A parallel composite control method and system for intelligent networked vehicle platoon

Abstract

The application relates to the technical field of vehicle control, and discloses a parallel composite control method and system for an intelligent networked vehicle queue. The method comprises the following steps: acquiring running state information of each vehicle in the vehicle queue and communication states between the vehicles; determining a communication connection probability according to the communication states, and calculating a communication coefficient and a communication weight; establishing a vehicle queue following dynamics model that fuses the influence of communication quality; designing a linear compensator based on the model, and outputting a steady-state reference control quantity; constructing an integral sliding mode variable, and using an adaptive integral sliding mode controller to perform gain adjustment, so as to obtain a disturbance compensation control quantity; and superimposing the two to obtain a composite control output for adjusting the running state of each vehicle. Through parallel division of the linear compensator and the adaptive integral sliding mode controller, the functions of steady-state convergence and disturbance suppression are decoupled, and the rapid convergence, string stability and anti-disturbance performance of the vehicle queue can be ensured simultaneously.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, specifically to a parallel composite control method and system for intelligent connected vehicle platoons. 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] Intelligent connected vehicle platoons achieve multi-vehicle coordinated following through inter-vehicle communication, improving road traffic efficiency and reducing vehicle operating energy consumption. Existing vehicle platoon control methods mostly employ sliding mode control or composite control structures, but they typically place steady-state error convergence and external disturbance suppression in the same control channel, lacking a division of control functions for different operating conditions. Under disturbance-free or normal communication conditions, disturbance compensation control persists, easily interacting with steady-state control and reducing the convergence speed of vehicle following errors. When external disturbances are strong, disturbance compensation relies primarily on a single control channel, limiting control margin and making it difficult to balance rapid platoon convergence and disturbance rejection stability. Furthermore, traditional sliding mode control is prone to significant control chattering, affecting vehicle ride smoothness.

[0004] Therefore, existing vehicle platoon control methods suffer from the problem of coupling steady-state convergence control and disturbance robust control, making it difficult to achieve effective division of control functions according to different operating conditions. Consequently, it is difficult to simultaneously guarantee the fast convergence, string stability, and disturbance rejection performance of the vehicle platoon. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a parallel composite control method and system for intelligent connected vehicle platoons, which can ensure the fast convergence, string stability and anti-disturbance performance of the vehicle platoon.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a parallel composite control method for intelligent connected vehicle platoons is provided, including: Obtain the operating status information of each vehicle in the vehicle queue and the communication status between vehicles; The communication connection probability between vehicles is determined based on the communication status between the vehicles, and the communication coefficient and communication weight are calculated based on the communication connection probability. Based on the communication coefficient, the communication weight, and the operating status information, a vehicle platoon following dynamics model is established; a linear compensator is designed based on the vehicle platoon following dynamics model, and the control parameters of the linear compensator are obtained through small disturbance stability analysis, and a steady-state reference control quantity is output. Based on the vehicle platoon following dynamics model and steady-state reference control quantity, an integral sliding mode variable is constructed; the integral sliding mode controller is used to adaptively adjust the gain of the integral sliding mode variable to obtain the disturbance compensation control quantity. The steady-state reference control quantity and the disturbance compensation control quantity are superimposed to obtain a composite control output, and the operating state of each vehicle is adjusted according to the composite control output.

[0007] Secondly, a parallel composite control system for intelligent connected vehicle platoons is provided, including: The information acquisition module is configured to acquire the operating status information of each vehicle in the vehicle queue and the communication status between vehicles. The communication status processing module is configured to: determine the communication connection probability between vehicles based on the communication status between the vehicles, and calculate the communication coefficient and communication weight based on the communication connection probability; The linear compensator design module is configured to: establish a vehicle platoon following dynamics model based on the communication coefficient, the communication weight, and the operating status information; design a linear compensator based on the vehicle platoon following dynamics model; obtain the control parameters of the linear compensator through small disturbance stability analysis; and output a steady-state reference control quantity. The integral sliding mode control module is configured to: construct integral sliding mode variables based on the vehicle platoon following dynamics model and steady-state reference control quantity; and use the integral sliding mode controller to adaptively adjust the gain of the integral sliding mode variables to obtain disturbance compensation control quantity. The composite control output module is configured to superimpose the steady-state reference control quantity and the disturbance compensation control quantity to obtain a composite control output, and adjust the operating state of each vehicle according to the composite control output.

[0008] Thirdly, an electronic device is also provided, comprising: Memory is used to store computer-readable instructions in a non-transitory manner. 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: In the parallel composite control architecture designed in this invention, the linear compensator and the integral sliding mode controller are completely decoupled: the linear compensator is specifically responsible for the rapid convergence of steady-state error under the condition of no disturbance and normal communication, and optimizes parameters through small disturbance stability analysis to ensure the stability of the series; the adaptive integral sliding mode controller is only dynamically activated when external disturbances occur, and uses a hyperbolic tangent adaptive gain function with upper limit constraint to smoothly adjust the compensation intensity, which avoids the continuous chattering in traditional sliding mode control and eliminates the mutual confrontation between the two types of control commands, so that the queue achieves the best balance between fast response and strong robustness.

[0012] This invention introduces communication connection probability and communication weights to quantify and embed non-ideal factors such as signal attenuation and packet loss in actual V2V communication into the vehicle queuing following dynamics model. This enables the control method to adapt to changes in communication quality and maintain queue stability even in communication-constrained scenarios, significantly improving its engineering applicability.

[0013] This invention does not rely on high-precision interference observers or multi-level state iterations, has low computational load, and is easy to deploy in real time on vehicle-mounted computing platforms, thus having good cost-effectiveness and promotional value. Attached Figure Description

[0014] 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.

[0015] Figure 1 This is a flowchart of a parallel composite control method for intelligent connected vehicle queues in a specific embodiment of the present invention; Figure 2 This is a schematic diagram of the vehicle communication model in a queue of intelligent connected vehicles. Figure 3 The simulation experiment shows the running state of the intelligent connected vehicle queue without a controller, where (a) is the position error of each vehicle in the queue, (b) is the speed of each vehicle in the queue, (c) is the acceleration of each vehicle in the queue, and (d) is the sliding mode variable. Figure 4 The diagram shows the operating state of the intelligent connected vehicle queue when using a linear compensator in a simulation experiment, where (a) is the position error of each vehicle in the queue, (b) is the velocity of each vehicle in the queue, (c) is the acceleration of each vehicle in the queue, and (d) is the sliding mode variable. Figure 5The diagram shows the running state of the intelligent connected vehicle queue when using an adaptive sliding mode controller in a simulation experiment. (a) represents the position error of each vehicle in the queue, (b) represents the speed of each vehicle in the queue, (c) represents the acceleration of each vehicle in the queue, and (d) represents the sliding mode variable. Figure 6 The diagram shows the operating state of the intelligent connected vehicle queue when using the method proposed in this invention in a simulation experiment, where (a) is the position error of each vehicle in the queue, (b) is the speed of each vehicle in the queue, (c) is the acceleration of each vehicle in the queue, and (d) is the sliding mode variable. Detailed Implementation

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0021] 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.

[0022] Terminology Explanation: Intelligent connected vehicle platoons: Multiple intelligent connected vehicles interact with each other through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies, and travel in platoons with a small distance between vehicles, which can improve road traffic efficiency and reduce energy consumption.

[0023] Linear compensator: A controller based on classical control theory that integrates proportional-derivative, velocity feedback and feedforward correction to improve the dynamic response speed and convergence stability of the system.

[0024] Adaptive integral sliding mode control: A nonlinear control method that combines integral sliding mode control with an adaptive gain adjustment mechanism, which can effectively suppress system chattering and enhance robustness to external disturbances and parameter uncertainties.

[0025] Train stability: The characteristic that the spacing error of the preceding vehicle in a platoon will not propagate and amplify along the platoon is the core indicator for safe platooning.

[0026] Example 1 like Figure 1 As shown, this embodiment provides a parallel composite control method for intelligent connected vehicle platoons, including: S101: Obtain the operating status information of each vehicle in the vehicle queue and the communication status between vehicles; S102: Determine the communication connection probability between vehicles based on the communication status between the vehicles, and calculate the communication coefficient and communication weight based on the communication connection probability; S103: Based on the communication coefficient, the communication weight, and the operating status information, establish a vehicle platoon following dynamics model; design a linear compensator based on the vehicle platoon following dynamics model; obtain the control parameters of the linear compensator through small disturbance stability analysis; and output a steady-state reference control quantity. S104: Based on the vehicle platoon following dynamics model and steady-state reference control quantity, construct integral sliding mode variables; use the integral sliding mode controller to adaptively adjust the gain of the integral sliding mode variables to obtain the disturbance compensation control quantity; S105: The steady-state reference control quantity and the disturbance compensation control quantity are superimposed to obtain a composite control output, and the operating state of each vehicle is adjusted according to the composite control output.

[0027] Step S101 is as follows: The system uses vehicle sensors and other data acquisition devices combined with V2V communication technology to obtain the operating status information of each vehicle in the vehicle queue and the communication status between vehicles.

[0028] The operational status information of each vehicle includes: the position, speed, and acceleration of each vehicle.

[0029] The communication status between vehicles includes: communication distance between vehicles, signal reception signal-to-noise ratio, path loss index, environmental compensation factor, noise interference intensity power, and transmission power.

[0030] Step S102 is as follows: Existing car-following dynamics models all assume completely reliable communication and fail to quantify signal attenuation and packet loss issues caused by road conditions and transmission distance. This step first defines the communication connection probability, communication coefficient, and communication weight, and then embeds these parameters into the subsequent vehicle platoon car-following dynamics model to quantify the impact of communication quality on vehicle car-following status.

[0031] S102.1: Calculate the probability of communication connections between vehicles.

[0032] If two vehicles can successfully exchange data packets during V2V communication, a valid communication connection is considered to exist. The probability of this communication connection event is defined as the communication connection probability. Assume the vehicle queue is a homogeneous group, with all vehicles having completely identical communication capabilities and radii. When any two vehicles in the intelligent connected vehicle queue are communicating and The communication distance between them is At that time, its communication model is as follows: Figure 2 As shown.

[0033] Two communication vehicles and The probability of communication connection between them is defined as This indicates that intelligent connected vehicles have a communication range The probability of successfully receiving and parsing target information within a certain range reflects the impact of the wireless communication environment on the reliability of vehicle communication. Its mathematical model is as follows: ; in, Indicates distance as The signal-to-noise ratio (SNR) of the vehicle-mounted receiver; The path loss index represents the rate at which signal power decreases with distance. Its value depends on the propagation environment, typically 3.5-4.5 for urban roads and 2.7-3.5 for highways. This represents the environmental compensation factor, which reflects the effectiveness of the receiver in counteracting environmental interference through signal processing gain. It is typically set to 0.8 in a static environment and 1.5 when traveling at high speeds. Indicates the power of noise interference intensity; Indicates transmission power; Indicates vehicle With vehicles The average distance between vehicles; represent of Power of 1.

[0034] S102.2: Calculate the communication coefficient and communication weight.

[0035] Vehicle and vehicles The communication signal strength between them is defined as : ; Considering the impact of communication signal strength on connection probability, the probability of establishing a communication link between vehicles can be expressed as: ; In practical vehicle-to-everything (V2X) communication scenarios, information can be considered successfully transmitted when the probability of establishing a communication link between vehicles reaches a certain threshold. This threshold is typically set at 0.6. This is used to define an intelligent connected vehicle. and The communication coefficient between them is ,but: ; in, For the Heaviside function, when hour, This indicates the vehicle The information can be successfully transmitted to the vehicle. ,opposite At that time, If the error message is 0, it means that the information could not be transmitted successfully.

[0036] Define vehicle With vehicles The communication weighting coefficient between them is Its definition is as follows: ; in, This represents the number of increments in the accumulation operation.

[0037] Step S103 is as follows: Traditional sliding mode design does not incorporate the optimal speed following model, and the adaptive law lacks smoothness constraints. When the speed of the lead car changes abruptly or there is a short-term communication failure, the fleet fluctuates violently and cannot meet engineering safety standards.

[0038] This embodiment establishes a vehicle platoon car-following dynamics model based on the optimal speed car-following model, integrating communication coefficients, communication weights, and operational status information. : ; ; in, , For the first i The vehicle's position and speed; for The first derivative; for The first derivative; Indicates in Time vehicle With vehicles The average distance between vehicles; For vehicles The optimal speed; Indicates the vehicle's maximum speed; Indicates the safe distance between vehicles; This refers to the vehicle sensitivity coefficient. The optimal velocity function; For composite control input; This refers to lumped disturbances (including measurement errors, communication interference, and natural environmental disturbances).

[0039] This embodiment independently designs a linear compensator that integrates proportional-derivative, speed feedback, and feedforward correction. It solves for the optimal control coefficients through small disturbance stability analysis and is specifically responsible for the rapid convergence of errors in scenarios with normal communication and no external disturbances, ensuring the stability of the vehicle train. It has no additional disturbance compensation function and only outputs the steady-state reference control quantity.

[0040] For the established vehicle platoon following dynamics model, a triple mechanism of proportional-derivative + speed feedback + feedforward correction is introduced to design a linear compensator:

[0041] in, , , These are the proportional-derivative coefficient, velocity feedback coefficient, and feedforward coefficient, respectively. Indicates in Time vehicle With vehicles The speed difference between them; The steady-state velocity of the queue is determined. The neutral stability condition is derived through small-perturbation stability analysis, and the above is optimized. , , To ensure the system converges quickly to equilibrium without disturbance, the output of the linear compensator... As a steady-state reference control quantity.

[0042] In some embodiments, the linear compensator that integrates proportional derivative, speed feedback, and feedforward correction can be replaced with a PID controller or a linear quadratic regulator. Stability optimization can be achieved by adjusting the weight matrix, which is suitable for low-end vehicle platforms with limited computing resources.

[0043] Step S104 is as follows: To address the issues of overshoot, large chattering, and lack of consideration for actual traffic conditions in traditional sliding mode control, this step employs a three-layer design to simultaneously resolve these shortcomings: First, an integral sliding mode variable embedded in the vehicle queuing dynamics model is constructed, ensuring the control strategy considers the actual traffic environment and better reflects real-world traffic conditions. Second, a hyperbolic tangent adaptive gain function with upper limit constraints is designed. The maximum gain is constrained by the hyperbolic tangent square term, capping the gain when the deviation is large and smoothly decreasing it as the deviation approaches zero. Simultaneously, the hyperbolic tangent function replaces the sign function, further smoothing the control output and significantly reducing acceleration chattering. Third, relying on "integral sliding mode + adaptive gain" directly cancels lumped disturbances, eliminating the need for multi-order observer iterative calculations and reducing the computational load on the vehicle controller.

[0044] Construct integral sliding mode variables that include a vehicle platooning and car-following dynamics model :

[0045] Where τ is the integration variable.

[0046] An adaptive reaching law is introduced to replace the traditional exponential reaching law, and an adaptive integral sliding mode controller is designed, employing a hyperbolic tangent function. Substitution symbol function To suppress chattering, no additional interference observer is required; an adaptive integral sliding mode controller is used to adaptively adjust the gain of the integral sliding mode variable to obtain the disturbance compensation control quantity. : ; in, k To control the gain, an adaptive gain adjustment function is used. , , , For controller coefficients, ; , k The specific size is set by those skilled in the art based on the actual state of the intelligent connected vehicle queue.

[0047] In some embodiments, the adaptive gain adjustment function It can be replaced by a fuzzy adaptive law or a neural network adaptive law, which optimizes the gain through online learning to further improve the adaptability to complex time-varying disturbances.

[0048] In some embodiments, the integral sliding mode variable can be replaced with the terminal sliding mode variable or the non-singular terminal sliding mode variable to achieve finite-time convergence of the system state, which is suitable for scenarios with extremely high requirements for response speed.

[0049] Step S105 is as follows: The output steady-state reference control quantity of the linear compensator is superimposed with the output disturbance compensation control quantity of the adaptive integral sliding mode controller to obtain the composite control output. : ; When there is no external disturbance The linear compensator ensures rapid convergence; when external disturbances occur, the adaptive integral sliding mode controller is dynamically activated to compensate for the disturbances and maintain system stability. The two controllers have a clear division of labor and automatically switch the dominant unit according to the operating conditions, without command offsetting.

[0050] The vehicle's throttle and brakes are adjusted according to the composite control output to achieve vehicle acceleration and deceleration control.

[0051] The method proposed in this invention can be extended to scenarios such as bidirectional communication topology and multi-queue collaborative topology, and queue control under different topologies can be achieved by adjusting the communication coefficient matrix and the communication weight coefficient matrix.

[0052] Simulation verification The effectiveness of the proposed method was verified through numerical simulation experiments. The intelligent connected vehicle convoy consists of one lead vehicle and nine following vehicles, with the safe distance between vehicles and the maximum speed set as follows: and The initial driving state of the vehicle platoon is set to have a uniform vehicle spacing. Arranged, with all vehicles having an initial velocity of... The simulation step size is set to 0.01 seconds, and the total simulation duration is 5000 time steps, or 50 seconds. The speed change of the lead car is given by the Gaussian equation, namely:

[0053] in, , , , This indicates that a [something] was introduced at the 10th and 35th seconds. The speed fluctuation. The parameters of the linear compensator are set to... , The parameters of the adaptive integral sliding mode controller are set as follows: The disturbance setting in the intelligent connected vehicle queue is... ,in These are the disturbance parameters.

[0054] like Figure 3 As shown, without the use of a controller, the state variables of vehicles in the intelligent connected vehicle platoon, including position error, speed, acceleration, and sliding mode variables, all exhibited significant fluctuations. These fluctuations were influenced by changes in the speed of the lead vehicle, with the fluctuations intensifying around the 10th and 35th seconds. Furthermore, due to continuous disturbances, the intelligent connected vehicle platoon experienced jittering throughout its operation.

[0055] After introducing a linear compensator, such as Figure 4 As shown, the fluctuation amplitude of the state variables of the intelligent connected vehicle queue is significantly reduced, and the amplification trend of fluctuations around the 10th and 35th seconds disappears. According to the definition of stability, these phenomena indicate that the system's ability to restore an equilibrium state is significantly improved, and the system's stability is enhanced. However, from... Figure 4 The initial stage of (a) and Figure 4 (b) Figure 4 (c) Figure 4 As can be seen in (d), there is still a jitter phenomenon during the operation of the intelligent connected vehicle platoon. This is mainly due to the influence of external disturbances and the weak robustness of the linear compensator.

[0056] When an intelligent connected vehicle platoon uses an adaptive integral sliding mode controller, such as Figure 5 As shown, the fluctuations of the state variable around the 10th and 35th seconds did not resemble... Figure 4 Such a rapid recovery to stability indicates a decrease in system stability. However, from Figure 5 The initial stage of (a) and Figure 5 (b) Figure 5 (c) Figure 5 As can be seen in (d), the jitter amplitude during the operation of the intelligent connected vehicle platoon is higher than that in (d). Figure 4 In the case of small and medium-sized vehicles, according to the definition of robustness, this indicates that the entire intelligent connected vehicle fleet has enhanced anti-interference capabilities and improved robustness. From Figure 5 It can be seen that the adaptive integral sliding mode controller can meet the robustness requirements of the system, but its stability is insufficient.

[0057] like Figure 6 As shown, when the method proposed in this invention is used to control the intelligent connected vehicle queue, the intelligent connected vehicle queue not only maintains good stability, but also significantly reduces the amplitude of jitter during operation. Figure 6 The initial fluctuations in (a) also disappeared. Figure 6 (b) Figure 6 (c) Figure 6The jitter amplitude in (d) was also significantly reduced, reaching an acceptable level. According to the definitions of stability and robustness, after using the composite controller, the intelligent connected vehicle convoy can not only quickly recover to a stable state, but also has strong anti-interference ability, and possesses good stability and robustness.

[0058] Example 2 This embodiment provides a parallel composite control system for intelligent connected vehicle platoons, including: The information acquisition module is configured to acquire the operating status information of each vehicle in the vehicle queue and the communication status between vehicles. The communication status processing module is configured to: determine the communication connection probability between vehicles based on the communication status between the vehicles, and calculate the communication coefficient and communication weight based on the communication connection probability; The linear compensator design module is configured to: establish a vehicle platoon following dynamics model based on the communication coefficient, the communication weight, and the operating status information; design a linear compensator based on the vehicle platoon following dynamics model; obtain the control parameters of the linear compensator through small disturbance stability analysis; and output a steady-state reference control quantity. The integral sliding mode control module is configured to: construct integral sliding mode variables based on the vehicle platoon following dynamics model and steady-state reference control quantity; and use the integral sliding mode controller to adaptively adjust the gain of the integral sliding mode variables to obtain disturbance compensation control quantity. The composite control output module is configured to superimpose the steady-state reference control quantity and the disturbance compensation control quantity to obtain a composite control output, and adjust the operating state of each vehicle according to the composite control output.

[0059] It should be noted that the information acquisition module, communication status processing module, linear compensator design module, integral sliding mode control module, and composite control output module mentioned above 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 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] Example 4 Embodiment 4 of the present invention provides a computer-readable storage medium.

[0069] 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.

[0070] The detailed steps are the same as those provided in Example 1, and will not be repeated here.

[0071] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0072] 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.

[0073] 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 parallel composite control method for intelligent connected vehicle platoons, characterized in that, include: Obtain the operating status information of each vehicle in the vehicle queue and the communication status between vehicles; The communication connection probability between vehicles is determined based on the communication status between the vehicles, and the communication coefficient and communication weight are calculated based on the communication connection probability. Based on the communication coefficient, the communication weight, and the operating status information, a vehicle platoon following dynamics model is established; a linear compensator is designed based on the vehicle platoon following dynamics model, and the control parameters of the linear compensator are obtained through small disturbance stability analysis, and a steady-state reference control quantity is output. Based on the vehicle platoon following dynamics model and steady-state baseline control variables, integral sliding mode variables are constructed. The disturbance compensation control quantity is obtained by adaptively adjusting the gain of the integral sliding mode variable using an integral sliding mode controller. The steady-state reference control quantity and the disturbance compensation control quantity are superimposed to obtain a composite control output, and the operating state of each vehicle is adjusted according to the composite control output.

2. The parallel composite control method for intelligent connected vehicle platoons as described in claim 1, characterized in that, The probability of communication connection between vehicles is determined based on the communication status between the vehicles. Specifically, the probability of communication connection between vehicles is determined based on the communication distance between the vehicles, the signal-to-noise ratio of the received signal, the path loss index, the environmental compensation factor, the noise interference intensity power, and the transmission power.

3. The parallel composite control method for intelligent connected vehicle platoons as described in claim 1, characterized in that, The communication coefficient and communication weight are calculated based on the communication connection probability. Specifically, the establishment status of the communication link between vehicles is determined based on the communication signal strength between vehicles and the communication connection probability, and the communication coefficient is determined based on a preset communication threshold. The communication weight between vehicles is determined based on the communication coefficient and the communication connection status between vehicles.

4. The parallel composite control method for intelligent connected vehicle platoons as described in claim 1, characterized in that, A linear compensator is designed based on the vehicle platoon car-following dynamics model. The control parameters of the linear compensator are obtained through small disturbance stability analysis, and the steady-state reference control quantity is output. Specifically, a linear compensator including proportional-derivative adjustment, speed feedback and feedforward correction is constructed based on the vehicle platoon car-following dynamics model. Small disturbance stability analysis is performed on the operating characteristics of the vehicle platoon near the equilibrium state, and the proportional derivative coefficient, speed feedback coefficient and feedforward coefficient of the linear compensator are determined based on the analysis results. Based on the determined control parameters and the following state between vehicles, the output of the linear compensator is calculated, and the output is determined as the steady-state reference control quantity.

5. The parallel composite control method for intelligent connected vehicle platoons as described in claim 1, characterized in that, The disturbance compensation control quantity is obtained by adaptively adjusting the gain of the integral sliding mode variable using an integral sliding mode controller, specifically as follows: in, This is the disturbance compensation control quantity; k To control the gain; For integral sliding mode variables; It is an adaptive gain adjustment function, and , , , For controller coefficients, .

6. The parallel composite control method for intelligent connected vehicle platoons as described in claim 1, characterized in that, The operational status information includes: the position, speed, and acceleration of each vehicle; the communication status between vehicles includes: the communication distance between vehicles, the signal-to-noise ratio, the path loss index, the environmental compensation factor, the noise interference intensity power, and the transmission power.

7. A parallel composite control system for intelligent connected vehicle platoons, characterized in that, include: The information acquisition module is configured to acquire the operating status information of each vehicle in the vehicle queue and the communication status between vehicles. The communication status processing module is configured to: determine the communication connection probability between vehicles based on the communication status between the vehicles, and calculate the communication coefficient and communication weight based on the communication connection probability; The linear compensator design module is configured to: establish a vehicle platoon following dynamics model based on the communication coefficient, the communication weight, and the operating status information; design a linear compensator based on the vehicle platoon following dynamics model; obtain the control parameters of the linear compensator through small disturbance stability analysis; and output a steady-state reference control quantity. The integral sliding mode control module is configured to: construct integral sliding mode variables based on the vehicle platoon following dynamics model and the steady-state reference control quantity; The disturbance compensation control quantity is obtained by adaptively adjusting the gain of the integral sliding mode variable using an integral sliding mode controller. The composite control output module is configured to superimpose the steady-state reference control quantity and the disturbance compensation control quantity to obtain a composite control output, and adjust the operating state of each vehicle according to the composite control output.

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 parallel composite control method for intelligent connected vehicle platoons 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 the program is executed by the processor, it implements the steps of the parallel composite control method for intelligent connected vehicle platoons 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 parallel composite control method for intelligent connected vehicle queues as described in any one of claims 1-6.

Images