Active fault-tolerant control method for intelligent connected vehicle platoon under communication delay

Abstract

This paper presents an active fault-tolerant control method for intelligent connected vehicle platoons under communication delay, focusing on intelligent vehicle safety and autonomous driving. Utilizing onboard sensors and the V2X wireless communication system to enable information exchange between the vehicle and other vehicles, this method employs inverse model compensation and feedback linearization techniques to establish a closed-loop control model for vehicle platoons that accommodates both sensor and actuator failures. A distributed observer-based active fault-tolerant control method is designed under the influence of communication delay. Based on a dynamic proportional-integral observer, sensor and actuator failures are estimated. Communication delay and fault compensation are considered in the controller to achieve platoon system stability and ensure the desired inter-vehicle spacing and speed for each vehicle in the platoon. A distributed controller is designed based on the fault estimate and the delayed output information from other vehicles to mitigate the impact of sensor and actuator failures and achieve platoon control.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent automobile safety and autonomous driving, and in particular relates to an active fault-tolerant control method for an intelligent connected vehicle platoon under the combined influence of communication delay, actuator failure, and sensor failure. Background Art

[0002] As the number of private cars continues to rise, existing road infrastructure is struggling to effectively handle the growing demand. This situation directly leads to frequent traffic jams, forcing vehicles to frequently start and stop. This not only significantly increases energy consumption but also indirectly contributes to an increase in road accidents, significantly increasing travel costs and the burden on the public. Intelligent connected vehicle platooning can adjust spacing to improve road utilization and mitigate energy waste through speed coordination, offering significant potential for addressing these pain points.

[0003] Intelligent connected vehicle platoon control technology uses V2X (Vehicle to Everything) technology to share vehicle information. Each vehicle in the platoon adjusts its motion state based on the shared information to achieve the desired spacing with adjacent vehicles and a speed consistent with the lead vehicle. Reference 1 (Yu G, Wong PK, Huang W, et al. Distributed adaptive consensus protocol for connected vehicle platoon with heterogeneous time-varying delays and switching topologies [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 17620-17631.) proposes an adaptive control method for vehicle platoons with heterogeneous time-varying delays. Reference 2 (Liu C, Zhao J, Patton RJ. Distributed antittack fault-tolerant tracking control for vehicle platoon systems under cyber-physical threats[J]. IEEE Transactions on Industrial Informatics, 2023, 19(6): 7825-7834.) proposes a control method based on unknown input observer to address actuator faults and switching topologies.

[0004] Intelligent connected vehicle platoons use V2X vehicle-to-everything (V2X) technology to improve the perception capabilities of vehicles. However, the introduction of wireless communications inevitably brings about the problem of information transmission delay, that is, communication delay, which may lead to information synchronization problems between vehicles, increase the risk of rear-end collisions, and reduce the efficiency of platoon coordination. In addition, in actual applications, the sensors and actuators of intelligent connected vehicles may malfunction due to various factors, resulting in the inability of vehicles to accurately perceive environmental information or execute control instructions, resulting in a decrease in control performance or even failure. In view of the impact of communication delay, sensor failure, and actuator failure on intelligent connected vehicle platoons, the present invention proposes an active fault-tolerant control method for intelligent connected vehicle platoons under communication delay. Summary of the Invention

[0005] The present invention aims to address the existing problem of vehicle platooning systems facing communication delays, sensor failures, and actuator failures. It provides an active fault-tolerant control method for intelligent connected vehicle platoons in the presence of communication delays. This method maintains stable platoon operation and ensures safe distances and speeds between vehicles, even in the event of sensor or actuator failures. The present invention first designs a distributed dynamic proportional-integral observer to estimate the sensor and actuator failures of each vehicle in the platoon. Then, based on the fault estimates and delayed output information from other vehicles, a distributed controller is designed to mitigate the impact of sensor and actuator failures and achieve platoon control.

[0006] The active fault-tolerant control method for an intelligent connected vehicle platoon under communication delay of the present invention comprises the following steps:

[0007] Step 1: Formation and Information Collection of a Vehicle Platoon: A vehicle platoon consists of N+1 vehicles, including a lead vehicle and N following vehicles. Vehicle status information, including location, speed, and acceleration, is acquired using onboard sensors. The vehicle uses the V2X wireless communication network to collect real-time information from neighboring vehicles and the lead vehicle, receives information from connected vehicles in real time, and broadcasts the vehicle's own information.

[0008] Step 2: Based on information obtained from on-board sensors and the V2X wireless communication network, a state-space model of the longitudinal dynamics of a single vehicle with actuator and sensor failures is established. Specifically, a nonlinear longitudinal dynamic expression for a single vehicle is derived based on Newton's second law, and feedback linearization is performed using inverse model compensation techniques to derive a linearized longitudinal dynamic model for the single vehicle. The position, velocity, and acceleration information of the ego vehicle are used as the state vector, and a vehicle longitudinal motion model including actuator failures is established. Furthermore, sensor failures are considered and sensor output information is constructed to estimate and compensate for these failures during the control process.

[0009] Step 3: Establish the objective function of intelligent connected vehicle platoon control based on the information interaction between intelligent connected vehicles described by graph theory. Specifically: define the communication topology of the vehicle platoon based on graph theory, and give the definitions of the adjacency matrix and the Laplace matrix; set the speed difference between the ego vehicle and the pilot vehicle, and the distance difference between the ego vehicle and the preceding vehicle as independent variables, and construct the objective function of the vehicle platoon control to ensure that the platoon maintains a constant desired formation.

[0010] Step 4: For the vehicle platoon system affected by sensor and actuator failures, establish a distributed error and fault observer, design a distributed active fault-tolerant controller without delay, design a distributed active fault-tolerant controller affected by communication delay, and solve the wheel motor drive torque required for platoon control in real time. Specifically:

[0011] 4.1) Construct an augmented system that includes sensor failures, design a distributed dynamic proportional-integral observer, and establish a closed-loop error system for the vehicle platoon observer;

[0012] 4.2) Design a distributed active fault-tolerant controller without communication delay and incorporate it into the platooning model to establish a closed-loop error control system for the vehicle platoon.

[0013] 4.3) Based on Lyapunov stability theory and linear matrix inequality methods, the conditions for achieving stability of the queue closed-loop error system under the condition of no time-varying delay are given, and the design method of the controller gain matrix and the decentralized observer gain is obtained;

[0014] 4.4) Design a distributed active fault-tolerant controller under the influence of communication delay. Substitute it into the formation model to establish a closed-loop error control system for the vehicle platoon under the influence of time-varying communication delay.

[0015] 4.5) Based on Lyapunov stability theory, linear matrix inequality method and Halanay inequality, the conditions for the active fault-tolerant controller to achieve stability of the queue closed-loop error system under time-varying delay are given, that is, the upper bound of the delay that can be tolerated by the controller and observer gains obtained in step 4.3) is obtained.

[0016] 4.6) Substitute the active fault-tolerant controller into the feedback linearization model in step 2 to calculate the desired wheel driving torque of the vehicle in real time, realizing the formation control of intelligent connected vehicles.

[0017] Compared with the prior art, the present invention has the following outstanding technical effects and advantages:

[0018] This invention utilizes on-board sensors and a V2X wireless communication system to enable information exchange between the vehicle itself and other vehicles. It employs inverse model compensation and feedback linearization techniques to establish a closed-loop control model for vehicle platoons that accommodates both sensor and actuator failures. Influenced by the communications industry, an active fault-tolerant control method with a distributed observer is designed. Through feedback linearization, this invention transforms a complex nonlinear control system into a relatively simple linear control system, facilitating control design. Based on a dynamic proportional-integral observer, this invention estimates sensor and actuator failures. The controller considers communication delays and compensates for these failures, achieving platoon system stability and ensuring the desired inter-vehicle spacing and speed for each vehicle in the platoon. By accounting for actuator and sensor failures, the designed controller can better address these uncertainties, improving system robustness and reliability. By utilizing on-board sensors and the V2X wireless communication network to collect and exchange information in real time, this invention enables rapid response to vehicle status changes and improves the system's real-time performance. This method is applicable not only to the control of individual vehicles but can also be extended to the coordinated control of entire fleets, contributing to the automation and intelligent management of fleets. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of an active fault-tolerant control system for an intelligent connected vehicle platoon under communication delay according to an embodiment of the present invention.

[0020] Figure 2 This is a flowchart of a method according to an embodiment of the present invention.

[0021] Figure 3 This is a diagram showing the fault signal tracking result of the first following vehicle under the influence of communication delay and fault in the embodiment of the present invention. Figure 3 In the figure, (a) compares the estimated dynamic tracking curve of the first following vehicle’s actuator fault (sinusoidal signal) with the true value; (b) compares the estimated dynamic tracking curve of the first following vehicle’s sensor fault (sinusoidal signal) with the true value.

[0022] Figure 4 This is a diagram showing the fault signal tracking result of the third following vehicle under the influence of communication delay and fault in the embodiment of the present invention. Figure 4 In the figure, (a) compares the estimated dynamic tracking curve of the third following vehicle actuator failure (combined signal) with the true value; (b) compares the estimated dynamic tracking curve of the third following vehicle sensor failure (combined signal) with the true value.

[0023] Figure 5 This is a simulation result diagram of active fault-tolerant control of a queue system under the influence of communication delay in an embodiment of the present invention. Figure 5In the figure, (a) is the simulation result curve of the distance tracking error of the queue system; (b) is the simulation result curve of the speed tracking error of the queue system; and (c) is the simulation result curve of the acceleration of the queue system. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the following embodiments will be further described in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0025] like Figure 1 As shown, the platooning control system of an embodiment of the present invention comprises a typical hierarchical structure consisting of a distributed observer i, a distributed active fault-tolerant controller i, sensors, actuators, and other modules. At the lowest level is the physical layer, where sensor and actuator modules may be affected by unknown faults. Next is the distributed fault estimation layer, where a distributed proportional-integral observer deployed on each vehicle independently estimates sensor and actuator faults. Next is the distributed active fault-tolerant control layer, which interacts bidirectionally with the distributed fault estimation layer. The distributed fault-tolerant control layer receives vehicle information via the V2X wireless communication network and simultaneously transmits information from the vehicle itself and the distributed observer to other vehicles in the platoon, thereby achieving coordinated control of the platoon.

[0026] like Figure 2 As shown, the present invention proposes an active fault-tolerant control method for an intelligent connected vehicle queue under communication delay, comprising the following steps:

[0027] Step 1: A platoon consists of N+1 vehicles, numbered 0,...N. Vehicle 0 is the lead vehicle, and vehicles 1,...,N are followers. Vehicle sensors and the V2X wireless communication network collect real-time information about the vehicle's motion state, as well as output information from neighboring vehicles and the lead vehicle.

[0028] Step 1.1: Utilize advanced on-board sensor technology integrated with GPS positioning to capture and analyze the vehicle's status information. This includes determining the vehicle's position, measuring its speed and acceleration, and calculating its output information.

[0029] Step 1.2: The ego vehicle exchanges information with the pilot vehicle and other following vehicles in the platoon via the V2X wireless communication network, receiving output information from the connected vehicle in real time and broadcasting the output information of the ego vehicle.

[0030] Step 2: Based on the information obtained from the on-board sensors and the V2X wireless communication network, a longitudinal dynamic state space model of a single vehicle with actuator failure and sensor failure is established.

[0031] Step 2.1: Perform a dynamic analysis of the longitudinal motion of the vehicle. Based on Newton's second law, a nonlinear dynamic model of the i-th vehicle in the platoon can be obtained.

[0032]

[0033]

[0034] Among them, m i is the mass of the vehicle, F i is the driving force of the vehicle, v i is the vehicle speed, μ i is the mechanical efficiency of the transmission system, T i is the actual driving / braking torque of the vehicle, R i is the wheel radius, c fi is the lumped air resistance coefficient, g is the gravitational acceleration constant, f i is the rolling resistance coefficient, ι i is the vehicle dynamic time lag constant, T ie is the expected torque.

[0035] Design the feedback linearization module:

[0036]

[0037] Combining equations (10), (11), (12) and (13), and assuming that the dynamics of the vehicles in the platoon are isomorphic, we have ι i =ι>0, the linear dynamic model of the i-th electric car can be obtained:

[0038]

[0039] Among them, a i is the vehicle acceleration, u i is the control input, ι i is the vehicle dynamic delay constant.

[0040] Step 2.2: Take the position, velocity, and acceleration of the ego vehicle as the state vector: Considering the impact of actuator failure and sensor failure on the system, a feedback linearization model of the i-th intelligent connected vehicle is established:

[0041]

[0042] in, is the output of the system; is an output matrix with full column rank and (A, C) observable; represents the unknown time-varying actuator fault (in a vehicle platoon, generally E = B), where q represents the number of types of vehicle actuator faults. Without loss of generality, we assume that the actuator fault f ai (t) The matching condition is satisfied, and the constraint is rank(B,F) = rank(B) = 1. In addition, the actuator fault f ai (t) belongs to That is, the energy accumulated by the fault over time is controllable, and f ai (t) is a continuous smooth function whose first-order derivative is bounded, that is, represents unknown time-varying sensor faults, where r represents the number of types of vehicle sensor faults. Without loss of generality, it is assumed that the sensor fault matrix E satisfies and with a time-invariant system matrix of compatible dimensions.

[0043] For the sensor fault matrix, when r = 3 and This means that the vehicle's position sensor, speed sensor, and acceleration sensor channels are affected by three different, unknown fault inputs.

[0044] Define the feedback linearization model of the pilot vehicle:

[0045]

[0046] Here, the definitions of A and B are the same as those of the following vehicles, x0 is the state of the leader vehicle, and y0(t) is the input of the leader vehicle. In our model, the leader vehicle is a virtual and complete leader that is not affected by sensor-actuator failures.

[0047] Step 3: Based on graph theory, describe the information interaction between intelligent connected vehicles and establish the objective function of intelligent connected vehicle formation control.

[0048] Step 3.1: Based on graph theory, describe the communication connection topology of the N following vehicles in the platoon system as a directed graph in Represents the set of following vehicles in the queue, Represents the set of edges with direct connectivity between vehicles; edge (i, j)∈ε indicates that vehicle j can receive information from vehicle i; based on graph theory, the communication relationship between N following vehicles can be expressed using the adjacency matrix The communication connection Laplacian matrix of N following vehicles can be defined as in is the in-degree matrix.

[0049] Based on graph theory, the traction matrix of the following vehicle and the leading vehicle can be expressed as diag is used to construct diagonal elements into a diagonal matrix, m i0 =1 means there is a communication connection, otherwise there is no communication connection. Therefore, the Laplace matrix of the pilot vehicle and the N following vehicles can be defined as:

[0050] When the above directed graph Contains a directed spanning tree with the leader as the root node. Then, is reversible, and there exists a matrix G = diag{g1,…,g N}>0, so that:

[0051]

[0052] in, As defined above, it represents the Laplace matrix of the lead vehicle and the N following vehicles;

[0053] For the convenience of description, For {g1,…,g N}The maximum and minimum elements. for The maximum and minimum eigenvalues ​​of .

[0054] Step 3.2: Using the speed difference between the ego vehicle and the pilot vehicle, and the distance difference between the ego vehicle and the preceding vehicle as independent variables, establish the objective function for platooning control, which satisfies:

[0055]

[0056] Among them, p i With v i denote the position and velocity of vehicle i, d i,i-1 represents the desired distance between the i-th vehicle and the i-1-th vehicle. When the objective function is satisfied, the queue can achieve the desired formation. In the present invention, d i,i-1 =d0 is a constant value, which means that the distance between the vehicle and the preceding vehicle is a constant value.

[0057] Step 4: For a vehicle platoon system affected by sensor and actuator failures, first construct an augmented system and establish a distributed error and fault observer. Next, design a distributed active fault-tolerant controller without delay. Next, design a distributed active fault-tolerant controller affected by communication delay. Finally, solve the wheel motor drive torque required for formation control in real time.

[0058] Step 4.1: To simultaneously observe the state and sensor failure of vehicle i in the queue, construct the following augmented system:

[0059]

[0060] in, It can be concluded that

[0061] Then construct an unknown row full rank matrix To make the following conditions hold:

[0062]

[0063] Unknown full row rank matrix Can be designed as:

[0064]

[0065] in:

[0066]

[0067] Then, the augmented system can be written as:

[0068]

[0069] For the above augmented system, a dynamic proportional-integral observer is constructed to estimate the actuator fault and augmented state:

[0070]

[0071] in, and They are f ai (t) and y i (t) observation, η i (t) is the auxiliary variable; M1 is the proportional gain, is the integral gain; for the derivative of the actuator fault, the observation effect is enhanced by introducing the dynamic quantity v, M2 and is the estimated gain of actuator fault, where ω is a variable selected empirically.

[0072] The estimation error of augmented state and actuator fault is defined as:

[0073]

[0074] and

[0075]

[0076] Among them, the augmented state estimation error is The estimated error of actuator failure is

[0077] Therefore, the actuator fault error dynamic system can be derived as:

[0078]

[0079] Among them, the estimation error of actuator failure is

[0080] Combined with actuator fault estimation, we further construct an augmented vector containing actuator faults and augmented state estimation including actuator faults The augmented state estimation error including actuator faults can be defined as:

[0081]

[0082] Among them, e fi (t) is the augmented state estimation error.

[0083] Its kinetic equation is:

[0084]

[0085] in, Obviously J0 is reversible, so:

[0086]

[0087] in,

[0088] Then the i-th decoupled observation error dynamics in the decentralized proportional-integral fault observer can be rewritten as:

[0089]

[0090] in,

[0091] Its compact form is:

[0092]

[0093] in, represents the Kronecker product,

[0094] Step 4.2: Define I a =[0 q×3 ,0 q×r ,I q×q ]、I s =[0 r×3 ,I r×r ,0 r×q ],So The distributed active fault-tolerant controller of the i-th following vehicle is designed as:

[0095]

[0096] Among them, the actuator fault compensation gain K is a constant gain matrix, which is designed later, and δ>0 is the coupling strength; To achieve compensation for actuator failure, The item compensates for sensor failure and realizes active fault-tolerant control through the above compensation.

[0097] Define the queue tracking error variable:

[0098] e i (t) = x i (t)-x0(t)D i,0 , (34)

[0099] Among them, D i,0 =[d i,0 ,0,0], with a constant spacing d i,0 = -i × d0, d0 is the desired constant spacing, define d i,j =-(ij)×d0, then D ij =[d i,j ,0,0].

[0100] Then, according to the above state tracking error e i (t), the state tracking error dynamics can be obtained as:

[0101]

[0102] Among them, e i (t) is the state tracking error.

[0103] Written in compact form:

[0104]

[0105] in, Represents the Kronecker product operator. N is the N×N identity matrix. e(t) is the compact form of the state tracking error. f (t) is a compact form of the state estimation error.

[0106] Step 4.3: Consider the Lyapunov function V(e(t),e f (t))=V1(e(t))+V2(e f (t)), where the Lyapunov function V1(e(t)) with symmetric positive definite matrices P and G is defined as:

[0107]

[0108] Among them, P -1 is the inverse of the positive definite matrix P, and G is a diagonal matrix defined in step 3.1.

[0109] Another Lyapunov function V2(e f (t)) is selected as:

[0110]

[0111] Among them, ρ is a positive scalar that reflects the convergence rate of the queue error system, and Q is a positive definite matrix.

[0112] The car platoon system (36) can be realized with the following bounded condition: for a given positive scalar ρ, if there exists a symmetric positive definite matrix P, Q and positive scalars c, ω that satisfies the following inequality conditions:

[0113]

[0114] Among them, λ max (·) represents the maximum eigenvalue of the matrix, c is a positive scalar, and the positive coupling strength is limited to The constant gain matrix is ​​designed as Observer gain ρ is a positive scalar, and the remaining parameters have been defined previously.

[0115] Proof: Definition Taking the derivative of the first Lyapunov function V1(e(t)) we have:

[0116]

[0117] For the second Lyapunov function V2(e f (t)) is derived as follows:

[0118]

[0119] comprehensive and We can get V(e(t),e f The time derivative of (t) is:

[0120]

[0121] Where θ is the upper bound of the first-order derivative of the actuator fault, and no information about the upper bound of the sensor is needed here.

[0122] When equation (39) holds true, the queue error system is uniformly eventually bounded.

[0123] Step 4.4: Consider the impact of communication delay, that is, only the information y of the neighboring vehicle at time t-τ(t) can be obtained j (t-τ(t)), construct the following controller:

[0124]

[0125] Among them, the actuator fault compensation gain K is the constant gain matrix, δ>0 is the coupling strength, To achieve compensation for actuator failure, since the output information is delayed, the output delay information is used to obtain the sensor failure estimate, so The two compensations are used to compensate for sensor failures, and active fault-tolerant control is achieved through the above two compensations.

[0126] Then, according to the state tracking error e in (34) i (t), the state tracking error dynamic equation can be obtained as

[0127]

[0128] Among them, e i (t-τ(t)) is the state tracking error at time t-τ(t), e fi (t-τ(t)) is the augmented state estimation error at time t-τ(t).

[0129] Its compact form is:

[0130]

[0131] where e(t-τ(t)) is the compact form of the state tracking error at time t-τ(t), e f (t-τ(t)) is a compact form of the augmented state estimation error at time t-τ(t).

[0132] According to the Newton-Leibniz formula, we have:

[0133]

[0134] The delay term in (45) can be converted into the following form:

[0135]

[0136] where e(t-τ(t)) is the compact form of the state tracking error at time t-τ(t).

[0137] Step 4.5: Design the Lyapunov function V3(e(t)) with symmetric positive definite matrices P and G,

[0138]

[0139] Among them, P -1 is the inverse of the positive definite matrix P, and G is a diagonal matrix defined in step 3.1.

[0140] The car platoon system can achieve consistent ultimate boundedness condition: for a given positive scalar ρ, if there exists a symmetric positive definite matrix P, Q, a positive scalar c, ω and an upper bound on the communication delay When the following inequality conditions are met

[0141]

[0142] The positive coupling strength is limited by The positive coupling strength is limited by The constant gain matrix is ​​designed as Observer gain ρ is a positive scalar,

[0143] Proof: The derivative of the Lyapunov function V3(e(t)) is:

[0144]

[0145] in,

[0146]

[0147] According to the Halanay inequality, we have:

[0148]

[0149] Among them, the specific forms of β1, β2, β3, β4, and β5 are as described above.

[0150] Solving the above inequality, we have:

[0151]

[0152] When the upper bound of the delay satisfies the above conditions, we have:

[0153]

[0154] in, It means taking the maximum value of the interval function. ∈ is a positive constant, and f is a positive constant related to ∈;

[0155] Further:

[0156]

[0157] Here, ||·|| represents the bi-norm of the matrix.

[0158] The car platoon error system is exponentially convergent and uniformly ultimately bounded, while the stability of the estimation error system of the decentralized observer is guaranteed by (39).

[0159] Step 4.6: Substitute the obtained controller into the feedback linearization strategy (13) to obtain the real-time desired control torque and achieve corresponding vehicle control.

[0160] Consider a platoon consisting of one leader vehicle and six following vehicles, whose communication topology is a typical predecessor-leader following (PLF). The dynamic model of the vehicles in the platoon is given by Equation (15), whose initial state values ​​are randomly selected. At the same time, the acceleration input a0 of the leader vehicle is set to:

[0161]

[0162] At the first vehicle's sensor fault input f s1 and actuator with fault input f a1 is set to:

[0163]

[0164]

[0165] The actuator of the third vehicle is connected to the fault input f a3 and sensor fault input f s3 Set to:

[0166]

[0167] The controller gains are set as:

[0168] K=[0.7039 1.3828 0.3542], δ=5.9538, (59)

[0169] The observer gain is set as:

[0170]

[0171] In addition, the upper bound of the time-varying delay obtained by the above gain is Therefore, the time-varying communication delay is set to τ(t) = || 0.0015sin(t) || + 0.00001, and the above delay meets the upper bound requirement.

[0172] Figure 3The demonstration shows the tracking effect of the distributed proportional integral observer for the actuator fault and sensor fault of the first following vehicle. Figure 3 In the figure, (a) is the dynamic tracking curve of the first following vehicle actuator fault (sinusoidal signal) compared with the estimated and true values; (b) is the dynamic tracking curve of the first following vehicle sensor fault (sinusoidal signal) compared with the estimated and true values. Figure 3 It can be seen that the designed distributed proportional-integral observer can quickly track large-amplitude, high-frequency sinusoidal fault signals, converge quickly when the fault signal switches, has good real-time tracking performance and high accuracy, and is less affected by time-varying communication delays.

[0173] Figure 4 The demonstration shows the tracking effect of the distributed proportional integral observer for the actuator fault and sensor fault of the third following vehicle. Figure 4 In the figure, (a) is the comparison between the estimated and true values ​​of the dynamic tracking curve of the actuator failure (combined signal) of the third following vehicle; (b) is the comparison between the estimated and true values ​​of the dynamic tracking curve of the sensor failure (combined signal) of the third following vehicle. Figure 4 It can be seen that the designed distributed proportional-integral observer can quickly track fault signals with large values ​​of steady-state fault signals, exponential signals, linear changes, etc. It converges quickly when the fault signal switches and is less affected by time-varying communication delay.

[0174] Figure 5 The distance error, speed error, speed and acceleration trajectory curves of the following vehicle are shown under the distributed controller designed by the present invention. Figure 5 In the figure, (a) is the simulation result curve of the distance tracking error of the queue system; (b) is the simulation result curve of the speed tracking error of the queue system; (c) is the simulation result curve of the acceleration of the queue system. Figure 5 As can be seen in the figure, with each adjustment of the lead vehicle's speed, the platoon's distance and speed errors converge to zero within approximately 10 seconds. This fact demonstrates that when the platoon is operating in an environment subject to sensor failures, actuator failures, and communication delays, the controller ensures system stability, ensuring that each following vehicle can track the lead vehicle and maintain the desired inter-vehicle distance.

[0175] The above embodiments are only preferred embodiments of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent of the present invention.

Claims

1. Active fault-tolerant control method for intelligent connected vehicle platoon under communication delay, characterized by The following steps are involved: Step 1: Formation of a platoon and information collection: A platoon consists of N+1 vehicles, including a lead vehicle and N following vehicles. The vehicle uses onboard sensors to obtain information about its own status, including its location, speed, and acceleration. It also uses the V2X wireless communication network to collect output information from neighboring vehicles and the pilot vehicle in real time, receive output information from connected vehicles in real time, and broadcast the vehicle's output information. Step 2: Based on information obtained from on-board sensors and the V2X wireless communication network, a state-space model of the longitudinal dynamics of a single vehicle with actuator and sensor failures is established. Specifically, a nonlinear longitudinal dynamic expression for a single vehicle is derived based on Newton's second law, and feedback linearization is performed using inverse model compensation techniques to derive a linearized longitudinal dynamic model for the single vehicle. The position, velocity, and acceleration information of the ego vehicle are used as the state vector, and a vehicle longitudinal motion model including actuator failures is established. Furthermore, sensor failures are considered and sensor output information is constructed to estimate and compensate for these failures during the control process. Step 3: Establish the objective function for platoon control based on graph theory to describe the information exchange between connected vehicles. Specifically, define the communication topology of the platoon based on graph theory and provide definitions of the adjacency matrix and the Laplace matrix. Set the speed difference between the ego vehicle and the pilot vehicle, and the distance difference between the ego vehicle and the preceding vehicle as independent variables, and construct the objective function for platoon control to ensure that the platoon maintains a constant desired formation. Step 4: For the vehicle platoon system affected by sensor and actuator failures, establish a distributed error and fault observer, design a distributed active fault-tolerant controller without delay, and design a distributed active fault-tolerant controller affected by communication delay, and solve the wheel driving torque required for formation control in real time.

2. The active fault-tolerant control method for intelligent connected vehicle platoons under communication delay as claimed in claim 1 is characterized in that In step 2, the longitudinal dynamics state-space model of a single vehicle with actuator failure and sensor failure is established, and the specific steps include: Step 2.1: Perform a dynamic analysis of the longitudinal motion of the vehicles and, based on Newton's second law, obtain a nonlinear dynamic model for the i-th vehicle in the platoon. Among them, m i is the mass of the vehicle, F i is the driving force of the vehicle, v i is the vehicle speed, μ i is the mechanical efficiency of the transmission system, T i is the actual driving / braking torque of the vehicle, R i is the wheel radius, c fi is the lumped air resistance coefficient, g is the gravitational acceleration constant, f i is the rolling resistance coefficient, ι i is the vehicle dynamic time lag constant, T ie is the desired torque; Design the feedback linearization module: Combining equations (10), (11), (12) and (13), and assuming that the dynamics of the vehicles in the platoon are isomorphic, we have ι i =ι>0, the linear dynamic model of the i-th electric vehicle is obtained: Among them, a i is the vehicle acceleration, u i is the control input, ι i is the vehicle dynamic time lag constant; Step 2.2: Take the position, velocity, and acceleration of the ego vehicle as the state vector: Considering the impact of actuator failure and sensor failure on the system, a feedback linearization model of the i-th intelligent connected vehicle is established: in, is the output of the system; is an output matrix with full column rank and (A, C) observable; represents the unknown time-varying actuator fault, where q represents the number of types of vehicle actuator faults. Without loss of generality, we assume that the actuator fault f ai (t) The matching condition is satisfied, and the constraint is rank(B,F) = rank(B) = 1. In addition, the actuator fault f ai (t) belongs to That is, the energy accumulated by the fault over time is controllable, and f ai (t) is a continuous smooth function whose first-order derivative is bounded, that is, represents unknown time-varying sensor faults, where r represents the number of types of vehicle sensor faults. Without loss of generality, it is assumed that the sensor fault matrix E satisfies and with a time-invariant system matrix of compatible dimensions; Define the feedback linearization model of the pilot vehicle: Here, the definitions of A and B are the same as those of the following vehicles, x0 is the state of the leader vehicle, and y0(t) is the input of the leader vehicle. In our model, the leader vehicle is a virtual and complete leader that is not affected by sensor-actuator failures.

3. The active fault-tolerant control method for intelligent connected vehicle platoons under communication delay as claimed in claim 1 is characterized in that In step 3, the objective function of intelligent connected vehicle platoon control is established based on the graph theory description of the information interaction between intelligent connected vehicles, including the following steps: Step 3.1: Based on graph theory, describe the communication connection topology of the N following vehicles in the platoon system as a directed graph in Represents the set of following vehicles in the queue, Represents the set of edges with direct connectivity between vehicles; edge (i, j)∈ε indicates that vehicle j can receive information from vehicle i; based on graph theory, the communication relationship between N following vehicles is represented by the adjacency matrix The communication connection Laplace matrix of N following vehicles is defined as in is the in-degree matrix; Based on graph theory, the traction matrix of the following vehicle and the leading vehicle is used to calculate the Among them, diag means constructing a diagonal matrix from diagonal elements, m i0 =1 indicates that there is a communication connection, otherwise there is no communication connection. The Laplace matrix of the pilot vehicle and the N following vehicles is defined as: When the above directed graph Contains a directed spanning tree with the leader as the root node; then, is reversible, and there exists a matrix G = diag{g1,…,g N }>0, so that: in, As defined above, it represents the Laplace matrix of the lead vehicle and the N following vehicles; remember For {g1,…,g N }'s maximum and minimum elements; for the maximum and minimum eigenvalues ​​of ; Step 3.2: Using the speed difference between the ego vehicle and the pilot vehicle, and the distance difference between the ego vehicle and the preceding vehicle as independent variables, establish the objective function for platooning control, which satisfies: Among them, p i With v i denote the position and velocity of vehicle i, d i,i-1 It represents the expected distance between the i-th vehicle and the i-1-th vehicle. When the objective function is satisfied, the queue can achieve the desired formation.

4. The active fault-tolerant control method for intelligent connected vehicle platoons under communication delay as claimed in claim 1 is characterized in that In step 4, the specific steps for solving the wheel motor driving torque required for formation control in real time include: 4.1) Construct an augmented system that includes sensor failures, design a distributed dynamic proportional-integral observer, and establish a closed-loop error system for the vehicle platoon observer; 4.2) Design a distributed active fault-tolerant controller without communication delay and incorporate it into the platooning model to establish a closed-loop error control system for the vehicle platoon. 4.3) Based on Lyapunov stability theory and linear matrix inequality methods, the conditions for achieving stability of the queue closed-loop error system under the condition of no time-varying delay are given, and the design method of the controller gain matrix and the decentralized observer gain is obtained; 4.4) Design a distributed active fault-tolerant controller under the influence of communication delay. Substitute it into the formation model to establish a closed-loop error control system for the vehicle platoon under the influence of time-varying communication delay. 4.5) Based on Lyapunov stability theory, linear matrix inequality methods, and the Halanay inequality, conditions are given for the stability of the queue closed-loop error system under time-varying delays using the active fault-tolerant controller. This means that the upper bound on the delay that the controller and observer gains obtained in step 4.3 can tolerate is obtained. 4.6) Substitute the active fault-tolerant controller into the feedback linearization model in step 2 to calculate the desired wheel driving torque of the vehicle in real time, realizing the formation control of intelligent connected vehicles.

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