Stability control system and method considering time varying and time lag of steering mechanism

Abstract

The invention discloses a stability control system and method considering time varying and time lag of a steering mechanism, and the system comprises a rotation angle generation module, a reference model module, an error fusion module, a stability control module, and an EKF side slip angle observation module. Wherein the steering angle generation module adopts a model predictive control (MPC) algorithm to track an expected trajectory and generate a front wheel steering angle, and the stability control module adopts a Smith pre-estimation compensation method to design active front wheel steering (Smith-AFS) and direct yawing moment control (DYC) so as to realize the stability control of the intelligent vehicle under the condition that an execution mechanism has time-varying and time-lag. According to the stability control system and method considering the time-varying time-lag of the steering mechanism, the tracking precision of the side slip angle and the yaw velocity is greatly improved, and the time-lag interference problem of a stability controller can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle stability control, and in particular relates to a stability control system and method taking into account the time-varying and time-delay of a steering mechanism. Background Art

[0002] With the deep integration of electronic information technology and the automotive industry, the direction of automotive intelligence has developed rapidly, which has become a hot topic in current research. With the rapid rise of autonomous driving, a series of safety accidents have also occurred, so the stability control of intelligent vehicles cannot be underestimated.

[0003] Most of the current research on autonomous driving control systems takes trajectory tracking accuracy as the control target, and rarely considers the problem of vehicle stability, which makes it easy for the vehicle to become unstable during the tracking process. In addition, when designing stability controllers, the time lag problem that occurs when the actual vehicle is executed is rarely considered, which greatly reduces the stability control effect. The current mainstream stability control mainly includes: active front steering control (AFS) and direct yaw moment control (DYC), both of which can act separately or simultaneously, but most control strategies often do not consider the lag link of the actuator, making the vehicle stability state more difficult to control. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention discloses a stability control system taking into account the time-varying and time-delay of the steering mechanism, which is characterized by comprising a steering angle generation module, a reference model module, an error fusion module, a stability control module, an EKF center of mass sideslip angle observation module,

[0005] in,

[0006] The turning angle generation module includes a trajectory planning submodule and a trajectory tracking submodule, which are used to generate the front wheel turning angle and send the front wheel turning angle to the reference model module.

[0007] The reference model module is used to generate the expected center of mass sideslip angle and the expected yaw rate, and send them together with the estimated center of mass sideslip angle and the actual yaw rate to the error fusion module.

[0008] The error fusion module is used to process the dimensionality of the center of mass sideslip angle error and the yaw rate error, and fuse the two into a single error and send it to the stability control module.

[0009] The stability control module includes an active front steering Smith compensation control submodule (Smith-AFS), a direct yaw moment control submodule (DYC), a linear extended state observer and an adaptive fuzzy control submodule, which are used to solve the intelligent vehicle stability control under the condition of time-varying and time-delay of the actuator.

[0010] The EKF center of mass sideslip angle observation module is used to estimate the vehicle center of mass sideslip angle and send the estimated center of mass sideslip angle to the error fusion module.

[0011] Furthermore, the active front steering Smith compensation control submodule (Smith-AFS) includes an active front steering controller and a Smith compensator, wherein the input of the active front steering controller is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows:

[0012]

[0013] In the formula, δ is the front wheel steering angle, Δδ is the additional front wheel steering angle, ε and K ω is the sliding mode AFS controller parameter, v x is the longitudinal speed, ω and ω d I is the actual yaw rate of the vehicle and the expected yaw rate of the vehicle, β is the sideslip angle of the vehicle center of mass, and I Z is the vehicle's yaw moment of inertia, a and b are the distances from the vehicle's center of mass to the front and rear axles, k1 and k2 are the equivalent cornering stiffnesses of the front and rear wheels, E is the fusion error, and ρ is the weight coefficient of the yaw rate error and the center of mass sideslip angle error.

[0014] The transfer function of the Smith compensator is as follows:

[0015]

[0016] In the formula, is the estimated transfer function of the vehicle system, The direct yaw moment control submodule (DYC) includes a direct yaw moment controller, wherein the input of the controller is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows:

[0017]

[0018] In the formula, ε and K β is the sliding mode DYC controller parameter

[0019] The linear extended state observer model is as follows:

[0020]

[0021] In the formula, x1 = ω, is the state variable of the system, are the estimated values ​​of the state variables x1, x2, d, d is the time-delay disturbance, z1, z2, z3 are the gain coefficients of the observer.

[0022] The turn execution lag time is as follows:

[0023]

[0024] In the formula, Estimated value for the steering execution delay.

[0025] The adaptive fuzzy control submodule is constructed as follows:

[0026] The function of fuzzy controller 1 is to adjust the value of control parameter ε in active front wheel steering controller and direct yaw moment controller so that the controller can achieve adaptive control. The basic domains of its input and output are: e ω :[-10,10],e β :[-4,4],ε:[0,100]. The corresponding fuzzy subsets are all {NB,NM,NS,ZO,PS,PM,PB}, that is, {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. The fuzzy rules of ε are shown in the following table:

[0027] Fuzzy rules for ε

[0028]

[0029] The function of fuzzy controller 2 is to adjust the value of weight coefficient ρ in the error fusion module. The basic domains of its input and output are: e ω :[-10,10],e β :[-4,4],ρ:[0,1]. The input fuzzy subsets are all {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}, and the output fuzzy subsets are {ZO (zero), PS (positive small), PM (positive middle), PB (positive large)}. The fuzzy rules of ρ are shown in the following table:

[0030] Fuzzy rules for ρ

[0031]

[0033] The control method of the stability control system considering the time-varying and time-delay of the steering mechanism is characterized by comprising the following steps:

[0034] S1, the desired trajectory is generated by the trajectory planning module, and then the front wheel turning angle is generated by the trajectory tracking module;

[0035] S2, generating a desired yaw rate and a desired center-of-mass sideslip angle through a reference model module; estimating the center-of-mass sideslip angle through an EKF center-of-mass sideslip angle estimation module;

[0036] S3, generating a fusion error by using an error fusion module to combine the expected yaw rate, the expected center of mass sideslip angle, the estimated center of mass sideslip angle, and the measured yaw rate;

[0037] S4, performing vehicle stability control according to the magnitude of the lateral acceleration;

[0038] Further, in step S1, the method for generating a corner includes the steps of:

[0039] S1.1. The optimal obstacle avoidance path is determined by establishing a multifunctional evaluation function that takes safety and smoothness into consideration. The solution space of the subsequent path optimization problem is determined based on the path decision result and the virtual contour of the obstacle collision. A quadratic programming problem with a faster solution speed is established in the convex space. A smooth feasible trajectory that satisfies kinematic constraints is obtained by relying on the optimization method.

[0040] S1.2. Track the trajectory planned in step S1.1 through the model predictive control algorithm to generate the front wheel steering angle.

[0041] Further, the desired yaw rate and the desired sideslip angle of the center of mass generated in step S2 are as follows:

[0042] The expected yaw rate is:

[0043]

[0044] The expected sideslip angle is:

[0045]

[0046] Further, the process of estimating the center of mass sideslip angle by the EKF center of mass sideslip angle estimation module in step S2 is as follows:

[0047] First, according to the initialization conditions, the estimated value of the previous moment is used to predict the state and error covariance, then the required gain matrix is ​​obtained, and then the corrected state and error covariance are updated by the observed value at the current moment. The prediction module and the update module are continuously iterated and looped, so that the filtering process gradually converges, and finally the estimated value of the vehicle's center of mass sideslip angle is obtained.

[0048] Further, in step S3, the specific process of error fusion is as follows:

[0049] S3.1, dimension normalization processing, get dimensionless error:

[0050]

[0051] In the formula, e i is the error after unification of dimensions, e is is the current actual error, e imax is the maximum error, e imin is the minimum error, i = ω, β

[0052] S3.2, calculate the error fusion, the calculation formula is:

[0053] E=ρe ω +(1-ρ)e β

[0054] Where E is the fusion error, ρ is the weight coefficient of the yaw rate error and the center of mass sideslip angle error

[0055] Furthermore, the weight coefficient ρ in step S3.2 is adjusted by a fuzzy controller 2, and the composition of the fuzzy controller 2 is as follows:

[0056] The basic domains of input and output of fuzzy control 2 are: ω :[-10,10],e β :[-4,4],ρ:[0,1]. The input fuzzy subsets are all {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}, and the output fuzzy subsets are {ZO (zero), PS (positive small), PM (positive middle), PB (positive large)}. The fuzzy rules of ρ are shown in the following table:

[0057] Fuzzy rules for ρ

[0058]

[0059] Further, in step S4, the stability controller intervenes in the judgment mode as follows: if the lateral acceleration a y >0.6g, DYC is used to control vehicle stability. If 0.4g < a y <0.6g, Smith-AFS is used to control vehicle stability, otherwise no control is applied;

[0060] Further, in step S4, the stability control method is:

[0061] S4.1. Since the time delay in the steering system is time-varying, a linear extended state observer is used to estimate the time delay of steering execution in the vehicle. The observer model is as follows:

[0062]

[0063] In the formula, x1 = ω, is the state variable of the system, are the estimated values ​​of the state variables x1, x2, d, d is the time-delay disturbance, z1, z2, z3 are the gain coefficients of the observer.

[0064] The turn execution lag time is as follows:

[0065]

[0066] In the formula, Estimated value for the steering execution delay.

[0067] S4.2, the Smith estimated compensation transfer function is obtained as follows:

[0068]

[0069] In the formula, is the estimated transfer function of the vehicle system, is the estimated value of the time delay in the controlled object

[0070] S4.3, when the lateral acceleration is 0.4g<a y When the vehicle speed is less than 0.6g, Smith-AFS is used to control the vehicle stability. The input of Smith-AFS control is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows:

[0071]

[0072] S4.4, when the lateral acceleration is a y When the load is greater than 0.6g, DYC is used to control vehicle stability. The input of DYC control is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows:

[0073]

[0074] S4.5, the control parameter ε in steps S4.2 and S4.3 is adjusted by fuzzy controller 1, and the composition of fuzzy controller 1 is as follows:

[0075] The basic domains of the input and output of the fuzzy controller 1 are: ω :[-10,10],e β :[-4,4],ε:[0,100]. The corresponding fuzzy subsets are all {NB,NM,NS,ZO,PS,PM,PB}, that is, {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. The fuzzy rules of ε are shown in the following table:

[0076] Fuzzy rules for ε

[0077]

[0078] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0079] The present invention adopts the Smith predictive compensation method to design an intelligent vehicle stability control that takes into account the time-varying time delay of the actuator, wherein the trajectory planning module is used to plan a reference trajectory of the vehicle; the trajectory tracking module adopts a model predictive control algorithm to track the reference trajectory and generate a front wheel steering angle; the stability control module is used to control the vehicle state to keep the vehicle in a stable state. This algorithm adopts the principle based on Smith predictive compensation, takes the time delay of the steering execution link into account, and predicts and compensates for the time delay of the steering execution link when designing the control algorithm, which greatly improves the performance of the stability controller under the condition of variable time delay of the steering mechanism and the tracking accuracy of the sideslip angle and yaw angular velocity of the center of mass. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a schematic diagram of a stability control system taking into account the time-varying delay of the actuator.

[0081] Figure 2 It is a flow chart of the stability control method considering the time-varying delay of the actuator.

[0082] Figure 3 yes Figure 1 Further schematic diagram of the stability control module.

[0083] Figure 4 It is the Smith predictive compensation principle diagram, where: D(s) is the transfer function of the AFS controller, G(s) is the transfer function of the vehicle system, is the estimated transfer function of the vehicle system, e -τs It is the lag link of the steering actuator. Estimate the hysteresis of the steering actuator, ω and ω d are the actual yaw rate and the desired yaw rate, β and β d are the actual center of mass sideslip angle and the expected center of mass sideslip angle respectively, and E is the fusion error. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0085] The present invention proposes a stability control system that takes into account the time-varying and time-delay of the steering mechanism. The overall framework is as follows: Figure 1 As shown, it includes a turning angle generation module, a reference model module, an error fusion module, a stability control module, and an EKF center of mass sideslip angle observation module.

[0086] The turning angle generation module includes a trajectory planning submodule and a trajectory tracking submodule, which are used to generate the front wheel turning angle and send the front wheel turning angle to the reference model module.

[0087] The reference model module is used to generate the expected center of mass sideslip angle and the expected yaw rate, and send them together with the estimated center of mass sideslip angle and the actual yaw rate to the error fusion module.

[0088] The error fusion module is used to process the dimensionality of the center of mass sideslip angle error and the yaw rate error, and fuse the two into a single error and send it to the stability control module.

[0089] The stability control module is as follows Figure 3 As shown, it includes an active front steering Smith compensation control submodule (Smith-AFS), a direct yaw moment control submodule (DYC), a linear extended state observer and an adaptive fuzzy control submodule to solve the intelligent vehicle stability control under the condition of time-varying and time-delay of the actuator.

[0090] The EKF center of mass sideslip angle observation module is used to estimate the vehicle center of mass sideslip angle and send the estimated center of mass sideslip angle to the error fusion module.

[0091] Furthermore, the active front steering Smith compensation control submodule (Smith-AFS) includes an active front steering controller and a Smith compensator. The Smith-AFS algorithm refers to connecting the Smith compensator in parallel with the controlled object and treating it as a new controlled object controlled by Smith-AFS. At this time, the system feedback is changed from the original controlled object output to the parallel output of the Smith compensator and the controlled object. The control result of the closed-loop control system is exactly the same as when there is no delay in the controlled vehicle, eliminating the influence of the lag link on the system.

[0092] Among them, the input of the active front wheel steering controller is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows:

[0093]

[0094] In the formula, δ is the front wheel steering angle, Δδ is the additional front wheel steering angle, ε and K ω is the sliding mode AFS controller parameter, v x is the longitudinal speed, ω and ω d They are respectively represented as the actual yaw rate of the vehicle and the expected yaw rate of the vehicle, β is the sideslip angle of the vehicle center of mass, I Zis the vehicle's yaw moment of inertia, a and b are the distances from the vehicle's center of mass to the front and rear axles, k1 and k2 are the equivalent cornering stiffnesses of the vehicle's front and rear wheels, E is the fusion error, and ρ is the weight coefficient of the yaw rate error and the center of mass sideslip angle error. The Smith compensator principle is as follows: Figure 4 As shown, its transfer function is as follows:

[0095]

[0096] In the formula, is the estimated transfer function of the vehicle system, Estimated turn execution delay

[0097] The direct yaw moment control submodule (DYC) includes a direct yaw moment controller, wherein the input of the controller is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows:

[0098]

[0099] In the formula, ε and K β is the sliding mode DYC controller parameter

[0100] The linear extended state observer model is as follows:

[0101]

[0102] In the formula, x1 = ω, is the state variable of the system, are the estimated values ​​of the state variables x1, x2, d, d is the time-delay disturbance, z1, z2, z3 are the gain coefficients of the observer. The steering execution time delay is as follows:

[0103]

[0104] In the formula, Estimated value for the steering execution delay.

[0105] The adaptive fuzzy control submodule is constructed as follows:

[0106] The function of fuzzy controller 1 is to adjust the value of control parameter ε in active front wheel steering controller and direct yaw moment controller so that the controller can achieve adaptive control. The basic domains of its input and output are: e ω :[-10,10],e β :[-4,4],ε:[0,100]. The corresponding fuzzy subsets are all {NB,NM,NS,ZO,PS,PM,PB}, that is, {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. The fuzzy rules of ε are shown in the following table:

[0107] Fuzzy rules for ε

[0108]

[0109]

[0110] The function of fuzzy controller 2 is to adjust the value of weight coefficient ρ in the error fusion module. The basic domains of its input and output are: e ω :[-10,10],e β :[-4,4],ρ:[0,1]. The input fuzzy subsets are all {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}, and the output fuzzy subsets are {ZO (zero), PS (positive small), PM (positive middle), PB (positive large)}. The fuzzy rules of ρ are shown in the following table:

[0111] Fuzzy rules for ρ

[0112]

[0113] The control method of the stability control system considering the time-varying and time-delay of the steering mechanism has the following overall process: Figure 2 As shown, it is characterized in that it includes the following steps:

[0114] S1, the desired trajectory is generated by the trajectory planning module, and then the front wheel turning angle is generated by the trajectory tracking module;

[0115] S2, generating a desired yaw rate and a desired center-of-mass sideslip angle through a reference model module; estimating the center-of-mass sideslip angle through an EKF center-of-mass sideslip angle estimation module;

[0116] S3, generating a fusion error by using the expected yaw angular velocity, the expected center of mass sideslip angle and the estimated center of mass sideslip angle obtained in step S2 through an error fusion module;

[0117] S4, performing vehicle stability control according to the magnitude of the lateral acceleration;

[0118] Further, in step S1, the method for generating a corner includes the steps of:

[0119] S1.1. The optimal obstacle avoidance path is determined by establishing a multifunctional evaluation function that takes safety and smoothness into consideration. The solution space of the subsequent path optimization problem is determined based on the path decision result and the virtual contour of the obstacle collision. A quadratic programming problem with a faster solution speed is established in the convex space. A smooth feasible trajectory that satisfies kinematic constraints is obtained by relying on the optimization method.

[0120] S1.2. Track the trajectory planned in step S1.1 through the model predictive control algorithm to generate the front wheel steering angle.

[0121] Further, the desired yaw rate and the desired sideslip angle of the center of mass generated in step S2 are as follows:

[0122] The expected yaw rate is:

[0123]

[0124] The expected sideslip angle is:

[0125]

[0126] Further, the process of estimating the center of mass sideslip angle by the EKF center of mass sideslip angle estimation module in step S2 is as follows:

[0127] First, according to the initialization conditions, the estimated value of the previous moment is used to predict the state and error covariance, then the required gain matrix is ​​obtained, and then the corrected state and error covariance are updated by the observed value at the current moment. The prediction module and the update module are continuously iterated and looped, so that the filtering process gradually converges, and finally the estimated value of the vehicle's center of mass sideslip angle is obtained.

[0128] Further, in step S3, the specific process of error fusion is as follows:

[0129] S3.1, dimension normalization processing, get dimensionless error:

[0130]

[0131] In the formula, e i is the error after unification of dimensions, e is is the current actual error, e imax is the maximum error, e imin is the minimum error, i = ω, β

[0132] S3.2, error fusion is:

[0133] E=ρe ω +(1-ρ)e β

[0134] Where E is the fusion error, ρ is the weight coefficient of the yaw rate error and the center of mass sideslip angle error

[0135] The weight coefficient ρ in step S3.2 is adjusted by fuzzy controller 2, and the fuzzy rules are formulated as follows:

[0136] The function of fuzzy controller 2 is to adjust the value of weight coefficient ρ in the error fusion module. The basic domains of its input and output are: e ω :[-10,10],e β:[-4,4],ρ:[0,1]. The input fuzzy subsets are all {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}, and the output fuzzy subsets are {ZO (zero), PS (positive small), PM (positive middle), PB (positive large)}. The fuzzy rules of ρ are shown in the following table:

[0137] Fuzzy rules for ρ

[0138]

[0139] Further, in step S4, the stability controller intervenes in the judgment mode as follows: if the lateral acceleration a y >0.6g, DYC is used to control vehicle stability. If 0.4g < a y <0.6g, Smith-AFS is used to control vehicle stability, otherwise no control is applied;

[0140] Further, in step S4, the stability control method is:

[0141] S4.1. Since the time delay in the steering system is time-varying, a linear extended state observer is used to estimate the time delay of steering execution in the vehicle. The observer model is as follows:

[0142]

[0143] In the formula, x1 = ω, is the state variable of the system, are the estimated values ​​of the state variables x1, x2, d, d is the time-delay disturbance, z1, z2, z3 are the gain coefficients of the observer.

[0144] The turn execution lag time is as follows:

[0145]

[0146] In the formula, Estimated value for the steering execution delay.

[0147] S4.2, Smith estimated the compensation transfer function as follows:

[0148]

[0149] In the formula, is the estimated transfer function of the vehicle system, is the estimated value of the time delay in the controlled object,

[0150] S4.3, when the lateral acceleration is 0.4g<a yWhen <0.6g, the input of the AFS submodule based on Smith-sliding mode control is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows:

[0151]

[0152] S4.4, when the lateral acceleration is a y When >0.6g, the input of the DYC submodule based on sliding mode control is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows:

[0153]

[0154] S4.5. Control parameters in steps S4.2 and S4.3 ε Fuzzy controller 1 is used for adjustment, and the fuzzy rules are formulated as follows:

[0155] The function of fuzzy controller 1 is to adjust the value of control parameter ε in active front wheel steering controller and direct yaw moment controller so that the controller can achieve adaptive control. The basic domains of its input and output are: e ω :[-10,10],e β :[-4,4],ε:[0,100]. The corresponding fuzzy subsets are all {NB,NM,NS,ZO,PS,PM,PB}, that is, {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. The fuzzy rules of ε are shown in the following table:

[0156] Fuzzy rules for ε

[0157]

[0158] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A stability control system taking into account the time-varying and time-delay of a steering mechanism, characterized in that: It includes the turning angle generation module, reference model module, error fusion module, stability control module, and EKF center of mass sideslip angle observation module. in, The turning angle generation module includes a trajectory planning submodule and a trajectory tracking submodule, which are used to generate the front wheel turning angle and send the front wheel turning angle to the reference model module; The EKF center of mass sideslip angle observation module is used to estimate the vehicle center of mass sideslip angle and send the estimated center of mass sideslip angle to the error fusion module; The reference model module is used to generate a desired center of mass sideslip angle and a desired yaw rate, and send them together with the estimated center of mass sideslip angle and the actual yaw rate to the error fusion module; The error fusion module is used to process the dimensionality problem of the center of mass sideslip angle error and the yaw rate error, and fuse the two into a single error and send it to the stability control module; The stability control module includes an active front steering Smith compensation control submodule (Smith-AFS), a direct yaw moment control submodule (DYC), a linear extended state observer and an adaptive fuzzy control submodule, which are used to solve the intelligent vehicle stability control under the condition of time-varying and time-delay of the actuator.

2. The stability control system considering the time-varying and time-delay of the steering mechanism according to claim 1, characterized in that: The active front steering Smith compensation control submodule (Smith-AFS) includes an active front steering controller and a Smith compensator. Among them, the input of the active front wheel steering controller is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows: In the formula, δ is the front wheel steering angle, Δδ is the additional front wheel steering angle, ε and K ω is the sliding mode AFS controller parameter, v x is the longitudinal speed, ω and ω d They are respectively represented as the actual yaw rate of the vehicle and the expected yaw rate of the vehicle, β is the sideslip angle of the vehicle center of mass, I Z is the vehicle's yaw moment of inertia, a and b are the distances from the vehicle's center of mass to the front and rear axles, k1 and k2 are the equivalent cornering stiffnesses of the vehicle's front and rear wheels, E is the fusion error, ρ is the weight coefficient of the yaw rate error and the center of mass sideslip angle error, The transfer function of the Smith compensator is as follows: In the formula, is the estimated transfer function of the vehicle system, is the estimated steering execution delay, The direct yaw moment control submodule (DYC) includes a direct yaw moment controller, wherein the input of the controller is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows: In the formula, ε and K β are the sliding mode DYC controller parameters, The linear extended state observer model is as follows: In the formula, x1 = ω, is the state variable of the system, are the estimated values ​​of the state variables x1, x2, d, d is the time-delay disturbance, z1, z2, z3 are the gain coefficients of the observer, The turn execution lag time is as follows: In the formula, Estimated turn execution delay The adaptive fuzzy control submodule is constructed as follows: The function of fuzzy controller 1 is to adjust the value of control parameter ε in active front wheel steering controller and direct yaw moment controller so that the controller can realize adaptive control. The basic domains of its input and output are: ω :[-10,10],e β :[-4,4],ε:[0,100], the corresponding fuzzy subsets are all {NB,NM,NS,ZO,PS,PM,PB}, that is, {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and the fuzzy rules of ε are shown in the following table: Fuzzy rules for ε The function of fuzzy controller 2 is to adjust the value of weight coefficient ρ in the error fusion module. The basic domains of its input and output are: e ω :[-10,10],e β :[-4,4],ρ:[0,1], the input fuzzy subsets are all {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}, the output fuzzy subsets are {ZO (zero), PS (positive small), PM (positive middle), PB (positive large)}, and the fuzzy rules of ρ are shown in the following table: Fuzzy rules for ρ 3. A control method based on the stability control system of claim 1 taking into account the time-varying and time-delay of the steering mechanism, characterized in that: The steps include: S1, the desired trajectory is generated by the trajectory planning module, and then the front wheel turning angle is generated by the trajectory tracking module; S2, generating a desired yaw rate and a desired center-of-mass sideslip angle through a reference model module, and estimating the center-of-mass sideslip angle through an EKF center-of-mass sideslip angle estimation module; S3, generating a fusion error by using the expected yaw angular velocity, the expected center of mass sideslip angle and the estimated center of mass sideslip angle obtained in step S2 through an error fusion module; S4. Perform vehicle stability control according to the magnitude of the lateral acceleration.

4. A control method for a stability control system taking into account the time-varying and time-delay of a steering mechanism according to claim 3, characterized in that: In step S1, the method of generating the corner is: S1.

1. The optimal obstacle avoidance path is determined by establishing a multifunctional evaluation function that takes safety and smoothness into consideration. The solution space of the subsequent path optimization problem is determined based on the path decision result and the virtual contour of the obstacle collision. A quadratic programming problem with a faster solution speed is established in the convex space. A smooth feasible trajectory that satisfies kinematic constraints is obtained by relying on the optimization method. S1.

2. Track the trajectory planned in step S1.1 through the model predictive control algorithm to generate the front wheel steering angle.

5. The control method of a stability control system taking into account the time-varying and time-delay of a steering mechanism according to claim 3, characterized in that: In step S2, the generated desired yaw rate and desired center of mass sideslip angle are as follows: The expected yaw rate is: The expected sideslip angle is: The process of estimating the center of mass sideslip angle through the EKF center of mass sideslip angle estimation module is as follows: First, according to the initialization conditions, the state and error covariance are predicted using the estimated value of the previous moment, and then the required gain matrix is ​​obtained. The state and error covariance are updated and corrected through the observed value at the current moment. The prediction module and the update module are continuously iterated and cycled, so that the filtering process finally converges gradually, and finally the estimated value of the vehicle's center of mass sideslip angle is obtained.

6. The control method of a stability control system taking into account the time-varying and time-delay of a steering mechanism according to claim 3, characterized in that: In step S3, the specific process of error fusion is as follows: S3.1, dimension normalization processing, get dimensionless error: In the formula, e i is the error after unification of dimensions, e is is the current actual error, e imax is the maximum error, e imin is the minimum error, i = ω, β; S3.2, error fusion is: E=ρe ω +(1-p)e β Where, E is the fusion error, ρ is the weight coefficient of the yaw rate error and the center of mass sideslip angle error; S3.

3. The weight coefficient ρ in step S3.2 is adjusted by fuzzy controller 2, and the fuzzy rules are formulated as follows: The function of fuzzy controller 2 is to adjust the value of weight coefficient ρ in the error fusion module. The basic domains of its input and output are: e ω :[-10,10],e β :[-4,4],ρ:[0,1], the input fuzzy subsets are all {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}, the output fuzzy subsets are {ZO (zero), PS (positive small), PM (positive middle), PB (positive large)}, and the fuzzy rules of ρ are shown in the following table: Fuzzy rules for ρ 7. The control method of a stability control system taking into account the time-varying and time-delay of a steering mechanism according to claim 3, characterized in that: In step S4, the stability control method is: S4.

1. Since the time delay in the steering system is time-varying, a linear extended state observer is used to estimate the time delay of steering execution in the vehicle. The observer model is as follows: In the formula, x1 = ω, is the state variable of the system, are the estimated values ​​of the state variables x1, x2, d, d is the time-delay disturbance, z1, z2, z3 are the gain coefficients of the observer, The turn execution lag time is as follows: In the formula, An estimate of the turn execution delay; S4.2, Smith estimated the compensation transfer function as follows: In the formula, is the estimated transfer function of the vehicle system, is the estimated value of the time delay in the controlled object; S4.3, when the lateral acceleration is 0.4g<a y When <0.6g, the input of the AFS submodule based on Smith-sliding mode control is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows: S4.4, when the lateral acceleration is a y When >0.6g, the input of the DYC submodule based on sliding mode control is the fusion error E, the control method is sliding mode control (SMC), and the control output is as follows: S4.

5. Control parameters in steps S4.2 and S4.3 ε Fuzzy controller 1 is used for adjustment, and the fuzzy rules are formulated as follows: The function of fuzzy controller 1 is to adjust the value of control parameter ε in active front wheel steering controller and direct yaw moment controller so that the controller can realize adaptive control. The basic domains of its input and output are: ω :[-10,10],e β :[-4,4],ε:[0,100], the corresponding fuzzy subsets are all {NB,NM,NS,ZO,PS,PM,PB}, that is, {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and the fuzzy rules of ε are shown in the following table: Fuzzy rules for ε

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