A connected vehicle fault performance self-healing control method and system considering physical constraints

Abstract

The application relates to a connected vehicle fault performance self-healing control method and system considering physical constraints in the technical field of intelligent transportation and vehicle control. The method comprises the following steps: constructing a heterogeneous connected vehicle nonlinear system model based on displacement, speed, nonlinear dynamics of acceleration, actuator fault influence and external disturbance; constructing an auxiliary dynamic variable representing the overall influence of the fault, deriving its time derivative, designing an adaptive fault influence identifier, and performing online identification of the system fault influence; constructing a lower bound parameter associated with unknown control coefficients, designing a parameter boundary estimator, and performing adaptive estimation of the unknown parameter boundary; based on the fault influence identification and parameter boundary estimation information, integrating the actuator physical constraint function, designing a connected vehicle formation self-healing controller, and realizing fault influence compensation, actuator physical constraint guarantee and connected vehicle formation stable control. The reliability and control accuracy of the connected vehicle cooperative control system under complex working conditions are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation and vehicle control, in particular to a fault performance self-healing control method and system for connected vehicles considering physical constraints. BACKGROUND

[0002] Under the technical development trend of deep integration of Internet of Vehicles technology and automatic driving technology, the connected vehicle cooperative control system has become the core technology direction to improve traffic efficiency and ensure traffic safety. Relying on information interaction sharing and cooperative decision mechanism between vehicles, this kind of system can realize path optimization, emergency avoidance, vehicle platoon driving and other core functions, effectively reduce the traffic accident rate and optimize the traffic flow distribution, providing important technical support for the development of intelligent transportation field.

[0003] However, in actual working condition application, the problems of actuator failure and physical constraints significantly restrict the robustness and control accuracy of the connected vehicle cooperative control system. Especially in typical complex scenarios such as complex road conditions, extreme weather or vehicle heterogeneity, the above problems are more prominent, which directly affects the reliability and operation stability of the connected vehicle cooperative control system, and becomes a key bottleneck restricting the large-scale application of this technology.

[0004] Specifically, in the process of connected vehicle cooperative control, the real-time response performance of actuators such as brakes and drive motors is the core basis to ensure the accurate execution of control commands. However, due to the limitations of actuator hardware performance and the complex interference of dynamic driving environment, the existing technical solutions still have the following key technical defects:

[0005] First, the actuator failure has the characteristics of suddenness and concealment. For example, brake actuators are prone to local failure due to mechanical wear, electronic signal interference and other factors, which can cause sudden changes in vehicle acceleration and disrupt the cooperative operation state of vehicles in the vehicle platoon. Since this kind of fault signal is difficult to be detected in real time and accurately, the abnormal motion state of the faulty vehicle may be transmitted to the adjacent vehicles through the Internet of Vehicles communication link, causing a chain of interference. This nonlinear fault diffusion mechanism makes it difficult for traditional model prediction-based cooperative control algorithms to quickly locate the fault source and dynamically adjust the control strategy; in high-density vehicle interaction scenarios, the system fault recovery takes too long, which can easily lead to the disintegration of vehicle platoon, vehicle collision and other safety risks.

[0006] Second, there is a dynamic conflict problem in the physical constraints of actuators. Actuators generally have physical limit constraints such as maximum output force, response speed, etc. such as torque upper limit of a drive motor, angular velocity threshold of a steering system, etc. In typical scenarios such as multi-vehicle cooperative braking and cooperative steering, adjacent vehicles may not be able to reach the system preset target acceleration or steering angle due to their own actuator physical constraint limitations, resulting in continuous accumulation of path tracking deviation. More seriously, when the multi-vehicle cooperative task requires acceleration and steering angle of the actuator beyond its local physical constraint range, the control command is prone to saturation failure, forcing the system to use a conservative control strategy, which significantly weakens the performance advantage of the cooperative control of the connected vehicles.

[0007] Third, there is an inherent contradiction between safety redundancy design and system real-time performance. To cope with actuator failure risks, existing technologies usually need to design a redundancy identification module for each type of fault parameter, which significantly increases the communication data load and computational complexity of the system. Taking the multi-vehicle cooperative formation scenario as an example, the faulty vehicle needs to broadcast state abnormal 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 delays, further exacerbating the deterioration of cooperative control performance.

[0008] Fourth, the coupling of actuator failure and physical constraints significantly increases the vulnerability of the control system. Existing cooperative control algorithms for connected vehicles (such as distributed model predictive control algorithms and sliding mode control algorithms) are generally designed based on the ideal assumption that the actuator is in normal working condition and the actuator meets the physical constraints. When the actuator temporarily reduces its output capacity due to failure, such traditional algorithms cannot achieve effective control under the constraint of limited output torque of the actuator, which easily leads to loss of stability of the connected vehicle formation and causes cooperative control failure.

[0009] Therefore, in the related art, there is an urgent need for a way to improve the reliability and control accuracy of the cooperative control system of connected vehicles under complex working conditions. SUMMARY

[0010] Therefore, it is necessary to provide a connected vehicle fault performance self-healing control method and system considering physical constraints to improve the reliability and control accuracy of the cooperative control system of connected vehicles under complex working conditions.

[0011] In a first aspect, the present application provides a connected vehicle fault performance self-healing control method considering physical constraints. The method comprises:

[0012] Constructing a heterogeneous connected vehicle nonlinear system model based on displacement, velocity, acceleration, nonlinear dynamics, actuator failure influence, and external disturbance;

[0013] 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.

[0014] Construct the infimum parameter associated with the unknown control coefficient, design the parameter boundary estimator, and perform adaptive estimation of the unknown parameter boundary;

[0015] 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.

[0016] Optionally, in one embodiment of this application, constructing the heterogeneous connected vehicle nonlinear system model includes:

[0017] 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.

[0018] 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.

[0019] Optionally, in one embodiment of this application, the heterogeneous connected vehicle nonlinear system model is represented as:

[0020]

[0021] 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.

[0022] Optionally, in one embodiment of this application, the auxiliary dynamic variable is represented as:

[0023]

[0024] 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;

[0025] Its time derivative is expressed as:

[0026]

[0027] in, For the first The derivatives of the vehicle's auxiliary dynamic variables. For the first Overall impact of actuator multiplicative and additive faults in the vehicle on the system The derivative of .

[0028] Optionally, in one embodiment of this application, the adaptive fault impact identifier is represented as:

[0029]

[0030] 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, For the first Overall impact of actuator multiplicative and additive faults in the vehicle on the system The estimated value.

[0031] Optionally, in an embodiment of the present application, the lower bound parameter associated with the unknown control coefficient is represented as:

[0032] ,

[0033] wherein, is the lower bound of the unknown control coefficient; is the inverse of the lower bound of the unknown control coefficient.

[0034] Optionally, in an embodiment of the present application, the parameter boundary estimator is represented as:

[0035]

[0036] wherein, is the estimated value of the parameter ; is the derivative of the estimated value; and is a positive scalar.

[0037] Optionally, in an embodiment of the present application, the platoon self-healing controller is represented as:

[0038]

[0039] wherein, is the physical constraint value of the th connected vehicle, , is the actuator physical constraint function; , is a positive scalar; is a positive scalar; is the indirect control law designed as , is a positive scalar.

[0040] Optionally, in an embodiment of the present application, the actuator physical constraint function is represented as:

[0041]

[0042] wherein, .

[0043] In a second aspect, the present application also provides a platoon fault performance self-healing control system considering physical constraints. The system comprises:

[0044] a heterogeneous system construction module for constructing a heterogeneous platoon nonlinear system model based on displacement, velocity, nonlinear dynamics of acceleration, actuator fault influence and external disturbance;

[0045] a fault influence identification module for constructing an auxiliary dynamic variable representing the overall influence of the fault, deriving a time derivative thereof, designing an adaptive fault influence identifier, and performing online identification of the influence of the fault on the system;

[0046] a parameter boundary estimation module for constructing a lower bound parameter associated with the unknown control coefficient, designing a parameter boundary estimator, and performing adaptive estimation of the boundary of the unknown parameter;

[0047] a platoon constraint control module for synthesizing an actuator physical constraint function based on the fault influence identification and parameter boundary estimation information, designing a platoon self-healing controller for the connected vehicles, and achieving fault influence compensation, actuator physical constraint guarantee, and stable control of the platoon of connected vehicles.

[0048] Compared with the prior art, the present application has the following advantages:

[0049] First, by quantitatively analyzing the physical limit of the available control torque of the actuator, an actuator model with clear constraints is established, effectively avoiding saturation failure caused by control commands exceeding the physical range of the actuator. Further, the actuator failure and bias fault are comprehensively modeled, covering the common actuator fault modes in the platoon of connected vehicles, improving the fault tolerance of the system and the executability of the control command, and ensuring the reliability of multi-vehicle cooperative control.

[0050] Second, a heterogeneous connected vehicle nonlinear system model is designed, which comprehensively considers the nonlinear dynamics of displacement, speed, acceleration, actuator fault influence, and external disturbance.

[0051] Third, by constructing an auxiliary dynamic variable representing the overall influence of the fault and deriving its derivative, combined with the design of the fault influence identifier, real-time online estimation of the fault influence is achieved, breaking the dependence on prior fault types in traditional fault estimation and effectively simplifying the design complexity of the fault identification system, providing accurate fault information input for subsequent control compensation.

[0052] Fourth, by constructing a lower bound dynamic estimator of the unknown parameter, the adaptive mechanism is used to update the parameter boundary estimation value in real time, effectively solving the problem of uncertain control coefficients in the heterogeneous connected vehicle system.

[0053] Fifth, the fault influence identification result, parameter boundary estimation value, and actuator physical constraint condition are organically integrated, and a platoon controller with fault compensation, constraint guarantee, and platoon stability is designed, which not only ensures the gradual convergence of the platoon tracking error, but also avoids secondary faults caused by actuator over-limit, significantly improving the overall stability and fault tolerance of the multi-vehicle platoon system. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1An application environment diagram of a physical constraint considering connected vehicle fault performance self-healing control method in an embodiment;

[0055] Figure 2 An illustration of displacement curves of a lead vehicle and a following vehicle 1-4 in an embodiment;

[0056] Figure 3 An illustration of velocity curves of a lead vehicle and a following vehicle 1-4 in an embodiment;

[0057] Figure 4 An illustration of inter-vehicle distance error curves of a following vehicle 1-4 in an embodiment;

[0058] Figure 5 An illustration of control torque curves of a following vehicle 1-4 in an embodiment;

[0059] Figure 6 An illustration of fault influence identification curves of a following vehicle 1 in an embodiment;

[0060] Figure 7 A structural block diagram of a physical constraint considering connected vehicle fault performance self-healing control system in an embodiment;

[0061] Figure 8 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0062] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0063] In an embodiment, as shown in Figure 1 , a physical constraint considering connected vehicle fault performance self-healing control method is provided, comprising the following steps:

[0064] S101: Construct a heterogeneous connected vehicle nonlinear system model based on displacement, velocity, nonlinear dynamics of acceleration, actuator fault influence and external disturbance.

[0065] In an embodiment of the present application, the construction of the heterogeneous connected vehicle nonlinear system model comprises:

[0066] S201: Establish a connected vehicle actuator physical constraint model by limiting the physical boundary conditions of the control torque that the actuator can generate.

[0067] S203: Construct an actuator fault model based on the actuator physical constraint model, and comprehensively consider the common failure and bias faults of the connected vehicle actuator.

[0068] In an embodiment of the present application, considering that the safety limits and physical constraints inherent in the throttle or brake system of the connected vehicle inevitably lead to actuator saturation problems, which must be addressed in control design, by defining the physical constraints of the actuator as the physical boundary of the control torque that the actuator can generate, the actuator physical constraint model is defined as:

[0069]

[0070] wherein, is time; is the vehicle in the platoon; is the vehicle in the platoon, .

[0071] The actuator fault model constructed based on the actuator physical constraint model is:

[0072]

[0073] wherein, is the uncertain fault occurrence time, reflecting the randomness of fault occurrence; is the failure factor, indicating that the efficiency of the actuator is lost, satisfying , and is a positive scalar. is the additive bias fault, indicating sudden faults caused by constant torque caused by hydraulic cylinder or mechanical transmission lock leakage, periodic faults caused by damage to insulated gate bipolar transistors, and unpredictable intermittent faults that occur repeatedly.

[0074] Considering that the dynamic parameters in the mechanism model are partially known or even completely unknown, and constantly change with changes in the external environment, in an embodiment of the present application, the heterogeneous connected vehicle nonlinear system model is represented as:

[0075]

[0076] wherein, are the derivatives of the displacement, velocity and acceleration of the vehicle, respectively; are the displacement, velocity and acceleration of the vehicle, respectively; indicates the coupling of the velocity and acceleration nonlinear dynamics; is an unknown control coefficient; is the vehicle in the platoon;​​​​ 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.

[0077] 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.

[0078] 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.

[0079] To characterize the overall impact of the fault, in one embodiment of this application, the auxiliary dynamic variable is represented as:

[0080]

[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;

[0082] Its time derivative is expressed as:

[0083]

[0084] 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 .

[0085] In one embodiment of this application, the adaptive fault effect identifier is represented as:

[0086]

[0087] wherein, , are design parameters, is a self-healing controller of the connected vehicle platoon, is an estimated value of an auxiliary dynamic variable , is a derivative of the estimated value of the auxiliary dynamic variable of the i-th vehicle , is an overall impact of the actuator multiplicative fault and additive fault on the system of the i-th vehicle , is an estimated value of the overall impact of the actuator multiplicative fault and additive fault on the system of the i-th vehicle .

[0088] wherein, due to the unknown nature of the residual term , an additional complementary term is introduced to approximately offset and compensate, is replaced by .

[0089] S103: constructing a lower bound parameter associated with the unknown control coefficient, designing a parameter boundary estimator, and performing adaptive estimation of the boundary of the unknown parameter.

[0090] In an embodiment of the present application, the lower bound parameter associated with the unknown control coefficient is represented as:

[0091] ,

[0092] wherein, is the lower bound of the unknown control coefficient; is the reciprocal of the lower bound of the unknown control coefficient.

[0093] In an embodiment of the present application, the parameter boundary estimator is represented as:

[0094]

[0095] wherein, is an estimated value of the parameter ; is a derivative of the estimated value; and are positive scalars.

[0096] S104: based on the fault impact identification and parameter boundary estimation information, synthesizing an actuator physical constraint function, designing a self-healing controller of the connected vehicle platoon, and realizing fault impact compensation, actuator physical constraint guarantee, and stable control of the connected vehicle platoon.

[0097] In an embodiment of the present application, for the heterogeneous platoon nonlinear system model, a platoon control algorithm considering physical constraints is constructed based on actuator fault influence identification information. Based on fault influence identification information and parameter boundary estimation , a platoon self-healing controller is designed, specifically:

[0098]

[0099] wherein, is the physical constraint value of the th platoon vehicle, , is the actuator physical constraint function; , is a positive scalar; is a positive scalar; is an indirect control law, designed as , is a positive scalar.

[0100] The actuator physical constraint function is specifically:

[0101]

[0102] wherein, is a positive scalar.

[0103] In an embodiment of the present application, in order to prove the effectiveness of the method, the following simulation test is performed for verification:

[0104] A set of heterogeneous platoon system consisting of 1 leader vehicle and 4 follower vehicles is considered, and the initial state of the system is set as shown in Table 1:

[0105] Table 1 Initial state of the system

[0106]

[0107] In this simulation experiment, the safe vehicle distance is set to 7.98 meters; the external environmental disturbance is ; the platoon physical constraint value is ; the leader vehicle acceleration is:

[0108]

[0109] For the first follower vehicle, consider that the actuator has a 50% partial efficiency loss and a bias fault at , i.e. and .

[0110] The effectiveness of the self-healing control method for the failure performance of the connected vehicle considering physical constraints proposed in the embodiment is verified by using MATLAB2023a and Simulink simulation software, and Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 and Figure 6 .

[0111] In the snapshot of the longitudinal displacement trajectory of the lead vehicle and the following vehicles 1-4 shown in Figure 2 , it can be observed that all connected vehicles always run in a longitudinal formation, maintaining a safe distance between vehicles, and no collision occurs.

[0112] In the snapshot of the speed of the lead vehicle and the following vehicles 1-4 shown in Figure 3 , it can be observed that the method can ensure the consistency of the speed of all formation vehicles.

[0113] In the snapshot of the inter-vehicle distance error of the following vehicles 1-4 shown in Figure 4 , it can be observed that under the proposed self-healing control method for the failure performance of the connected vehicle considering physical constraints, all following vehicles in the formation can achieve the expected tracking performance and are not affected by the actuator failure in the following vehicle 1.

[0114] In the snapshot of the inter-vehicle distance error of the following vehicles 1-4 shown in Figure 5 , it can be observed that the control torque generated by each following vehicle can be constrained in the physical interval , i.e., the proposed method can guarantee the physical constraint , .

[0115] In the snapshot of the actuator failure influence identification of the following vehicle 1 shown in Figure 6 , it can be observed that the overall influence trend of the actuator failure can be identified online, and the effectiveness of the designed failure influence identifier is verified.

[0116] It should be understood that although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless explicitly stated herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0117] Based on the same inventive concept, the embodiments of the present application also provide a physical constraint considering connected vehicle fault performance self-healing control system for implementing the above-mentioned method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme described in the above-mentioned method, so the specific limitations in one or more physical constraint considering connected vehicle fault performance self-healing control system embodiments provided below can be referred to the limitations of the physical constraint considering connected vehicle fault performance self-healing control method in the above, which will not be described here again.

[0118] In one embodiment, as shown in Figure 7 A physical constraint considering connected vehicle fault performance self-healing control system 700 is provided, comprising: a heterogeneous system construction module 701, a fault influence identification module 702, a parameter boundary estimation module 703 and a platoon constraint control module 704, wherein:

[0119] The heterogeneous system construction module 701 is configured to construct a heterogeneous connected vehicle nonlinear system model based on displacement, velocity, nonlinear dynamics of acceleration, actuator fault influence and external disturbance.

[0120] The fault influence identification module 702 is configured to construct an auxiliary dynamic variable representing the overall influence of the fault, derive its time derivative, design an adaptive fault influence identifier, and perform online identification of the system fault influence.

[0121] The parameter boundary estimation module 703 is configured to construct a lower bound parameter associated with the unknown control coefficient, design a parameter boundary estimator, and perform adaptive estimation of the unknown parameter boundary.

[0122] The platoon constraint control module 704 is configured to integrate actuator physical constraint functions based on fault influence identification and parameter boundary estimation information, design a connected vehicle platoon self-healing controller, and realize fault influence compensation, actuator physical constraint guarantee and connected vehicle platoon stability control.

[0123] In one embodiment of the present application, the construction of the heterogeneous connected vehicle nonlinear system model comprises:

[0124] By limiting the physical boundary conditions of the control torque that the actuator can generate, an actuator physical constraint model of the connected vehicle is established;

[0125] Based on the actuator physical constraint model, an actuator fault model is constructed, considering common failure and bias faults of the connected vehicle actuator.

[0126] In one embodiment of the present application, the heterogeneous connected vehicle nonlinear system model is represented as:

[0127]

[0128] 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.

[0129] In one embodiment of this application, the auxiliary dynamic variable is represented as:

[0130]

[0131] 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;

[0132] Its time derivative is expressed as:

[0133]

[0134] 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 .

[0135] In one embodiment of this application, the adaptive fault effect identifier is represented as:

[0136]

[0137] in, , These are all design parameters. For connected vehicle platooning self-healing controller, 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.

[0138] In one embodiment of this application, the infimum parameter associated with the unknown control coefficient is expressed as:

[0139] ,

[0140] in, This represents the lower bound of the unknown control coefficient; It is the reciprocal of the underfimum of the unknown control coefficient.

[0141] In one embodiment of this application, the parameter boundary estimator is represented as:

[0142]

[0143] in, For parameters The estimated value; The derivative of the estimated value; and It is a positive scalar.

[0144] In one embodiment of this application, the connected vehicle platoon self-healing controller is represented as:

[0145]

[0146] 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; is an indirect control law, designed as , is a positive scalar.

[0147] In an embodiment of the present application, the actuator physical constraint function is expressed as:

[0148]

[0149] wherein, .

[0150] The above-mentioned modules in the vehicle-to-vehicle fault performance self-healing control system considering physical constraints can be implemented wholly or partially by software, hardware or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules.

[0151] In an embodiment, a computer device is provided, which can be a terminal, and an internal structure diagram thereof can be as shown in Figure 8 The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, mobile cellular network, NFC (Near Field Communication) or other technologies. The computer program is executed by the processor to implement a vehicle-to-vehicle fault performance self-healing control method considering physical constraints. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball or touchpad arranged on the shell of the computer device, or can be an external keyboard, touchpad or mouse, etc.

[0152] Those skilled in the art can understand that Figure 8 the structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0153] In an embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0154] In an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0155] In an embodiment, a computer program product is provided, comprising a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0156] 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 for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0157] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present 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 storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0158] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0159] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for self-healing control of connected vehicle failure performance 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. 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. Based on the physical constraint model of the actuator, an actuator fault model is constructed, taking into account common failures and bias faults of actuators in connected vehicles. 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. An additive bias fault indicates a sudden fault caused by constant torque due to leakage in a hydraulic cylinder or mechanical transmission lock, a periodic fault caused by damage to an insulated gate bipolar transistor, or a recurring, unpredictable intermittent fault. The auxiliary dynamic variable is represented as follows: wherein, is a positive design parameter; is a tracking error; is an ideal constant inter-vehicle distance; ; is an inter-vehicle distance between the first vehicle and the second vehicle; is an inter-vehicle distance between the first vehicle and the second vehicle; is an inter-vehicle distance between the first vehicle and the second vehicle; is a body length of the first vehicle; is a body length of the first vehicle; Its time derivative is expressed as: wherein, is the derivative of the auxiliary dynamic variable of the vehicle, is the derivative of the overall effect on the system of the multiplicative fault and the additive fault of the actuator of the vehicle ​ The adaptive fault effect identifier is represented as follows: wherein, , are design parameters, is a self-healing controller for a platoon of connected vehicles, is an estimate of an auxiliary dynamic variable , is a derivative of an estimate of an auxiliary dynamic variable of the i-th vehicle , is an estimate of the overall effect of actuator multiplicative faults and additive faults on the system for the i-th vehicle , is an estimate of the overall effect of actuator multiplicative faults and additive faults on the system for the i-th vehicle , 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 infimum of the unknown control coefficient; 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; 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.

2. The self-healing control method for connected vehicle fault performance considering physical constraints according to claim 1, characterized in that, The actuator physical constraint function is expressed as follows: in, .

3. 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, so as to realize fault impact compensation, actuator physical constraint protection and stable control of connected vehicle formations. 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. Based on the physical constraint model of the actuator, an actuator fault model is constructed, taking into account common failures and bias faults of actuators in connected vehicles. 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. An additive bias fault indicates a sudden fault caused by constant torque due to leakage in a hydraulic cylinder or mechanical transmission lock, a periodic fault caused by damage to an insulated gate bipolar transistor, or a recurring, unpredictable intermittent fault. 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; The adaptive fault effect identifier is represented as follows: in, , These are all design parameters. For connected vehicle platooning self-healing controller, 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; 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 infimum of the unknown control coefficient; 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; 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.

Images