Distributed control method for connected vehicle platoon based on bandwidth-aware self-triggered communication under denial-of-service attack
By distinguishing between communication status and denial-of-service attack time in the vehicle network, a bandwidth-aware self-triggering control strategy was designed, which solved the stability problem of fleet collaborative control under communication constraints and attacks, and realized stable tracking and efficient operation of the fleet system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANJING UNIV OF POSTS & TELECOMM
- Filing Date
- 2026-04-28
- Publication Date
- 2026-07-24
AI Technical Summary
Existing vehicle fleet collaborative control methods struggle to guarantee system stability and continuous operation when communication links are limited or when subjected to denial-of-service attacks. In particular, the lack of adaptive control strategies under denial-of-service attacks may lead to system instability or Zeno behavior.
By constructing state-space equations and communication link reachability determination mechanisms, the system distinguishes between normal communication and denial-of-service attack time intervals, designs bandwidth-aware self-triggered control strategies, reduces unnecessary communication during normal intervals, and switches to preset sampling methods during attack intervals. Combined with a consistency controller and a distributed control framework, the system achieves stable tracking and control strategy switching for the fleet system.
In environments with limited communication and denial-of-service attacks, the stability and security of the fleet system are improved, the communication burden and computational complexity are reduced, Zeno behavior is avoided, and the continuous operation and collaborative control performance of the fleet are ensured.
Smart Images
Figure CN122457656A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of industrial process control and vehicle networking collaborative control technology, specifically relating to a distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks. Background Technology
[0002] With the development of vehicle-to-everything (V2X) and autonomous driving technologies, fleet cooperative control methods based on vehicle-to-vehicle (V2V) communication have been extensively studied. These methods achieve tracking of the lead vehicle and stable fleet formation control by exchanging state information such as position, speed, and acceleration between vehicles. In existing fleet cooperative control methods, most control strategies rely on periodic sampling or event-triggered mechanisms to achieve state updates and control input calculations. However, existing fleet cooperative control methods typically assume continuous availability of the V2X communication link or only consider mild communication unreliability scenarios such as random packet loss. However, in real-world open wireless network environments, communication links may be completely interrupted for certain time intervals due to bandwidth limitations, network congestion, or malicious interference, resulting in the inability to transmit state information between vehicles and making it difficult for control strategies relying on real-time communication to function properly.
[0003] To reduce communication load, some existing technologies have introduced self-triggering control strategies. These strategies predict future states and calculate the next update time in advance, thereby reducing continuous monitoring and frequent communication. However, most existing self-triggering fleet control methods do not incorporate the scenario of complete communication interruption into a unified modeling framework, particularly lacking system stability analysis and control strategy design for situations where denial-of-service attacks cause temporary disconnection of the communication topology. Furthermore, denial-of-service attacks are intermittent in time, with uncertain duration and frequency. Existing methods generally do not impose constraints on the attack duration and frequency to ensure system stability, nor do they distinguish between control update mechanisms during normal and disrupted communication periods. This can lead to a loss of coordinated stability in fleet systems, or even security risks, when attacks are frequent or prolonged. In addition, some existing self-triggering control methods do not adequately consider the lower bound of the trigger interval in their theoretical design, potentially leading to infinitely dense trigger times and Zeno-like behavior, which is difficult to implement in practical systems.
[0004] Therefore, existing technologies still lack a distributed fleet cooperative control method that can operate solely on a self-triggering mechanism under conditions of limited vehicle-to-everything (V2X) communication and the presence of denial-of-service attacks, can adaptively switch update strategies between two time intervals of normal and blocked communication, and simultaneously ensure system stability and Zeno-free behavior. Summary of the Invention
[0005] To address the issues of high reliance on continuous and reliable communication and difficulty in ensuring stable operation under denial-of-service attacks and limited communication bandwidth in existing vehicle-to-everything (V2X) fleet cooperative control technologies, this application provides a self-triggered formation control method for V2X cooperative vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks. This method models denial-of-service attacks, distinguishing between normal communication time intervals and effective attack time intervals. Within normal communication time intervals, a bandwidth-aware self-triggered control update strategy is designed based on communication bandwidth occupancy, enabling vehicles to perform control and information updates only when necessary, reducing unnecessary communication interactions. During time intervals affected by effective denial-of-service attacks, a preset sampling method is switched to ensure system feasibility. This allows for stable tracking of the lead vehicle's motion status within the fleet under conditions of limited attack duration and frequency, thereby reducing communication and computational burdens while improving system stability under communication constraints and attack environments.
[0006] To achieve the above objectives, this application employs the following technical solution:
[0007] This application discloses a distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks, specifically including the following steps:
[0008] Step 1: Construct the state space equations of the cooperative vehicles to obtain the expected formation state and actual state of the lead vehicle and following vehicles;
[0009] Step 2: Model potential denial-of-service (DoS) attacks in the vehicle-to-everything (V2X) network. Identify communication interruption scenarios through a communication link reachability determination mechanism. Define the effective DoS attack time intervals that cause the fleet communication topology to lose connectivity. Apply constraints to the effective DoS attack time intervals and their frequency. Specifically, when a DoS attack occurs but the fleet communication topology remains connected, the corresponding time interval is determined to be an invalid DoS attack time interval; when a DoS attack causes the fleet communication topology to lose connectivity, the corresponding time interval is determined to be an effective DoS attack time interval.
[0010] Step 3: Based on the actual status of the lead vehicle and following vehicles obtained in Step 1, within the time interval when communication is normal and the convoy communication topology remains connected, design a self-triggered control update strategy for each following vehicle. Each vehicle autonomously predicts and determines the next control input update time based solely on its own status and neighbor vehicle status information received most recently, without continuously monitoring status errors. During the time interval when communication is under effective denial-of-service attack, the self-triggered update strategy is stopped, and each following vehicle updates the control input according to a preset time sampling period to ensure the feasibility of the control system under communication obstruction conditions.
[0011] Step 4: Based on steps 1, 2 and 3, construct a fleet consistency controller. Under the condition that the denial-of-service attack meets the frequency and duration constraints, the fleet system can meet the control requirements.
[0012] A further improvement in this application is that: the actual states of the lead vehicle and the following vehicles are obtained, and the state-space equation of the cooperative vehicle constructed in step 1 is:
[0013]
[0014] in, For the first The longitudinal position of the vehicles Vertical position The derivative with respect to time, For the first The speed of the vehicles For speed The derivative with respect to time, For the first The acceleration of the vehicle For vehicle quality, For engine driving force, For air resistance, For vehicle rolling resistance, Given the dynamic coefficient, the state-space equations for the lead vehicle and the following vehicle are:
[0015]
[0016] in, This is the state vector of the vehicle being followed. Let be the state vector of the lead vehicle. The matrix of the third-order dynamics system of the vehicle. For vehicle control input matrix, For vehicle controllers.
[0017] A further improvement of this application is that: in step 1, the desired formation state of the lead vehicle and the following vehicles is obtained, wherein the desired formation state is the desired formation distance, specifically:
[0018] Definition of the first Distance between the vehicle and the lead vehicle:
[0019]
[0020] but For vehicles With a vehicle The spacing.
[0021] A further improvement in this application is that, in step 2, a model is created for potential denial-of-service attacks in the Internet of Vehicles (IoV), specifically:
[0022] Step 2.1, set In the time interval The duration of an effective denial-of-service attack. Time interval The number of attacks that occurred within the territory. and The following constraints need to be met:
[0023]
[0024] in, and These represent the start and end times of any chosen time interval, respectively. For the duration of the attack, As a margin for the number of attacks, This is an upper bound on the frequency of attacks. This is the upper bound parameter for the attack duty cycle;
[0025] Step 2.2: To ensure the stability and consistency of the fleet distributed control system in the event of an attack, an upper bound is set on the frequency of attacks. and attack duty cycle upper bound parameter Apply the following constraints:
[0026]
[0027] in, For the first The maximum control update interval for a following vehicle under the self-triggered control mechanism. This refers to the main stable attenuation rate parameter of the system under normal communication conditions. An additional attenuation parameter is introduced to address self-triggered sampling errors and communication discontinuities.
[0028] To design a margin constant for stability, This represents the gain scheduling parameter.
[0029] A further improvement in this application is that, in step 3, during the time interval when communication is normal and the fleet communication topology remains connected, a self-triggered control update strategy is designed for each following vehicle:
[0030]
[0031] in, For the first vehicle number The timing of the next update is controlled. Represents the state transition matrix. For trigger threshold coefficient, This is the bandwidth utilization coefficient. When the bandwidth utilization rate is greater than 50%, When bandwidth utilization is less than 50%, For error weight parameters, The attenuation coefficient is... For the trigger time interval, For vehicle controller, The distance between two adjacent vehicles This is the time integration variable.
[0032] A further improvement in this application is that: in step 3, during the effective denial-of-service attack time interval, the self-triggered update strategy is stopped, and each following vehicle updates the control input according to a preset time sampling period, specifically: The following vehicles were in the first Secondary control input update time Then, the next update time ,in For the first The maximum control update interval for a following vehicle under the self-triggered control mechanism.
[0033] A further improvement in this application is that, in step 4, for the first... Building a fleet consistency controller for each vehicle:
[0034]
[0035] in, For consistency control gain matrix, The first Vehicles and the first The vehicle's most recently updated status information, This represents the distance between two adjacent vehicles.
[0036] The control method of this application is implemented through a vehicle-to-everything (V2X) fleet cooperative control framework for denial-of-service (DoS) attack environments, which includes:
[0037] The local state perception module, corresponding to the sensors on the vehicle, is used to collect data in real time. The actual state of the vehicle, which includes at least the vehicle's position, speed, and acceleration, is used as the basic input for local control and predictive calculations.
[0038] The decision unit, connected to the local state perception module, is used to receive the actual state of the local vehicle and, based on the current communication state determination result, select to enable the self-triggered control update mode or the fixed time interval update mode to realize the switching of control strategy under normal communication and denial-of-service attack conditions.
[0039] The self-triggering control update module is used to predict the vehicle status based on the most recently successfully received local actual status and the actual status of neighboring vehicles within the time interval when communication is normal and the fleet communication topology remains connected. It also autonomously calculates and determines the next control input update time according to the self-triggering control update strategy, so that the vehicle does not need to continuously monitor the status error between adjacent update times.
[0040] The fixed time interval update module is used to update the control input according to a preset time sampling period during the time interval when a denial-of-service attack occurs and causes the fleet communication topology to lose connectivity, so as to ensure the feasibility and continuous operation capability of the control system under communication obstruction conditions.
[0041] The communication transmission module is used to send the actual status or expected platooning status of local vehicles to the vehicle ad hoc network when communication conditions permit, so that adjacent vehicles can perform cooperative control and status prediction.
[0042] The communication receiving module is used to receive the actual status or expected platooning status sent by other vehicles from the vehicle ad hoc network, and to identify communication interruption situations when a denial-of-service attack occurs, thereby providing the decision-making unit with a basis for determining the communication status.
[0043] The distributed controller module is used to integrate the local actual state, the received actual state of neighboring vehicles, and the desired platooning state, calculate the corresponding control input, and output the control input to the vehicle actuators.
[0044] The vehicle actuator, corresponding to the vehicle's power execution unit, is used to receive control inputs and drive the vehicle's longitudinal movement, enabling the vehicle to achieve stable fleet cooperative operation under the action of self-triggered control update and denial-of-service attack switching mechanism.
[0045] The beneficial effects of this application are:
[0046] This application constructs a vehicle-to-everything (V2X) fleet cooperative control framework for denial-of-service (DoS) attack environments, unifying the modeling of vehicle longitudinal dynamics, fleet communication topology, and communication interruption behavior under DoS attack conditions. By introducing a communication link reachability determination mechanism, it clearly distinguishes between normal communication time intervals and effective DoS attack time intervals that cause the fleet communication topology to lose connectivity. This ensures that the fleet control strategy maintains a clear analytical structure and stability guarantee even under communication constraints or temporary interruptions, thereby improving the applicability and reliability of the method in complex network environments.
[0047] This application employs a bandwidth-aware, self-triggered control update strategy to improve fleet communication efficiency and control resource utilization. During periods of normal communication and topology connectivity, each following vehicle autonomously predicts and determines the next control update time based solely on its most recently successfully received state and neighboring vehicle state information, combined with communication bandwidth usage. This avoids the redundant overhead of continuous state monitoring and frequent communication; while ensuring fleet collaborative control performance, it effectively reduces communication burden and computational complexity.
[0048] This application introduces a control update mechanism switching strategy within the time interval of a denial-of-service attack and imposes constraints on the duration and frequency of the attack, ensuring that the fleet system maintains continuous operation and stable tracking performance even under communication attacks. Combined with consistency error system analysis, the method can achieve stable tracking of the lead vehicle's motion state under given attack constraints and avoid Zeno behavior caused by self-triggered control strategies, thereby improving the operational stability and security of the fleet system in unreliable communication and attack environments. Attached Figure Description
[0049] Figure 1 This is the overall flowchart of the control method of this application.
[0050] Figure 2 This application describes the vehicle location tracking trajectory under the LBPF communication topology.
[0051] Figure 3 This application describes the changes in the triggering frequency of each vehicle in scenarios with high bandwidth utilization.
[0052] Figure 4 This application describes the changes in trigger frequency for each vehicle in scenarios with low bandwidth utilization.
[0053] Figure 5 This is a framework diagram of the control system of this application. Detailed Implementation
[0054] The embodiments of the present invention will be disclosed below with reference to the drawings. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential.
[0055] like Figure 1 As shown, this application discloses a distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under a denial-of-service attack. The distributed control method for connected vehicle fleets specifically includes the following steps:
[0056] Step 1: Construct the state-space equations for the cooperative vehicles, obtaining the desired formation state and actual state of the lead vehicle and following vehicles. The desired formation state is the desired formation distance, and the actual state includes vehicle position, speed, and acceleration. The constructed state-space equations for the cooperative vehicles are as follows:
[0057]
[0058] in, For the first The longitudinal position of the vehicles Vertical position The derivative with respect to time, For the first The speed of the vehicles For speed The derivative with respect to time, For the first The acceleration of the vehicle For vehicle quality, For engine driving force, For air resistance, For the rolling resistance of the vehicles, the state-space equations for the lead vehicle and the following vehicles are:
[0059]
[0060] in, This is the state vector of the vehicle being followed. Let be the state vector of the lead vehicle. The matrix of the third-order dynamics system of the vehicle. For vehicle control input matrix, For vehicle controller, This is the dynamic coefficient.
[0061] In this step, the desired formation state of the lead vehicle and following vehicles is obtained, where the desired formation state is the desired vehicle spacing, specifically:
[0062] Definition of the first Distance between the vehicle and the lead vehicle:
[0063]
[0064] but For vehicles With a vehicle The spacing.
[0065] Step 2: Model potential denial-of-service (DoS) attacks in the vehicle-to-everything (V2X) network. Identify communication interruption scenarios through a communication link reachability determination mechanism. Define the effective DoS attack time intervals that cause the fleet communication topology to lose connectivity. Apply constraints to the effective DoS attack time intervals and their frequency. Specifically, when a DoS attack occurs but the fleet communication topology remains connected, the corresponding time interval is determined to be an invalid DoS attack time interval; when a DoS attack causes the fleet communication topology to lose connectivity, the corresponding time interval is determined to be an effective DoS attack time interval.
[0066] In this step, a denial-of-service attack that may occur in the Internet of Vehicles is modeled, specifically as follows:
[0067] Step 2.1, set In the time interval The duration of an effective denial-of-service attack. Time interval The number of attacks that occurred within the territory. and The following constraints need to be met:
[0068]
[0069] in, and These represent the start and end times of any chosen time interval, respectively. For the duration of the attack, As a margin for the number of attacks, This is an upper bound on the frequency of attacks. This is the upper bound parameter for the attack duty cycle;
[0070] Step 2.2: To ensure the stability and consistency of the fleet distributed control system in the event of an attack, an upper bound is set on the frequency of attacks. and attack duty cycle upper bound parameter Apply the following constraints:
[0071]
[0072] in, For the first The maximum control update interval for a following vehicle under the self-triggered control mechanism. This refers to the main stable attenuation rate parameter of the system under normal communication conditions. An additional attenuation parameter is introduced to address self-triggered sampling errors and communication discontinuities.
[0073] To design a margin constant for stability, This represents the gain scheduling parameter.
[0074] Step 3: Based on the actual status of the lead vehicle and following vehicles obtained in Step 1, design a self-triggered control update strategy for each following vehicle within the time interval when communication is normal and the fleet communication topology remains connected:
[0075]
[0076] in, For the first vehicle number The timing of the next update is controlled. Represents the state transition matrix. For trigger threshold coefficient, This is the bandwidth utilization coefficient. When the bandwidth utilization rate is greater than 50%, When bandwidth utilization is less than 50%, For error weight parameters, The attenuation coefficient is... For the trigger time interval, For vehicle controller, The distance between two adjacent vehicles. This is the time integration variable.
[0077] Each vehicle autonomously predicts and determines the next control input update time based solely on its own state and neighboring vehicle state information received most recently, without needing to continuously monitor state errors.
[0078] During the effective denial-of-service attack period, the self-triggered update strategy is stopped, and each following vehicle updates the control input according to a preset time sampling period to ensure the feasibility of the control system under communication disruption conditions. Specifically: The following vehicles were in the first Secondary control input update time Then, the next update time ,in For the first The maximum control update interval for a following vehicle under the self-triggered control mechanism.
[0079] Step 4: Based on steps 1, 2, and 3, construct a fleet consistency controller. Under the condition that denial-of-service attacks satisfy frequency and duration constraints, the fleet system can meet the control requirements. Specifically, for the... Building a fleet consistency controller for each vehicle:
[0080]
[0081] in, For consistency control gain matrix, The first Vehicles and the first The vehicle's most recently updated status information, This represents the distance between two adjacent vehicles.
[0082] The vehicle-to-everything (V2X) fleet cooperative control framework for denial-of-service (DoS) attack environments includes:
[0083] The local state perception module, corresponding to the sensors on the vehicle, is used to collect data in real time. The actual state of the vehicle, which includes at least the vehicle's position, speed, and acceleration, is used as the basic input for local control and predictive calculations.
[0084] The decision unit, connected to the local state perception module, is used to receive the actual state of the local vehicle and, based on the current communication state determination result, select to enable the self-triggered control update mode or the fixed time interval update mode to realize the switching of control strategy under normal communication and denial-of-service attack conditions.
[0085] The self-triggering control update module is used to predict the vehicle status based on the most recently successfully received local actual status and the actual status of neighboring vehicles within the time interval when communication is normal and the fleet communication topology remains connected. It also autonomously calculates and determines the next control input update time according to the self-triggering control update strategy, so that the vehicle does not need to continuously monitor the status error between adjacent update times.
[0086] The fixed time interval update module is used to update the control input according to a preset time sampling period during the time interval when a denial-of-service attack occurs and causes the fleet communication topology to lose connectivity, so as to ensure the feasibility and continuous operation capability of the control system under communication obstruction conditions.
[0087] The communication transmission module is used to send the actual status or expected platooning status of local vehicles to the vehicle ad hoc network when communication conditions permit, so that adjacent vehicles can perform cooperative control and status prediction.
[0088] The communication receiving module is used to receive the actual status or expected platooning status sent by other vehicles from the vehicle ad hoc network, and to identify communication interruption situations when a denial-of-service attack occurs, thereby providing the decision-making unit with a basis for determining the communication status.
[0089] The distributed controller module is used to integrate the local actual state, the received actual state of neighboring vehicles, and the desired platooning state, calculate the corresponding control input, and output the control input to the vehicle actuators.
[0090] The vehicle actuator, corresponding to the vehicle's power execution unit, is used to receive control inputs and drive the vehicle's longitudinal movement, enabling the vehicle to achieve stable fleet cooperative operation under the action of self-triggered control update and denial-of-service attack switching mechanism.
[0091] To verify this application, this application considers the vehicle dynamics coefficient of a convoy system consisting of 4 following vehicles and 1 lead vehicle. In the LBPF communication topology, an edge-level asynchronous DoS attack is considered, where communication interruptions occur only between specific vehicle pairs and asynchronously at different time periods. Specifically, communication links (1,2), (2,3), and (3,4) are subjected to DoS attacks in the intervals [5,6][5,6][5,6], [17,18][17,18][17,18], and [32,33][32,33][32,33], respectively, while communication remains normal at other times. This setting is used to characterize asynchronous, non-holistic attack scenarios that more closely resemble real-world network environments. The simulation uses an Euler integration step size of Δt = 0.001 s and a total duration of T = 50 s.
[0092] like Figure 2 The vehicle position tracking trajectory shown clearly demonstrates that, under the proposed control strategy, the convoy can successfully maintain the desired distance from the lead vehicle.
[0093] Figure 3 and Figure 4 The diagram illustrates the changes in trigger frequency for each vehicle under two scenarios: high bandwidth utilization and low bandwidth utilization. It can be seen that the trigger frequency is lower when bandwidth utilization is greater than 50%, and higher when bandwidth utilization is less than 50%. This demonstrates that this application can proactively reduce communication when the network is reliable and automatically increase communication when the network deteriorates, thereby effectively balancing control performance and communication overhead.
[0094] The above description is merely an 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 principle of the present invention should be included within the scope of the claims of the present invention.
Claims
1. A distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks, characterized in that: The networked vehicle distributed control method specifically includes the following steps: Step 1: Construct the state space equations of the cooperative vehicles to obtain the expected formation state and actual state of the lead vehicle and following vehicles; Step 2: Model potential denial-of-service (DoS) attacks in the vehicle-to-everything (V2X) network. Identify communication interruption scenarios through a communication link reachability determination mechanism. Define the effective DoS attack time intervals that cause the fleet communication topology to lose connectivity. Apply constraints to the effective DoS attack time intervals and their frequency. Specifically, when a DoS attack occurs but the fleet communication topology remains connected, the corresponding time interval is determined to be an invalid DoS attack time interval; when a DoS attack causes the fleet communication topology to lose connectivity, the corresponding time interval is determined to be an effective DoS attack time interval. Step 3: Based on the actual status of the lead vehicle and following vehicles obtained in Step 1, within the time interval when communication is normal and the fleet communication topology remains connected, design a self-triggered control update strategy for each following vehicle. Each vehicle autonomously predicts and determines the next control input update time based solely on its own status and neighbor vehicle status information received most recently, without continuously monitoring status errors. During the time interval when communication is under effective denial-of-service attack, the self-triggered update strategy is stopped, and each following vehicle updates the control input according to a preset time sampling period. Step 4: Based on Steps 1, 2 and 3, construct a fleet consistency controller. Under the condition that the denial-of-service attack meets the frequency and duration constraints, the fleet system meets the control requirements.
2. The distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks as described in claim 1, characterized in that: To obtain the actual states of the lead vehicle and the following vehicles, the state-space equation of the cooperative vehicle constructed in step 1 is as follows: in, For the first The longitudinal position of the vehicles Vertical position The derivative with respect to time, For the first The speed of the vehicles For speed The derivative with respect to time, The first The acceleration of the vehicle For vehicle quality, For engine driving force, For air resistance, For the rolling resistance of the vehicles, the state-space equations for the lead vehicle and the following vehicle are expressed as: in, This is the state vector of the vehicle being followed. Load the equipment for the lead vehicle. The matrix of the third-order dynamics system of the vehicle. For vehicle control input matrix, For vehicle controller, This is the dynamic coefficient.
3. The distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks as described in claim 1, characterized in that: In step 1, the desired formation state of the lead vehicle and following vehicles is obtained. The desired formation state is the desired formation distance, specifically: Definition of the first Distance between following vehicles and the lead vehicle: but For vehicles With a vehicle The spacing.
4. The distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks as described in claim 1, characterized in that: In step 2, a denial-of-service attack model is constructed to address potential threats in the Internet of Vehicles (IoV) system. Specifically: Step 2.1, set In the time interval The duration of an effective denial-of-service attack. Time interval The number of attacks that occurred within the territory. and The following constraints need to be met: in, and These represent the start and end times of any chosen time interval, respectively. For the duration of the attack, As a margin for the number of attacks, This is an upper bound on the frequency of attacks. This is the upper bound parameter for the attack duty cycle; Step 2.2: Upper bound on the frequency of attack occurrence and attack duty cycle upper bound parameter Apply the following constraints: in, For the first The maximum control update interval for a following vehicle under the self-triggered control mechanism. The main stable decay rate parameter To add attenuation parameters, To design a margin constant for stability, This represents the gain scheduling parameter.
5. The distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks as described in claim 1, characterized in that: In step 3, during the time interval when communication is normal and the fleet communication topology remains connected, a self-triggered control update strategy is designed for each following vehicle: in, For the first vehicle number The timing of the next update is controlled. Represents the state transition matrix. For trigger threshold coefficient, This is the bandwidth utilization coefficient. When the bandwidth utilization rate is greater than 50%, When bandwidth utilization is less than 50%, For error weight parameters, The attenuation coefficient is... For the trigger time interval, For vehicle controller, The distance between two adjacent vehicles. This is the time integration variable.
6. The distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks as described in claim 5, characterized in that: In step 3, during the effective denial-of-service attack period, the self-triggered update strategy is stopped, and each following vehicle updates the control input according to a preset time sampling period, specifically: The following vehicles were in the first Secondary control input update time Then, the next update time ,in For the first The maximum control update interval for a following vehicle under the self-triggered control mechanism.
7. The distributed control method for connected vehicle fleets based on bandwidth-aware self-triggered communication under denial-of-service attacks as described in claim 1, characterized in that: In step 4, for the first Building a fleet consistency controller for each vehicle: in, For consistency control gain matrix, The first Vehicles and the first The vehicle's most recently updated status information, This represents the distance between two adjacent vehicles.
8. The connected vehicle fleet cooperative control method based on self-triggered control update under denial-of-service attack as described in claim 1, characterized in that: The control method is implemented through a vehicle-to-everything (V2X) fleet cooperative control framework for denial-of-service (DoS) attack environments, which includes: The local state perception module, corresponding to the sensors on the vehicle, is used to collect data in real time. The actual state of the vehicle, which includes at least the vehicle's position, speed, and acceleration, is used as the basic input for local control and predictive calculations. The decision unit, connected to the local state perception module, is used to receive the actual state of the local vehicle and, based on the current communication state determination result, select to enable the self-triggered control update mode or the fixed time interval update mode to realize the switching of control strategy under normal communication and denial-of-service attack conditions. The self-triggering control update module is used to predict the vehicle status based on the most recently successfully received local actual status and the actual status of neighboring vehicles within the time interval when communication is normal and the fleet communication topology remains connected. It also autonomously calculates and determines the next control input update time according to the self-triggering control update strategy, so that the vehicle does not need to continuously monitor the status error between adjacent update times. The fixed time interval update module is used to update the control input according to a preset time sampling period during the time interval when a denial-of-service attack occurs and causes the fleet communication topology to lose connectivity, so as to ensure the feasibility and continuous operation capability of the control system under communication obstruction conditions. The communication transmission module is used to send the actual status or expected platooning status of local vehicles to the vehicle ad hoc network when communication conditions permit, so that adjacent vehicles can perform cooperative control and status prediction. The communication receiving module is used to receive the actual status or expected platooning status sent by other vehicles from the vehicle ad hoc network, and to identify communication interruption situations when a denial-of-service attack occurs, thereby providing the decision-making unit with a basis for determining the communication status. The distributed controller module is used to integrate the local actual state, the received actual state of neighboring vehicles, and the desired platooning state, calculate the corresponding control input, and output the control input to the vehicle actuators. The vehicle actuator, corresponding to the vehicle's power execution unit, is used to receive control inputs and drive the vehicle's longitudinal movement, enabling the vehicle to achieve stable fleet cooperative operation under the action of self-triggered control update and denial-of-service attack switching mechanism.