Network connection vehicle fault performance self-healing control method and system considering physical constraint

Abstract

The invention relates to a network connection vehicle fault performance self-healing control method and system considering physical constraints in the technical field of intelligent traffic and vehicle control. The method comprises the following steps: constructing a heterogeneous networked vehicle nonlinear system model based on nonlinear dynamics of displacement, speed and acceleration, actuator fault influence and external disturbance; constructing an auxiliary dynamic variable representing the overall influence of the fault, deducing a time derivative of the auxiliary dynamic variable, designing a self-adaptive fault influence identifier, and carrying out the online identification of the system fault influence; constructing a lower definite bound parameter associated with the unknown control coefficient, designing a parameter boundary estimator, and performing unknown parameter boundary adaptive estimation; and on the basis of fault influence identification and parameter boundary estimation information, a physical constraint function of the actuator is integrated, a self-healing controller of the networked vehicle formation is designed, and fault influence compensation, physical constraint guarantee of the actuator and stable control of the networked vehicle formation are realized. And the reliability and the control precision of the networked vehicle cooperative control system under complex working conditions are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation and vehicle control technology, and in particular to a method and system for self-healing control of fault performance of connected vehicles that takes into account physical constraints. Background Technology

[0002] With the deep integration of vehicle-to-everything (V2X) technology and autonomous driving technology, connected vehicle cooperative control systems have become a core technology for improving traffic efficiency and ensuring traffic safety. Relying on information sharing and collaborative decision-making mechanisms between vehicles, these systems can achieve multiple core functions such as path optimization, emergency avoidance, and platooning, effectively reducing traffic accident rates and optimizing traffic flow distribution, providing crucial technical support for the development of intelligent transportation.

[0003] However, in practical applications, actuator failures and physical constraint-related issues significantly limit the robustness and control accuracy of connected vehicle cooperative control systems. These problems are particularly pronounced in complex scenarios such as challenging road conditions, extreme weather, or heterogeneous vehicle configurations, directly impacting the reliability and operational stability of the connected vehicle cooperative control system and becoming a key bottleneck restricting the large-scale application of this technology.

[0004] Specifically, in the collaborative control of connected vehicles, the real-time response performance of actuators such as brakes and drive motors is the core foundation for ensuring the accurate execution of control commands. However, due to limitations in the hardware performance of the actuators themselves and the complex interference of the dynamic driving environment, existing technical solutions still have the following key technical defects: First, actuator failures are characterized by their suddenness and insidious nature. For example, brake actuators are prone to partial failure due to mechanical wear, electronic signal interference, and other factors, leading to sudden changes in vehicle acceleration and disrupting the coordinated operation of vehicles within the platoon. Because such fault signals are difficult to detect in real time and accurately, the abnormal motion of a faulty vehicle may be transmitted to neighboring vehicles through the vehicle-to-everything (V2X) communication link, triggering a chain reaction of interference. This nonlinear fault propagation mechanism makes it difficult for traditional model-based predictive collaborative control algorithms to quickly locate the fault source and dynamically adjust the control strategy. In high-density vehicle interaction scenarios, system fault recovery takes too long, easily leading to safety risks such as platoon breakup and vehicle collisions.

[0005] Second, there are dynamic conflicts arising from the physical constraints of actuators. Actuators generally have physical limits such as maximum output force and response speed, including the upper limit of the torque of the drive motor and the angular velocity threshold of the steering system. In typical scenarios such as multi-vehicle cooperative braking and cooperative steering, adjacent vehicles may be unable to synchronously reach the system's preset target acceleration or steering angle due to the physical constraints of their own actuators, leading to a continuous accumulation of path tracking deviations. More seriously, when the acceleration and steering angle requirements of multi-vehicle cooperative tasks exceed the local physical constraints of the actuators, control commands are prone to saturation failure, forcing the system to adopt a conservative control strategy, which significantly weakens the performance advantages of connected vehicle cooperative control.

[0006] Third, there is an inherent contradiction between safety redundancy design and system real-time performance. To address the risk of actuator failure, existing technologies typically require designing redundant identification modules for each type of fault parameter. However, this design approach significantly increases the system's communication data load and computational complexity. Taking a multi-vehicle collaborative platooning scenario as an example, a faulty vehicle needs to broadcast abnormal status information to other vehicles in the vehicle network. During the real-time transmission of fault identification data, data transmission conflicts are easily caused by network latency, further exacerbating the deterioration of collaborative control performance.

[0007] Fourth, the coupling between actuator failure and physical constraints leads to significant vulnerability of the control system. Existing connected vehicle cooperative control algorithms (such as distributed model predictive control and sliding mode control) are generally designed based on the ideal assumptions of "actuator in normal operating condition" and "actuator meeting physical constraints". When the actuator temporarily reduces its output capacity due to failure, these traditional algorithms cannot achieve effective control under the constraint of the actuator's limited output torque, which can easily lead to the loss of stability of connected vehicle platooning and cause cooperative control failure.

[0008] Therefore, there is an urgent need in related technologies for a way to improve the reliability and control accuracy of the connected vehicle cooperative control system under complex working conditions. Summary of the Invention

[0009] Therefore, it is necessary to provide a self-healing control method and system for connected vehicle fault performance that considers physical constraints, which can improve the reliability and control accuracy of the connected vehicle cooperative control system under complex working conditions, in order to address the above-mentioned technical problems.

[0010] Firstly, this application provides a self-healing control method for fault performance of connected vehicles that considers physical constraints. The method includes: A nonlinear system model of heterogeneous connected vehicles is constructed based on nonlinear dynamics of displacement, velocity, and acceleration, the effects of actuator failure, and external disturbances. We construct auxiliary dynamic variables to characterize the overall impact of faults, derive their time derivatives, design an adaptive fault impact identifier, and perform online identification of the impact of system faults. Construct the infimum parameter associated with the unknown control coefficient, design the parameter boundary estimator, and perform adaptive estimation of the unknown parameter boundary; Based on fault impact identification and parameter boundary estimation information, and by integrating actuator physical constraint functions, a self-healing controller for connected vehicle platooning is designed to achieve fault impact compensation, actuator physical constraint protection, and stable control of connected vehicle platooning.

[0011] Optionally, in one embodiment of this application, constructing the heterogeneous connected vehicle nonlinear system model includes: By defining the physical boundary conditions under which the actuator can generate control torque, a physical constraint model for the connected vehicle actuator is established. An actuator fault model is constructed based on the aforementioned actuator physical constraint model, taking into account common failures and bias faults of connected vehicle actuators.

[0012] Optionally, in one embodiment of this application, the heterogeneous connected vehicle nonlinear system model is represented as:

[0013] in, The first The derivatives of the vehicle's displacement, velocity, and acceleration. ; , , The first The vehicle's displacement, velocity, and acceleration; This represents the coupled nonlinear dynamics of velocity and acceleration; For unknown control coefficients; For the first A self-healing controller for platooning connected vehicles; This indicates external disturbances caused by the environment; For the first The overall impact of multiplicative and additive actuator faults on the system in a vehicle. The failure factor indicates a loss of actuator efficiency, satisfying the following conditions: ,and and It is a positive scalar. Because of additive bias, it indicates sudden faults caused by constant torque due to leakage in hydraulic cylinders or mechanical transmission locks, periodic faults caused by damage to insulated gate bipolar transistors, and recurring unpredictable intermittent faults.

[0014] Optionally, in one embodiment of this application, the auxiliary dynamic variable is represented as:

[0015] in, Positive design parameters; For tracking error; For an ideal constant workshop distance; ; For the first Vehicle and the The distance between vehicles; For the first The length of the vehicle body; Its time derivative is expressed as:

[0016] in, For the first The derivatives of the vehicle's auxiliary dynamic variables. For the first Overall impact of multiplicative and additive actuator faults in a vehicle on the system The derivative of .

[0017] Optionally, in one embodiment of this application, the adaptive fault impact identifier is represented as:

[0018] in, , These are all design parameters. For the first The platoon self-healing controller for connected vehicles For auxiliary dynamic variables The estimated value, For the first Vehicle auxiliary dynamic variable estimates The derivative of For the first Overall impact of multiplicative and additive actuator faults in a vehicle on the system The estimated value.

[0019] Optionally, in one embodiment of this application, the infimum parameter associated with the unknown control coefficient is expressed as: ,

[0020] in, This represents the lower bound of the unknown control coefficient; It is the reciprocal of the underfimum of the unknown control coefficient.

[0021] Optionally, in one embodiment of this application, the parameter boundary estimator is represented as:

[0022] in, For parameters The estimated value; The derivative of the estimated value; and It is a positive scalar.

[0023] Optionally, in one embodiment of this application, the connected vehicle platoon self-healing controller is represented as:

[0024] in, For the first The physical constraints of connected vehicles , For actuator physical constraint functions; , It is a positive scalar; It is a positive scalar; For indirect control laws, designed as , It is a positive scalar.

[0025] Optionally, in one embodiment of this application, the actuator physical constraint function is expressed as:

[0026] in, .

[0027] Secondly, this application also provides a self-healing control system for connected vehicle fault performance that considers physical constraints. The system includes: The heterogeneous system construction module is used to construct a nonlinear system model of a heterogeneous connected vehicle based on nonlinear dynamics of displacement, velocity, and acceleration, the effects of actuator failure, and external disturbances. The fault impact identification module is used to construct auxiliary dynamic variables that characterize the overall impact of faults, derive their time derivatives, design an adaptive fault impact identifier, and perform online identification of the impact of system faults. The parameter boundary estimation module is used to construct the infimum parameter associated with the unknown control coefficient, design the parameter boundary estimator, and perform adaptive estimation of the unknown parameter boundary. The formation constraint control module is used to design a self-healing controller for connected vehicle formations based on fault impact identification and parameter boundary estimation information, and by integrating actuator physical constraint functions. This controller achieves fault impact compensation, actuator physical constraint protection, and stable control of connected vehicle formations.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: First, by quantitatively analyzing the physical limits of the available control torque of the actuator, a well-constrained actuator model was established, effectively avoiding saturation failure caused by control commands exceeding the physical range of the actuator. Furthermore, comprehensive modeling was conducted for actuator failure and bias faults, covering common actuator failure modes in connected vehicle platooning. This improved the system's fault tolerance and the executability of control commands, ensuring the reliability of multi-vehicle collaborative control.

[0029] Second, a nonlinear system model of heterogeneous connected vehicles was designed, which comprehensively considered the nonlinear dynamics of displacement, velocity, and acceleration, the impact of actuator failure, and external disturbances.

[0030] Third, by constructing auxiliary dynamic variables that characterize the overall impact of the fault and deriving their derivatives, and combining this with the design of a fault impact identifier, real-time online estimation of the fault impact is achieved. This breaks through the dependence of traditional fault estimation on prior fault types, effectively simplifies the design complexity of the fault identification system, and provides accurate fault information input for subsequent control compensation.

[0031] Fourth, by constructing a dynamic estimator for the lower bound of unknown parameters and using an adaptive mechanism to update the parameter boundary estimates in real time, the problem of uncertain control coefficients in heterogeneous connected vehicle systems is effectively solved.

[0032] Fifth, by organically integrating the fault impact identification results, parameter boundary estimates, and actuator physical constraints, a formation controller with fault compensation, constraint protection, and formation stability was designed. This not only ensures the gradual convergence of formation tracking errors but also avoids secondary faults caused by actuators exceeding limits, significantly improving the overall stability and fault tolerance of the multi-vehicle formation system. Attached Figure Description

[0033] Figure 1 This is an application environment diagram of a self-healing control method for fault performance of connected vehicles that considers physical constraints in one embodiment. Figure 2 This is a schematic diagram of the displacement curves of the lead vehicle and the following vehicles 1-4 in one embodiment; Figure 3 This is a schematic diagram of the speed curves of the lead vehicle and follower vehicles 1-4 in one embodiment; Figure 4 This is a schematic diagram of the vehicle spacing error curves for following vehicles 1-4 in one embodiment; Figure 5 This is a schematic diagram of the control torque curves of following vehicles 1-4 in one embodiment; Figure 6 This is a schematic diagram of the fault impact identification curve of the following vehicle 1 in one embodiment; Figure 7This is a block diagram of a self-healing control system for connected vehicle fault performance considering physical constraints in one embodiment. Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0035] In one embodiment, such as Figure 1 As shown, a self-healing control method for fault performance of connected vehicles considering physical constraints is provided, including the following steps: S101: Construct a nonlinear system model of heterogeneous connected vehicles based on nonlinear dynamics of displacement, velocity, and acceleration, the effects of actuator failure, and external disturbances.

[0036] In one embodiment of this application, constructing a heterogeneous connected vehicle nonlinear system model includes: S201: By defining the physical boundary conditions under which the actuator can generate control torque, a physical constraint model for the connected vehicle actuator is established.

[0037] S203: Construct an actuator fault model based on the actuator physical constraint model, taking into account common failures and bias faults of connected vehicle actuators.

[0038] In one embodiment of this application, considering that the inherent safety limitations and physical constraints of the throttle or braking system of a connected vehicle inevitably lead to actuator saturation, which is a problem that must be solved in control design, the actuator physical constraint model is defined as follows by limiting the physical boundary of the control torque that the actuator can generate:

[0039] in, For time; For the first A self-healing controller for platooning connected vehicles; For the first The physical constraints of connected vehicles .

[0040] The actuator fault model constructed based on the aforementioned actuator physical constraint model is as follows:

[0041] in, The uncertain timing of the failure reflects the randomness of its occurrence. The failure factor indicates a loss of actuator efficiency, satisfying the following conditions: ,and and It is a positive scalar. Because of additive bias, it indicates sudden faults caused by constant torque due to leakage in hydraulic cylinders or mechanical transmission locks, periodic faults caused by damage to insulated gate bipolar transistors, and recurring unpredictable intermittent faults.

[0042] Considering that the dynamic parameters in the mechanism model are partially known or even completely unknown, and constantly change with the external environment, in one embodiment of this application, the nonlinear system model of the heterogeneous connected vehicle is expressed as:

[0043] in, The first The derivatives of the vehicle's displacement, velocity, and acceleration. ; , , The first The vehicle's displacement, velocity, and acceleration; This represents the coupled nonlinear dynamics of velocity and acceleration; For unknown control coefficients; For the first A self-healing controller for platooning connected vehicles; This indicates external disturbances caused by the environment; For the first The overall impact of multiplicative and additive actuator faults on the system in a vehicle. The failure factor indicates a loss of actuator efficiency, satisfying the following conditions: ,and and It is a positive scalar. Because of additive bias, it indicates sudden faults caused by constant torque due to leakage in hydraulic cylinders or mechanical transmission locks, periodic faults caused by damage to insulated gate bipolar transistors, and recurring unpredictable intermittent faults.

[0044] S102: Construct auxiliary dynamic variables to characterize the overall impact of faults, derive their time derivatives, design an adaptive fault impact identifier, and perform online identification of the impact of system faults.

[0045] To address the actuator failure problem encountered in the formation control of heterogeneous connected vehicles, a fault impact identifier was constructed based on the actuator constraint model.

[0046] To characterize the overall impact of the fault, in one embodiment of this application, the auxiliary dynamic variable is represented as:

[0047] in, Positive design parameters; For tracking error; For an ideal constant workshop distance; ; For the first Vehicle and the The distance between vehicles; For the first The length of the vehicle body; Its time derivative is expressed as:

[0048] in, For the first The derivatives of the vehicle's auxiliary dynamic variables. For the first Overall impact of multiplicative and additive actuator faults in a vehicle on the system The derivative of .

[0049] In one embodiment of this application, the adaptive fault effect identifier is represented as:

[0050] in, , These are all design parameters. For connected vehicle platooning self-healing controllers, For auxiliary dynamic variables The estimated value, For the first Vehicle auxiliary dynamic variable estimates The derivative of For the first Overall impact of multiplicative and additive actuator faults in a vehicle on the system The estimated value.

[0051] Among them, due to the residual term Due to the unknown nature of the problem, additional supplementary terms are introduced. Approximate offset compensation is performed. Replaced with .

[0052] S103: Construct the infimum parameter associated with the unknown control coefficient, design the parameter boundary estimator, and perform adaptive estimation of the unknown parameter boundary.

[0053] In one embodiment of this application, the infimum parameter associated with the unknown control coefficient is expressed as: ,

[0054] in, This represents the lower bound of the unknown control coefficient; It is the reciprocal of the underfimum of the unknown control coefficient.

[0055] In one embodiment of this application, the parameter boundary estimator is represented as:

[0056] in, For parameters The estimated value; The derivative of the estimated value; and It is a positive scalar.

[0057] S104: Based on fault impact identification and parameter boundary estimation information, and integrating actuator physical constraint functions, a self-healing controller for connected vehicle platooning is designed to achieve fault impact compensation, actuator physical constraint protection, and stable control of connected vehicle platooning.

[0058] In one embodiment of this application, a connected vehicle platooning control algorithm considering physical constraints is constructed based on actuator fault impact identification information for a heterogeneous connected vehicle nonlinear system model. and parameter boundary estimation The design of the connected vehicle platoon self-healing controller is as follows:

[0059] in, For the first The physical constraints of connected vehicles , For actuator physical constraint functions; , It is a positive scalar; It is a positive scalar; For indirect control laws, designed as , It is a positive scalar.

[0060] The actuator physical constraint function is as follows:

[0061] in, It is a positive scalar.

[0062] In one embodiment of this application, to demonstrate the effectiveness of the method, the following simulation experiment was conducted for verification: Consider a heterogeneous connected vehicle platooning system consisting of one lead vehicle and four follower vehicles. The initial settings of the system are shown in Table 1: Table 1 System Initial State

[0063] In this simulation experiment, the safe vehicle spacing was set at 7.98 meters; the external environmental disturbance was... The physical constraint value of connected vehicles is ; Navigator acceleration for:

[0064] For the first following vehicle, consider the actuator in At this time, a 50% partial efficiency loss and bias fault occur, that is and .

[0065] The effectiveness of the proposed self-healing control method for connected vehicle fault performance considering physical constraints, as described in this embodiment, was verified using MATLAB 2023a and Simulink simulation software. Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 .

[0066] exist Figure 2 The snapshots of the longitudinal displacement trajectories of the lead car and follower cars 1-4 show that all connected vehicles always operate in longitudinal formation, maintaining a safe distance between vehicles and no collisions occur.

[0067] exist Figure 3 As can be observed from the speed snapshots of the lead vehicle and follower vehicles 1-4, this method can ensure the consistency of speed for all vehicles in the formation.

[0068] exist Figure 4 As can be observed in the snapshot of the inter-vehicle distance error of following vehicles 1-4, under the proposed self-healing control method for connected vehicle fault performance considering physical constraints, all following vehicles in the formation can achieve the desired tracking performance and are not affected by the actuator failure in following vehicle 1.

[0069] exist Figure 5 As can be observed in the snapshots of the inter-vehicle distance errors of following vehicles 1-4, the control torque generated by each following vehicle can be constrained within the physical range. That is, the proposed method can guarantee physical constraints. , .

[0070] exist Figure 6As can be observed in the snapshot of actuator failure impact identification of the following vehicle 1, the overall impact trend of actuator failure can be identified online, and the effectiveness of the designed failure impact identifier is verified.

[0071] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0072] Based on the same inventive concept, this application also provides a physical constraint-considered connected vehicle fault performance self-healing control system for implementing the aforementioned physical constraint-considered connected vehicle fault performance self-healing control method. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the physical constraint-considered connected vehicle fault performance self-healing control system provided below can be found in the above-described limitations of the physical constraint-considered connected vehicle fault performance self-healing control method, and will not be repeated here.

[0073] In one embodiment, such as Figure 7 As shown, a self-healing control system 700 for connected vehicle fault performance considering physical constraints is provided, including: a heterogeneous system construction module 701, a fault impact identification module 702, a parameter boundary estimation module 703, and a formation constraint control module 704, wherein: The heterogeneous system construction module 701 is used to construct a nonlinear system model of a heterogeneous connected vehicle based on nonlinear dynamics of displacement, velocity, and acceleration, the effects of actuator failure, and external disturbances.

[0074] The fault impact identification module 702 is used to construct auxiliary dynamic variables that characterize the overall impact of faults, derive their time derivatives, design an adaptive fault impact identifier, and perform online identification of the impact of system faults.

[0075] The parameter boundary estimation module 703 is used to construct the infimum parameter associated with the unknown control coefficient, design the parameter boundary estimator, and perform adaptive estimation of the unknown parameter boundary.

[0076] The formation constraint control module 704 is used to design a self-healing controller for connected vehicle formation based on fault impact identification and parameter boundary estimation information, and by integrating actuator physical constraint functions, so as to realize fault impact compensation, actuator physical constraint protection and stable control of connected vehicle formation.

[0077] In one embodiment of this application, constructing a heterogeneous connected vehicle nonlinear system model includes: By defining the physical boundary conditions under which the actuator can generate control torque, a physical constraint model for the connected vehicle actuator is established. An actuator fault model is constructed based on the aforementioned actuator physical constraint model, taking into account common failures and bias faults of connected vehicle actuators.

[0078] In one embodiment of this application, the heterogeneous connected vehicle nonlinear system model is represented as:

[0079] in, The first The derivatives of the vehicle's displacement, velocity, and acceleration. ; , , The first The vehicle's displacement, velocity, and acceleration; This represents the coupled nonlinear dynamics of velocity and acceleration; For unknown control coefficients; For the first A self-healing controller for platooning connected vehicles; This indicates external disturbances caused by the environment; For the first The overall impact of multiplicative and additive actuator faults on the system in a vehicle. The failure factor indicates a loss of actuator efficiency, satisfying the following conditions: ,and and It is a positive scalar. Because of additive bias, it indicates sudden faults caused by constant torque due to leakage in hydraulic cylinders or mechanical transmission locks, periodic faults caused by damage to insulated gate bipolar transistors, and recurring unpredictable intermittent faults.

[0080] In one embodiment of this application, the auxiliary dynamic variable is represented as:

[0081] in, Positive design parameters; For tracking error; For an ideal constant workshop distance; ; For the first Vehicle and the The distance between vehicles; For the first The length of the vehicle body; Its time derivative is expressed as:

[0082] in, For the first The derivatives of the vehicle's auxiliary dynamic variables. For the first Overall impact of multiplicative and additive actuator faults in a vehicle on the system The derivative of .

[0083] In one embodiment of this application, the adaptive fault effect identifier is represented as:

[0084] in, , These are all design parameters. For connected vehicle platooning self-healing controllers, For auxiliary dynamic variables The estimated value, For the first Vehicle auxiliary dynamic variable estimates The derivative of For the first Overall impact of multiplicative and additive actuator faults in a vehicle on the system The estimated value.

[0085] In one embodiment of this application, the infimum parameter associated with the unknown control coefficient is expressed as: ,

[0086] in, This represents the lower bound of the unknown control coefficient; It is the reciprocal of the underfimum of the unknown control coefficient.

[0087] In one embodiment of this application, the parameter boundary estimator is represented as:

[0088] in, For parameters The estimated value; The derivative of the estimated value; and It is a positive scalar.

[0089] In one embodiment of this application, the connected vehicle platoon self-healing controller is represented as:

[0090] in, For the first The physical constraints of connected vehicles , For actuator physical constraint functions; , It is a positive scalar; It is a positive scalar; For indirect control laws, designed as , It is a positive scalar.

[0091] In one embodiment of this application, the actuator physical constraint function is expressed as:

[0092] in, .

[0093] The modules in the aforementioned self-healing control system for connected vehicle fault performance considering physical constraints can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0094] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a self-healing control method for connected vehicle fault performance considering physical constraints. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0095] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0096] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0097] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0098] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0099] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0100] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0101] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A self-healing control method for fault performance of connected vehicles considering physical constraints, characterized in that, The method includes: A nonlinear system model of heterogeneous connected vehicles is constructed based on nonlinear dynamics of displacement, velocity, and acceleration, the effects of actuator failure, and external disturbances. We construct auxiliary dynamic variables to characterize the overall impact of faults, derive their time derivatives, design an adaptive fault impact identifier, and perform online identification of the impact of system faults. Construct the infimum parameter associated with the unknown control coefficient, design the parameter boundary estimator, and perform adaptive estimation of the unknown parameter boundary; Based on fault impact identification and parameter boundary estimation information, and by integrating actuator physical constraint functions, a self-healing controller for connected vehicle platooning is designed to achieve fault impact compensation, actuator physical constraint protection, and stable control of connected vehicle platooning.

2. The self-healing control method for connected vehicle fault performance considering physical constraints according to claim 1, characterized in that, The construction of the heterogeneous connected vehicle nonlinear system model includes: By defining the physical boundary conditions under which the actuator can generate control torque, a physical constraint model for the connected vehicle actuator is established. An actuator fault model is constructed based on the aforementioned actuator physical constraint model, taking into account common failures and bias faults of connected vehicle actuators.

3. The self-healing control method for connected vehicle fault performance considering physical constraints according to claim 1, characterized in that, The nonlinear system model of the heterogeneous connected vehicle is represented as follows: in, The first The derivatives of the vehicle's displacement, velocity, and acceleration. ; , , The first The vehicle's displacement, velocity, and acceleration; This represents the coupled nonlinear dynamics of velocity and acceleration; For unknown control coefficients; For the first A self-healing controller for platooning connected vehicles; This indicates external disturbances caused by the environment; For the first The overall impact of multiplicative and additive actuator faults on the system of the vehicle. The failure factor indicates a loss of actuator efficiency, satisfying the following conditions: ,and and It is a positive scalar. Because of additive bias, it indicates sudden faults caused by constant torque due to leakage in hydraulic cylinders or mechanical transmission locks, periodic faults caused by damage to insulated gate bipolar transistors, and recurring unpredictable intermittent faults.

4. The self-healing control method for connected vehicle fault performance considering physical constraints according to claim 1, characterized in that, The auxiliary dynamic variable is represented as follows: in, Positive design parameters; For tracking error; For an ideal constant workshop distance; ; For the first Vehicle and the The distance between vehicles; For the first The length of the vehicle body; Its time derivative is expressed as: in, For the first The derivatives of the vehicle's auxiliary dynamic variables. For the first Overall impact of multiplicative and additive actuator faults in a vehicle on the system The derivative of .

5. The self-healing control method for connected vehicle fault performance considering physical constraints according to claim 4, characterized in that, The adaptive fault effect identifier is represented as follows: in, , These are all design parameters. For connected vehicle platooning self-healing controllers, For auxiliary dynamic variables The estimated value, For the first Vehicle auxiliary dynamic variable estimates The derivative, For the first Overall impact of multiplicative and additive actuator faults in a vehicle on the system The estimated value.

6. The self-healing control method for connected vehicle fault performance considering physical constraints according to claim 1, characterized in that, The infimum parameter associated with the unknown control coefficient is expressed as: , in, This represents the lower bound of the unknown control coefficient; It is the reciprocal of the underfimum of the unknown control coefficient.

7. The self-healing control method for connected vehicle fault performance considering physical constraints according to claim 6, characterized in that, The parameter boundary estimator is expressed as: in, For parameters The estimated value; The derivative of the estimated value; and It is a positive scalar.

8. The self-healing control method for connected vehicle fault performance considering physical constraints according to claim 1, characterized in that, The connected vehicle platoon self-healing controller is represented as follows: in, For the first The physical constraints of connected vehicles , For actuator physical constraint functions; , It is a positive scalar; It is a positive scalar; For indirect control laws, designed as , It is a positive scalar.

9. A self-healing control method for connected vehicle fault performance considering physical constraints according to claim 8, characterized in that, The actuator physical constraint function is expressed as follows: in, .

10. A self-healing control system for connected vehicle fault performance considering physical constraints, characterized in that, The system includes: The heterogeneous system construction module is used to construct a nonlinear system model of a heterogeneous connected vehicle based on nonlinear dynamics of displacement, velocity, and acceleration, the effects of actuator failure, and external disturbances. The fault impact identification module is used to construct auxiliary dynamic variables that characterize the overall impact of faults, derive their time derivatives, design an adaptive fault impact identifier, and perform online identification of the impact of system faults. The parameter boundary estimation module is used to construct the infimum parameter associated with the unknown control coefficient, design the parameter boundary estimator, and perform adaptive estimation of the unknown parameter boundary. The formation constraint control module is used to design a self-healing controller for connected vehicle formations based on fault impact identification and parameter boundary estimation information, and by integrating actuator physical constraint functions. This controller achieves fault impact compensation, actuator physical constraint protection, and stable control of connected vehicle formations.

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