Road feedback control method and apparatus

Abstract

Disclosed in the present invention are a road feedback control method and apparatus. The method comprises: by means of a torque sensor stiffness-based steering angle configuration strategy, constructing a driver input torque expression; then acquiring a driver input torque, and, on the basis of the driver input torque expression, calculating a motor rotation angle; then constructing a road feedback motor output torque expression with respect to the motor rotation angle and steering resistance torque expressions for a steering system; and finally, on the basis of a particle swarm optimization, the road feedback motor output torque expression and the steering resistance torque expressions, optimizing parameters of a PID controller. Thus, the present invention uses a swarm intelligent algorithm to achieve rapid optimization of PID control parameters, so as to eliminate reliance on debugging by technicians, thus greatly shortening parameter adjustment periods.

Description

Road sense control method and device Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a road feel control method and device. Background Art

[0002] Steer-by-wire systems eliminate the mechanical connection between the steering wheel and the steering actuator, and instead generate road feel through simulation. By building a complete steer-by-wire vehicle model and applying dynamic methods based on the principle of steering resistance torque generated during steering in traditional steering systems, the target steering resistance torque is calculated. PID control is used to simulate the steering resistance torque using a road feel motor. Determining the PID control parameters is crucial. Existing methods often rely on designers modifying and adjusting these parameters through multiple rounds of testing. These parameters rely heavily on the designer's subjective perception, resulting in a stiff or soft steering feel and a lengthy parameter adjustment cycle. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a road feel control method and device, which uses a swarm intelligence algorithm to quickly optimize PID control parameters and get rid of the dependence on expert debugging.

[0004] According to a first aspect of the present invention, a road feel control method is provided, comprising:

[0005] The driver input torque expression is constructed based on the steering angle setting strategy of the torque sensor stiffness;

[0006] Obtaining a driver input torque, and calculating a motor rotation angle according to an expression of the driver input torque;

[0007] Constructing an expression for the output torque of the road-sensing motor and a steering resistance torque expression of the steering system with respect to the motor rotation angle;

[0008] Based on the particle swarm algorithm, the parameters of the PID controller are optimized according to the output torque expression of the road sensing motor and the steering resistance torque expression.

[0009] Optionally, the torque sensor stiffness angle setting strategy includes:

[0010] Set the torque sensor stiffness according to the following expression:

[0011]

[0012] Among them, r is a random number between [0,1], K s is the torque sensor stiffness, θ sw is the steering wheel angle, k2 is a constant.

[0013] Optionally, constructing the driver input torque expression includes:

[0014] The driver input torque expression is constructed according to the following expression:

[0015]

[0016] Among them, J sw is the steering wheel moment of inertia; B sw is the steering wheel damping; K s is the torque sensor stiffness; θ sw is the steering wheel angle; T f is the steering wheel friction torque; T sw Input torque for the driver; is the motor rotation angle; is the road-sensing motor reduction ratio.

[0017] Optionally, constructing an expression for the output torque of the road-sensing motor related to the motor rotation angle includes:

[0018] The output torque expression of the road sense motor with respect to the motor rotation angle is constructed by the following expression:

[0019]

[0020] Among them, T m1 Output torque for the road sensing motor; B is the moment of inertia of the road sensor motor; m1 is the road sense motor damping; θ m1 is the motor angle; g1 is the road sense motor reduction ratio; θ sw is the steering wheel angle; Ks is the torque sensor stiffness.

[0021] Optionally, the steering resistance torque includes a return resistance torque and a friction resistance torque.

[0022] Optionally, the tire drag torque and the self-aligning resistance torque caused by wheel alignment are represented as a linear relationship between the wheel deviation angle and the camber angle. The following expression is used to construct an approximate linear expression for the self-aligning resistance torque:

[0023]

[0024] Therefore, the aligning torque is:

[0025] Among them, M1 is the return resistance torque, M s is the self-aligning resistance torque caused by tire drag torque and wheel alignment, k is the lateral load proportional constant; f s is the lateral adhesion coefficient; P is the vertical load on the wheel; l1 is the maximum length of the wheel contact area, and r is the wheel rolling radius; is the kingpin caster angle; is the vehicle heading angle; is the wheel angle; l r is the distance from the center of mass to the axis of rotation; is the kingpin camber angle; i is the steering system torque transmission ratio.

[0026] Optionally, construct the friction torque expression using the following expression:

[0027]

[0028] Among them, T f is the friction resistance torque; τ s is the friction torque of the steering system, which is 2N·m; v is the vehicle speed.

[0029] According to a second aspect of the present invention, there is provided a road feel control device, comprising:

[0030] a first construction unit configured to construct a driver input torque expression based on a steering angle setting strategy of a torque sensor stiffness;

[0031] a calculation unit configured to obtain a driver input torque and calculate a motor rotation angle according to an expression of the driver input torque;

[0032] A second constructing unit is configured to construct an expression for the output torque of the road-sensing motor and a steering resistance torque of the steering system with respect to the motor rotation angle;

[0033] The optimization unit is configured to optimize the parameters of the PID controller based on the particle swarm algorithm according to the output torque expression of the road sensing motor and the steering resistance torque expression.

[0034] Optionally, in the particle swarm algorithm, the population size is determined to be n, where n is the number of potential solutions; the maximum number of iterations is ; Search space, i.e. parameters 、 、 Upper and lower limits;

[0035] Let the three parameters of PID controller be 、 、 A combination of is a particle, and all particle positions are randomly initialized to obtain the set X:

[0036]

[0037] The position of the i-th particle is expressed as follows:

[0038] Initial global optimal GBest=minJ{ i=1, 2...n, represents the optimal solution of the i-th parameter at the current iteration number, that is, the number of parameters to be solved; minJ is the optimization objective function;

[0039] Individual initial optimality , i=1,2,..,n, that is, the value when the iteration number t=0 is the individual optimal solution;

[0040] The particle position update strategy is as follows:

[0041]

[0042] in, Represents a set of PID controller parameters obtained at the t+1th iteration; i=1, 2, 3...n; j=1, 2, 3...n; represents the Levy flight crossover coefficient; PD represents the full development enhancement coefficient; DS represents the Levy flight variation coefficient; e represents the perturbation parameter; PE represents the local search enhancement coefficient; Levy(n) represents the standard Levy flight; represents the warning threshold; r represents a random number, which takes -1 when the number of iterations is odd and takes 1 when the number of iterations is even; represents a random number between [-1,1]; rand represents a random number between [0,1]; t represents the number of iterations.

[0043] According to a third aspect of the present invention, a controller is provided. The controller includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the aforementioned road feel control method.

[0044] According to a fourth aspect of the present invention, a vehicle is provided, comprising a vehicle body and a controller installed in the vehicle body, wherein the controller executes the aforementioned road feel control method.

[0045] The above one or more technical solutions in the embodiments of this specification have at least the following technical effects:

[0046] The embodiments of this specification provide a road feel control method and device. This method constructs a driver input torque expression using a torque sensor stiffness-based angle setting strategy. The driver input torque is then acquired and the motor angle is calculated based on the driver input torque expression. Furthermore, an expression for the road feel motor output torque and the steering system's steering resistance torque relative to the motor angle is constructed. Finally, using a particle swarm algorithm, the PID controller parameters are optimized based on the road feel motor output torque and steering resistance torque expressions. This swarm intelligence algorithm allows for rapid optimization of PID control parameters, eliminating reliance on technical personnel for debugging and significantly reducing parameter adjustment cycles.

[0047] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference figures denote the same components. In the drawings:

[0049] FIG1 shows a flow chart of a road feel control method according to an embodiment of the present invention.

[0050] FIG2 is a schematic diagram showing the optimization of PID control parameters by using a particle swarm algorithm in an embodiment of the present invention.

[0051] FIG3 shows a block diagram of a road feel control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0054] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0055] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0056] An embodiment of the present invention provides a road feel control method. Referring to the flowchart shown in FIG1 , the road feel control method includes steps 101 to 104:

[0057] Step 101: Construct a driver input torque expression based on a torque sensor stiffness angle setting strategy.

[0058] A steer-by-wire system physically decouples the steering wheel from the steering gear. It primarily consists of a steering wheel assembly, a controller (ECU), and a steering actuator assembly. There's no mechanical connection between the steering wheel and the steering gear; steering is achieved through wiring harnesses that control the steering electromechanical system.

[0059] This embodiment first constructs a mathematical model for the steering wheel, specifically an expression for the driver's input torque. Driver input torque refers to the torque applied by the driver when turning the steering wheel. When turning, the driver must apply force to the steering wheel to control the vehicle's turn. Specifically, the driver input torque expression is:

[0060]

[0061] Among them, J sw is the steering wheel moment of inertia; B sw is the steering wheel damping; K s is the torque sensor stiffness; θ sw is the steering wheel angle; T f is the steering wheel friction torque; T sw Input torque for the driver; is the motor rotation angle; is the road-sensing motor reduction ratio.

[0062] It's easy to see that the driver input torque expression includes the torque sensor stiffness. It's important to note that to accurately reflect the steering gear meshing clearance when the steering wheel angle is at zero and the increased stiffness caused by the steering mechanism's position limit during extreme steering, the torque sensor stiffness setting strategy is as follows:

[0063] Set the torque sensor stiffness according to the following expression:

[0064]

[0065] Among them, r is a random number between [0,1], K s is the torque sensor stiffness, θ sw is the steering wheel angle, k2 is a constant.

[0066] exist When the steering wheel is in zero position, the torque sensor stiffness is as shown in the expression. When , the gear is in full meshing section, and the torque sensor stiffness is a constant k2, which can be obtained through experimental testing. When , it is in the extreme steering area of ​​the steering wheel, and the torque sensor stiffness is infinite.

[0067] Step 102: Obtain the driver input torque, and calculate the motor rotation angle according to the driver input torque expression.

[0068] In this embodiment, the driver input torque applied by the driver can be measured. Substituting the measured driver input torque into the driver input torque expression can calculate the motor angle. The motor angle can be used to construct the road-sensing motor output torque expression, but the motor angle is difficult to measure directly. However, the motor angle can be calculated using the driver input torque expression.

[0069] Step 103: Construct an expression for the output torque of the road-sensing motor and a steering resistance torque of the steering system in relation to the motor rotation angle.

[0070] In this embodiment, after calculating the motor angle using the driver input torque expression, the road sense motor output torque can be constructed from the motor angle. The road sense motor is used to simulate the steering resistance torque, which is fed back to the steering wheel to give the driver a steering feel and facilitate steering control.

[0071] During the steering process, the torque generated by the friction between the tire and the road can be calculated by the dynamic model, and this value is the output torque of the road sensing motor.

[0072] Optionally, an expression for the output torque of the road-sensing motor related to the motor rotation angle is constructed using the following expression:

[0073]

[0074] Among them, T m1 Output torque for the road sensing motor; B is the moment of inertia of the road sensor motor; m1 is the road sense motor damping; θ m1 is the motor angle; g1 is the road sense motor reduction ratio; θ sw is the steering wheel angle; Ks is the torque sensor stiffness.

[0075] Optionally, in this embodiment, the steering resistance torque of a conventional steering system is primarily composed of the self-aligning resistance torque and the friction resistance torque of the steering system. However, the tire self-aligning resistance torque includes the self-aligning resistance torque caused by tire trail and the self-aligning resistance torque caused by tire alignment. The self-aligning resistance torque caused by tire trail and wheel alignment is represented as a linear relationship between the wheel slip angle and the camber angle. Using the following expression, an approximate linear expression for the self-aligning resistance torque is constructed:

[0076]

[0077] Therefore, the M1 caused by the return resistance torque can be written as:

[0078]

[0079] Among them, M1 is the return resistance torque, M s is the self-aligning resistance torque caused by tire drag torque and wheel alignment, k is the lateral load proportional constant; f s is the lateral adhesion coefficient; P is the vertical load on the wheel; l1 is the maximum length of the wheel contact area, and r is the wheel rolling radius; is the kingpin caster angle; is the vehicle heading angle; is the wheel angle; l r is the distance from the center of mass to the axis of rotation; is the kingpin camber angle; i is the steering system torque transmission ratio.

[0080] In this embodiment, the friction resistance torque expression can be:

[0081]

[0082] Among them, T f is the friction resistance torque; τ s is the friction torque of the steering system, which is 2N·m; v is the vehicle speed.

[0083] Steering resistance torque M can be expressed as M=M1+T f ,The steering resistance torque is the simulated target torque of the road sensing motor.

[0084] Step 104: Based on the particle swarm algorithm, the parameters of the PID controller are optimized according to the expression of the output torque of the road sensing motor and the expression of the steering resistance torque.

[0085] Furthermore, the reduction mechanism between the steering wheel and the road-sensing motor forms a dynamic system, typically controlled by PID. However, current PID parameters are typically adjusted by designers through multiple rounds of testing. This heavily relies on the designer's subjective experience, resulting in a stiff or soft steering feel, and the entire parameter adjustment cycle is lengthy. To avoid human interference, a swarm intelligence algorithm is employed to rapidly optimize and obtain the optimal solution.

[0086] In this embodiment, as shown in FIG2 , the PID controller is based on the ideal torque r(t) (ie, the steering resistance torque M) and the actual feedback torque value c(t) (the output torque T of the road sense motor). m1 ), and we get the deviation e(t)=r(t)-c(t).

[0087] The primary challenge with PID controllers is parameter setting. Adjusting PID parameters often relies on technical expertise and trial-and-error methods, which is not only time-consuming but also doesn't guarantee optimal performance. To address this issue, as shown in Figure 2, this embodiment uses a particle swarm optimization algorithm to optimize the PID controller. The particle swarm optimization algorithm (PSO) originates from research on bird hunting behavior and offers faster computation speed and stronger global search capabilities than traditional optimization algorithms.

[0088] This embodiment uses the integral ITAE index of time multiplied by absolute error as the output performance index J, which is defined as:

[0089]

[0090] The optimization objective function is minJ, and the optimization algorithm steps are as follows:

[0091] Initialize algorithm parameters: n, m, 、 .

[0092] 1. Determine the population size as n (the number of potential solutions); the maximum number of iterations ; Search space, i.e. parameters 、 、 upper and lower limits.

[0093] 2. Let the three parameters of PID 、 、 A combination of is a particle, and all particle positions are randomly initialized to obtain the set X.

[0094]

[0095] The position of the i-th particle is expressed as follows:

[0096] Initial global optimal GBest=minJ{ i=1, 2...n, represents the optimal solution for the i-th parameter at the current iteration number, that is, the number of parameters to be solved.

[0097] Individual initial optimality , i=1,2,..,n, that is, the value when the iteration number t=0 is the individual optimal solution.

[0098] 3. Update the position of each particle (parameter to be determined). The strategy for updating the velocity and position of the particle in the search space is as follows:

[0099]

[0100] Here, one x represents a PID parameter solution. This embodiment uses particle swarm intelligence algorithm optimization to replace the designer's subjective debugging work. Represents a set of PID parameters obtained at the t+1th iteration; i=1, 2, 3...n; j=1, 2, 3...n; represents the Levy flight cross coefficient; PD represents the full development enhancement coefficient; DS represents the Levy flight variation coefficient; e represents the perturbation parameter; PE represents the local search enhancement coefficient.

[0101]

[0102]

[0103]

[0104]

[0105] Where Levy(n) is the standard Levy flight; is the warning threshold, which can generally be set to 0.1; is a random number between [-1,1]; Represents a very small number; r is a random number, which takes -1 when the number of iterations is odd and 1 when the number of iterations is even; rand is a random number between [0,1]; is the mean of the sum of all elements of X; is the upper limit of the jth parameter; is the lower limit of the jth parameter.

[0106] The optimization goal is minJ. When J reaches the minimum or the number of iterations reaches the preset value, the PID parameters are determined.

[0107] In summary, the road feel control method provided in the embodiments of this specification constructs a driver input torque expression through an angle setting strategy based on the torque sensor stiffness. The driver input torque is then acquired and the motor angle is calculated based on this driver input torque expression. Furthermore, an expression for the road feel motor output torque and the steering resistance torque of the steering system relative to the motor angle is constructed. Finally, based on the road feel motor output torque expression and the steering resistance torque expression, the PID controller parameters are optimized using a particle swarm algorithm. This swarm intelligence algorithm allows for rapid optimization of PID control parameters, eliminating reliance on technical personnel for debugging and significantly reducing the parameter adjustment cycle.

[0108] Based on the same inventive concept, in combination with FIG3 , an embodiment of the present invention further provides a road feel control device, comprising:

[0109] a first construction unit configured to construct a driver input torque expression based on a steering angle setting strategy of a torque sensor stiffness;

[0110] a calculation unit configured to obtain a driver input torque and calculate a motor rotation angle according to an expression of the driver input torque;

[0111] A second constructing unit is configured to construct an expression for the output torque of the road-sensing motor and a steering resistance torque of the steering system with respect to the motor rotation angle;

[0112] The optimization unit is configured to optimize the parameters of the PID controller based on the particle swarm algorithm according to the output torque expression of the road sensing motor and the steering resistance torque expression.

[0113] Optionally, the torque sensor stiffness angle setting strategy includes:

[0114] Set the torque sensor stiffness according to the following expression:

[0115]

[0116] Among them, r is a random number between [0,1], K s is the torque sensor stiffness, θ sw is the steering wheel angle, k2 is a constant.

[0117] Optionally, constructing the driver input torque expression includes:

[0118] The driver input torque expression is constructed according to the following expression:

[0119]

[0120] Among them, J sw is the steering wheel moment of inertia; B sw is the steering wheel damping; K s is the torque sensor stiffness; θ sw is the steering wheel angle; T f is the steering wheel friction torque; T sw Input torque for the driver; is the motor rotation angle; is the road-sensing motor reduction ratio.

[0121] Optionally, constructing an expression for the output torque of the road-sensing motor related to the motor rotation angle includes:

[0122] The output torque expression of the road sense motor with respect to the motor rotation angle is constructed by the following expression:

[0123]

[0124] Among them, T m1 Output torque for the road sensing motor; B is the moment of inertia of the road sensor motor; m1 is the road sense motor damping; θ m1 is the motor angle; g1 is the road sense motor reduction ratio; θ sw is the steering wheel angle.

[0125] Optionally, the steering resistance torque includes a return resistance torque and a friction resistance torque.

[0126] Optionally, the tire drag torque and the self-aligning resistance torque caused by wheel alignment are represented as a linear relationship between the wheel deviation angle and the camber angle. The following expression is used to construct an approximate linear expression for the self-aligning resistance torque:

[0127]

[0128] Therefore, the aligning torque is:

[0129] Among them, M1 is the return resistance torque, M s is the self-aligning resistance torque caused by tire drag torque and wheel alignment, k is the lateral load proportional constant; f s is the lateral adhesion coefficient; P is the vertical load on the wheel; l1 is the maximum length of the wheel contact area, and r is the wheel rolling radius; is the kingpin caster angle; is the vehicle heading angle; is the wheel angle; l r is the distance from the center of mass to the axis of rotation; is the kingpin camber angle; i is the steering system torque transmission ratio.

[0130] Optionally, construct the friction torque expression using the following expression:

[0131]

[0132] Among them, T f is the friction resistance torque; τ s is the friction torque of the steering system, which is 2N·m; v is the vehicle speed.

[0133] Optionally, the optimization unit is further configured to:

[0134] In the particle swarm algorithm, the population size is determined to be n, where n is the number of potential solutions; the maximum number of iterations is ; Search space, i.e. parameters 、 、 Upper and lower limits;

[0135] Let the three parameters of PID controller be 、 、 A combination of is a particle, and all particle positions are randomly initialized to obtain the set X:

[0136]

[0137] The position of the i-th particle is expressed as follows:

[0138] Initial global optimal GBest=minJ{ i=1, 2...n, represents the optimal solution of the i-th parameter at the current iteration number, that is, the number of parameters to be solved;

[0139] Individual initial optimality , i=1,2,..,n, that is, the value when the iteration number t=0 is the individual optimal solution;

[0140] The particle position update strategy is as follows:

[0141]

[0142] in, Represents a set of PID controller parameters obtained at the t+1th iteration; i=1, 2, 3...n; j=1, 2, 3...n; represents the Levy flight crossover coefficient; PD represents the full development enhancement coefficient; DS represents the Levy flight variation coefficient; e represents the perturbation parameter; PE represents the local search enhancement coefficient; Levy(n) represents the standard Levy flight; represents the warning threshold; r represents a random number, which takes -1 when the number of iterations is odd and takes 1 when the number of iterations is even; represents a random number between [-1,1]; rand represents a random number between [0,1]; t represents the number of iterations.

[0143] In summary, the road feel control device provided in the embodiments of this specification constructs a driver input torque expression through an angle setting strategy based on the torque sensor stiffness. The driver input torque is then acquired and the motor angle is calculated based on this driver input torque expression. Furthermore, an expression for the road feel motor output torque and the steering resistance torque of the steering system relative to the motor angle is constructed. Finally, based on the road feel motor output torque expression and the steering resistance torque expression, the PID controller parameters are optimized using a particle swarm algorithm. This swarm intelligence algorithm allows for rapid optimization of PID control parameters, eliminating reliance on technical personnel for debugging and significantly reducing the parameter adjustment cycle.

[0144] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the road feel control device described above can refer to the corresponding process in the aforementioned road feel control method, and will not be elaborated here.

[0145] According to a third aspect of the present invention, a controller is provided, which includes a road feel control device, a memory, a processor and a communication unit. The memory stores machine-readable instructions executable by the processor. When the controller is running, the processor and the memory communicate via a bus, and the processor executes the machine-readable instructions and performs a road feel control method.

[0146] The memory, processor, and communication unit components are electrically connected to each other, directly or indirectly, to enable signal transmission or interaction. For example, these components may be electrically connected via one or more communication buses or signal lines. The road feel control device includes at least one software function module, which may be stored in the memory in the form of software or firmware. The processor is configured to execute the executable module stored in the memory.

[0147] The memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0148] In some embodiments, the processor is used to perform one or more functions described in this embodiment. In some embodiments, the processor may include one or more processing cores (eg, a single-core processor (S) or a multi-core processor (S)).

[0149] In this embodiment, the memory is configured to store a program, and the processor is configured to execute the program after receiving an execution instruction. The process definition method disclosed in any implementation of this embodiment can be applied to a processor or implemented by a processor.

[0150] The communication unit is configured to establish a communication connection between the controller and other devices through a network, and is used to send and receive data through the network.

[0151] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the controller described above can refer to the corresponding process in the aforementioned method, and will not be elaborated here.

[0152] According to a fourth aspect of the present invention, a vehicle is provided, comprising a vehicle body and a controller installed in the vehicle body, wherein the controller is configured to implement the aforementioned road feel control method.

[0153] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the vehicle controller described above can refer to the corresponding process in the aforementioned method and will not be elaborated here.

[0154] The above are merely various embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A road feel control method, characterized in that: include: The driver input torque expression is constructed based on the steering angle setting strategy of the torque sensor stiffness; Obtaining a driver input torque, and calculating a motor rotation angle according to an expression of the driver input torque; Constructing an expression for the output torque of the road-sensing motor and a steering resistance torque expression of the steering system with respect to the motor rotation angle; Based on the particle swarm algorithm, the parameters of the PID controller are optimized according to the output torque expression of the road sensing motor and the steering resistance torque expression.

2. The method according to claim 1, characterized in that The torque sensor stiffness angle setting strategy includes: Set the torque sensor stiffness according to the following expression: Among them, r is a random number between [0,1], K s is the torque sensor stiffness, θ sw is the steering wheel angle, k2 is a constant.

3. The method according to claim 1, characterized in that The constructing of the driver input torque expression comprises: The driver input torque expression is constructed according to the following expression: Among them, J sw is the steering wheel moment of inertia; B sw is the steering wheel damping; K s is the torque sensor stiffness; θ sw is the steering wheel angle; T f is the steering wheel friction torque; T sw Input torque for the driver; is the motor rotation angle; is the road-sensing motor reduction ratio.

4. The method according to claim 1, wherein The constructing of the road sense motor output torque expression related to the motor rotation angle includes: The output torque expression of the road sense motor with respect to the motor rotation angle is constructed by the following expression: Among them, T m1 Output torque for the road sensing motor; B is the moment of inertia of the road sensor motor; m1 is the road sense motor damping; θ m1 is the motor angle; g1 is the road sense motor reduction ratio; θ sw is the steering wheel angle; Ks is the torque sensor stiffness.

5. The method according to claim 1, wherein The steering resistance torque includes a return resistance torque and a friction resistance torque.

6. The method according to claim 5, characterized in that The tire drag torque and the self-aligning resistance torque caused by wheel alignment are characterized as a linear relationship between the wheel deviation angle and the camber angle. The approximate linear expression of the self-aligning resistance torque is constructed through the following expression: Therefore, the aligning torque is: Among them, M1 is the return resistance torque, M s is the self-aligning resistance torque caused by tire drag torque and wheel alignment, k is the lateral load proportional constant; f s is the lateral adhesion coefficient; P is the vertical load on the wheel; l1 is the maximum length of the wheel contact area, and r is the wheel rolling radius; is the kingpin caster angle; is the vehicle heading angle; is the wheel angle; l r is the distance from the center of mass to the axis of rotation; is the kingpin camber angle; i is the steering system torque transmission ratio.

7. The method according to claim 5, characterized in that The friction resistance torque expression is constructed by the following expression: Among them, T f is the friction resistance torque; τ s is the friction torque of the steering system, which is 2N·m; v is the vehicle speed.

8. A road feel control device, characterized in that: include: a first construction unit configured to construct a driver input torque expression based on a steering angle setting strategy of a torque sensor stiffness; a calculation unit configured to obtain a driver input torque and calculate a motor rotation angle according to an expression of the driver input torque; A second constructing unit is configured to construct an expression for the output torque of the road-sensing motor and a steering resistance torque of the steering system with respect to the motor rotation angle; The optimization unit is configured to optimize the parameters of the PID controller based on the particle swarm algorithm according to the output torque expression of the road sensing motor and the steering resistance torque expression.

9. The device according to claim 8, characterized in that In the particle swarm algorithm, the population size is determined to be n, where n is the number of potential solutions; the maximum number of iterations is ; Search space, i.e. parameters 、 、 Upper and lower limits; Let the three parameters of PID controller be 、 、 A combination of is a particle, and all particle positions are randomly initialized to obtain the set X: The position of the i-th particle is expressed as follows: Initial global optimal GBest=minJ{ , i=1,2...n, represents the optimal solution of the i-th parameter at the current iteration number, that is, the number of parameters to be solved; minJ represents the optimization objective function; Individual initial optimality , i=1,2,..,n, that is, the value when the iteration number t=0 is the individual optimal solution; The particle position update strategy is as follows: in, Represents a set of PID controller parameters obtained at the t+1th iteration; i=1, 2, 3...n; j=1, 2, 3...n; represents the Levy flight crossover coefficient; PD represents the full development enhancement coefficient; DS represents the Levy flight variation coefficient; e represents the perturbation parameter; PE represents the local search enhancement coefficient; Levy(n) represents the standard Levy flight; represents the warning threshold; r represents a random number, which takes -1 when the number of iterations is odd and takes 1 when the number of iterations is even; represents a random number between [-1,1]; rand represents a random number between [0,1]; t represents the number of iterations.

10. A vehicle, characterized in that: The vehicle includes a vehicle body and a controller installed in the vehicle body, wherein the controller executes the road feel control method according to any one of claims 1 to 7.

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