Intelligent automobile double-event trigger control method for double communication networks

Abstract

The invention discloses an intelligent automobile double-event trigger control method aiming at double communication networks. The method comprises the following steps: initializing; establishing intelligence to judge whether an SC event triggering condition and a CA event triggering condition are met or not; solving a front wheel steering angle control instruction; and performing intelligent automobile double-event trigger control. According to the invention, a double-event trigger condition is designed to determine whether a state signal of an intelligent automobile path tracking type 1 fuzzy model is updated to a type 1 fuzzy time-lag controller or not and determine whether a front wheel steering angle control instruction calculated by the type 1 fuzzy time-lag controller is updated to an actuator module or not; communication resources of double communication networks are effectively saved; the method has the capability of efficiently avoiding the problem of communication congestion, can save a lot of communication resources, and improves the reliability of the intelligent automobile in the path tracking control process. The fuzzy time-delay controller is designed to calculate a front wheel steering angle control instruction, and the reliability of path tracking control of the intelligent automobile is ensured.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle control technology, and in particular to a dual-event triggering control method for intelligent vehicles with dual communication networks. Background Technology

[0002] As intelligent vehicles become increasingly complex, their safe and reliable motion control relies more heavily on communication networks. Typically, intelligent vehicles connect their hardware and software via communication networks between sensors and controller modules, and between controller modules and actuator modules, enabling their operation. However, communication networks are constrained by limited bandwidth, resulting in very limited communication resources. Traditional time-triggered intelligent vehicle control methods update sensor measurements and transmit them to the controller module at fixed sampling intervals, and update the controller module's front wheel steering angle control commands to the actuator module at fixed sampling intervals. This wastes significant amounts of unnecessary communication resources, potentially leading to network congestion in both the sensor-controller and controller-actuator communication networks. Furthermore, traditional event-triggered intelligent vehicle control methods only address the limited communication network resources between sensors and controller modules, neglecting the resource constraints of the communication network between controller and actuator modules, which also results in insufficient communication resources. Therefore, if the resource constraints of these dual communication networks are not properly addressed, the intelligent vehicle's communication network may be blocked, rendering it unable to function properly. The sensor measurement status refers to lateral velocity, yaw rate, position information, heading angle, and longitudinal velocity. The dual communication network refers to the communication network between the sensor and the controller module, and the communication network between the controller module and the actuator module. Summary of the Invention

[0003] To address the aforementioned problems in the existing technology, the present invention aims to provide a dual-event triggering control method for intelligent vehicles with dual communication networks, so as to effectively solve the resource-constrained problem of dual communication networks and enable intelligent vehicles to operate reliably.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows: A dual-event triggering control method for intelligent vehicles with dual communication networks is disclosed. The method utilizes a dual-event triggering control system for intelligent vehicle path tracking, which includes an initialization module, a dual-event triggering module, a data buffer module, a controller module, and an actuator module.

[0005] The initialization module is used to check whether the signal transmission and reception of the sensor, planning module, and actuator module are normal; check whether the dual communication network and data cache module are operating normally; check whether the loading of reference path information is normal, the reference path information including reference position, reference road curvature, and reference heading angle; load the parameters of the intelligent vehicle path tracking fuzzy model; load the parameters of the dual event triggering module; and load the parameters of the controller module.

[0006] The dual-event triggering module consists of SC event triggering conditions and CA event triggering conditions. The SC event triggering conditions refer to event triggering conditions defined for the communication network between the sensor and the controller module, used to determine whether to update the state signal of the intelligent vehicle path tracking fuzzy model to the controller module, where the controller module is a fuzzy time-delay controller. The SC event triggering conditions are designed based on an adaptive storage threshold function and the state signal of the intelligent vehicle path tracking fuzzy model, which is designed based on the measurement state of the sensor. The adaptive storage threshold function is designed based on the state signal of the intelligent vehicle path tracking fuzzy model and stored historical event triggering state signals, where the stored historical event triggering state signals refer to the state signals of the SC event triggering conditions at the current and previous triggering times. The CA event triggering condition refers to the event triggering condition formulated for the communication network between the controller module and the actuator module, which is used to determine whether to update the front wheel steering angle control command of the fuzzy time-delay controller to the actuator module. The CA event triggering condition is designed based on the dynamic threshold function and the state signal of the intelligent vehicle path tracking fuzzy model. The dynamic threshold function is designed based on the state signal of the intelligent vehicle path tracking fuzzy model and the current state signal triggered by the CA event triggering condition.

[0007] The elements of the state signal of the intelligent vehicle path tracking fuzzy model include lateral error, lateral error rate of change, heading angle error, heading angle error rate of change, the front wheel steering angle control command actually executed by the intelligent vehicle, and the reference road curvature. The lateral error refers to the difference between the current position information of the intelligent vehicle and the reference position, and the heading angle error refers to the difference between the current heading angle of the intelligent vehicle and the reference heading angle. The current state information of the intelligent vehicle includes position information, heading angle, longitudinal velocity, lateral velocity, and yaw rate. The data caching module is used to cache the state signal of the intelligent vehicle path tracking fuzzy model triggered at the previous moment when the SC event triggering condition is not met, and the front wheel steering angle control command of the fuzzy time-delay controller at the previous moment when the CA event triggering condition is not met.

[0008] The intelligent vehicle path tracking fuzzy model is obtained by mathematical calculations based on the current state information of the intelligent vehicle and the reference path information, and modeled in the form of mathematical equations. The fuzzy time-delay controller is designed based on the intelligent vehicle path tracking fuzzy model, and the fuzzy time-delay controller uses the state signals of the intelligent vehicle path tracking fuzzy model to solve for the front wheel steering angle control command.

[0009] The intelligent vehicle dual-event triggering control method includes the following steps: Step 1: Initialization The initialization module checks whether the signal transmission and reception of the sensors, planning module, and actuator module are normal; checks whether the dual communication network and data cache module are operating normally; and checks whether the loading of reference path information is normal. The sensor information refers to environmental obstacle information and the current status information of the intelligent vehicle, and the planning module information refers to reference path information. The reference path information includes reference position, reference road curvature, and reference heading angle. The actuator module signal includes the front wheel steering angle actually executed by the actuator.

[0010] The initialization module loads parameters for a type-one fuzzy model of intelligent vehicle path tracking; loads parameters for a dual-event triggering module; and loads parameters for a type-one fuzzy time-delay controller. The parameters of the type-one fuzzy model of intelligent vehicle path tracking include the mass of the intelligent vehicle, the distance from the center of the front and rear axles to the center of mass, and the lateral stiffness and moment of inertia of the front and rear axle tires. The parameters of the dual-event triggering module include the weight matrix of the SC event triggering condition, different preset constants in the adaptive stored threshold function, the weight matrix of the CA event triggering condition, and different preset constants in the dynamic threshold function. The parameters of the type-one fuzzy time-delay controller include the maximum and minimum values ​​of the actuator delay and the network-induced delay, as well as the sampling period.

[0011] Step 2: Establish a type I fuzzy model for intelligent vehicle path tracking Based on the current state information of the intelligent vehicle obtained in real time in step 1 and the reference path information, mathematical calculations are performed, and the following fuzzy model for intelligent vehicle path tracking, containing 16 sub-models, is established in the form of mathematical equations:

[0012] In the formula: Refers to a type of fuzzy model for intelligent vehicle path tracking based on sampling period The continuous time of execution;

[0013]

[0014]

[0015]

[0016]

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[0018]

[0019]

[0020]

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[0026]

[0027]

[0028] in, The weight function is given by a fuzzy model for intelligent vehicle path tracking. , i =1, 2, 3, ..., 15, 16, i This refers to the sequence number of the corresponding sub-model in a type of fuzzy model for intelligent vehicle path tracking. , , , , , , and The mathematical expression is as follows:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] Define the first predecessor variable = , and All Membership function; define the second antecedent variable. = , and All Membership function; define the third antecedent variable. = , and All Membership function; define the fourth antecedent variable. = , and All Membership function; , , , , , , and Each satisfies , , , .

[0037] In the formula: and They are respectively maximum value and minimum value ; and They are respectively maximum value and minimum value ; and They are respectively maximum value and minimum value ; and They are respectively maximum value and minimum value ; State signals and definitions for a fuzzy model of path tracking for intelligent vehicles ; for The first derivative; The actual front wheel steering angle control commands executed by the intelligent vehicle; The lateral speed of the intelligent vehicle; For the yaw rate of intelligent vehicles; Refers to lateral error. Refers to the error in heading angle. for The first-order differential, for The first-order differential, and The second-order differential form is as follows:

[0038]

[0039] In the formula: The longitudinal speed of the intelligent vehicle; For reference road curvature, To mitigate the uncertainty interference affecting the second-order derivative of the lateral error; To mitigate the uncertainty interference affecting the second-order derivative of the heading angle error; The front wheel steering angle control command is calculated by a type of fuzzy time-delay controller. for The coefficient matrix, for The coefficient matrix, This is considered a perturbation in a fuzzy model of intelligent vehicle path tracking.

[0040] For ease of calculation, a matrix is ​​defined. Matrices of the same order as follows:

[0041] Then matrix By using or , or , or , or Replace the matrix respectively In , , , Sure; and The expression is as follows:

[0042]

[0043] In the formula: For the quality of intelligent vehicles; The moment of inertia of an intelligent vehicle; and These are the distances from the front and rear axles to the center of gravity, respectively. and These are the lateral stiffness of the front and rear axle tires, respectively. This refers to the actuator time delay.

[0044] Step 3: Design adaptive stored threshold function and dynamic threshold function Based on the state signal of the intelligent vehicle path tracking type I fuzzy model obtained in step 2 and the stored historical event trigger signal, design an adaptive stored threshold function; based on the state signal of the intelligent vehicle path tracking type I fuzzy model obtained in step 2 and the current state signal triggered by the CA event trigger condition, design a dynamic threshold function.

[0045] The adaptive storage-type threshold function is composed of variables. and The components, and their mathematical equation expressions are as follows:

[0046]

[0047] In the formula:

[0048]

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[0050]

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[0055]

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[0057] in, and Both are variables in the adaptive stored threshold function that is the trigger condition for SC events and satisfy the following conditions: and , for The first-order differential, for The first derivative; For variables The weighted trigger-induced error term in the data stores historical event trigger state signals; For variables The weighted trigger-induced error term in the data stores historical event trigger state signals; is the weight matrix of the adaptive storage threshold function, and it is positive definite; All are preset, different constant values ​​and satisfy... ; For the sensor at the current sampling time The status signal; For the first Network-induced latency at the moment of event triggering For the first Network-induced latency at the moment of event triggering For the first Network-induced latency at the moment of event triggering For the first Network-induced latency at the moment of event triggering For the first Network-induced latency at the moment of event triggering The length of the sampling period. Refers to the moment the event is triggered Start of The next sampling time; The SC event triggering condition is in the first The status signal sampled at all times; The SC event triggering condition is in the first The status signal sampled at all times; The SC event triggering condition is in the first The status signal sampled at all times; The SC event triggering condition is in the first The status signal sampled at all times; The SC event triggering condition is in the first The status signal sampled at all times; The triggering condition for the SC event is at the triggering time. The status signal, The triggering condition for the SC event is at the triggering time. The status signal, The triggering condition for the SC event is at the triggering time. The status signal, The triggering condition for the SC event is at the triggering time. The status signal, The triggering condition for the SC event is at the triggering time. The status signal; The SC trigger condition at the current sampling time , No. Triggering time , No. Triggering time , No. Triggering time , No. Triggering time and the Triggering time The weighted average of the state signals. , All of them are set with different weight constants and satisfy the following conditions: , . It is the arctangent function. To calculate the sign of the norm, Pi is the mathematical constant of a circle.

[0058] The mathematical equation for the dynamic threshold function is as follows:

[0059] In the formula:

[0060]

[0061]

[0062] in, The variable in the dynamic threshold function that determines the CA event trigger condition and satisfies , For variables The trigger-induced error term in the middle, for The first-order differential, The triggering condition for the CA event is at the triggering time. Induced error and satisfying ; All are preset, different constant values ​​and satisfy... .

[0063] Step 4: Determine whether the SC event triggering conditions and CA event triggering conditions are met. The dual-event triggering module consists of SC event triggering conditions and CA event triggering conditions. The SC event triggering conditions are designed based on the adaptive storage-type threshold function in step 3 and the state signal of the intelligent vehicle path tracking type I fuzzy model calculated in step 2; the CA event triggering conditions are designed based on the dynamic threshold function in step 3 and the state signal of the intelligent vehicle path tracking type I fuzzy model calculated in step 2.

[0064] The mathematical expression for the SC event triggering condition is as follows:

[0065] In the formula: Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time; The triggering condition for the SC event is at the triggering time. Induced error, The triggering condition for the SC event is at the triggering time. Induced error, The triggering condition for the SC event is at the triggering time. Induced error, The triggering condition for the SC event is at the triggering time. Induced error, The triggering condition for the SC event is at the triggering time. Induced error; for The weight constants, for The weight constants, for The weight constants, for The weight constants, for The weight constants, , , , and satisfy , , , , , .

[0066] The mathematical expression for the CA event triggering condition is as follows:

[0067] Before each calculation of the front wheel steering angle control command, the Type I fuzzy time-delay controller determines whether the SC event triggering condition is met. If the condition is met, the state signal of the Type I fuzzy model for intelligent vehicle path tracking is updated in the Type I fuzzy time-delay controller to calculate the front wheel steering angle control command. If the condition is not met, no update is made, and the state signal of the Type I fuzzy model for intelligent vehicle path tracking triggered at the previous moment is used and stored in the data cache module.

[0068] Step 5: Solve for the front wheel steering angle control command Based on the state signal of the intelligent vehicle path tracking fuzzy model triggered in step 4, the control instructions of the fuzzy time-delay controller containing 16 sub-controllers are calculated as follows:

[0069] In the formula: The gain matrix of a type-1 fuzzy time-delay controller is given by the coefficient matrix in a type-1 fuzzy model for path tracking in intelligent vehicles. and The design was obtained.

[0070] Step 6: Perform dual-event trigger control for intelligent vehicles The front wheel steering angle control command determined in step 5 is sent to the actuator module to realize the safety control of the intelligent vehicle. Before receiving the front wheel steering angle control command calculated by the type fuzzy time delay controller, the actuator module determines whether the CA event triggering condition in step 4 is met. If it is met, the front wheel steering angle control command calculated by the type fuzzy time delay controller is updated to the actuator module. If it is not met, it is not updated, and the front wheel steering angle control command calculated by the type fuzzy time delay controller at the previous moment is used and stored in the data cache module.

[0071] Further, determine whether the intelligent vehicle has reached its destination. If so, complete the control task; otherwise, proceed to step 1.

[0072] The present invention has the following beneficial effects: 1. This invention addresses the communication network congestion problem between the sensor and controller modules, and between the controller and actuator modules. It designs a novel dual-event triggering condition to determine whether the state signal of a fuzzy model for intelligent vehicle path tracking is updated in a fuzzy time-delay controller, and whether the front wheel steering angle control command calculated by the fuzzy time-delay controller is updated in the actuator module. This effectively saves communication resources in the dual communication networks. In particular, it solves the problem that traditional event-triggered intelligent vehicle control methods only focus on the limited communication network resources between the sensor and controller modules. The SC event triggering condition also stores historical event triggering state signals, fully considering the impact of historical triggering state signals on the dual-event triggering control system for intelligent vehicle path tracking. This provides efficient avoidance of communication congestion, saves significant communication resources, and improves the reliability of intelligent vehicles in path tracking control.

[0073] 2. This invention differs from traditional intelligent vehicle path tracking models. The designed fuzzy model for intelligent vehicle path tracking incorporates lateral error, lateral error rate of change, heading angle error, heading angle error rate of change, the actual front wheel steering angle control command executed by the intelligent vehicle, and the reference road curvature into the state signal of the fuzzy model, thus possessing the ability to reflect the nonlinear dynamic characteristics of the intelligent vehicle. Simultaneously, it fully considers the time-varying speed of the intelligent vehicle, the uncertainty of the lateral stiffness of the front and rear axle tires, uncertainty interference, reference road curvature, actuator time delay, and network-induced time delay. Based on the fuzzy model for intelligent vehicle path tracking, a fuzzy time-delay controller is designed to calculate the front wheel steering angle control command, ensuring the reliability of the intelligent vehicle path tracking control. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the present invention.

[0075] Figure 2 This is a schematic diagram of the structure of the intelligent vehicle path tracking dual-event triggering control system of the present invention. Detailed Implementation

[0076] The following is for reference only. Figure 1-2 The present invention will be further described below.

[0077] Figure 1The diagram shows the principle of this invention. It implements dual-event triggering control for intelligent vehicles through a fuzzy time-delay controller, a dual-event triggering module, and a data buffering module, effectively saving communication resources. The dual-event triggering module consists of SC event triggering conditions and CA event triggering conditions. The SC event triggering conditions refer to event triggering conditions established for the communication network between the sensor and controller modules, used to determine whether to update the state signal of the intelligent vehicle path tracking fuzzy model to the fuzzy time-delay controller. The CA event triggering conditions refer to event triggering conditions established for the communication network between the controller module and the actuator module, used to determine whether to update the front wheel steering angle control command of the fuzzy time-delay controller to the actuator module. The fuzzy time-delay controller calculates the front wheel steering angle control command based on the state signal triggered by the SC event triggering module and the gain matrix of the fuzzy time-delay controller.

[0078] Before each calculation of the front wheel steering angle control command, the type-1 fuzzy time-delay controller needs to determine whether the SC event triggering condition in step 4 of the invention is met. If it is met, the state signal of the intelligent vehicle path tracking type-1 fuzzy model is updated to the type-1 fuzzy time-delay controller to calculate the front wheel steering angle control command. If it is not met, it is not updated, and the state signal of the intelligent vehicle path tracking type-1 fuzzy model triggered at the previous moment is used and stored in the data cache module.

[0079] Before receiving the front wheel angle control command calculated by the fuzzy time-delay controller, the actuator module determines whether the CA event triggering condition in step 4 of the invention is met. If it is met, the front wheel angle control command calculated by the fuzzy time-delay controller is updated to the actuator module. If it is not met, it is not updated, and the front wheel angle control command calculated by the fuzzy time-delay controller at the previous moment is used and stored in the data cache module.

[0080] This invention utilizes Figure 2 The intelligent vehicle path tracking dual-event triggered control system shown in the figure operates through the following specific steps: Step 1: Initialization Step 2: Establish a type I fuzzy model for intelligent vehicle path tracking In the steps of the invention, By using or , or , or , or Replace each In , , , To determine, In the type I fuzzy model for path tracking of intelligent vehicles, the first... i The state vector of each sub-model The coefficient matrix, i= 1, 2, 3, ..., 15, 16, as shown in Table 1, represent the values ​​in a type-I fuzzy model for intelligent vehicle path tracking. Determined by the 16 fuzzy rules in Table 1, i In the type I fuzzy model for path tracking of intelligent vehicles, the first... i The sequence number of the sub-model corresponding to each fuzzy rule; in Table 1, "S" refers to the antecedent variable. Depend on Updated to Precedence variable Depend on Updated to Precedence variable Depend on Updated to Precedence variable Depend on Updated to "B" refers to the antecedent variable. Depend on Updated to Precedence variable Depend on Updated to Precedence variable Depend on Updated to Precedence variable Depend on Updated to .

[0081] Table 1. 16 Fuzzy Rules in a Type I Fuzzy Model for Intelligent Vehicle Path Tracking

[0082] Step 3: Design adaptive stored threshold function and dynamic threshold function In step 3 of the invention, the adaptive storage-type threshold function is composed of variables. and The components, and their mathematical equation expressions are as follows:

[0083]

[0084] In the formula:

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[0094] The adaptive storage-type threshold function in variables and It stores historical event trigger status signals. , , , and The design fully considers the impact of historical trigger moment state signals on the dual-event trigger control system for intelligent vehicle path tracking. It also includes a... , The SC trigger condition at the current sampling time , No. Triggering time , No. Triggering time , No. Triggering time , No. Triggering time and the Triggering time The weighted average of the state signals; through design This can effectively enhance the smoothness of the triggered state signal. and It will follow the sensor at the current sampling time status signal Adaptive adjustment and It will follow the weighted average. The adaptive adjustment further enhances the adaptability of the SC event triggering conditions.

[0095] The mathematical equation for the dynamic threshold function is as follows:

[0096] In the formula:

[0097]

[0098]

[0099] in, It will follow the sensor at the current sampling time status signal Adaptive adjustment It will follow the weighted average. The adaptive adjustment further enhances the adaptability of the CA event triggering conditions.

[0100] Step 4: Determine whether the SC event triggering conditions and CA event triggering conditions are met. In step 4 of the invention, the mathematical expression for the CA event triggering condition is as follows:

[0101] By designing CA event triggering conditions, the problem of neglecting the communication network resources between the controller module and the actuator module in traditional event-triggered intelligent vehicle control methods is effectively solved, further saving the communication resources of dual communication networks.

[0102] Step 5: Solve for the front wheel steering angle control command Step 6: Perform dual-event trigger control for intelligent vehicles The steps in this embodiment are consistent with the steps in the invention.

[0103] This invention is not limited to the details of the above embodiments. Any equivalent concept or modification within the technical scope disclosed in this invention shall be included within the protection scope of this invention.

Claims

1. A dual-event triggering control method for intelligent vehicles with dual communication networks, characterized in that: Control is achieved using a dual-event triggering control system for intelligent vehicle path tracking, including the following steps: Step 1: Initialization; Step 2: Establish a type I fuzzy model for intelligent vehicle path tracking; Step 3: Design adaptive stored threshold functions and dynamic threshold functions; Step 4: Determine whether the SC event triggering conditions and CA event triggering conditions are met; Step 5: Solve for the front wheel steering angle control command; Step 6: Perform dual-event trigger control for the intelligent vehicle; further, determine whether the intelligent vehicle has reached its destination. If so, complete the control task; otherwise, proceed to step 1.

2. The intelligent vehicle dual-event triggering control method for dual communication networks according to claim 1, characterized in that: The intelligent vehicle path tracking dual-event triggering control system includes an initialization module, a dual-event triggering module, a data cache module, a controller module, and an actuator module; The initialization module is used to check whether the signal transmission and reception of the sensor, planning module and actuator module are normal; to check whether the dual communication network and data cache module are operating normally; and to check whether the loading of the reference path information is normal. The reference path information includes reference position, reference road curvature and reference heading angle. Load the parameters of the intelligent vehicle path tracking fuzzy model; load the parameters of the dual-event triggering module; Load the parameters of the controller module; The dual-event triggering module consists of SC event triggering conditions and CA event triggering conditions. The SC event triggering conditions refer to event triggering conditions defined for the communication network between the sensor and the controller module, used to determine whether to update the state signal of the intelligent vehicle path tracking fuzzy model to the controller module, where the controller module is a fuzzy time-delay controller. The SC event triggering conditions are designed based on an adaptive storage threshold function and the state signal of the intelligent vehicle path tracking fuzzy model, which is designed from the measurement state of the sensor. The adaptive storage threshold function is based on the state signal of the intelligent vehicle path tracking fuzzy model... The state signals and stored historical event trigger state signals are designed to represent the state signals of the SC event trigger conditions at the current and previous trigger times. The CA event trigger conditions refer to the event trigger conditions formulated for the communication network between the controller module and the actuator module, used to determine whether to update the front wheel steering angle control command of the type-1 fuzzy time-delay controller to the actuator module. The CA event trigger conditions are designed based on the dynamic threshold function and the state signals of the intelligent vehicle path tracking type-1 fuzzy model. The dynamic threshold function is designed based on the state signals of the intelligent vehicle path tracking type-1 fuzzy model and the current state signal triggered by the CA event trigger conditions. The elements of the state signal of the intelligent vehicle path tracking fuzzy model include lateral error, lateral error rate of change, heading angle error, heading angle error rate of change, the front wheel steering angle control command actually executed by the intelligent vehicle, and the reference road curvature. The lateral error refers to the difference between the current position information of the intelligent vehicle and the reference position, and the heading angle error refers to the difference between the current heading angle of the intelligent vehicle and the reference heading angle. The current state information of the intelligent vehicle includes position information, heading angle, longitudinal speed, lateral speed, and yaw rate. The data caching module is used to cache the state signal of the intelligent vehicle path tracking fuzzy model triggered at the previous moment when the SC event triggering condition is not met, and the front wheel steering angle control command of the fuzzy time delay controller at the previous moment when the CA event triggering condition is not met. The intelligent vehicle path tracking fuzzy model is obtained by mathematical calculation of the current state information of the intelligent vehicle and the reference path information and modeling it in the form of mathematical equations. The aforementioned fuzzy time-delay controller is designed based on a fuzzy model for intelligent vehicle path tracking. The fuzzy time-delay controller uses the state signals of the fuzzy model for intelligent vehicle path tracking to solve for the front wheel steering angle control command.

3. The intelligent vehicle dual-event triggering control method for dual communication networks according to claim 1, characterized in that: The initialization method described in step 1 is as follows: The initialization module checks whether the signal transmission and reception of the sensors, planning module, and actuator module are normal; checks whether the dual communication network and data cache module are operating normally; and checks whether the loading of reference path information is normal. The sensor information refers to environmental obstacle information and the current status information of the intelligent vehicle, and the planning module information refers to reference path information. The reference path information includes reference position, reference road curvature, and reference heading angle. The actuator module signal includes the actual front wheel steering angle executed by the actuator. The initialization module loads the parameters of a type-I fuzzy model for intelligent vehicle path tracking; it also loads the parameters of the dual-event triggering module. The parameters of a type-1 fuzzy time-delay controller are loaded; the parameters of the intelligent vehicle path tracking type-1 fuzzy model include the mass of the intelligent vehicle, the distance from the center of the front and rear axles to the center of mass, the lateral stiffness and moment of inertia of the front and rear axle tires; the parameters of the dual-event triggering module include the weight matrix of the SC event triggering condition, different preset constants in the adaptive storage threshold function, the weight matrix of the CA event triggering condition, and different preset constants in the dynamic threshold function; the parameters of the type-1 fuzzy time-delay controller include the maximum and minimum values ​​of the actuator time delay, the network-induced time delay, and the sampling period.

4. The intelligent vehicle dual-event triggering control method for dual communication networks according to claim 1, characterized in that: The method for establishing a type I fuzzy model for intelligent vehicle path tracking in step 2 is as follows: Based on the current state information of the intelligent vehicle obtained in real time in step 1 and the reference path information, mathematical calculations are performed, and the following fuzzy model for intelligent vehicle path tracking, containing 16 sub-models, is established in the form of mathematical equations: In the formula: Refers to a type of fuzzy model for intelligent vehicle path tracking based on sampling period The continuous time of execution; in, The weight function is given by a fuzzy model for intelligent vehicle path tracking. , i =1, 2, 3, ..., 15, 16, i This refers to the sequence number of the corresponding sub-model in a type of fuzzy model for intelligent vehicle path tracking. , , , , , , and The mathematical expression is as follows: Define the first predecessor variable = , and All Membership function; define the second antecedent variable. = , and All Membership function; define the third antecedent variable. = , and All Membership function; define the fourth antecedent variable. = , and All Membership function; , , , , , , and Each satisfies , , , ; In the formula: and They are respectively maximum value and minimum value ; and They are respectively maximum value and minimum value ; and They are respectively maximum value and minimum value ; and They are respectively maximum value and minimum value ; State signals and definitions for a fuzzy model of path tracking for intelligent vehicles ; for The first derivative; The actual front wheel steering angle control commands executed by the intelligent vehicle; The lateral speed of the intelligent vehicle; For the yaw rate of intelligent vehicles; Refers to lateral error. Refers to the error in heading angle. for The first-order differential, for The first-order differential, and The second-order differential form is as follows: In the formula: The longitudinal speed of the intelligent vehicle; For reference road curvature, To mitigate the uncertainty interference affecting the second-order derivative of the lateral error; To mitigate the uncertainty interference affecting the second-order derivative of the heading angle error; The front wheel steering angle control command is calculated by a type of fuzzy time-delay controller. for The coefficient matrix, for The coefficient matrix, The perturbation is considered a type of fuzzy model for intelligent vehicle path tracking; For ease of calculation, a matrix is ​​defined. Matrices of the same order as follows: Then matrix By using or , or , or , or Replace the matrix respectively In , , , Sure; and The expression is as follows: In the formula: For the quality of intelligent vehicles; The moment of inertia of an intelligent vehicle; and These are the distances from the front and rear axles to the center of gravity, respectively. and These are the lateral stiffness of the front and rear axle tires, respectively. This refers to the actuator time delay.

5. The intelligent vehicle dual-event triggering control method for dual communication networks according to claim 1, characterized in that: The method for designing the adaptive stored threshold function and the dynamic threshold function described in step 3 is as follows: Based on the state signal of the intelligent vehicle path tracking type I fuzzy model obtained in step 2 and the stored historical event trigger signal, design an adaptive stored threshold function; based on the state signal of the intelligent vehicle path tracking type I fuzzy model obtained in step 2 and the current state signal triggered by the CA event trigger condition, design a dynamic threshold function. The adaptive storage-type threshold function is composed of variables. and The components, and their mathematical equation expressions are as follows: In the formula: in, and Both are variables in the adaptive stored threshold function that is the trigger condition for SC events and satisfy the following conditions: and , for The first-order differential, for The first derivative; For variables The weighted trigger-induced error term in the data stores historical event trigger state signals; For variables The weighted trigger-induced error term in the data stores historical event trigger state signals; is the weight matrix of the adaptive storage threshold function, and it is positive definite; All are preset, different constant values ​​and satisfy... ; For the sensor at the current sampling time The status signal; For the first Network-induced latency at the moment of event triggering For the first Network-induced latency at the moment of event triggering For the first Network-induced latency at the moment of event triggering For the first Network-induced latency at the moment of event triggering For the first Network-induced latency at the moment of event triggering The length of the sampling period. Refers to the moment the event is triggered Start of The next sampling time; The SC event triggering condition is in the first The status signal sampled at all times; The SC event triggering condition is in the first The status signal sampled at all times; The SC event triggering condition is in the first The status signal sampled at all times; The SC event triggering condition is in the first The status signal sampled at all times; The SC event triggering condition is in the first The status signal sampled at all times; The triggering condition for the SC event is at the triggering time. The status signal, The triggering condition for the SC event is at the triggering time. The status signal, The triggering condition for the SC event is at the triggering time. The status signal, The triggering condition for the SC event is at the triggering time. The status signal, The triggering condition for the SC event is at the triggering time. The status signal; The SC trigger condition at the current sampling time , No. Triggering time , No. Triggering time , No. Triggering time , No. Triggering time and the Triggering time The weighted average of the state signals; , All of them are set with different weight constants and satisfy the following conditions: , ; It is the arctangent function. To calculate the sign of the norm, Pi; The mathematical equation for the dynamic threshold function is as follows: In the formula: in, The variable in the dynamic threshold function that determines the CA event trigger condition and satisfies , For variables The trigger-induced error term in the middle, for The first-order differential, The triggering condition for the CA event is at the triggering time. Induced error and satisfying ; All are preset, different constant values ​​and satisfy... .

6. The intelligent vehicle dual-event triggering control method for dual communication networks according to claim 1, characterized in that: The method for determining whether the SC event triggering condition and the CA event triggering condition are met in step 4 is as follows: The dual-event triggering module consists of SC event triggering conditions and CA event triggering conditions; the SC event triggering conditions are designed based on the adaptive storage-type threshold function in step 3 and the state signal of the intelligent vehicle path tracking type I fuzzy model calculated in step 2; the CA event triggering conditions are designed based on the dynamic threshold function in step 3 and the state signal of the intelligent vehicle path tracking type I fuzzy model calculated in step 2. The mathematical expression for the SC event triggering condition is as follows: In the formula: Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time. Refers to the first Each event triggers the sampling time; The triggering condition for the SC event is at the triggering time. Induced error, The triggering condition for the SC event is at the triggering time. Induced error, The triggering condition for the SC event is at the triggering time. Induced error, The triggering condition for the SC event is at the triggering time. Induced error, The triggering condition for the SC event is at the triggering time. Induced error; for The weight constants, for The weight constants, for The weight constants, for The weight constants, for The weight constants, , , , and satisfy , , , , , ; The mathematical expression for the CA event triggering condition is as follows: Before each calculation of the front wheel steering angle control command, the Type I fuzzy time-delay controller determines whether the SC event triggering condition is met. If the condition is met, the state signal of the Type I fuzzy model for intelligent vehicle path tracking is updated in the Type I fuzzy time-delay controller to calculate the front wheel steering angle control command. If the condition is not met, no update is made, and the state signal of the Type I fuzzy model for intelligent vehicle path tracking triggered at the previous moment is used and stored in the data cache module.

7. The intelligent vehicle dual-event triggering control method for dual communication networks according to claim 1, characterized in that: The method for solving the front wheel steering angle control command in step 5 is as follows: Based on the state signal of the intelligent vehicle path tracking fuzzy model triggered in step 4, the control instructions of the fuzzy time-delay controller containing 16 sub-controllers are calculated as follows: In the formula: The gain matrix of a type-1 fuzzy time-delay controller is given by the coefficient matrix in a type-1 fuzzy model for path tracking in intelligent vehicles. and The design was obtained.

8. The intelligent vehicle dual-event triggering control method for dual communication networks according to claim 1, characterized in that: The method for performing dual-event triggering control of intelligent vehicles described in step 6 is as follows: The front wheel steering angle control command determined in step 5 is sent to the actuator module to realize the safety control of the intelligent vehicle. Before receiving the front wheel steering angle control command calculated by the type fuzzy time delay controller, the actuator module determines whether the CA event triggering condition in step 4 is met. If it is met, the front wheel steering angle control command calculated by the type fuzzy time delay controller is updated to the actuator module. If it is not met, it is not updated, and the front wheel steering angle control command calculated by the type fuzzy time delay controller at the previous moment is used and stored in the data cache module.

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