematics. zf This is the return torque of the front tires.

[0125] Optionally, the first rack force estimate includes the steering rack force component and the road surface rack force component.

[0126] Optionally, when the vehicle is traveling on a smooth road surface, the steering rack force component is obtained by rotating the front wheel angle; when the front wheel angle is zero and the vehicle is traveling on an uneven road surface, the road surface rack force component is obtained.

[0127] It should be noted that the foregoing explanation of the embodiment of the fuzzy control-based steerable rack force fusion estimation method for different working conditions also applies to the fuzzy control-based steerable rack force fusion estimation device for different working conditions in this embodiment, and will not be repeated here.

[0128] The fuzzy control-based rack-and-pinion force fusion estimation device for different operating conditions proposed in this invention has the following advantages:

[0129] This paper thoroughly studies the applicability of rack force estimators under bumpy roads, different vehicle speeds, and different steering angles, and provides a fuzzy control-based method for fusion estimation of rack force under different working conditions for steer-by-wire. It achieves rack force classification estimation under different working conditions, especially under different vehicle speeds, different steering angles, and different road surface smoothness. It incorporates driving habits to avoid frequent switching of estimation methods caused by high-frequency steering angles at low speeds, which would prevent the driver from fully grasping the vehicle's condition.

[0130] Compared to rack force estimators based on steering models, this embodiment of the invention pre-judges road conditions and transforms them into rack force estimators based on vehicle-tire models when the road surface is bumpy. This allows the driver to estimate the rack force components caused by the road surface, enabling the driver to fully grasp the vehicle's driving status, and making it less dangerous, especially at medium and high speeds or on steep slopes.

[0131] Compared to rack force estimators based on vehicle-tire models, this embodiment of the invention transforms the model into a rack force estimator based on a steering model at medium vehicle speeds and large steering angles, avoiding the degradation of model estimation performance at large steering angles. In particular, at low vehicle speeds, the rack force estimator based on the steering model is directly used, resulting in good estimation performance and avoiding the driver's inability to fully grasp the vehicle's condition due to frequent switching of estimation methods caused by high-frequency steering angles at low speeds.

[0132] Compared to the simple rack force fusion method, the embodiments of the present invention provide a detailed description of rack force classification estimation under different working conditions, especially under different vehicle speeds, different turning angles and different road surface smoothness. This method cannot quickly obtain the estimation method under specific working conditions and cannot meet the estimation accuracy requirements for specific working conditions, thus improving the adaptability to complex working conditions.

[0133] In the road feel simulation method, the embodiment of the present invention has better estimation performance under specific working conditions. By introducing the rack force estimation value into the road feel model, it is indirectly reflected by the rack force under simple working conditions, and directly reflected by the self-aligning torque of the tire under complex working conditions. The driver can fully grasp the coupling force information between the wheel and the ground to generate road feel, enhance the driver's judgment of road information, and thus help driving decision-making.

[0134] In the steering execution control method, the embodiments of the present invention can use the real-time estimated rack force for feedforward compensation, and can effectively estimate the rack force under complex working conditions to maintain tracking performance. When driving at high speeds or on bumpy roads, the rack force estimation value based on the vehicle-tire model is used to improve the accuracy of steering execution and enable the driver to fully grasp the road conditions. When driving at low speeds or with large turning angles, the rack force estimation value based on the steering system is used. By switching the estimation method under different working conditions, the speed and accuracy of steering execution can be effectively met.

[0135] To prevent frequent switching from affecting steering feel, fuzzy control is used to fuse the two estimates. The inputs are vehicle speed and steering wheel angle, and the output is the fusion weight. The fusion weight value is substituted into the rack force fusion formula to obtain the final rack force value.

[0136] Figure 11 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. The electronic device may include:

[0137] The memory 1101, the processor 1102, and the computer program stored on the memory 1101 and executable on the processor 1102.

[0138] When the processor 1102 executes the program, it implements the fuzzy control-based steerable rack force fusion estimation method for different working conditions provided in the above embodiments.

[0139] Furthermore, electronic devices also include:

[0140] Communication interface 1103 is used for communication between memory 1101 and processor 1102.

[0141] The memory 1101 is used to store computer programs that can run on the processor 1102.

[0142] The memory 1101 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0143] If the memory 1101, processor 1102, and communication interface 1103 are implemented independently, then the communication interface 1103, memory 1101, and processor 1102 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0144] Optionally, in a specific implementation, if the memory 1101, processor 1102, and communication interface 1103 are integrated on a single chip, then the memory 1101, processor 1102, and communication interface 1103 can communicate with each other through an internal interface.

[0145] The processor 1102 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0146] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described fuzzy control-based steer-by-wire force fusion estimation method for different operating conditions.

[0147] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0148] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0149] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0150] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0151] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0152] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0153] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0154] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for fusion estimation of rack and pinion forces in steer-by-wire under different operating conditions based on fuzzy control, characterized in that, Includes the following steps: Based on preset sensors located on the outside of the tire, the unevenness of the current road surface is collected. Determine whether the unevenness is greater than or equal to a first threshold. If the unevenness is greater than or equal to the first threshold, then use a rack force estimator based on a vehicle-tire model to measure the first rack force estimate of the vehicle and add the first rack force estimate to the road feel feedback design. Otherwise, collect the actual vehicle speed through the preset sensor. The system determines whether the actual vehicle speed is less than a second threshold. If the actual vehicle speed is less than the second threshold, a rack force estimator based on a steering system model is used to measure the estimated second rack force of the vehicle, and the estimated second rack force is added to the road feel feedback design. Otherwise, the system determines whether the actual vehicle speed is less than a third threshold. If the vehicle speed is less than the third threshold, a rack force estimator based on a vehicle-tire model is used to measure the estimated first rack force, and the estimated first rack force is added to the road feel feedback design. Otherwise, the system measures the front wheel angle of the vehicle using the preset sensor. Determine whether the front wheel steering angle is less than or equal to a fourth threshold. If the front wheel steering angle is less than or equal to the fourth threshold, then the first rack force estimate is measured using the rack force estimator based on the vehicle-tire model, and the first rack force estimate is added to the road feel feedback design. Otherwise, the second rack force estimate is measured using the rack force estimator based on the steering system model, and the second rack force estimate is added to the road feel feedback design.

2. The method for fusion estimation of rack and pinion forces under different operating conditions based on fuzzy control according to claim 1, characterized in that, Also includes: Before incorporating the road feel feedback design, the first rack force estimate and the second rack force estimate are fused based on fuzzy control to smoothly connect the first rack force estimate and the second rack force estimate under different working conditions.

3. The method for fusion estimation of rack and pinion forces under different operating conditions based on fuzzy control according to claim 2, characterized in that, The fusion of the first rack force estimate and the second rack force estimate based on fuzzy control includes: The preset vehicle speed and preset steering wheel angle are input into the fuzzy controller to obtain the fusion weights; The rack force fusion formula is determined based on the fusion weights. The first rack force estimate and the second rack force estimate are then fused by weights to obtain the fused rack force estimate.

4. The method for fusion estimation of rack and pinion forces under different operating conditions based on fuzzy control according to claim 2, characterized in that, The formula for rack force fusion is: F rack =k*F rack-steer +(1-k)*F rack-tire Among them, F rack F is the estimated value of the rack force after fusion. rack-steer F is the estimated value of the second rack force. rack-tire is the estimated force of the first rack, and k is the fusion weight.

5. The method for fusion estimation of rack and pinion forces under different operating conditions based on fuzzy control according to claim 1, characterized in that, The rack force estimator based on the vehicle-tire model is as follows: F rack-tire =i p *M zf Among them, F rack-tire Let i be the estimated force of the first rack. p M is the ratio of tire torque to rack force transmission given to the vehicle's steering kinematics. zf This is the return torque of the front tires.

6. The method for fusion estimation of rack and pinion forces under different operating conditions based on fuzzy control according to claim 5, characterized in that, The first rack force estimate includes the steering rack force component and the road surface rack force component.

7. The method for fusion estimation of rack and pinion forces under different operating conditions based on fuzzy control according to claim 6, characterized in that, When the vehicle is traveling on a smooth road surface, the steering rack force component is obtained by rotating the front wheel angle. When the front wheel angle is zero and the vehicle is traveling on an uneven road surface, the road surface rack force component is obtained.

8. A fuzzy control-based steerable rack force fusion estimation device for different operating conditions, characterized in that, include: The measurement module is used to collect the unevenness of the current road surface profile based on preset sensors set on the outside of the tire; The unevenness comparison module is used to determine whether the unevenness is greater than or equal to a first threshold. If the unevenness is greater than or equal to the first threshold, a rack force estimator based on a vehicle-tire model is used to measure the first rack force estimate of the vehicle and the first rack force estimate is added to the road feel feedback design. Otherwise, the actual vehicle speed is collected by the preset sensor. A speed comparison module is used to determine whether the actual vehicle speed is less than a second threshold. If the actual vehicle speed is less than the second threshold, a rack force estimator based on a steering system model is used to measure the estimated second rack force of the vehicle, and the estimated second rack force is added to the road feel feedback design. Otherwise, it is determined whether the actual vehicle speed is less than a third threshold. If the vehicle speed is less than the third threshold, a rack force estimator based on a vehicle-tire model is used to measure the estimated first rack force, and the estimated first rack force is added to the road feel feedback design. Otherwise, the front wheel steering angle of the vehicle is measured by the preset sensor. The front wheel steering angle comparison module is used to determine whether the front wheel steering angle is less than or equal to a fourth threshold. If the front wheel steering angle is less than or equal to the fourth threshold, the first rack force estimate is measured using the rack force estimator based on the vehicle-tire model, and the first rack force estimate is added to the road feel feedback design. Otherwise, the second rack force estimate is measured using the rack force estimator based on the steering system model, and the second rack force estimate is added to the road feel feedback design.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the fuzzy control-based steer-by-wire force fusion estimation method for different operating conditions as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the fuzzy control-based steerable rack force fusion estimation method for different operating conditions as described in any one of claims 1-7.

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