A lateral control method for intelligent driving of EPS system

Abstract

The present invention relates to the field of automobile steering gears, and specifically discloses a lateral control method for intelligent driving of an EPS system, including: collecting operating parameters; obtaining the expected steering angle and expected steering angular velocity of the next control cycle; establishing a prediction model; traversing and taking values ​​of the electromagnetic torque with a step length of b; obtaining the predicted steering angle and predicted steering angular velocity; calculating the prediction deviation, and selecting the motor target torque corresponding to the minimum value of the prediction deviation as the motor target electromagnetic torque of the next cycle. The present invention determines the range of the electromagnetic torque of the motor, traverses within this range, and substitutes each group of steering angles and steering angular velocities obtained by the traversal into the calculation of the prediction deviation, thereby selecting the motor torque with the smallest deviation within the motor torque range, so that the motor torque changes as little as possible while achieving the power assist target in the next control cycle, thereby increasing the smoothness of the electric power assist operation process.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power steering devices, and in particular to a lateral control method for intelligent driving of an EPS system. Background Art

[0002] With the development of the electronic stability system, the current electronic stability system can send request torque to the electronic power steering system (EPS), interact with the dynamic torque assist function of the electronic power steering system (EPS), and apply dynamic torque through the electronic power steering system (EPS) to assist the driver in steering. When the left and right adhesion coefficients of the road are inconsistent, the driver brakes or accelerates to cause the vehicle to become unstable, which improves the lateral stability of the vehicle. When understeer or oversteer conditions occur during cornering, lateral torque adjustment is performed to assist in cornering and improve the driving experience.

[0003] In the prior art, a common method to improve driving feel is to optimize the control algorithm, and a common method is to adjust the control parameters according to real-time data. However, during the adjustment of the control parameters, the torque of the motor changes significantly, and the control of the vehicle state is not precise enough. Therefore, this method is difficult to smoothly control the torque of the motor, which will affect the life of the motor itself and the feel of the operation, resulting in a short motor life and an inability to ensure smoothness during the operation of the electric power assist. Summary of the invention

[0004] The purpose of the present invention is to provide a lateral control method for intelligent driving of an EPS system to solve the problems of short motor life and inability to ensure smoothness during electric power operation in the prior art.

[0005] Further, the current state of the EPS lateral control state machine is obtained, and if the current EPS lateral control state machine is in a state of responding to a request, the operating parameters of the EPS lateral control state machine in the current control range are collected;

[0006] Obtaining the desired steering angle and desired steering angular velocity of the next control cycle of the EPS system;

[0007] Establish a prediction model based on EPS dynamics and steering motor dynamics;

[0008] In the current control cycle, the electromagnetic torque is traversed and taken values ​​with a step size of b within the electromagnetic torque range of the steering motor;

[0009] Substituting the traversal values ​​into the prediction model respectively to obtain a predicted steering angle and a predicted steering angular velocity;

[0010] The prediction deviation is calculated according to the expected steering angle, the expected steering angular velocity, the predicted steering angle, and the predicted steering angular velocity, and the motor target torque corresponding to the minimum value of the prediction deviation is selected as the motor target electromagnetic torque for the next cycle.

[0011] Furthermore, the prediction model is specifically:

[0012] ,in:

[0013] is the steering angle of the current control cycle, is the steering angular velocity, The driver's hand torque, is the steering resistance torque, is the rotor angle of the steering motor, is the motor speed of the steering motor, To predict the steering angle, To predict the steering angular velocity, To predict the steering angular acceleration, To control the cycle, is the electromagnetic torque of the steering motor in the current control cycle, is the moment of inertia of the steering shaft, is the moment of inertia of the steering motor, The transmission ratio of the worm gear reducer, is the steering motor damping coefficient, is the steering motor angular acceleration, is the damping coefficient of the steering axis.

[0014] Furthermore, the step of traversing and taking values ​​of the electromagnetic torque within the electromagnetic torque range of the steering motor with a step length of b in the current control cycle includes:

[0015] Get the electromagnetic torque range of the steering motor in the current control range [-a, a];

[0016] Get the step length b;

[0017] According to the step length b, traverse and obtain values ​​from [-a, a] and count the obtained values ​​as T mk。

[0018] Furthermore, the , where: k=0,1, … , 2a / b.

[0019] Furthermore, substituting the traversal values ​​into the prediction model respectively to obtain the predicted steering angle and the predicted steering angular velocity also includes:

[0020] Will Tm1 Substitute the value of into the prediction model to obtain the predicted steering angle and predicted steering angular velocity ;

[0021] right T mk The value of is traversed, and the above steps are repeated to obtain the predicted steering angle Predicted steering angle velocity .

[0022] Furthermore, the calculation of the prediction deviation and the selection of the motor target torque corresponding to the minimum value of the prediction deviation as the motor target electromagnetic torque for the next cycle also include the following steps:

[0023] Get the predicted steering angle Predicted steering angle velocity ;

[0024] Acquiring the desired steering angle and the desired steering angular velocity;

[0025] Calculate the prediction deviation ;

[0026] Determine the prediction deviation The minimum value of

[0027] Select the minimum of T mk As the target torque of the motor in the next control cycle .

[0028] Further, the prediction deviation is determined according to the following formula: ,

[0029] ;

[0030] Among them: α is the normalization coefficient, β1 is the weight coefficient for predicting the steering angle, β2 is the weight coefficient for predicting the steering angular velocity, is the desired steering angle, is the desired steering angular velocity.

[0031] Furthermore, the value of the step length b is selected according to the computing power of the microprocessor.

[0032] Furthermore, the current state of the EPS lateral control state machine includes: no request state, response request state, temporary non-response request state, and permanent fault state.

[0033] Furthermore, the EPS lateral control state machine is based on the vehicle speed, the driver's hand torque, the lateral control request command , EPS fault state and other conditions, the lateral control state machine switches between no request state, response request state, temporary response request not allowed state, and permanent fault state.

[0034] Compared with the prior art, the invention has the beneficial effect that the invention determines the electromagnetic torque range of the motor and T mk A traversal is performed, and each group of steering angles and steering angular velocities obtained by the traversal are substituted into the calculation of the predicted deviation, so as to select the motor torque with the smallest deviation within the motor torque range, so that the change of the motor torque can be as small as possible while achieving the power assist target in the next control cycle, thereby increasing the smoothness of the electric power assist operation process.

[0035] Furthermore, the smaller the amplitude of the change in the electromagnetic torque of the motor is, the smaller the load generated at the moment of the state change during the operation of the motor is, thereby increasing the service life of the steering motor during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of the steps of a lateral control method for intelligent driving of an EPS system according to an embodiment of the present invention;

[0037] Figure 2 A flowchart of the operating state and state transition conditions described in an embodiment of the present invention;

[0038] Figure 3 This is a flowchart of the steps of traversing and obtaining values ​​with a step length of b as described in step S3 of an embodiment of the present invention;

[0039] Figure 4 This is a control block diagram of the implementation process of the lateral control method for intelligent driving of the EPS system described in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. Among them, the terms "first position" and "second position" are two different positions, and the first feature "above", "above" and "above" the second feature include the first feature being directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. The first feature "below", "below" and "below" the second feature include the first feature being directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0042] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0044] See also Figure 1 As shown, it is a flowchart of the steps of a lateral control method for intelligent driving of an EPS system according to an embodiment of the present invention, including:

[0045] Step S10, obtaining the current state of the EPS lateral control state machine, if the current EPS lateral control state machine is in a state of responding to a request, collecting the operating parameters of the EPS lateral control state machine in the current control range;

[0046] Step S20, obtaining the desired steering angle and desired steering angular velocity of the next control cycle of the EPS system;

[0047] Step S30, establishing a prediction model according to the EPS dynamics principle and the steering motor dynamics principle;

[0048] Step S40, in the current control cycle, taking a step length of b within the electromagnetic torque range of the steering motor, traverse and obtain values ​​of the electromagnetic torque;

[0049] Step S50, substituting the traversed values ​​into the prediction model respectively to obtain a predicted steering angle and a predicted steering angular velocity;

[0050] Step S60, calculating the prediction deviation according to the expected steering angle, the expected steering angular velocity, the predicted steering angle, and the predicted steering angular velocity, and selecting the motor target torque corresponding to the minimum value of the prediction deviation as the motor target electromagnetic torque for the next cycle.

[0051] In the above embodiment, by traversing the electromagnetic torque of the steering motor, predicting the steering angle and steering angular velocity, designing and solving the performance index function, and selecting the optimal electromagnetic torque of the steering motor according to the result, the response speed of the control system is improved and overshoot is avoided, so as to quickly find the electromagnetic torque of the motor that can meet the expected angle and the expected angular velocity, thereby increasing the smoothness of the electric power steering operation process.

[0052] The current EPS lateral control state machine is in the following states: no request state S1, response request state S2, temporary non-response request state S3, permanent fault state S4. The EPS lateral control state machine is based on the vehicle speed, the driver's hand torque, the lateral control request command , EPS fault state, etc., the lateral control state machine switches between the no request state S1, the response request state S2, the temporary non-response request state S3, and the permanent fault state S4. Figure 2 As shown, it is a flow chart of the operating state and its state transition conditions according to an embodiment of the present invention.

[0053] When the EPS is in the no-request state S1, T1 takes effect when the following conditions are met at the same time, and the state machine switches from the no-request state S1 to the response request state S2:

[0054] Lateral Control Request Command efficient;

[0055] Driver hand torque , and the duration is greater than the set value ;

[0056] The vehicle speed message is normal;

[0057] The vehicle speed V is between Vmin and Vmax;

[0058] Note: , , Vminimum value, and Vmaximum value are all parameters, which are determined through actual vehicle calibration.

[0059] Specifically, if the EPS is in the response request state S2, when the lateral control request command invalidation T2 takes effect, the state machine switches from the response request state S2 to the no request state S1.

[0060] Specifically, when the EPS is in the no-request state S1, if any of the following conditions is met, T3 takes effect and the state machine switches from the no-request state S1 to the temporary non-response request state S3:

[0061] Driver hand torque , and the duration is greater than the set value ;

[0062] Vehicle speed V > Vmax;

[0063] EPS has recoverable faults, such as overvoltage, undervoltage, overtemperature, etc.

[0064] Vehicle speed message is lost;

[0065] in: , , V maximum values ​​are parameters and are determined through actual vehicle calibration.

[0066] Specifically, when the EPS is in the response request state S2, if any of the following conditions is met, T4 takes effect, and the state machine switches from the response request state S2 to the temporary non-response request state S3;

[0067] The message of the lateral control request command is lost;

[0068] , and the duration is greater than the set value ;

[0069] Vehicle speed V > Vmax;

[0070] Vehicle speed message is lost;

[0071] EPS has recoverable faults, such as overvoltage, undervoltage, overtemperature, etc.

[0072] Specifically, when the EPS is in the temporary non-response request state S3, when the following conditions are met at the same time, T5 takes effect, and the state machine switches from the temporary non-response request state S3 to the no request state S1;

[0073] The message of the lateral control request command is normal;

[0074] Driver's hand torque T s ≤Ts -max , and the duration is greater than the set value t Ts-max1 ;

[0075] The vehicle speed message is normal;

[0076] EPS is fault-free.

[0077] Specifically, if there is a permanent fault in EPS, such as EPS hardware failure, sensor failure, etc., T6 takes effect, and the state machine switches from the current state to the permanent non-response request state S4. EPS can only restore the lateral control function after restarting and without permanent faults.

[0078] Specifically, the step of establishing a prediction model according to the EPS dynamics principle and the steering motor dynamics principle in step S3 includes:

[0079] Step S301, establish the differential equation formula 1 according to the principle of dynamics: ;

[0080] Step S302, establishing the steering motor differential equation formula 2 according to the steering motor dynamics principle: ;

[0081] Among them, θ s is the steering angle, T s is the driver's hand torque detected by the torque sensor, G is the transmission ratio of the worm gear reducer, and J is s is the moment of inertia of the steering shaft, B s is the moment of inertia damping coefficient of the steering shaft, T a is the output torque of the steering motor, T m is the electromagnetic torque of the steering motor, J m is the moment of inertia of the steering motor, B m is the steering motor damping coefficient, T r It is the steering resistance torque, which is calculated by the vehicle electronic control unit and transmitted to the EPS via CAN communication;

[0082] Step S303, combining Formula 1 and Formula 2 to obtain Formula 3: ;

[0083] Step S304, combining formula 3 with the calculation formulas for predicting the steering angular velocity and the predicted steering angle to obtain formula 4 for the prediction model: ;

[0084] in: is the steering angle of the current control cycle, is the steering angular velocity, The driver's hand torque, is the steering resistance torque, is the rotor angle of the steering motor, is the motor speed of the steering motor, To predict the steering angle, To predict the steering angular velocity, To predict the steering angular acceleration, To control the cycle, is the electromagnetic torque of the steering motor in the current control cycle, is the moment of inertia of the steering shaft, is the moment of inertia of the steering motor, The transmission ratio of the worm gear reducer, is the steering motor damping coefficient, is the steering motor angular acceleration, is the damping coefficient of the steering axis.

[0085] in , Obtained through angle and torque sensors, Obtained through the motor position sensor, Obtained through the car CAN network;

[0086] See also Figure 3 As shown, it is a flowchart of the steps of traversing and obtaining values ​​with a step length of b as described in step S40 of an embodiment of the present invention, including:

[0087] Step S401, obtaining the electromagnetic torque range of the steering motor [-a, a] within the current control interval;

[0088] Step S402, obtaining a step length b;

[0089] Step S403: traverse and obtain values ​​from [-a, a] according to the step length b and count the obtained values ​​as T mk .

[0090] Specifically, the , where: k=0,1, … , 2a / b.

[0091] Specifically, the T mk Substituting into equation 4, the prediction is T mk The steering angle of the next control cycle is the input condition and the value of the steering angular velocity .

[0092] Specifically, in step S50, the step of calculating the predicted steering angle and the predicted steering angular velocity includes:

[0093] Step S501, substitute the value of Tm1 into the prediction model to obtain the predicted steering angle and predicted steering angular velocity ;

[0094] Step S502: mkThe predicted steering angle is obtained by traversing the values ​​of and predicted steering angular velocity .

[0095] Specifically, in step S60, the calculation of the prediction deviation and the selection of the motor target torque corresponding to the minimum value of the prediction deviation as the motor target electromagnetic torque for the next cycle further include the following steps:

[0096] Step S601, obtaining the predicted steering angle and predicted steering angular velocity ;

[0097] Step S602: Obtain the desired steering angle and the desired steering angular velocity ;

[0098] Step S603, calculating the prediction deviation ;

[0099] Step S604, determining the prediction deviation The minimum value of

[0100] Step S605, select the minimum of T mk As the target torque of the motor in the next control cycle .

[0101] Specifically, the prediction bias The calculation method is: ;

[0102] Among them: α is the normalization coefficient, β1 is the weight coefficient for predicting the steering angle, β2 is the weight coefficient for predicting the steering angular velocity, is the desired steering angle, is the desired steering angular velocity;

[0103] in: The bigger, More dependent on the error in the predicted steering angle, The larger the More dependent on the error in predicting the steering angular velocity.

[0104] In the above embodiment, The smaller the value, the closer the prediction result is to the target. Traverse and select The optimal motor target torque corresponding to the minimum value can select the optimal torque that meets the next control cycle within the motor torque range, while making the torque change curve in the lateral control process smoother, thereby increasing the smoothness of the electric power-assisted operation process.

[0105] Specifically, the steering motor compensation torque for lateral control is obtained through closed-loop control of the steering angle. , The time domain expression of the value at any time is: ;

[0106] in, For time, is the proportionality coefficient, is the integration coefficient.

[0107] Let T d is the total torque of the EPS lateral control steering motor, then: T d =T b +T c ;

[0108] Let T e The target power torque calculated by the EPS power control unit. is the target electromagnetic torque of the EPS system steering motor, then: ;

[0109] If the result of the lateral control state machine is the response request state S2, the lateral control function of the intelligent driving takes effect, and the response to T d Perform self-attenuation, gradually reduce it to 0, and gradually increase the limit value, so that T e It gradually works, causing the EPS system to gradually switch to the intelligent driving lateral control mode;

[0110] If the result of the lateral control state machine is no request state S1, temporary non-response request state S3 or permanent non-response request state S4, the intelligent driving lateral control function does not work and the response T e Perform self-attenuation, gradually reduce it to 0, and gradually increase the limit value, so that T d It gradually works, causing the EPS system to gradually switch to the power steering control mode;

[0111] In the above embodiment, the steering motor compensation electromagnetic torque is calculated through steering angle closed-loop control. This dynamic adjustment mechanism enables the system to respond to changes in complex environments in real time and ensure high-precision control effects. Finally, the optimal steering motor target electromagnetic torque is added to the steering motor compensation electromagnetic torque to jointly control the steering motor. This method not only improves the system's responsiveness, but also ensures that the requirement of steady-state zero error is achieved under various road conditions.

[0112] Specifically, the embodiment of the present invention further provides a specific implementation method. It is known that the equipment and parameters used in the current embodiment are: a column-EPS (C-EPS) system, a passenger car weighing 2.5 tons, an EPS controller with TC397 of Infineon Technologies as the main MCU, and a lateral control method for intelligent driving of the EPS system are implemented. The steering motor is a three-pole permanent magnet synchronous motor (PMSM), with a rated torque of 4.3Nm and a rated speed of 1050r / min;

[0113] Working process: please refer to Figure 4 As shown, it is a control block diagram of the implementation process of the EPS system intelligent driving lateral control method according to an embodiment of the present invention. The EPS controller reads the signals of the steering angle and torque sensors and calculates the driver's hand torque. , Steering wheel steering angle ,right Differentiate the time to get the steering wheel steering angular velocity ; Get the vehicle speed by reading the vehicle CAN network data , Lateral control request command , expected steering angle , expected steering angular velocity , Steering resistance torque ;

[0114] By reading the rotor position sensor signal and calculating the rotor angle of the steering motor ,right Differentiate the time to get the motor speed ;

[0115] Get the three-phase motor current of the steering motor through the current sensor signal , , ,right , , and Perform coordinate transformation to obtain the D-axis feedback current of the motor and Q-axis feedback current EPS real-time detection and calculation of load torque ,Will and As input, check the motor's Tn curve to get the D-axis target current ,Depend on and Perform D-axis current control to obtain D-axis voltage .

[0116] Meet the maximum electromagnetic torque in EPS lateral control application scenarios The range is [-2.1, 2.1], unit Nm. With a step of 0.1Nm, the sequence of -2.1Nm, -2.0Nm, ..., 2.1 Nm Traverse and get values ​​from [-4.3,4.3], and record the value obtained each time as T mk , where subscript k = 0, 1, 2, ..., 42; T mk Substituting into equation 4, we get T mk The steering angle of the next control cycle is the input condition and steering angular velocity Due to the steering wheel angle The range is [-80,80], the unit is degree, the steering wheel steering angular velocity The range is [-540, 540], and the unit is degree / second, so take ;hope More dependent on the error in the predicted steering angle, so , . The calculated , Substituting prediction bias In, get T mk Corresponding performance indicators , select the minimum of T mk Optimal target torque of the motor .

[0117] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A lateral control method for intelligent driving of an EPS system, characterized in that: include: Get the current state of the EPS lateral control state machine. If the current EPS lateral control state machine is in the state of responding to a request, collect the operating parameters of the EPS lateral control state machine in the current control range. Obtaining the expected steering angle and expected steering angular velocity of the next control cycle of the EPS system; Establish a prediction model based on EPS dynamics and steering motor dynamics; In the current control cycle, the electromagnetic torque is traversed and taken values ​​with a step size of b within the electromagnetic torque range of the steering motor; Substituting the traversal values ​​into the prediction model respectively to obtain a predicted steering angle and a predicted steering angular velocity; Calculate the prediction deviation according to the expected steering angle, the expected steering angular velocity, the predicted steering angle, and the predicted steering angular velocity, and select the motor target torque corresponding to the minimum value of the prediction deviation as the motor target electromagnetic torque for the next cycle; The prediction model is specifically: ,in: is the steering angle of the current control cycle, is the steering angular velocity, The driver's hand torque, is the steering resistance torque, is the rotor angle of the steering motor, is the motor speed of the steering motor, To predict the steering angle, To predict the steering angular velocity, To predict the steering angular acceleration, To control the cycle, is the electromagnetic torque of the steering motor in the current control cycle, is the moment of inertia of the steering shaft, is the moment of inertia of the steering motor, The transmission ratio of the worm gear reducer, Steering motor damping coefficient, is the steering motor angular acceleration, is the damping coefficient of the steering axis.

2. The lateral control method of EPS system intelligent driving according to claim 1, characterized in that: The step of traversing and taking values ​​of the electromagnetic torque within the electromagnetic torque range of the steering motor with a step length of b in the current control cycle comprises: Get the electromagnetic torque range of the steering motor in the current control range [-a, a]; Get the step length b; According to the step length b, traverse and obtain values ​​from [-a, a] and count the obtained values ​​as .

3. The lateral control method of EPS system intelligent driving according to claim 2, characterized in that: Said , where: k=0, 1,…, 2a / b.

4. The lateral control method of EPS system intelligent driving according to claim 2, characterized in that: Substituting the traversal values ​​into the prediction model to obtain the predicted steering angle and the predicted steering angular velocity also includes: Will Substitute the value of into the prediction model to obtain the predicted steering angle and predicted steering angular velocity ; right The value of is traversed, and the above steps are repeated to obtain the predicted steering angle and predicted steering angular velocity .

5. The lateral control method of EPS system intelligent driving according to claim 3, characterized in that: The calculation of the prediction deviation and the selection of the motor target torque corresponding to the minimum value of the prediction deviation as the motor target electromagnetic torque for the next cycle also include the following steps: Get the predicted steering angle and predicted steering angular velocity ; Acquiring the desired steering angle and the desired steering angular velocity; Calculate the prediction deviation ; Determine the prediction deviation The minimum value of Select the minimum of As the motor target torque for the next control cycle ; The prediction deviation is determined according to the following formula , ； Among them: α is the normalization coefficient, β1 is the weight coefficient of the predicted steering angle, β2 is the weight coefficient of the predicted steering angular velocity, is the desired steering angle, is the desired steering angular velocity, is the steering angle of the next control cycle, is the steering angular velocity of the next control cycle.

6. The EPS system intelligent driving lateral control method according to claim 2, characterized in that: The step length b is selected according to the computing power of the microprocessor.

7. The EPS system intelligent driving lateral control method according to claim 1, characterized in that: The states of the current EPS lateral control state machine include: no request state (S1), response request state (S2), temporary non-allowed response request state (S3), and permanent fault state (S4).

8. The EPS system intelligent driving lateral control method according to claim 7, characterized in that: The EPS lateral control state machine is based on the vehicle speed, the driver's hand torque, the lateral control request command and EPS fault state, so that the lateral control state machine switches between no request state (S1), response request state (S2), temporary non-allowed response request state (S3), and permanent fault state (S4).

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