Control method of steering instruction based on adaptive event triggering mechanism and automobile thereof

Abstract

The application discloses a control method of steering instruction based on an adaptive event triggering mechanism and a car thereof, and relates to the technical field of vehicle control. last The application comprises the following steps: judging whether the interval between the current time k and the last steering control instruction triggering time k last is greater than the minimum triggering interval; judging whether the absolute value of the difference between the current steering control instruction at the current time k and the last steering control instruction at the last triggering time k last is greater than the current adaptive threshold value; when > and, updating the triggering time and issuing the current steering control instruction to the drive-by-wire steering executor; otherwise, the drive-by-wire steering executor of the vehicle maintains the last steering control instruction at the last triggering time k last . The application introduces an adaptive triggering threshold adjustment rule in the framework of the extended state observer. The total disturbance change is gentle in the steady state, the threshold value is lifted to reduce the communication frequency, and the threshold value is contracted to improve the control update density when the disturbance mutates.

Description

Technical Field

[0001] This invention relates to the technical field of vehicle control, and more particularly to a control method for steering commands based on an adaptive event triggering mechanism and the vehicle thereof. Background Technology

[0002] In the classic active disturbance rejection control framework, the extended state observer needs to estimate the lateral error, the rate of change of error, and the total system disturbance in real time. The controller then calculates and updates the steering command based on the estimates in each sampling period. However, in a distributed architecture with limited onboard network bandwidth and numerous nodes, this periodic high-frequency communication continuously occupies bus resources, exacerbates network load, and causes fretting wear on the steering actuator, shortening the service life of electromechanical components.

[0003] Therefore, in order to reduce communication frequency, event-triggered mechanisms have been introduced into control systems in existing technologies. The core idea is to update control commands only when the system state changes significantly. However, most existing event-triggered strategies use fixed thresholds, i.e., updates are triggered when the change in control commands exceeds a preset constant. But fixed thresholds have certain drawbacks: if the threshold is set too high, the response is sluggish during sudden disturbances, leading to decreased tracking accuracy; if the threshold is set too low, it degenerates into quasi-periodic sampling, failing to effectively reduce communication load. Although some studies have attempted to correlate the trigger threshold with the tracking error amplitude, the error amplitude reflects the consequences of the control effect rather than the cause of the disturbance, and its indication of system instability risk is lagging. Especially when a vehicle encounters sudden changes in road adhesion or parameter perturbations, the total disturbance estimate of the extended state observer changes drastically. If the triggering mechanism fails to detect this change in time and increase the update frequency, the controller will struggle to intervene and compensate in the early stages of disturbance evolution, leading to rapid divergence of lateral errors.

[0004] Therefore, there is an urgent need for a steering command control method based on an adaptive event triggering mechanism to achieve dynamic matching between trigger sensitivity and system instability risk, effectively reducing the average transmission frequency of steering commands while ensuring tracking accuracy, thereby solving the aforementioned problems. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] In view of the problems existing in the prior art, the present invention is proposed.

[0007] To address the technical problem of lacking dynamic matching between trigger sensitivity and system instability risk in existing technologies, this invention provides a steering command control method based on an adaptive event triggering mechanism. The invention provides the following technical solution: determining the current time k and the trigger time k of the previous steering control command. last interval Is it greater than the minimum trigger interval? ; Determine the current steering control command at time k. Compared to the last trigger time k last Steering control commands issued Is the absolute value of the difference greater than the current adaptive threshold? ; when > and At that time, the steering command received by the steer-by-wire actuator is Otherwise maintain ; in, In the formula, Based on the steady-state threshold, This is the sensitivity adjustment coefficient. for The magnitude of the total rate of change of the disturbance at any given time.

[0008] As a preferred embodiment of the steering command control method based on an adaptive event triggering mechanism described in this invention, wherein: the amplitude of the total disturbance change rate is... The calculation method, based on the first-order difference approximation, is designed as follows: ; in, The magnitude of the total rate of change of disturbance. The current sampling time The total disturbance estimate output by the extended state observer. The previous sampling time The total disturbance estimate is the total disturbance estimate corresponding to the previous control cycle at the current moment. The system sampling period is the time interval between two adjacent control periods.

[0009] As a preferred embodiment of the control method for steering commands based on an adaptive event triggering mechanism described in this invention, the method further includes: designing a third-order linear extended state observer to estimate the system state and total disturbance in real time; The third-order linear extended state observer is designed as follows: ; in, , , All of these are the gain coefficients of the observer. This represents the actual lateral tracking error. and These represent the estimated lateral tracking error and its derivative, i.e., the velocity tracking error. This is the real-time estimate of the total disturbance. The rate of change of the lateral tracking error. The rate of change of the speed tracking error. The rate of change of the real-time estimate of the total disturbance. This is the input for front wheel steering angle control; The nominal estimate of the control channel gain; Using the forward Euler method with sampling period The observer is discretized to obtain a discrete domain recursive equation, and the real-time estimate of the total disturbance of the system at the current moment is calculated using the discrete domain recursive equation.

[0010] As a preferred embodiment of the steering command control method based on the adaptive event triggering mechanism described in this invention, the gain of the third-order linear extended state observer is configured using a bandwidth parameterization method. ; in This represents the observer bandwidth.

[0011] As a preferred embodiment of the steering command control method based on the adaptive event triggering mechanism described in this invention, the discrete domain recursive equation is designed as follows: ; Among them, the observation error is recorded. The current sampling time The observation error is defined as the current lateral tracking error measurement. Compared with the observer's estimate The difference, The lateral tracking error at the center of the vehicle's front axle at the current moment is the distance from the vehicle's actual position to the reference path normal, which is obtained in real time by the path perception module. This is the estimated value of the first state variable of the observer at the current moment, enabling the assessment of the lateral tracking error. Tracking This is the estimated value of the second state variable of the observer at the current moment, realizing the rate of change of error. Tracking This is the estimated value of the third state variable of the observer at the current moment, thus realizing the total disturbance of the system. Real-time estimation, For the next sampling time The recursive value, For the next sampling time The recursive value, For the next sampling time The recursive value.

[0012] As a preferred embodiment of the steering command control method based on the adaptive event triggering mechanism described in this invention, the method further includes: applying a physical limit to the actuator to obtain the current vehicle steering control command value issued to the steer-by-wire system, the expression of which is: ; in, It is a saturation function. , These represent the negative and positive physical limit angles of the steering actuator, respectively. For Ackermann feedforward angle, This is the original steering command value.

[0013] As a preferred embodiment of the steering command control method based on the adaptive event triggering mechanism described in this invention, wherein: Designed as follows: ; in, For the system's virtual control variables; To control the nominal estimate of channel gain, This is the real-time estimate of the total disturbance; The Designed as follows: ; in, and These are the proportional and differential gains, respectively. For heading deviation feedback gain, This represents the heading angle deviation.

[0014] As a preferred embodiment of the steering command control method based on the adaptive event triggering mechanism described in this invention, the proportional and differential gains are configured using a bandwidth parameterization method. , ; in, This refers to the controller bandwidth.

[0015] As a preferred embodiment of the steering command control method based on the adaptive event triggering mechanism described in this invention, wherein: Designed as follows: ; in, This refers to the vehicle's wheelbase. The path curvature of the reference path at the current projection point.

[0016] The present invention also provides an automobile, wherein the automobile executes the steps of the control method for steering commands based on the above-described adaptive event triggering mechanism in vehicle control via a steer-by-wire actuator.

[0017] The beneficial effects of this invention are as follows: Addressing the limitations of fixed-period communication and fixed-threshold event triggering, this invention introduces an adaptive trigger threshold adjustment law within the extended state observer framework. This threshold is constructed as a monotonically decreasing function of the rate of change of the total disturbance estimate: in steady state, when the total disturbance changes gradually, the threshold is raised to reduce communication frequency; when the disturbance changes abruptly, the threshold shrinks to increase control update density. Simultaneously, to avoid Zeno's phenomenon, a minimum trigger interval constraint is set in the system. This mechanism achieves a dynamic match between trigger sensitivity and system instability risk, effectively reducing the average transmission frequency of steering commands while ensuring tracking accuracy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the 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. Wherein: Figure 1 This is a schematic diagram of the overall process of the steering command control method based on the adaptive event triggering mechanism proposed in this invention; Figure 2 This is a simulation diagram of global trajectory tracking in the method of the present invention; Figure 3 This is a simulation diagram illustrating the lateral tracking error in the method of the present invention; Figure 4 This is a simulation diagram of the yaw rate response in the method of the present invention; Figure 5 This is a simulation diagram illustrating the event triggering moment in the method of the present invention; Figure 6 This is a simulation diagram illustrating the steering command and total disturbance estimation in the method of the present invention; Figure 7 This is a simulation diagram illustrating the communication load comparison in the method of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1

[0023] Reference Figure 1 As an embodiment of the present invention, a steering command control method based on an adaptive event triggering mechanism is provided. This method is based on the following vehicle dynamics models: a vehicle lateral dynamics model and a steer-by-wire actuator model.

[0024] The vehicle dynamics model is used to demonstrate the physical basis of rear axle differential cooperation. The core output of this model is the yaw moment. With yaw rate The transmission relationship between them. From the vehicle's lateral dynamics model, it can be seen that the additional yaw moment... We directly enter the yaw motion equation, which, together with the lateral forces on the front and rear axles, determines the vehicle's yaw response. Current axle lateral force When it decreases due to performance degradation, if If the yaw moment is kept at zero, the yaw moment balance is disrupted, and the vehicle will deviate from the desired yaw trajectory. Therefore, the essence of rear axle differential cooperation is to actively adjust... To compensate The absence of.

[0025] Considering the coupling characteristics of lateral and yaw motion in a distributed drive electric vehicle, a two-degree-of-freedom single-track vehicle dynamics model is established. Assume a longitudinal vehicle speed... If the change is gradual within the control cycle or maintained by an independent longitudinal controller, the lateral motion equation of the system can be described as follows: ; In the formula, For the overall vehicle quality, To bypass The yaw moment of inertia of the axis and These are the distances from the center of mass to the front and rear axles, respectively. The sideslip angle is the angle of the center of mass. The yaw rate is angular velocity. The additional yaw moment generated by the rear axle differential drive and These represent the lateral forces of the front and rear axle tires, respectively. Within the tire's linear operating range, the lateral forces can be approximated as... , ,in , For the equivalent lateral stiffness of the front and rear axles, , These are the front and rear tire slip angles. The front and rear tire slip angles are determined by kinematic relationships. , Provided.

[0026] In this model, the front axle lateral force Depends on front wheel steering angle This is the primary source of control for path tracing. When When the actual output is limited due to changes in actuator performance, The shortcomings will be directly reflected in the lateral tracking error. The increase in [something]. This causal relationship provides the physical basis for the subsequent estimation of the total perturbation by the extended state observer.

[0027] The steer-by-wire actuator model is used to demonstrate that ADRC has a certain degree of passive fault tolerance. The core feature of this model is the first-order inertial element. .in, The target front wheel steering angle calculated by the controller, This refers to the actual steering angle applied to the front wheels of the vehicle. As the steering system time constant, in the event triggering part of the steering command control method based on an adaptive event triggering mechanism, the steer-by-wire system has an inherent dynamic response delay, whose time constant is... This determines the actual steering angle and the speed at which the command is followed. When the steering efficiency coefficient... When a gradual change occurs, its impact on lateral error is not instantaneous, but rather gradually permeates the vehicle response dynamically through the actuators. The total disturbance estimation channel of the extended state observer continuously senses error changes during this process and cancels them out in the feedforward channel. Therefore, ADRC has a natural passive fault tolerance capability for gradually changing steering performance. That is, as long as the rate of performance change is within the observer bandwidth, the controller can maintain tracking accuracy without adjusting parameters.

[0028] Having understood the principles of the above dynamic model, the method of this invention will now be discussed in detail: Traditional periodic control updates result in wasted communication resources and actuator wear due to fretting under steady-state conditions. While fixed-threshold event triggering can reduce communication frequency, it suffers from lag in response to sudden disturbances. This invention proposes an adaptive triggering threshold adjustment law with the rate of change of the total disturbance estimate as a sensitive factor, i.e., an adaptive event triggering mechanism based on the rate of change of the total disturbance. The specific reasoning process is as follows: S1: To describe the vehicle's tracking behavior relative to the reference path, the lateral tracking error at the center of the front axle is defined. deviation from heading angle Under the assumption of small deviation, the second-order dynamics of the lateral error can be approximated as: ; In the formula, These are known or partially unknown nonlinear functions related to vehicle lateral dynamics, tire nonlinearity, and path curvature variation; This is the input for front wheel steering angle control; The nominal estimate of the control channel gain is physically represented as the lateral acceleration generated per unit front wheel rotation angle. This refers to the bounded unknowns that include external disturbances such as cross wind and road surface cross slope. This refers to the lateral tracking error at the center of the vehicle's front axle. This is the first time derivative of the lateral tracking error. For the longitudinal speed of the vehicle, Let yaw rate be the vehicle's angular velocity. The path curvature of the reference path at the current projection point.

[0029] When the steer-by-wire actuator experiences gain attenuation fault, the actual steering angle applied to the front wheels is: ,in Let this be the steering efficiency coefficient. By incorporating the fault effects into the lumped disturbance term, the error dynamics can be rewritten as: ; Among them, the total system disturbance Defined as: ; In the above formula, The nominal estimate of the control channel gain; This refers to the steering efficiency coefficient; The input is the front wheel steering angle control input; through the above transformation, the system nonlinearity, parameter uncertainty, external environmental disturbances, and actuator performance degradation (total system disturbance) are uniformly reduced to a one-dimensional scalar time-varying function. The complex vehicle dynamics coupling and actuator failures are equivalently merged into a single lumped disturbance term, simplifying the original nonlinear system into a second-order integral model. This processing allows the extended state observer to directly treat this lumped disturbance as a new state variable for real-time observation and estimation.

[0030] S2: Total disturbance The expanded state variable is the third state variable of the system, denoted as the expanded state vector. Under the assumptions that the total disturbance is differentiable and its derivative is bounded, the system state-space equations are: ; in Unknown but bounded The first component of the extended state vector The time derivative. From Therefore ; The second component of the extended state vector is defined as the first derivative of the lateral error, i.e. ; for The time derivative. From the original system and , can be obtained ; The third component of the extended state vector, i.e., the total system disturbance resulting from the extension. ; for The time derivative is equal to the rate of change of the total disturbance. ; Total system disturbance The first time derivative of represents the time-varying rate of lumped perturbation; The nominal estimate for controlling channel gain has been defined above.

[0031] Transition from state-space equations to extended state observer: Transforming the total perturbation After expanding to the third state variable of the system, we obtain the third-order extended state-space equation: ; This equation can be written in matrix form: ; in The coefficient matrix is ​​as follows: ; The system output equation is That is, only the lateral error can be directly measured, while the error derivative and the total disturbance need to be estimated by the observer.

[0032] For the above system, a linearly extended state observer of the Romberg form is constructed: ; in For the state estimation vector, For the observer's estimate of the output, Let be the observer gain vector to be tuned. Expanding the matrix yields the observer equation in fractional form: ; In the formula, The rate of change of the lateral tracking error. The rate of change of the speed tracking error. The core of the observer design lies in the gain vector, which represents the rate of change of the real-time estimate of the total disturbance. Tuning. Defining observation error. Subtracting the state-space equation from the observer equation yields the error dynamic equation: ; in This is the output matrix. The convergence rate of the error system is determined by the matrix. The eigenvalues ​​determine the matrix. Using a bandwidth parameterization method, the eigenvalues ​​of this matrix are uniformly allocated on the negative real axis of the complex plane. Location: ; By comparing the coefficients, we can obtain the analytical expression for the observer gain: ; in The observer bandwidth (rad / s) is the only tuning parameter for the observer. When bounded, observation error It will asymptotically converge to a neighborhood centered at zero, with the convergence rate increasing. Increase and accelerate.

[0033] To implement this in an onboard digital controller, the forward Euler method is used with a sampling period of... Discretize the continuous-domain observer. Denote the observation error. Replacing the differential with the difference Substituting into the continuous observer equation and rearranging, we obtain the discrete domain recurrence equation as follows: ; In the above formula, For the first Observational error at the sampling time; For the first Lateral tracking error at the sampling time; For the first The estimated value of the first state variable of the time-time observer; For the first The estimated value of the second state variable of the time-time observer; For the first The estimated value of the third state variable (expanded state) of the time-observer; For the next sampling time The recursive value; For the next sampling time The recursive value; For the next sampling time The recursive value.

[0034] S3: The third state of the extended state observer The rate of change of the lumped disturbance acting on the transverse dynamics was estimated in real time. This reflects the degree to which the system deviates from steady state. When tire force saturates, road adhesion changes abruptly, or actuator performance drops sharply, If the value increases significantly, the trigger threshold needs to be lowered to increase the control command update frequency. Therefore, a trigger threshold is constructed. It is a monotonically decreasing function of the magnitude of the total disturbance rate of change. ; In the formula, Based on the steady-state threshold, This is the sensitivity adjustment coefficient. In the discrete-time implementation, the rate of change of the disturbance is calculated by the first-order difference approximation: ; also, For the magnitude of the total disturbance rate of change, the triggering decision of the adaptive event triggering mechanism mentioned above must simultaneously satisfy the time interval constraint and the state increment constraint: Time interval constraint: Compare the current time k with the time k triggered by the last steering control command. last interval Is it greater than the minimum trigger interval? This prevents the Zeno phenomenon and allows for dynamic response time for the actuator (steer-by-wire actuator).

[0035] It should be noted that in event-triggered control, the Zeno phenomenon refers to the situation where control commands are triggered and updated an infinite number of times within a finite time period. This occurs because if the triggering condition depends only on a signal increment threshold without a lower time limit constraint, when the system state fluctuates frequently and slightly around the threshold, each fluctuation satisfies the triggering condition, causing the trigger interval to approach zero. This phenomenon cannot be realized in a physical system and would lead to controller overload and actuator failure. To avoid the Zeno phenomenon, this invention introduces a minimum trigger interval in the event triggering decision. This forces that the time span between two consecutive triggers must not be less than this value, thus eliminating the possibility of infinitely fast sampling from a mechanism perspective.

[0036] State increment constraint: Determine the current steering control command at time k. (The target front wheel steering angle calculated by the controller at time k) and the previous trigger time k last Steering control commands issued (k) last Whether the absolute value of the difference between the actual front wheel steering angle applied to the vehicle at any given moment is greater than the current adaptive threshold. ; when > (Time interval constraint) and (During state increment constraints), the steering command received by the steer-by-wire actuator is: Otherwise maintain For example: update trigger time and the current steering control command Send the signal to the steer-by-wire actuator; otherwise, the vehicle's steer-by-wire system maintains the state of the last trigger point k. last Steering control commands issued .

[0037] Specifically In the formula It is a saturation function. , These represent the negative and positive physical limit angles of the steering actuator, respectively; , For the virtual control quantity of the system, , and These are the proportional and differential gains, respectively. For heading deviation feedback gain, This represents the heading angle deviation. Using a bandwidth parameterization method, the closed-loop characteristic equation is configured as follows: We can obtain: Controller bandwidth The response speed of the closed-loop system to the reference command is determined. The virtual control quantity is compensated for by total disturbance feedforward and divided by the control channel gain to obtain the original steering command. ; , This refers to the vehicle's wheelbase. The path curvature of the reference path at the current projection point.

[0038] In summary, the adaptive event triggering mechanism described above achieves a dynamic match between the triggering frequency and the risk of system instability: the threshold is high and communication is sparse under steady-state conditions; the threshold shrinks and control updates are intensive under transient disturbances, thereby effectively reducing the average communication load while ensuring tracking accuracy.

[0039] To further verify the technical effects of the present invention, the following simulation examples are used to verify the performance of event-triggered active disturbance rejection path tracking control.

[0040] I. Simulation Operating Condition Settings

[0041] The simulation uses a distributed drive electric vehicle model, and the vehicle parameters are shown in Table 1. The reference path is a variable curvature double-line-change trajectory that includes straight sections and curves, with a total length of approximately 250m and a maximum lateral offset of 3.5m. The target cruising speed of the vehicle is set to 20m / s, and the peak road adhesion coefficient is 0.85. The simulation sampling period is 0.01s, and the total duration is 15s.

[0042] Table 1 Main physical parameters of the vehicle

[0043] II. Comparison Mode

[0044] To verify the effectiveness of the event triggering mechanism, two control modes were set up for comparison: Periodic sampling: The active disturbance rejection steering controller updates and issues steering commands in each sampling period, i.e., full communication mode.

[0045] Event triggering: The event-triggered active disturbance rejection controller proposed in this invention is adopted. The triggering threshold is adaptively adjusted according to the total disturbance change rate, and the minimum triggering interval is set to 0.02s.

[0046] III. Reference Figures 2-7 The simulation results were analyzed, specifically: Reference Figure 2 Global trajectory tracking: The reference path and the actual trajectory highly overlap. The vehicle completed a double-lane-shifting path with curvature changes within the X-axis range of 0-250m. This demonstrates that the designed ADRC controller possesses excellent command tracking capabilities. Even after adding an event-triggered mechanism, the closed-loop system can still guide the vehicle to precisely follow the target trajectory without significant steady-state deviation or oscillation divergence, proving the strong convergence of the control architecture.

[0047] Reference Figure 3Lateral tracking error: The actual error is greatly compressed to within 0.1m or even smaller, far below the preset safety boundary (red dashed line). Although the frequency of control commands is deliberately reduced by the event-triggered mechanism, the lateral error is not amplified as a result. The active disturbance rejection characteristics of ADRC absorb external disturbances and internal unmodeled dynamics, ensuring that even discrete and non-periodic command updates can maintain extremely high trajectory tracking accuracy.

[0048] Reference Figure 4 Yaw rate response: The purple curve shows the change in yaw rate during cornering and correction. Although the curve fluctuates, it quickly converges to near 0 without sustained high-frequency oscillations. Smooth convergence of yaw rate is crucial for vehicle lateral stability. This figure indicates that the system possesses good yaw damping characteristics, failing to induce high-frequency vibrations in the vehicle body during dynamic steering, resulting in smooth vehicle attitude control.

[0049] Reference Figure 5 Event Triggering Time: The red vertical bars represent the moments when the controller issues commands. It can be seen that triggering is very frequent during the 0-6 second period (entering curves, large curvature changes); while during the 8-15 second period (straight tracks, steady-state phase), triggering becomes very sparse, even with large gaps. This verifies the theoretical expectation of the adaptive ETM: when the system faces transient changes or large errors, the ETM keenly senses and triggers frequently to ensure control accuracy; when the system enters steady state or the error is within the tolerance range, the ETM actively cuts off communication and maintains the zero-order hold (ZOH) state. This is a typical on-demand communication allocation strategy.

[0050] Reference Figure 6 Steering command and total disturbance estimate: The blue line represents the front wheel steering angle command, and the orange line represents the total disturbance estimate output by the Extended State Observer (LESO). The two exhibit a clear reverse synchronization trend in the time domain. This confirms the feedforward compensation decoupling mechanism of ADRC. When the system encounters an unknown total disturbance, LESO observes it in real time; subsequently, the control law immediately generates a reverse action to cancel the disturbance. It is this observation-compensation mechanism that transforms the complex nonlinear vehicle model into a simple integral cascade system in the eyes of the controller.

[0051] Reference Figure 7 Communication load comparison: Traditional periodic sampling occurs nearly 1500 times, while the number of event triggers after adding ETM is only over 200. ETM successfully reduces network communication load by 84%. In distributed chassis architectures where in-vehicle network (such as CAN / vehicle Ethernet) bandwidth is increasingly strained, this mechanism can effectively alleviate network congestion, reduce the risk of packet loss, and significantly reduce high-frequency micro-motion wear of steer-by-wire actuators, extending hardware lifespan.

[0052] IV. Conclusion

[0053] In summary, simulation results show that the event-triggered active disturbance rejection controller proposed in this invention can reduce the frequency of steering command communication by approximately 78% while ensuring path tracking accuracy and vehicle stability, effectively alleviating the problems of onboard network load and actuator fretting wear. Furthermore, the inherent disturbance estimation and compensation mechanism of the active disturbance rejection controller endows the system with the ability to tolerate parameter perturbations within a certain range.

[0054] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A control method for steering commands based on an adaptive event-triggered mechanism, characterized in that, Includes the following steps: Determine the difference between the current time k and the time k triggered by the last steering control command. last interval Is it greater than the minimum trigger interval? ; Determine the current steering control command at time k. Compared to the last trigger time k last Steering control commands issued Is the absolute value of the difference greater than the current adaptive threshold? The steering control command is the target front wheel steering angle; when > and At that time, the steering command received by the steer-by-wire actuator is Otherwise maintain ; in, In the formula, Based on the steady-state threshold, This is the sensitivity adjustment coefficient. Let be the magnitude of the total rate of change of the disturbance at time k; Wherein, the amplitude of the total disturbance rate of change The calculation method, based on the first-order difference approximation, is designed as follows: ； in, The magnitude of the total rate of change of disturbance. The current sampling time The total disturbance estimate output by the extended state observer. The previous sampling time The total disturbance estimate is the total disturbance estimate corresponding to the previous control cycle at the current moment. The system sampling period is the time interval between two adjacent control cycles. The method also includes: designing a third-order linear extended state observer to estimate the system state and total disturbance in real time; The third-order linear extended state observer is designed as follows: ； in, , , All of these are the gain coefficients of the observer. This represents the actual lateral tracking error. and The estimated values ​​of lateral tracking error and velocity tracking error are respectively. This is the real-time estimate of the total disturbance. The rate of change of the lateral tracking error. The rate of change of the speed tracking error. The rate of change of the real-time estimate of the total disturbance. This is the input for front wheel steering angle control; The nominal estimate of the control channel gain; Using the forward Euler method with sampling period The observer is discretized to obtain the discrete domain recursive equation, and the real-time estimate of the total disturbance of the system at the current moment is calculated by the discrete domain recursive equation. The gain of the third-order linear extended state observer is configured using a bandwidth parameterization method. ； in This represents the observer bandwidth.

2. The control method for steering commands based on an adaptive event triggering mechanism according to claim 1, characterized in that: The discrete-domain recursive equation is designed as follows: ； Among them, the observation error is recorded. The current sampling time The observation error is defined as the current lateral tracking error measurement. Compared with the observer's estimate The difference, The lateral tracking error at the center of the vehicle's front axle at the current moment is the distance from the vehicle's actual position to the reference path normal, which is obtained in real time by the path perception module. This is the estimated value of the first state variable of the observer at the current moment, enabling the assessment of the lateral tracking error. Tracking This is the estimated value of the second state variable of the observer at the current moment, realizing the rate of change of error. Tracking This is the estimated value of the third state variable of the observer at the current moment, thus realizing the total disturbance of the system. Real-time estimation, For the next sampling time The recursive value, For the next sampling time The recursive value, For the next sampling time The recursive value.

3. The control method for steering commands based on an adaptive event triggering mechanism according to claim 1, characterized in that: The method also includes: applying physical limits to the actuators to obtain the current vehicle steering control command value sent to the steer-by-wire system. : ； in, It is a saturation function. , These represent the negative and positive physical limit angles of the steering actuator, respectively. For Ackermann feedforward angle, This is the original steering command value.

4. The control method for steering commands based on an adaptive event triggering mechanism according to claim 3, characterized in that: The Designed as follows: ； in, For the system's virtual control variables; To control the nominal estimate of channel gain, This is the real-time estimate of the total disturbance; The Designed as follows: ； in, and These are the proportional and differential gains, respectively. For heading deviation feedback gain, This represents the heading angle deviation.

5. The control method for steering commands based on an adaptive event triggering mechanism according to claim 4, characterized in that: The proportional and differential gains are configured using a bandwidth parameterization method. , ; in, This refers to the controller bandwidth.

6. The control method for steering commands based on an adaptive event triggering mechanism according to claim 3, characterized in that: The Designed as follows: ； in, This refers to the vehicle's wheelbase. The path curvature of the reference path at the current projection point.

7. A car, characterized in that: The steps of the vehicle control method using the steering command based on any one of claims 1 to 6, which employs a steer-by-wire actuator to execute vehicle control, are described.

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