Transverse movement safety control method and system for autonomous vehicle

Abstract

The invention discloses a transverse movement safety control method and system for an automatic driving vehicle. The method comprises the following steps: calculating a projection error by acquiring a course error and a transverse error between a controlled vehicle system and a reference path; inputting the projection error into an input channel of a dual-channel event triggering mechanism for sparse updating, and inputting a preset time fuzzy state observer to estimate unmeasurable dynamic data; based on the updated projection error and the estimated value, designing a self-adaptive safety controller through a backstepping method to obtain control input; a security control signal is reconstructed in combination with a spoofing attack model, and re-sparsification is carried out through an output channel of a dual-channel event triggering mechanism; and finally, realizing accurate and safe control on the transverse movement of the vehicle by utilizing the updated control signal. According to the invention, the communication and computing resources are effectively saved, the stability of the system state within the preset time is ensured, and the security of the control system under the network spoofing attack is enhanced.

Description

Technical Field

[0001] This invention belongs to the field of lateral motion control for autonomous vehicles, specifically relating to a method and system for lateral motion safety control of autonomous vehicles. Background Technology

[0002] With the increasing prevalence of automobiles, the automotive industry is accelerating its transformation and upgrading towards electrification, intelligence, and connectivity. Autonomous vehicles are playing an increasingly crucial role in modern transportation systems, greatly facilitating people's daily lives. At the same time, the implementation of smart city concepts is gradually making autonomous vehicles a focus of social attention. It is worth noting that the increased intelligence of vehicles places stringent demands on the communication efficiency between sensors, actuators, and vehicle controllers; coupled with the combined effects of uncertainties in vehicle model parameters, unknown external interference, and malicious network attacks, the performance of autonomous vehicle control systems also faces stringent requirements. Therefore, numerous scholars both domestically and internationally have conducted extensive and in-depth research on lateral motion control technology for autonomous vehicles, achieving a series of important research results.

[0003] In the field of lateral motion control, as a core technology for improving the intelligence and automation level of vehicles, active steering is achieved by adjusting the steering assist motor of the steering system to complete high-precision path tracking tasks. Currently, although many control technologies are used in the lateral motion control systems of autonomous vehicles, existing technologies still have the following problems:

[0004] (1) In the control methods of the lateral motion control system of autonomous vehicles, although the existing technologies can complete the task of high-precision path tracking, most of them do not take into account the consumption and utilization of communication resources during the tracking process. Dual-channel event-triggered control can effectively reduce the consumption of communication resources and greatly improve the utilization of communication resources.

[0005] (2) In existing methods for lateral motion control of autonomous vehicles, most methods are based on the ideal assumption that the state is completely measurable. However, in actual vehicle control scenarios, the key states inside the system are often difficult to obtain directly, requiring the installation of a large number of sensors. At the same time, existing control methods rarely consider achieving the ideal control effect within a preset time.

[0006] (3) Most existing methods for controlling the lateral motion of autonomous vehicles do not consider the impact of deception attacks during vehicle communication. From a practical point of view, vehicles communicate through networks, which means that vehicle communication is easily affected by deception attacks. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for lateral movement safety control of autonomous vehicles, which not only enables the vehicle system to reach stability within a preset time, but also effectively reduces the consumption of communication resources and greatly improves the utilization rate of communication resources.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for lateral movement safety control of autonomous vehicles, applied to the vehicle's lateral movement system, the method comprising the following steps:

[0010] Step 1: In the vehicle lateral motion system, confirm the heading error and lateral error between the controlled vehicle system and the reference path;

[0011] Step 2: Obtain the projection error based on the heading error and lateral error obtained in Step 1;

[0012] Step 3: Input the projection error obtained in Step 2 into the dual-channel event triggering mechanism to obtain the projection error after triggering threshold update;

[0013] Step 4: Input the updated projection error obtained in Step 3 into the preset time fuzzy state observer to obtain the estimated value of the dynamic data;

[0014] Step 5: Design an adaptive safety controller based on the estimated values ​​of the dynamic data obtained in Step 4, and obtain the adaptive control input;

[0015] Step 6: Reconstruct the adaptive control input obtained in Step 5 based on the deception attack model to obtain the security control signal;

[0016] Step 7: Input the safety control signal obtained in Step 6 into the dual-channel event triggering mechanism to obtain the control signal after updating the trigger threshold;

[0017] Step 8: Control the target system based on the updated control signal from Step 7 to achieve lateral motion control of the vehicle.

[0018] Furthermore, in step 1, the vehicle lateral motion system model is constructed through the following steps:

[0019] Based on dynamics theory, a two-degree-of-freedom model of the vehicle is constructed:

[0020]

[0021] Among them, variables Indicates the vehicle's slip angle. and These represent the lateral forces of the front and rear tires, respectively. Indicates longitudinal velocity. This indicates the yaw rate of the vehicle. Indicates the yaw moment of inertia; Indicates vehicle mass. This represents the distance from the center of gravity to the front axle. This indicates the distance from the center of mass to the rear axle;

[0022] Based on the two-degree-of-freedom model of the vehicle, the following vehicle kinematic model is constructed:

[0023]

[0024] Among them, variables Indicates heading error. Indicates the vehicle's heading. Indicates lateral velocity. Indicates lateral error. Indicates projection error. Indicates a constant projection distance. Indicates the distance along the reference path. Indicates the heading of the reference path;

[0025] Based on the above vehicle kinematics model, the nonlinear vehicle dynamics model is obtained as follows:

[0026]

[0027] Among them, defining variables , and , Indicates the steering angle of the front wheels. and These represent unknown disturbances in the front and rear axles, respectively. Indicates the curvature of the road surface. Indicates the front wheel slip angle. Indicates a constant projection distance. This indicates the lateral stiffness of the front axle tires. Indicates the lateral stiffness of the rear axle tires. Indicates the adhesion coefficient;

[0028] To stabilize the system, the unknown disturbance is transformed into the following form based on the nonlinear vehicle dynamics model:

[0029]

[0030] in, , To represent unknown external disturbances, based on the above derivation, the nonlinear vehicle dynamics model is transformed into the following form:

[0031]

[0032] To address the issue that vehicle dynamics models are insufficient to fully describe a vehicle's response to steering inputs, the following steering system model is established:

[0033]

[0034] Among them, variables Indicates the steering resistance torque. Indicates the coefficient of friction. Indicates the torque of the steering motor; For the steering angle of the front wheels yes The first derivative with respect to time, yes The second derivative with respect to time, Indicates the gear ratio of the steering system. Indicates the gear ratio of the steering motor;

[0035] The steering system model is then rewritten as follows:

[0036]

[0037] in, Indicates the damping of the steering system. This represents the equivalent inertia of the steering system;

[0038] In actual use, and Difficult to measure; therefore, the above-mentioned transformation system model... Rewritten as:

[0039]

[0040] in, , This indicates an unknown external disturbance;

[0041] Based on the above analysis, the rewritten steering system model can be transformed into the following form:

[0042]

[0043] Define variables , , ;

[0044] The lateral motion of the vehicle is determined by the input of the steering system, therefore a strict feedback system is used, which is combined with the steering system and the vehicle dynamics model as follows:

[0045]

[0046] Define intermediate variables , , and , Given constants, for The first derivative with respect to time, This indicates system output containing a deception attack. This represents the steering angular velocity of the front wheels.

[0047] Furthermore, in step 3, a dual-channel event triggering mechanism is designed based on the dual-channel event triggering control theory; the dual-channel event triggering mechanism is as follows:

[0048]

[0049] in, Indicates time Updated input signal, Indicates time, Indicates the update time. This represents a design parameter greater than 0. Indicates in The system output is constantly updated. This indicates the system output that has been updated via an event-triggered mechanism.

[0050] Furthermore, in step 4, the preset temporal fuzzy state observer includes:

[0051] Based on the vehicle's lateral motion system, a model transformation is performed to obtain a dynamic model, which includes unknown dynamic parameters; the characterization formulas of the dynamic model include:

[0052]

[0053] Define continuous functions and , , , , These are unknown parameters; therefore, nonlinear functions and It is completely unknown;

[0054] Based on the universal approximation principle of fuzzy logic systems, unknown dynamic parameters are solved to obtain a fuzzy state observer at a predetermined time; the characterization formula of the fuzzy state observer at the predetermined time includes:

[0055]

[0056] Among them, variables express The estimated value; It is a preset time scale function. , It is an integer. It is a positive constant about the preset time, therefore, It is monotonically increasing and ; , , , Indicates observation gain. Indicates system output The estimated value, Indicates parameters The estimated value, Representing fuzzy basis functions;

[0057] The formulas for characterizing dynamic parameters include:

[0058]

[0059]

[0060] in, It is an ideal weight vector. It is the approximation error, and satisfies , It is a normal number.

[0061] Furthermore, step 5 includes:

[0062] Step 51: Confirm and acquire the data from the preset time fuzzy state observer;

[0063] Step 52: Obtain the virtual control input based on the projection error, observer data, and preset time stability theory;

[0064] Step 53: Obtain the adaptive law of the target system based on the adaptive virtual control input and the observer data;

[0065] Step 54: Obtain adaptive control input based on the virtual control input and the adaptive law.

[0066] Furthermore, step 6 includes:

[0067] Step 61: Based on the projection error of the system, the observer data, and the preset time stability theory, obtain the error vector;

[0068] Step 62: Construct a Lyapunov function based on the error vector;

[0069] Step 63: Obtain the adaptive virtual control input based on the error vector and the Lyapunov function.

[0070] Furthermore, in step 7, the system control signal is obtained based on the adaptive control input, and the safety controller is designed using the backstepping method, defining the following coordinate transformation:

[0071]

[0072] in, For virtual controllers, It is the expected system input. ;

[0073] The system control signal is characterized by the following formulas:

[0074]

[0075] in, This indicates a design parameter that is greater than 0. Indicates virtual controller The derivative with respect to time;

[0076] Construct the following deception attack model:

[0077]

[0078] in, , This is a multiplicative attack signal. This is an additive attack signal;

[0079] Based on the deception attack model, system security control signals are obtained, and the characterization formulas for these signals include:

[0080] .

[0081] Furthermore, step 8 includes:

[0082] Step 81: Input the safety control signal into the dual-channel event triggering mechanism to obtain the control signal after updating the trigger threshold;

[0083] Step 82: Based on the dual-channel event triggering control theory, design a dual-channel event triggering mechanism; the dual-channel event triggering mechanism includes:

[0084]

[0085] in, This indicates the control signal after being updated by the event-triggered mechanism. Indicates in The control signal is updated constantly. This indicates a design parameter that is greater than 0;

[0086] Based on the designed dual-channel event triggering mechanism and system safety control signals, an updated control signal is obtained after meeting the triggering threshold. The updated control signal includes:

[0087]

[0088] A lateral movement safety control system for autonomous vehicles, applied to the vehicle's lateral movement system, comprising:

[0089] The heading error and lateral error acquisition module is used to confirm the heading error and lateral error between the controlled vehicle system and the reference path in the vehicle lateral motion system.

[0090] The projection error acquisition module is used to acquire the projection error based on the heading error and lateral error between the controlled vehicle system and the reference path.

[0091] The preset time fuzzy state observer module is used to input the updated projection error of the system into the preset time fuzzy observer to obtain the estimated value of the dynamic data;

[0092] The backstepping technique module is used to design an adaptive safety controller based on the estimated values ​​of the updated projection error and dynamic data of the system, and to obtain the adaptive control input.

[0093] The deception attack module is used to obtain security control signals based on the deception attack model and adaptive control input.

[0094] The dual-channel event triggering module is used to input the system's projection error and safety control signal into the dual-channel event triggering mechanism to obtain the updated projection error and control signal after triggering thresholds.

[0095] The output control module is used to control the target system based on the updated control signal to achieve lateral motion control of the vehicle.

[0096] Beneficial Effects: Compared with existing technologies, the lateral motion safety control method and system for autonomous vehicles of the present invention confirms the heading and lateral errors between the controlled vehicle system and the reference path, and calculates the projection error between the controlled vehicle system and the reference path; inputs the system's projection error into a preset time fuzzy state observer and a dual-channel event triggering mechanism to obtain an estimated value of the dynamic data and an updated projection error after triggering a threshold; designs an adaptive controller based on the updated projection error and the estimated value of the dynamic data to obtain adaptive control input; obtains a safety control signal based on a deception attack model and the adaptive control input, and inputs the safety control signal into the event triggering mechanism to obtain a control signal updated after meeting the triggering threshold. The present invention, through preset time theory, establishes a preset time fuzzy state observer and a preset time vehicle lateral motion safety controller with a dual-channel event triggering mechanism, solving the technical problems of unpredictable system states in actual systems, decreased control performance due to deception attacks, and high resource consumption during system communication. Attached Figure Description

[0097] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0098] Figure 1 A flowchart illustrating the steps of the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention;

[0099] Figure 2 This is a flowchart illustrating the steps of the preset vehicle lateral motion system in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention.

[0100] Figure 3 This is a flowchart illustrating the steps of setting up a fuzzy observer in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention.

[0101] Figure 4 A flowchart illustrating the steps of obtaining adaptive control input in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention;

[0102] Figure 5 This is a flowchart illustrating the steps for obtaining virtual control input in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention.

[0103] Figure 6 This is a flowchart illustrating the steps of acquiring safety control signals in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention.

[0104] Figure 7 This is a module connection diagram of the preset time fuzzy adaptive dual-channel event-triggered safety control system for the vehicle lateral motion system provided in an embodiment of the present invention;

[0105] Figure 8 A structural block diagram of a vehicle lateral motion control method;

[0106] Figure 9 Flowchart of the vehicle lateral motion control method

[0107] Reference numerals: 10, Heading error and lateral error acquisition module; 20, Projection error acquisition module; 30, Preset time fuzzy state observer module; 40, Backstepping technique module; 50, Spoofing attack module; 60, Dual-channel event triggering module; 70, Output control module. Detailed Implementation

[0108] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0109] With the increasing prevalence of automobiles, the automotive industry is accelerating its transformation and upgrading towards electrification, intelligence, and connectivity. The role of automobiles in modern transportation systems is becoming increasingly crucial, greatly facilitating people's daily lives. Simultaneously, the implementation and promotion of smart city concepts have propelled autonomous vehicles into the focus of social attention. It is worth noting that the increased intelligence of vehicles places stringent demands on the communication efficiency between sensors, actuators, and vehicle controllers. Furthermore, the combined effects of uncertainties in vehicle model parameters, unknown external interference, and malicious cyberattacks place equally stringent demands on the performance of autonomous vehicle control systems. Therefore, numerous scholars both domestically and internationally have conducted extensive and in-depth research on lateral motion control technology for autonomous vehicles, achieving a series of research results.

[0110] In the field of lateral motion control, as a core technology for improving the intelligence and automation level of vehicles, lateral motion control typically achieves active steering by regulating the steering assist motor of the steering system, thereby completing high-precision path tracking tasks. While existing technologies in vehicle lateral motion control systems can accomplish high-precision path tracking, most do not consider the consumption and utilization of communication resources during the tracking process. Furthermore, most existing vehicle lateral motion control methods are based on the ideal assumption of measurable states, but in actual vehicle control scenarios, critical internal states of the system are often difficult to obtain directly, and most methods do not consider achieving the desired effect within a preset time. Currently, there is a lack of relevant algorithmic inventions for preset-time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control. Therefore, the vehicle lateral motion safety control method of this invention has significant application value and practical significance.

[0111] The vehicle lateral motion preset time safety control method provided in this invention confirms the heading and lateral errors between the controlled vehicle system and the reference path, and calculates the projection error between the controlled vehicle system and the reference path. The system's projection error is input into a preset time fuzzy state observer and a dual-channel event triggering mechanism to obtain an estimated value of the dynamic data and an updated projection error after triggering a threshold. An adaptive controller is designed based on the updated projection error and the estimated value of the dynamic data to obtain adaptive control input. A safety control signal is obtained based on a deception attack model and the adaptive control input, and this safety control signal is input into the dual-channel event triggering mechanism to obtain a control signal updated after meeting the triggering threshold. This invention, through preset time theory, establishes a preset time fuzzy state observer and a preset time vehicle lateral motion safety controller with a dual-channel event triggering mechanism, solving the technical problems of unpredictable system states in actual systems, decreased control performance due to deception attacks, and high resource consumption during system communication.

[0112] In some embodiments, please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention. The preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided by this invention is applied to a vehicle lateral motion system and is specifically implemented through steps 100 to 800:

[0113] Step 100: In the vehicle lateral motion system, confirm the heading error and lateral error between the controlled vehicle system and the reference path;

[0114] In some embodiments, please refer to Figure 2 , Figure 2This is a flowchart illustrating the steps of setting up a vehicle lateral motion system in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention. The method for setting up a vehicle lateral motion system in this invention is specifically implemented through steps 110 to 120:

[0115] Step 110: Based on dynamics theory, construct a two-degree-of-freedom model of the vehicle;

[0116] In some embodiments, the two-degree-of-freedom model representation formula for a vehicle includes:

[0117]

[0118] Among them, variables Indicates the vehicle's slip angle. and These represent the lateral forces of the front and rear tires, respectively. Indicates longitudinal velocity. This indicates the yaw rate of the vehicle. Indicates the yaw moment of inertia; Indicates vehicle mass. This represents the distance from the center of gravity to the front axle. This indicates the distance from the center of mass to the rear axle.

[0119] Step 120: Based on the two-degree-of-freedom model of the vehicle, construct a nonlinear vehicle kinematic model;

[0120] In some embodiments, the representation formula of the vehicle kinematics model includes:

[0121]

[0122] Among them, variables Indicates heading error. Indicates the vehicle's heading. Indicates lateral velocity. Indicates lateral error. Indicates projection error. Indicates a constant projection distance. Indicates the distance along the reference path. Indicates the heading of the reference path.

[0123] Based on the above vehicle kinematics model, the nonlinear vehicle dynamics model can be obtained as follows:

[0124]

[0125] Among them, defining variables , and , Indicates the steering angle of the front wheels. and These represent unknown disturbances in the front and rear axles, respectively. Indicates the curvature of the road surface. Indicates the front wheel slip angle. Indicates a constant projection distance. This indicates the lateral stiffness of the front axle tires. Indicates the lateral stiffness of the rear axle tires. This represents the adhesion coefficient.

[0126] To stabilize the system, based on the nonlinear vehicle dynamics model, the unknown disturbance can be transformed into the following form:

[0127]

[0128] in, , This represents an unknown external disturbance. Based on the above derivation, the nonlinear vehicle dynamics model can be transformed into the following form:

[0129]

[0130] Step 130: Based on the problem that the vehicle dynamics model is insufficient to fully describe the vehicle's response to steering input, establish a steering system model;

[0131] In some embodiments, the characterization formula for the steering system model includes:

[0132]

[0133] Among them, variables Indicates the steering resistance torque. Indicates the coefficient of friction. Indicates the torque of the steering motor; For the steering angle of the front wheels yes The first derivative with respect to time, yes The second derivative with respect to time, Indicates the gear ratio of the steering system. This indicates the gear ratio of the steering motor.

[0134] The steering system model can then be rewritten as:

[0135]

[0136] in, Indicates the damping of the steering system. This represents the equivalent inertia of the steering system;

[0137] In actual use, and Difficult to measure. Therefore, the above-mentioned transformation system model... Rewritten as:

[0138]

[0139] Among them, variables , This indicates an unknown external disturbance.

[0140] Based on the above analysis, the rewritten steering system model can be transformed into the following form:

[0141]

[0142] Define variables , and , Indicates the damping of the steering system. Indicates the gear ratio of the steering system. This represents the equivalent inertia of the steering system. This indicates the gear ratio of the steering motor.

[0143] Step 140: The vehicle lateral motion system is obtained by combining a rigorous feedback system with the steering system and a vehicle dynamics model;

[0144] In some embodiments, the feedback system is combined with the steering system and the vehicle dynamics model as follows:

[0145]

[0146] Define intermediate variables , , and , Given constants, for The first derivative with respect to time, This indicates system output containing a deception attack. This represents the steering angular velocity of the front wheels.

[0147] Step 200: Obtain the projection error based on the heading error and lateral error between the controlled vehicle system and the reference path;

[0148] Step 300: Input the system's projection error into the dual-channel event triggering mechanism to obtain the updated projection error after triggering the threshold.

[0149] In some embodiments, the formula for representing the projection error after updating the trigger threshold is:

[0150]

[0151] in, Indicates time Updated input signal, Indicates time, Indicates the update time. This represents a design parameter greater than 0. Indicates in The system output is constantly updated. This indicates the system output that has been updated via an event-triggered mechanism.

[0152] Step 400: Input the updated projection error into the preset time fuzzy observer to obtain the estimated value of the dynamic data.

[0153] In some embodiments, please refer to Figure 3 , Figure 3 This is a flowchart illustrating the steps of setting a pre-set fuzzy observer in the pre-set time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention. The method of setting a pre-set fuzzy observer in this invention is specifically implemented through steps 410 to 420:

[0154] Step 410: Based on the vehicle's lateral motion system, perform model conversion to obtain a dynamic model, which includes unknown dynamic parameters;

[0155] In some embodiments, the characterization formula of the dynamic model includes:

[0156]

[0157] Define continuous functions and .because , , and Since the parameters are unknown, it is a nonlinear function. and It is completely unknown.

[0158] Step 420: Based on the universal approximation principle of fuzzy logic systems, solve for the unknown dynamic parameters and obtain the fuzzy state observer at a predetermined time.

[0159] In some embodiments, the characterization formula for the predetermined time fuzzy state observer includes:

[0160]

[0161] Among them, variables express The estimated value; It is a preset time scale function. , It is an integer. It is a positive constant about the preset time, therefore, It is monotonically increasing and ; , , , Indicates observation gain. Indicates system output The estimated value, Indicates parameters The estimated value, This represents a fuzzy basis function.

[0162] The formulas for characterizing dynamic parameters include:

[0163]

[0164]

[0165] Among them, variables It is an ideal weight vector. It is the approximation error, and satisfies , It is a normal number.

[0166] Step 500: Design an adaptive controller based on the estimated values ​​of the dynamic data and obtain the adaptive control input;

[0167] In some embodiments, please refer to Figure 4 , Figure 4 This is a flowchart illustrating the steps of obtaining adaptive control input in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention. Specifically, the method for obtaining adaptive control input in this invention is implemented through steps 510 to 540:

[0168] Step 510: Confirm and acquire the data from the preset time fuzzy state observer;

[0169] Step 520: Obtain the virtual control input based on the projection error, observer data, and preset time stability theory;

[0170] In some embodiments, please refer to Figure 5 , Figure 5 This is a flowchart illustrating the steps for obtaining virtual control input in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention. The method for obtaining virtual control input in this invention is specifically implemented through steps 511 to 513:

[0171] Step 511: Based on the projection error of the system, the observer data, and the preset time stability theory, obtain the error vector;

[0172] Step 512: Construct the Lyapunov function based on the error vector;

[0173] Step 513: Obtain the adaptive virtual control input based on the error vector and the Lyapunov function.

[0174] Step 530: Obtain the adaptive law of the target system based on the virtual control input and the observer data;

[0175] Step 540: Obtain adaptive control input based on the virtual control input and the adaptive law.

[0176] Step 600: Obtain the security control signal based on the deception attack model and adaptive control input;

[0177] In some embodiments, please refer to Figure 6 , Figure 6 This is a flowchart illustrating the steps for obtaining safety control signals in the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided in this embodiment of the invention. The method for obtaining safety control signals in this invention is specifically implemented through steps 610 to 620:

[0178] Step 610: Obtain the control signal based on the adaptive control input;

[0179] The safety controller is designed using the backstepping method, and the following coordinate transformation is defined:

[0180]

[0181] in, For virtual controllers, It is the expected system input. ;

[0182] In some embodiments, the characterization formula of the system control signal includes:

[0183]

[0184] in, This indicates a design parameter that is greater than 0. Indicates virtual controller The derivative with respect to time;

[0185] Step 620: Obtain security control signals based on the deception attack model and control signals;

[0186] In some embodiments, the representation formula of the deception attack model includes:

[0187]

[0188] in, , This is a multiplicative attack signal. This is an additive attack signal.

[0189] The characterization formula for the system safety control signal includes:

[0190]

[0191] Step 700: Input the safety control signal into the event triggering mechanism to obtain the control signal after updating the trigger threshold;

[0192] In some embodiments, the dual-channel event triggering mechanism includes:

[0193]

[0194] in, This indicates the control signal after being updated by the event-triggered mechanism. Indicates in The control signal is updated constantly. This indicates a design parameter that is greater than 0.

[0195] Based on the designed dual-channel event triggering mechanism and safety control signal, an updated control signal is obtained after meeting the triggering threshold. The updated control signal includes:

[0196]

[0197] Step 800: Control the target system based on the updated control signal to achieve lateral motion control of the vehicle.

[0198] Understandably, the vehicle lateral movement preset time safety control method provided in this embodiment of the invention confirms the heading error and lateral error between the controlled vehicle system and the reference path, and calculates the projection error between the controlled vehicle system and the reference path; inputs the system's projection error into a preset time fuzzy state observer and a dual-channel event triggering mechanism to obtain an estimated value of the dynamic data and an updated projection error after triggering a threshold; designs an adaptive controller based on the updated projection error and the estimated value of the dynamic data to obtain adaptive control input; obtains a safety control signal based on the deception attack model and the adaptive control input, and inputs the safety control signal into the dual-channel event triggering mechanism to obtain a control signal updated after meeting the triggering threshold. This invention, through preset time theory, establishes a preset time fuzzy state observer and a preset time vehicle lateral movement safety controller with a dual-channel event triggering mechanism, solving the technical problems of unpredictable system states in actual systems, decreased control performance due to deception attacks, and high resource consumption during system communication.

[0199] For example, please refer to Figure 8 and Figure 9 , Figure 8 This is a block diagram of the vehicle lateral motion control method. Figure 9 The flowchart of the vehicle lateral motion control method is shown below. An example of the modeling steps of the preset time fuzzy adaptive dual-channel event-triggered vehicle lateral motion safety control method provided by the embodiments of the present invention is as follows:

[0200] 1. Establish a lateral motion model for the vehicle.

[0201] Based on dynamics theory, a two-degree-of-freedom model of the vehicle is constructed:

[0202] (1)

[0203] Among them, variables Indicates the vehicle's slip angle. and These represent the lateral forces of the front and rear tires, respectively. Indicates longitudinal velocity. This indicates the yaw rate of the vehicle. Indicates the yaw moment of inertia; Indicates vehicle mass. This represents the distance from the center of gravity to the front axle. This indicates the distance from the center of mass to the rear axle.

[0204] Based on the two-degree-of-freedom model of the vehicle, the following vehicle kinematic model is constructed:

[0205] (2)

[0206] Among them, variables Indicates heading error. Indicates the vehicle's heading. Indicates lateral velocity. Indicates lateral error. Indicates projection error. Indicates a constant projection distance. Indicates the distance along the reference path. Indicates the heading of the reference path.

[0207] Based on the above vehicle kinematics model, the nonlinear vehicle dynamics model can be obtained as follows:

[0208] (3)

[0209] Among them, defining variables , and , Indicates the steering angle of the front wheels. and These represent unknown disturbances in the front and rear axles, respectively. Indicates the curvature of the road surface. Indicates the front wheel slip angle. Indicates a constant projection distance. This indicates the lateral stiffness of the front axle tires. Indicates the lateral stiffness of the rear axle tires. This represents the adhesion coefficient.

[0210] To stabilize the system, based on the nonlinear vehicle dynamics model, the unknown disturbance can be transformed into the following form:

[0211] (4)

[0212] in, , This represents an unknown external disturbance. Based on the above derivation, the nonlinear vehicle dynamics model can be transformed into the following form:

[0213] (5)

[0214] To address the issue that vehicle dynamics models are insufficient to fully describe a vehicle's response to steering inputs, the following steering system model is established:

[0215] (6)

[0216] Among them, variables Indicates the steering resistance torque. Indicates the coefficient of friction. Indicates the torque of the steering motor; For the steering angle of the front wheels yes The first derivative with respect to time, yes The second derivative with respect to time, Indicates the gear ratio of the steering system. This indicates the gear ratio of the steering motor.

[0217] The steering system model can then be rewritten as:

[0218] (7)

[0219] in, Indicates the damping of the steering system. This represents the equivalent inertia of the steering system;

[0220] In actual use, and Difficult to measure. Therefore, the above-mentioned transformation system model... Rewritten as:

[0221] (8)

[0222] in, , This indicates an unknown external disturbance.

[0223] Based on the above analysis, the rewritten steering system model can be transformed into the following form:

[0224] (9)

[0225] Define variables , , , Indicates the damping of the steering system. Indicates the gear ratio of the steering system. This represents the equivalent inertia of the steering system. This indicates the gear ratio of the steering motor.

[0226] The lateral motion of the vehicle is determined by the input of the steering system, therefore a rigorous feedback system is used, which can be combined with the steering system and the vehicle dynamics model as follows:

[0227] (10)

[0228] Define intermediate variables , , and , Given constants, for The first derivative with respect to time, This indicates system output containing a deception attack. This represents the steering angular velocity of the front wheels.

[0229] 2. Design a deception attack model

[0230] (11)

[0231] in, , This is a multiplicative attack signal. This is an additive attack signal.

[0232] 3. Design a dual-channel event triggering mechanism

[0233] (12)

[0234] in, Indicates time Updated input signal, Indicates time, Indicates the update time. This represents a design parameter greater than 0. Indicates in The system output is constantly updated. This represents the system output updated via an event-triggered mechanism. This indicates the control signal after being updated by the event-triggered mechanism. Indicates in The control signal is updated constantly. This indicates a design parameter that is greater than 0.

[0235] 4. Design a pre-defined temporally fuzzy state observer

[0236] First, define and for:

[0237] (13)

[0238] (14)

[0239] Based on formulas (13) and (14), formula (10) can be rewritten as:

[0240] (15)

[0241] Among them, variables , , , These are unknown parameters. Therefore, it is a nonlinear function. and It is completely unknown. Using fuzzy logic systems... and Processing:

[0242] (16)

[0243] (17)

[0244] Among them, variables It is an ideal weight vector. It is the approximation error, and satisfies , It is a normal number.

[0245] Therefore, the following pre-defined temporally ambiguous state observer is constructed:

[0246] (18)

[0247] Among them, variables express The estimated value; It is a preset time scale function. , It is an integer. It is a positive constant about the preset time, therefore, It is monotonically increasing and ; , , , Indicates observation gain. Indicates system output The estimated value, Indicates parameters The estimated value, This represents a fuzzy basis function.

[0248] 5. Design a time-fuzzy vehicle lateral motion controller.

[0249] Based on (15), the safety controller will be designed using the reverse step method.

[0250] Define the following coordinate transformation:

[0251] (19)

[0252] in, For virtual controllers, It is the expected system input. .

[0253] Step 1: According to (18), the observation error can be obtained as follows: To achieve observation at a predetermined time, define Therefore, we have:

[0254] (20)

[0255] in,

[0256] , , , , , yes The estimation error.

[0257] because It is a Hurwitz matrix, a choice matrix. Then there exists a positive definite matrix. , so that:

[0258] (twenty one)

[0259] The Lyapunov function is chosen as follows:

[0260] (twenty two)

[0261] (22) time derivative The derivation is as follows:

[0262] (twenty three)

[0263] According to Young's inequality, we can obtain:

[0264] (twenty four)

[0265] (25)

[0266] Substituting (24) and (25) into (23), we get:

[0267] (26)

[0268] in,

[0269]

[0270]

[0271] Step 2: Based on (15) and (19), The time derivative is:

[0272] (27)

[0273] Choose the Lyapunov function for:

[0274] (28)

[0275] Combining the time derivatives of (27) and (28) The derivation is as follows:

[0276] (29)

[0277] According to Young's inequality, we can obtain:

[0278] (30)

[0279] An adaptive virtual control law can be designed as follows:

[0280] (31)

[0281] in, These are design parameters.

[0282] Then, substituting (30) and (31) into (29) yields:

[0283] (32)

[0284] Step 3: Based on (15) and (19), The time derivative is:

[0285] (33)

[0286] Choose the Lyapunov function for:

[0287] (34)

[0288] Combining the time derivatives of (33) and (34) The derivation is as follows:

[0289] (35)

[0290] According to Young's inequality, we can obtain:

[0291] (36)

[0292] Adaptive virtual control laws and adaptive laws can be designed as follows:

[0293] (37)

[0294] (38)

[0295] in, , These are design parameters.

[0296] Then, substituting (36)-(38) into (35) yields:

[0297] (39)

[0298] Step 4: Based on (15) and (19), The time derivative is:

[0299] (40)

[0300] Choose the Lyapunov function for:

[0301] (41)

[0302] Combining the time derivatives of (40) and (41) The derivation is as follows:

[0303] (42)

[0304] An adaptive virtual control law can be designed as follows:

[0305] (43)

[0306] in, These are design parameters.

[0307] Then, substituting (43) into (42) yields:

[0308] (44)

[0309] Step 5: Based on (15) and (19), The time derivative is:

[0310] (45)

[0311] Choose the Lyapunov function for:

[0312] (46)

[0313] Combining (12) and (45), the time derivative of (46) The derivation is as follows:

[0314] (47)

[0315] According to Young's inequality, we can obtain:

[0316] (48)

[0317] The adaptive safety actual control law and adaptive law can be designed as follows:

[0318] (49)

[0319] (50)

[0320] in, , These are design parameters. Then, substituting (48)-(50) into (47) yields:

[0321] (51)

[0322] According to Young's inequality, we can obtain:

[0323] (52)

[0324] Substituting (52) into (51) yields:

[0325] (53)

[0326] Based on (26) and (53), choose the Lyapunov function. for:

[0327] (55)

[0328] Then, the time derivative of (55) The derivation is as follows:

[0329] (56)

[0330] Accordingly, embodiments of the present invention also provide a preset time-fuzzy adaptive dual-channel event-triggered safety control system for the vehicle's lateral motion system; please refer to [link to relevant documentation]. Figure 7 , Figure 7 This is a module connection diagram of a preset time fuzzy adaptive dual-channel event-triggered safety control system for a vehicle lateral motion system provided in an embodiment of the present invention. The preset time fuzzy adaptive dual-channel event-triggered safety control system for a vehicle lateral motion system provided in an embodiment of the present invention includes:

[0331] The heading error and lateral error acquisition module 10 is used to confirm the heading error and lateral error between the controlled vehicle system and the reference path in the vehicle lateral motion system.

[0332] The projection error acquisition module 20 is used to acquire the projection error based on the heading error and lateral error between the controlled vehicle system and the reference path.

[0333] The preset time fuzzy state observer module 30 is used to input the updated projection error of the system into the preset time fuzzy observer to obtain the estimated value of the dynamic data;

[0334] The backstepping technique module 40 is used to design an adaptive controller based on the estimated values ​​of the updated projection error and dynamic data of the system, and to obtain the adaptive control input.

[0335] The deception attack module 50 is used to obtain a security control signal based on the deception attack model and adaptive control input.

[0336] The dual-channel event triggering module 60 is used to input the system's projection error and safety control signal into the dual-channel event triggering mechanism to obtain the control signal after updating the trigger threshold;

[0337] The output control module 70 is used to control the target system based on the updated control signal to achieve lateral motion control of the vehicle.

[0338] In some embodiments, the projection error acquisition module is specifically used for:

[0339] Based on dynamics theory, a two-degree-of-freedom model of the vehicle is constructed:

[0340]

[0341] Among them, variables Indicates the vehicle's slip angle. and These represent the lateral forces of the front and rear tires, respectively. Indicates longitudinal velocity. This indicates the yaw rate of the vehicle. Indicates the yaw moment of inertia; Indicates vehicle mass. This represents the distance from the center of gravity to the front axle. This indicates the distance from the center of mass to the rear axle;

[0342] Based on the two-degree-of-freedom model of the vehicle, the following vehicle kinematic model is constructed:

[0343]

[0344] Among them, variables Indicates heading error. Indicates the vehicle's heading. Indicates lateral velocity. Indicates lateral error. Indicates projection error. Indicates a constant projection distance. Indicates the distance along the reference path. Indicates the heading of the reference path;

[0345] Based on the above vehicle kinematics model, the nonlinear vehicle dynamics model can be obtained as follows:

[0346]

[0347] Among them, defining variables , and , Indicates the steering angle of the front wheels. and These represent unknown disturbances in the front and rear axles, respectively. Indicates the curvature of the road surface. Indicates the front wheel slip angle. Indicates a constant projection distance. This indicates the lateral stiffness of the front axle tires. Indicates the lateral stiffness of the rear axle tires. This represents the adhesion coefficient.

[0348] To stabilize the system, based on the nonlinear vehicle dynamics model, the unknown disturbance can be transformed into the following form:

[0349]

[0350] in, , To represent unknown external disturbances, based on the above derivation, the nonlinear vehicle dynamics model can be transformed into the following form:

[0351]

[0352] To address the issue that vehicle dynamics models are insufficient to fully describe a vehicle's response to steering inputs, the following steering system model is established:

[0353]

[0354] Among them, variables Indicates the steering resistance torque. Indicates the coefficient of friction. Indicates the torque of the steering motor; For the steering angle of the front wheels yes The first derivative with respect to time, yes The second derivative with respect to time, Indicates the gear ratio of the steering system. Indicates the gear ratio of the steering motor;

[0355] The steering system model can then be rewritten as:

[0356]

[0357] in, Indicates the damping of the steering system. This represents the equivalent inertia of the steering system;

[0358] In actual use, and Difficult to measure. Therefore, the above-mentioned transformation system model... Rewritten as:

[0359]

[0360] in, , This indicates an unknown external disturbance.

[0361] Based on the above analysis, the rewritten steering system model can be transformed into the following form:

[0362]

[0363] Define variables , , ;

[0364] The lateral motion of the vehicle is determined by the input of the steering system, therefore a rigorous feedback system is used, which can be combined with the steering system and the vehicle dynamics model as follows:

[0365]

[0366] Define intermediate variables , , and , Given constants, for The first derivative with respect to time, This indicates system output containing a deception attack. This represents the steering angular velocity of the front wheels.

[0367] In some embodiments, the preset time fuzzy state observer module is specifically used for:

[0368] Based on the vehicle's lateral motion system, a model transformation is performed to obtain a dynamic model, which includes unknown dynamic parameters; the characterization formulas of the dynamic model include:

[0369]

[0370] Define continuous functions , , , , , These are unknown parameters. Therefore, it is a nonlinear function. and It is completely unknown.

[0371] Based on the universal approximation principle of fuzzy logic systems, unknown dynamic parameters are solved to obtain a fuzzy state observer at a predetermined time; the characterization formula of the fuzzy state observer at the predetermined time includes:

[0372]

[0373] Among them, variables express The estimated value; It is a preset time scale function. , It is an integer. It is a positive constant about the preset time, therefore, It is monotonically increasing and ; , , , Indicates observation gain. Indicates system output The estimated value, Indicates parameters The estimated value, This represents a fuzzy basis function.

[0374] The formulas for characterizing dynamic parameters include:

[0375]

[0376]

[0377] in, It is an ideal weight vector. It is the approximation error, and satisfies , It is a normal number.

[0378] In some embodiments, the backstepping technique module is specifically used for:

[0379] Confirm and acquire the data from the preset time fuzzy state observer;

[0380] The virtual control input is obtained based on the projection error, observer data, and the preset time stability theory.

[0381] The adaptive law of the target system is obtained based on the virtual control input and the observer data;

[0382] The adaptive control input is obtained based on the virtual control input and the adaptive law.

[0383] In some embodiments, the step of obtaining virtual control input based on the projection error includes:

[0384] Based on the system's projection error, observer data, and a preset time stability theory, an error vector is obtained.

[0385] Construct a Lyapunov function based on the error vector;

[0386] The adaptive virtual control input is obtained based on the error vector and the Lyapunov function.

[0387] In some embodiments, the deception attack module is specifically used for:

[0388] Construct the following deception attack model:

[0389]

[0390] in, , This is a multiplicative attack signal. This is an additive attack signal.

[0391] In some embodiments, the dual-channel event triggering module is specifically used for:

[0392] The dual-channel event triggering mechanism is designed as follows:

[0393]

[0394] in, Indicates time Updated input signal, Indicates time, Indicates the update time. This represents a design parameter greater than 0. Indicates in The system output is constantly updated. This represents the system output updated via an event-triggered mechanism. This indicates the control signal after being updated by the event-triggered mechanism. Indicates in The control signal is updated constantly. This represents a design parameter greater than 0. In some embodiments, the output control module is specifically used for:

[0395] Based on the designed dual-channel event triggering mechanism and system safety control signals, an updated control signal is obtained after meeting the triggering threshold. The updated control signal includes:

[0396]

[0397] The present invention has provided a detailed description of the preset time fuzzy adaptive dual-channel event-triggered safety control method and system for the vehicle lateral motion system provided in the embodiments of the present invention. Specific examples have been used in the present invention to illustrate the principle and implementation of the present invention. The above description of the embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for lateral movement safety control of autonomous vehicles, characterized in that: Applied to a vehicle lateral movement system, the method includes the following steps: Step 1: In the vehicle lateral motion system, confirm the heading error and lateral error between the controlled vehicle system and the reference path; Step 2: Obtain the projection error based on the heading error and lateral error obtained in Step 1; Step 3: Input the projection error obtained in Step 2 into the dual-channel event triggering mechanism to obtain the projection error after triggering threshold update; Step 4: Input the updated projection error obtained in Step 3 into the preset time fuzzy state observer to obtain the estimated value of the dynamic data; Step 5: Design an adaptive safety controller based on the estimated values ​​of the dynamic data obtained in Step 4, and obtain the adaptive control input; Step 6: Reconstruct the adaptive control input obtained in Step 5 based on the deception attack model to obtain the security control signal; Step 7: Input the safety control signal obtained in Step 6 into the dual-channel event triggering mechanism to obtain the control signal after updating the trigger threshold; Step 8: Control the target system based on the updated control signal from Step 7 to achieve lateral motion control of the vehicle.

2. The method according to claim 1, characterized in that: In step 1, the vehicle lateral motion system model is constructed through the following steps: Based on dynamics theory, a two-degree-of-freedom model of the vehicle is constructed: Among them, variables Indicates the vehicle's slip angle. and These represent the lateral forces of the front and rear tires, respectively. Indicates longitudinal velocity. This indicates the yaw rate of the vehicle. Indicates the yaw moment of inertia; Indicates vehicle mass. This represents the distance from the center of gravity to the front axle. This indicates the distance from the center of mass to the rear axle; Based on the two-degree-of-freedom model of the vehicle, the following vehicle kinematic model is constructed: Among them, variables Indicates heading error. Indicates the vehicle's heading. Indicates lateral velocity. Indicates lateral error. Indicates projection error. Indicates a constant projection distance. Indicates the distance along the reference path. Indicates the heading of the reference path; Based on the above vehicle kinematics model, the nonlinear vehicle dynamics model is obtained as follows: Among them, defining variables , and , Indicates the steering angle of the front wheels. and These represent unknown disturbances in the front and rear axles, respectively. Indicates the curvature of the road surface. Indicates the front wheel slip angle. Indicates a constant projection distance. This indicates the lateral stiffness of the front axle tires. Indicates the rear axle tire lateral stiffness. Indicates the adhesion coefficient; To stabilize the system, the unknown disturbance is transformed into the following form based on the nonlinear vehicle dynamics model: in, , To represent unknown external disturbances, based on the above derivation, the nonlinear vehicle dynamics model is transformed into the following form: To address the issue that vehicle dynamics models are insufficient to fully describe a vehicle's response to steering inputs, the following steering system model is established: Among them, variables Indicates the steering resistance torque. Indicates the coefficient of friction. Indicates the torque of the steering motor; For the steering angle of the front wheels yes The first derivative with respect to time, yes The second derivative with respect to time, Indicates the gear ratio of the steering system. Indicates the gear ratio of the steering motor; The steering system model is then rewritten as follows: in, Indicates the damping of the steering system. This represents the equivalent inertia of the steering system; In actual use, and Difficult to measure; therefore, the above-mentioned transformation system model... Rewritten as: in, , This indicates an unknown external disturbance; Based on the above analysis, the rewritten steering system model can be transformed into the following form: Define variables , , ; The lateral motion of the vehicle is determined by the input of the steering system, therefore a strict feedback system is used, which is combined with the steering system and the vehicle dynamics model as follows: Define intermediate variables , , and , Given constants, for The first derivative with respect to time, This indicates system output containing a deception attack. This represents the steering angular velocity of the front wheels.

3. The method according to claim 1, characterized in that: In step 3, a dual-channel event triggering mechanism is designed based on the dual-channel event triggering control theory; the dual-channel event triggering mechanism is as follows: in, Indicates time Updated input signal, Indicates time, Indicates the update time. This represents a design parameter greater than 0. Indicates in The system output is constantly updated. This indicates the system output that has been updated via an event-triggered mechanism.

4. The method according to claim 1, characterized in that: In step 4, the preset time fuzzy state observer includes: Based on the vehicle's lateral motion system, a model transformation is performed to obtain a dynamic model, which includes unknown dynamic parameters; the characterization formulas of the dynamic model include: Define continuous functions and , , , , These are unknown parameters; therefore, nonlinear functions and It is completely unknown; Based on the universal approximation principle of fuzzy logic systems, unknown dynamic parameters are solved to obtain a fuzzy state observer at a predetermined time; the characterization formula of the fuzzy state observer at the predetermined time includes: Among them, variables express The estimated value; It is a preset time scale function. , It is an integer. It is a positive constant about the preset time, therefore, It is monotonically increasing and ; , , , Indicates observation gain. Indicates system output The estimated value, Indicates parameters The estimated value, Representing fuzzy basis functions; The formulas for characterizing dynamic parameters include: in, It is an ideal weight vector. It is the approximation error, and satisfies , It is a normal number.

5. The method according to claim 1, characterized in that: Step 5 includes: Step 51: Confirm and acquire the data from the preset time fuzzy state observer; Step 52: Obtain the virtual control input based on the projection error, observer data, and preset time stability theory; Step 53: Obtain the adaptive law of the target system based on the adaptive virtual control input and the observer data; Step 54: Obtain adaptive control input based on the virtual control input and the adaptive law.

6. The method according to claim 1, characterized in that: Step 6 includes: Step 61: Based on the projection error of the system, the observer data, and the preset time stability theory, obtain the error vector; Step 62: Construct a Lyapunov function based on the error vector; Step 63: Obtain the adaptive virtual control input based on the error vector and the Lyapunov function.

7. The method according to claim 1, characterized in that: In step 7, the system control signal is obtained based on the adaptive control input, and a safety controller is designed using the backstepping method, defining the following coordinate transformation: in, For virtual controllers, It is the expected system input. ; The system control signal is characterized by the following formulas: in, This indicates a design parameter that is greater than 0. Indicates virtual controller The derivative with respect to time; Construct the following deception attack model: in, , This is a multiplicative attack signal. This is an additive attack signal; Based on the deception attack model, system security control signals are obtained, and the characterization formulas for these signals include: 。 8. The method according to claim 1, characterized in that: Step 8 includes: Step 81: Input the safety control signal into the dual-channel event triggering mechanism to obtain the control signal after updating the trigger threshold; Step 82: Based on the dual-channel event triggering control theory, design a dual-channel event triggering mechanism; the dual-channel event triggering mechanism includes: in, This indicates the control signal after being updated by the event-triggered mechanism. Indicates in The control signal is updated constantly. This indicates a design parameter that is greater than 0; Based on the designed dual-channel event triggering mechanism and system safety control signals, an updated control signal is obtained after meeting the triggering threshold. The updated control signal includes: 。 9. A lateral movement safety control system for autonomous vehicles, characterized in that: The lateral movement safety control system for the autonomous vehicle, applied to the vehicle's lateral movement system, includes: The heading error and lateral error acquisition module (10) is used to confirm the heading error and lateral error between the controlled vehicle system and the reference path in the vehicle lateral motion system; The projection error acquisition module (20) is used to acquire the projection error based on the heading error and lateral error between the controlled vehicle system and the reference path; The preset time fuzzy state observer module (30) is used to input the updated projection error of the system into the preset time fuzzy observer to obtain the estimated value of the dynamic data; The backstepping technique module (40) is used to design an adaptive safety controller based on the estimated values ​​of the updated projection error and dynamic data of the system, and to obtain the adaptive control input; The deception attack module (50) is used to obtain a security control signal based on the deception attack model and the adaptive control input; The dual-channel event triggering module (60) is used to input the system's projection error and safety control signal into the dual-channel event triggering mechanism to obtain the projection error and control signal after updating the trigger threshold; The output control module (70) is used to control the target system based on the updated control signal to realize the lateral motion control of the vehicle.

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