A Vehicle Queue Following Control Method and System Based on Adaptive Disturbance Observation

Abstract

This invention belongs to the field of vehicle control technology and discloses a vehicle platoon car-following control method and system based on adaptive disturbance observation. The method includes: acquiring vehicle platoon operating state information, establishing vehicle following motion equations containing nonlinear disturbances and determining spacing errors; constructing a vehicle car-following model, introducing non-singular integral sliding mode variables to construct coupled sliding mode variables; constructing an adaptive superspiral disturbance observer based on nonlinear disturbances, dynamically adjusting the observer gain according to the disturbance estimation error to obtain disturbance estimation results; constructing a sliding mode controller based on coupled sliding mode variables, operating state information, and disturbance estimation results, determining the control input for each following vehicle and iteratively updating it to achieve distributed car-following control. This invention improves the estimation accuracy of time-varying multi-source disturbances through an adaptive gain adjustment mechanism, effectively suppresses chattering by using coupled sliding mode variables and a hyperbolic tangent function, and significantly improves the car-following performance of vehicle platoons in complex environments.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, specifically to a vehicle platooning following control method and system based on adaptive disturbance observation. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of intelligent connected vehicle technology, vehicle platoon cooperative control obtains vehicle status information through inter-vehicle communication and achieves car-following control based on the operating status of adjacent vehicles, which is of great significance for improving road traffic efficiency and driving safety. However, in actual operation, vehicle platoons are easily affected by various factors such as measurement errors, vehicle parameter uncertainties, inter-vehicle communication errors, and changes in the natural environment, forming unknown nonlinear disturbances with time-varying characteristics.

[0004] Existing technologies typically treat the aforementioned multi-source disturbance factors as a single nonlinear term, making it difficult to accurately obtain the disturbance changes. Furthermore, existing disturbance observation methods often employ fixed observation gains, making it difficult to adjust observation parameters in a timely manner based on disturbance changes and estimation errors. This results in insufficient estimation accuracy and adaptability under time-varying complex disturbance conditions. Since vehicle platoon controllers usually rely on system models and state information for control, when unknown disturbances cannot be accurately estimated and effectively compensated, model mismatch can easily occur, reducing vehicle spacing and speed tracking accuracy, and consequently affecting the stable operation of the vehicle platoon system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a vehicle platoon following control method and system based on adaptive disturbance observation.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a vehicle platooning following control method based on adaptive disturbance observation is provided, including: The system acquires the operational status information of the vehicle platoon, establishes vehicle following motion equations containing nonlinear disturbances, and determines the spacing error between adjacent vehicles based on a preset headway strategy. A vehicle following model is constructed based on the operating status information; combined with the spacing error, non-singular integral sliding mode variables are introduced into the vehicle following model to construct coupled sliding mode variables; An adaptive superspiral perturbation observer is constructed based on the nonlinear perturbation. The gain of the adaptive superspiral perturbation observer is adjusted according to the perturbation estimation error to obtain the perturbation estimation result of the nonlinear perturbation. Based on the coupled sliding mode variables, the operating status information of the vehicle platoon, and the disturbance estimation results, a sliding mode controller is constructed to determine the control input for each following vehicle. The control inputs of each following vehicle are applied to the corresponding following vehicle, the operating status information of the vehicle queue is updated, and the control inputs of each following vehicle are determined again based on the updated operating status information, thereby realizing distributed car-following control of the vehicle queue.

[0007] Secondly, a vehicle platooning following control system based on adaptive disturbance observation is provided, including: The information acquisition and motion following module is configured to: acquire the running status information of the vehicle queue, establish the vehicle following motion equation containing nonlinear disturbances, and determine the spacing error between adjacent vehicles according to the preset headway strategy. The sliding mode variable construction module is configured to: construct a vehicle car-following model based on the running state information; and, in conjunction with the spacing error, introduce non-singular integral sliding mode variables into the vehicle car-following model to construct coupled sliding mode variables. The perturbation observer module is configured to: construct an adaptive superspiral perturbation observer based on the nonlinear perturbation, adjust the gain of the adaptive superspiral perturbation observer according to the perturbation estimation error, and obtain the perturbation estimation result of the nonlinear perturbation; The sliding mode controller module is configured to: construct a sliding mode controller based on the coupled sliding mode variables, the operating status information of the vehicle queue, and the disturbance estimation results, and determine the control input for each following vehicle; The control update module is configured to: apply the control input of each following vehicle to the corresponding following vehicle, update the running status information of the vehicle queue, and determine the control input of each following vehicle again based on the updated running status information, so as to realize the distributed car-following control of the vehicle queue.

[0008] Thirdly, an electronic device is also provided, comprising: Memory, used for non-transitory storage of computer-readable instructions; and Processor, for executing the computer-readable instructions, When the computer-readable instructions are executed by the processor, they perform the method described in the first aspect above.

[0009] Fourthly, a computer-readable storage medium is provided having a program stored thereon that, when executed by a processor, implements the method described in the first aspect above.

[0010] Fifthly, a computer program product is provided, employing the following technical solution: A computer program product includes software code, wherein a program in the software code performs the steps of the method described in the first aspect of the present invention.

[0011] The above technical solution has the following advantages or beneficial effects: (1) By introducing a vehicle following model into the integral sliding mode variable, the present invention makes the control strategy of the vehicle following queue fully consider the actual traffic flow operation law, and the controller design is highly consistent with the traffic flow theory. This avoids the problem of decoupling between the controller and the traffic system in the prior art, and makes the queue operation more in line with the actual road conditions.

[0012] (2) The adaptive superspiral perturbation observer proposed in this invention introduces an adaptive gain adjustment mechanism, which can dynamically adjust the observer gain according to the magnitude of the estimation error. When the estimation error is large, the gain is increased to accelerate the convergence speed; when the estimation error is small, the gain is decreased to suppress measurement noise, thus overcoming the shortcomings of existing fixed-gain perturbation observers in adapting to time-varying multi-source perturbations. Theoretical analysis and experiments have shown that the perturbation estimation accuracy of this observer is significantly better than that of existing perturbation observers.

[0013] (3) This invention effectively suppresses chattering in sliding mode control by introducing coupled sliding mode variables into the integral sliding mode controller and replacing the sign function with a hyperbolic tangent function. Theoretical analysis shows that under the action of the integral sliding mode controller, the vehicle spacing error can converge to zero in a finite time, and the system has finite-time stability.

[0014] (4) The theoretical analysis was verified by numerical simulation and physical experiments of intelligent micro-robot vehicles. The results show that the method proposed in this invention can achieve rapid error convergence, maintain safe vehicle spacing, and dynamically adapt to traffic flow changes, which is highly consistent with the theory of real traffic flow. Attached Figure Description

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0016] Figure 1 This is a flowchart of a vehicle platoon following control method based on adaptive disturbance observation in a specific embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of a vehicle following queue consisting of one lead vehicle and N following vehicles in a specific embodiment of the present invention.

[0018] Figure 3The figures show the simulation results of the vehicle queuing spacing in the vehicle queue compared with the existing methods based on adaptive disturbance observation proposed in this invention. (a) shows the simulation results of the vehicle queuing spacing without a disturbance observer, (b) shows the simulation results of the vehicle queuing spacing when using the method of this invention, and (c) shows the simulation results of the vehicle queuing spacing when using only an adaptive superspiral disturbance observer without introducing a car-following model.

[0019] Figure 4 The figures show the simulation results of the vehicle platoon following control method based on adaptive disturbance observation proposed in this invention and existing methods in terms of speed comparison in the vehicle platoon. Among them, (a) is the simulation result of the speed comparison of each vehicle in the vehicle platoon without a disturbance observer, (b) is the simulation result of the speed comparison of each vehicle in the vehicle platoon when using the method of this invention, and (c) is the simulation result of the speed comparison of each vehicle in the vehicle platoon when only an adaptive superspiral disturbance observer is used without introducing a following model.

[0020] Figure 5 This is for the experimental intelligent vehicle and its communication structure.

[0021] Figure 6 The figures show the experimental results of the vehicle platoon following control method based on adaptive disturbance observation proposed in this invention, under the scenario of sudden change in the speed of the leading vehicle, and the changes in vehicle spacing and speed. (a) shows the experimental results of the changes in vehicle spacing, and (b) shows the experimental results of the changes in speed.

[0022] Figure 7 The figures show the experimental results of the vehicle queuing following control method based on adaptive disturbance observation proposed in this invention, in the scenario of acceleration and deceleration in the vehicle following queue. (a) shows the experimental results of the vehicle spacing change, and (b) shows the experimental results of the speed change.

[0023] In the diagram, 1 is the WiFi signal receiving module, 2 is the camera, 3 is the LiDAR, and 4 is the lead vehicle. Detailed Implementation

[0024] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. The terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] In this embodiment of the invention, "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of this invention, "multiple" refers to two or more.

[0027] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0028] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0029] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0030] Terminology Explanation: Vehicle-to-Vehicle (V2V) wireless communication technology is used to enable real-time interaction of status information between vehicles.

[0031] An On-Board Unit (OBU) is a communication device installed on a vehicle for exchanging data with roadside units and other vehicles.

[0032] A roadside unit (RSU) is a communication device installed on both sides of a road to interact with vehicles.

[0033] Sliding Mode Control (SMC): A nonlinear variable structure control method that uses a sliding surface to make the system state move along a predetermined trajectory, exhibiting strong robustness.

[0034] Disturbance Observer (DOB): A control technique used to estimate and compensate for external disturbances and uncertainties in a system. It estimates disturbances by comparing the difference between the actual output of the system and the predicted output of the model.

[0035] Nonsingular Terminal Sliding Mode Control (NTSM): An improved sliding mode control method that avoids control divergence by designing a nonsingular sliding surface while maintaining finite-time convergence characteristics.

[0036] Super-Twisting Algorithm (STA): A second-order sliding mode control algorithm that can effectively suppress chattering while maintaining robustness to disturbances.

[0037] String stability: In a vehicle platooning system, the property that a disturbance experienced by the preceding vehicle does not amplify as it propagates along the platoon.

[0038] Example 1 like Figure 1 As shown, this embodiment provides a vehicle platooning following control method based on adaptive disturbance observation, including: S101: Obtain the running status information of the vehicle queue, establish the vehicle following motion equation containing nonlinear disturbances, and determine the spacing error between adjacent vehicles according to the preset headway strategy. S102: Construct a vehicle following model based on the operating status information; combine the spacing error with the non-singular integral sliding mode variable to introduce the vehicle following model and construct coupled sliding mode variables; S103: Construct an adaptive superspiral perturbation observer based on the nonlinear perturbation, adjust the gain of the adaptive superspiral perturbation observer according to the perturbation estimation error, and obtain the perturbation estimation result of the nonlinear perturbation; S104: Based on the coupled sliding mode variables, the operating status information of the vehicle queue, and the disturbance estimation results, construct a sliding mode controller and determine the control input for each following vehicle; S105: Apply the control input of each following vehicle to the corresponding following vehicle, update the running status information of the vehicle queue, and determine the control input of each following vehicle again based on the updated running status information to realize distributed car-following control of the vehicle queue.

[0039] In this embodiment, each vehicle in the vehicle platoon is equipped with autonomous driving capabilities.

[0040] Step S101 is as follows: S101.1: Acquire the operational status information of the vehicle platoon, including the position, speed, and acceleration of each vehicle in the platoon; and the adjustable sensitivity coefficient in a V2V environment. The adjustable sensitivity coefficient in a V2V environment is acquired through vehicle-to-vehicle wireless communication technology, on-board unit (OBU), and roadside unit.

[0041] S101.2: Establish the vehicle following motion equations that include nonlinear disturbances.

[0042] Consider a car convoy consisting of one lead car and N following cars, such as Figure 2 As shown, vehicle 0 is the lead vehicle, and all vehicles are traveling in a single lane with no overtaking or lane changing occurring.

[0043] The equation of motion for the lead car is described as follows: ; in, for The first derivative, and These represent the position and speed of the lead vehicle, respectively.

[0044] The equation of motion for the i-th following car out of N following cars is: ; in, for The first derivative, , and They represent t Time of the first Vehicle position, speed, and control inputs for The first derivative, This represents an unknown nonlinear disturbance caused by the superposition of measurement errors, parameter uncertainties, and natural environmental interference. Assume an unknown nonlinear disturbance. satisfy and ,in, and A positive number indicates that the controller of this invention controls an unknown nonlinear disturbance of finite size.

[0045] Using a constant headway spacing strategy, the spacing error is defined as: ; in, Let be the spacing error of the i-th vehicle at time t. This refers to the actual distance between two adjacent vehicles. h 1 represents a fixed headway. This represents the distance between two adjacent vehicles when they are stationary.

[0046] Step S102 is as follows: Construct a vehicle following model in a V2V environment based on operational status information. : ; Among them, vehicles Indicates with vehicles In this embodiment, the vehicle establishing communication only communicates with the vehicle ahead in the following queue. α A The adjustable sensitivity coefficient for autonomous vehicles in a V2V environment; μ i,l This refers to the communication weighting coefficient; Indicates in Time vehicle With vehicles The average distance between vehicles, for Time vehicle Location, for Time vehicle Location; Indicates in Time vehicle With vehicles The speed difference between them For the optimal velocity function, is the response coefficient to the relative velocity stimulus.

[0047] Communication weight coefficient μ i,l Specifically: ; in, This represents the number of increments in the accumulation operation.

[0048] This embodiment designs a non-singular integral sliding mode variable that considers vehicle car-following characteristics. The vehicle car-following model in a V2V environment is embedded into the integral sliding surface, ensuring that queue operation conforms to actual traffic flow characteristics. The non-singular integral sliding mode variable is designed as follows: ; in, Let be the non-singular integral sliding mode variable of the i-th vehicle at time t; Let be the spacing error of the i-th vehicle at time t; σ and These are positive parameters, all of which are sliding surface design parameters. These parameters vary under different environments and require adjustment by those skilled in the art based on the actual application environment. Furthermore, 0 < 0. <1; For integration variables, Represents the integral variable The spacing error of the i-th vehicle at time i; For symbolic functions, Represents the integral variable The vehicle following model for the i-th vehicle at time step.

[0049] To ensure the stability of the vehicle following queue, coupled sliding mode variables are further introduced. : ; in, p For positive parameters, 0 < p The value is ≤1. The parameter varies in different environments and requires those skilled in the art to set and adjust the parameter according to the actual application environment.

[0050] Step S103 is as follows: This embodiment designs an adaptive superhelical perturbation observer based on second-order superhelical theory to estimate unknown nonlinear perturbations experienced by the system in real time. The specific steps are as follows: First, based on the nonlinear perturbation, the following second-order nominal system is constructed: ; in, , It is a calculated value based on state reconstruction; , express The first derivative, express The first derivative, for The first derivative.

[0051] Secondly, the adaptive superspiral perturbation observer is designed as follows: ; ; in, , and For perturbation observer gain, μ , ω and θ To adaptively adjust parameters, and The estimated values ​​are respectively and , , , and All of these are disturbance estimation errors. and They are respectively and The first derivative, will This serves as the result of the perturbation estimation.

[0052] The core innovation of the adaptive superspiral perturbation observer lies in the introduction of an adaptive mechanism, through... Dynamically adjust the observer gain , and When the estimation error is large, the gain is increased to accelerate convergence; when the estimation error is small, the gain is decreased to suppress measurement noise, thereby achieving high-precision adaptive estimation of time-varying multi-source disturbances.

[0053] In some embodiments, the adaptive law of the adaptive superspiral perturbation observer can be replaced by other forms of nonlinear functions, such as... or Similarly, this method can also achieve the function of dynamically adjusting the observer gain based on the magnitude of the estimation error.

[0054] Step S104 is as follows: Based on the coupled sliding mode variables, the operating state information of the vehicle queue, and the disturbance estimation results, a distributed non-singular integral sliding mode controller is designed: ; in k For controller gain, distributed non-singular integral sliding mode controller A i ( t The expression for ) is:

[0055] in, For the first The first derivative of the vehicle spacing error; This represents the spacing error of the Nth vehicle.

[0056] To suppress chattering in sliding mode control, this embodiment employs a hyperbolic tangent function. Substitution symbol function ,in It is a small positive real number.

[0057] The final form of the distributed nonsingular integral sliding mode controller is: ; That is, it serves as the control input for the i-th vehicle at time t.

[0058] In some embodiments, an alternative to the sign function for suppressing chattering can be a saturation function. or S-shaped function By replacing the hyperbolic tangent function, the same effect of suppressing chattering can be achieved.

[0059] Simulation verification To verify the effectiveness of the proposed method, a queuing control system consisting of one lead vehicle and six follower vehicles was set up in MATLAB simulation software. The minimum safe distance between two adjacent vehicles was set as follows: The headway is The safe distance between vehicles and the maximum speed are set as follows: and The simulation step size was set to 0.001 seconds, and the total simulation time was 15 seconds. The parameters of the distributed non-singular integral sliding mode controller and the adaptive superspiral perturbation observer were selected as follows: , , , , , , , , , , In the initial stage of the simulation, the vehicle... Arranged at uniform intervals, the initial speed of the following vehicle is set to... The speed change of the lead car is represented by the following Gaussian equation:

[0060] in, The unit is m / s. Unknown nonlinear integrated perturbation The settings are as follows: ; like Figure 3 and Figure 4 As shown, despite the influence of external disturbances and changes in the speed of the lead vehicle, under the action of the distributed non-singular integral sliding mode controller, the speed of the following vehicles can follow the speed of the lead vehicle, and other state variables can also converge to the vicinity of the state of the lead vehicle. This indicates that under the action of the distributed non-singular integral sliding mode controller, the followers can stably follow the lead vehicle, which demonstrates the effectiveness of the distributed non-singular integral sliding mode controller.

[0061] When no disturbance observer is used, although the system state variables can converge, the oscillation amplitude during operation is still quite significant, such as... Figure 3 (a) and Figure 4 As shown in (a) above. When an adaptive superhelical perturbation observer is added, as shown in (a) below. Figure 3 (b) and Figure 4 As shown in (b), the fluctuation amplitude of the state variables is reduced during system operation, the influence of external disturbances on the system is reduced, and the system operates more smoothly.

[0062] Finally, comparison Figure 3 (b) and (c) in the middle and Figure 4 As shown in (b) and (c), when the car-following model is introduced, the following vehicles can follow the preceding vehicles better and converge to a steady state faster in terms of vehicle spacing and speed variables. The acceleration, sliding surface and spacing error can also reflect the following effect of the system. This shows that the following performance of the queuing system is better during operation, more in line with traffic flow characteristics and more in line with the actual situation.

[0063] Experimental verification The experiment used JetAcker-based intelligent vehicles. Each intelligent vehicle included a WiFi signal receiver module 1, a camera 2, and a LiDAR 3. The WiFi signal receiver module was used to communicate with the WiFi communication base station. The queuing system experimental platform included a main control device, a communication base station, and a queuing system consisting of one lead vehicle and three follower vehicles. The intelligent vehicles used in the experiment communicated with the main control device and other vehicles via a local area network based on the ROS system. The communication structure between the experimental vehicles and the experimental platform is as follows: Figure 5 As shown. Before the experiment, the stationary distance between the front ends of the vehicles in the queuing system was set to 0.6 meters. The experimental parameters were set as follows: , , , , , , , , , , , , , , This experiment simulates two common operational scenarios in a real-world environment: the lead vehicle suddenly stopping and restarting, and the queuing system smoothly operating – accelerating – smoothly operating – decelerating – smoothly operating. The time step was set to 0.1 seconds, and the total experiment duration was set to 40 to 50 seconds.

[0064] The scenario where the lead vehicle suddenly stops and then restarts occurs as follows: Initially, the queue is stationary, then the lead vehicle... Driving at a speed, the lead vehicle suddenly stopped during the journey, and then... The vehicle continued to move at the lead speed, with the other vehicles following the lead car. Figure 6 The driving situation of the experimental queue when using the controller proposed in this invention under such circumstances, wherein, Figure 6 In the diagram, (a) and (b) represent the changes in vehicle spacing and speed, respectively. From... Figure 6 It can be observed that the queuing system using the controller proposed in this invention can converge both before and after the speed of the lead vehicle changes. After the queuing system starts, the speed converges to... The distance between the front and rear of the vehicles also narrowed to the left and right. When the lead vehicle suddenly stops, the speed and spacing of the following vehicles decrease accordingly, but they recover to a stable state within a short period of time. This shows that, under the control of the controller, the queuing system can converge to a stable state within a limited time and can also quickly recover to a stable state after encountering disturbances, which proves the effectiveness of the controller.

[0065] The speed of the queue system changes as follows: initially, the queue is stationary, then the lead car moves... Moving at high speed, next, the maximum speed of the queuing system. from Increase to After stable operation, the vehicle's maximum speed will be... from Reduced back to When the maximum speed of a vehicle changes, the speed of vehicles and the headway between vehicles in the queuing system will also change accordingly. Figure 7 The driving status of the experimental queue when using the controller proposed in this invention under such circumstances. Figure 7 Figures (a) and (b) show the changes in vehicle spacing and speed, respectively. It can be seen from the figures that both disturbance observers can lead to system convergence. After the queuing system is started, the speed converges to... The time was basically stable, and the distance between the front of the vehicles also converged to With maximum speed As the size increases, the speed and spacing between vehicles in the queuing system also gradually increase. Increase to At that time, the speed of the queue system also converged to The distance between the front of the car has also narrowed. The queuing system reached another stable state, after which the maximum speed of the vehicles... Reduced back to The system's speed and headway also decrease accordingly, and eventually the speed converges back to [the desired value]. The distance between the front of the car converges to This demonstrates that the queuing system can converge to a steady state within a finite time, and can also regain a new steady state when the system's state changes. Furthermore, when the speed of the queuing system changes, the corresponding headway also changes, which is consistent with traffic flow theory.

[0066] Example 2 This embodiment provides a vehicle platooning and following control system based on adaptive disturbance observation, including: The information acquisition and motion following module is configured to: acquire the running status information of the vehicle queue, establish the vehicle following motion equation containing nonlinear disturbances, and determine the spacing error between adjacent vehicles according to the preset headway strategy. The sliding mode variable construction module is configured to: construct a vehicle car-following model based on the running state information; and, in conjunction with the spacing error, introduce non-singular integral sliding mode variables into the vehicle car-following model to construct coupled sliding mode variables. The perturbation observer module is configured to: construct an adaptive superspiral perturbation observer based on the nonlinear perturbation, adjust the gain of the adaptive superspiral perturbation observer according to the perturbation estimation error, and obtain the perturbation estimation result of the nonlinear perturbation; The sliding mode controller module is configured to: construct a sliding mode controller based on the coupled sliding mode variables, the operating status information of the vehicle queue, and the disturbance estimation results, and determine the control input for each following vehicle; The control update module is configured to: apply the control input of each following vehicle to the corresponding following vehicle, update the running status information of the vehicle queue, and determine the control input of each following vehicle again based on the updated running status information, so as to realize the distributed car-following control of the vehicle queue.

[0067] It should be noted that the aforementioned information acquisition and motion-following construction module, sliding mode variable construction module, disturbance observer module, sliding mode controller module, and control update module correspond to steps S101 to S105 in Embodiment 1. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should also be noted that these modules, as part of the system, can be executed in a computer system, such as a set of computer-executable instructions.

[0068] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0069] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0070] Example 3 This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the method described in Embodiment 1.

[0071] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0072] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0073] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0074] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0075] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0076] Example 4 Embodiment 4 of the present invention provides a computer-readable storage medium.

[0077] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the method as described in Embodiment 1 of the present invention.

[0078] The detailed steps are the same as those provided in Example 1, and will not be repeated here.

[0079] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0080] A computer program product includes software code, wherein the program in the software code performs the steps described in Embodiment 1 of the present invention.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A vehicle platooning following control method based on adaptive disturbance observation, characterized in that, include: The system acquires the operational status information of the vehicle platoon, establishes vehicle following motion equations containing nonlinear disturbances, and determines the spacing error between adjacent vehicles based on a preset headway strategy. A vehicle following model is constructed based on the operating status information; combined with the spacing error, non-singular integral sliding mode variables are introduced into the vehicle following model to construct coupled sliding mode variables; An adaptive superspiral perturbation observer is constructed based on the nonlinear perturbation. The gain of the adaptive superspiral perturbation observer is adjusted according to the perturbation estimation error to obtain the perturbation estimation result of the nonlinear perturbation. Based on the coupled sliding mode variables, the operating status information of the vehicle platoon, and the disturbance estimation results, a sliding mode controller is constructed to determine the control input for each following vehicle. The control inputs of each following vehicle are applied to the corresponding following vehicle, the operating status information of the vehicle queue is updated, and the control inputs of each following vehicle are determined again based on the updated operating status information, thereby realizing distributed car-following control of the vehicle queue.

2. The vehicle platooning following control method based on adaptive disturbance observation as described in claim 1, characterized in that, The operational status information of the vehicle queue includes the position, speed, and acceleration of each vehicle in the vehicle following queue, as well as the adjustable sensitivity coefficient in the V2V environment.

3. The vehicle platooning following control method based on adaptive disturbance observation as described in claim 1, characterized in that, In conjunction with the aforementioned spacing error, non-singular integral sliding mode variables are introduced into the vehicle car-following model, specifically as follows: ； in, Let be the non-singular integral sliding mode variable of the i-th vehicle at time t. Let be the spacing error of the i-th vehicle at time t. σ and For positive parameters, 0 < <1, For integration variables, Represents the integral variable The spacing error of the i-th vehicle at time i. For symbolic functions, Represents the integral variable The vehicle following model for the i-th vehicle at time step.

4. The vehicle platooning following control method based on adaptive disturbance observation as described in claim 1, characterized in that, The adaptive superspiral perturbation observer is constructed based on the nonlinear perturbation, specifically by: constructing a second-order nominal system based on the nonlinear perturbation; constructing an adaptive superspiral perturbation observer based on the second-order nominal system; obtaining the estimation results of the nonlinear perturbation and its variation; and determining the perturbation estimation error based on the state of the adaptive superspiral perturbation observer.

5. The vehicle platooning following control method based on adaptive disturbance observation as described in claim 1, characterized in that, The gain of the adaptive superspiral perturbation observer is adjusted according to the perturbation estimation error. Specifically, the direction and degree of gain adjustment of the adaptive superspiral perturbation observer are determined according to the perturbation estimation error, so that the gain of the observer is dynamically updated as the perturbation estimation error changes. The observer gain is increased when the perturbation estimation error is large, and decreased when the perturbation estimation error is small.

6. The vehicle platooning following control method based on adaptive disturbance observation as described in claim 1, characterized in that, Based on the coupled sliding mode variables, the operating state information of the vehicle platoon, and the disturbance estimation results, a sliding mode controller is constructed to determine the control input for each following vehicle, specifically: ； in, This represents the control input for the i-th vehicle at time t. h 1 represents a fixed headway; Let be the speed of the i-th vehicle at time t; Let be the speed of the (i-1)th vehicle at time t; p For positive parameters, 0 < p ≤1; It is a non-singular integral sliding mode controller; k For controller gain; It is the hyperbolic tangent function; For coupled sliding mode variables; It is a small positive real number; This is the result of the disturbance estimation.

7. A vehicle platooning following control system based on adaptive disturbance observation, characterized in that, include: The information acquisition and motion following module is configured to: acquire the running status information of the vehicle queue, establish the vehicle following motion equation containing nonlinear disturbances, and determine the spacing error between adjacent vehicles according to the preset headway strategy. The sliding mode variable construction module is configured to: construct a vehicle car-following model based on the running state information; and, in conjunction with the spacing error, introduce non-singular integral sliding mode variables into the vehicle car-following model to construct coupled sliding mode variables. The perturbation observer module is configured to: construct an adaptive superspiral perturbation observer based on the nonlinear perturbation, adjust the gain of the adaptive superspiral perturbation observer according to the perturbation estimation error, and obtain the perturbation estimation result of the nonlinear perturbation; The sliding mode controller module is configured to: construct a sliding mode controller based on the coupled sliding mode variables, the operating status information of the vehicle queue, and the disturbance estimation results, and determine the control input for each following vehicle; The control update module is configured to: apply the control input of each following vehicle to the corresponding following vehicle, update the running status information of the vehicle queue, and determine the control input of each following vehicle again based on the updated running status information, so as to realize the distributed car-following control of the vehicle queue.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the vehicle platoon following control method based on adaptive disturbance observation as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the vehicle platooning following control method based on adaptive disturbance observation as described in any one of claims 1-6.

10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of the vehicle platoon following control method based on adaptive disturbance observation as described in any one of claims 1-6.

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