A multi-agv event-triggered security path tracking method against false data injection attack

Abstract

The present application relates to a kind of multi autonomous guide vehicle (AGV) cluster security path tracking control method of resisting false data injection attack, belong to control engineering technical field.For the problem that system stability and safety are threatened by false data injection attack in unreliable network, the present application introduces dynamic event triggering mechanism, only updates control input when necessary, reduces communication burden and energy consumption;Design adaptive state estimator, recover normal system signal from tampered sensor and actuator signal;Adaptive attack compensation mechanism is built, to inhibit the negative influence of attack on system performance.The present application fully considers the application requirement of multi-AGV cluster system in unreliable network environment, by introducing dynamic event triggered control, designing adaptive state estimator and building adaptive attack compensation mechanism, false data injection attack can be effectively resisted, and strong guarantee is provided for multi-AGV cluster system security path tracking control.

Description

Technical Field

[0001] This invention relates to an event-triggered secure path tracking control method for a multi-autonomous guided vehicle (AGV) cluster system that can resist spoofed data injection attacks, belonging to the field of control engineering technology. Background Technology

[0002] With the development of intelligent transportation systems, multi-AGV path tracking has gradually become a research hotspot. Multi-AGV clusters coordinate the actions and behaviors of multiple AGVs to complete a given control task, improving transportation efficiency and optimizing resource consumption. However, with the increase in the number of AGVs and the complexity of communication between them, the security of the system under network attacks has become one of the key factors limiting its widespread application.

[0003] Multi-AGV cluster systems heavily rely on wireless network communication environments, with AGVs exchanging information with each other via the wireless network. False data injection attacks, a potential threat to communication networks, mislead the AGV control system by tampering with the data transmission of AGV sensors and actuators, causing deviations in AGV decision-making and behavior, thus affecting operational safety. Therefore, designing an effective attack defense mechanism to ensure that multi-AGV clusters can still achieve safe and accurate path tracking even when facing false data injection attacks is a pressing technical challenge in the field of intelligent transportation.

[0004] In practice, the bandwidth of wireless communication networks is usually limited, requiring the utilization of this limited bandwidth to accomplish the desired control tasks. Event-triggered mechanisms, by transmitting communication only when pre-defined events occur, can reduce the frequency of control input updates, thereby lowering the system's communication burden and energy consumption. Event-triggered mechanisms can dynamically adjust the timing of control signal transmission and updates based on parameters such as the operating status of the multi-AGV cluster, the relative positions of adjacent AGVs, and their speeds. By precisely controlling the timing of event triggers, unnecessary communication transmissions and data computation can be reduced while ensuring the safety and stability of the multi-AGV cluster.

[0005] In conclusion, there is a need to develop an effective event-triggered secure path tracking control method for multi-AGV clusters vulnerable to spoofed data injection attacks. This research will provide an efficient and secure path tracking solution for multi-AGV clusters, laying a solid foundation for the development of intelligent transportation systems. Summary of the Invention

[0006] The main objective of this invention is to provide an event-triggered secure path tracking control method for multi-AGV clusters that can resist spoofed data injection attacks, thereby ensuring the security and stability of multi-AGV clusters in unreliable network environments. By introducing a dynamic event-triggered mechanism, control inputs are updated when necessary, reducing the system's communication burden and energy consumption. An adaptive state estimator is constructed by fully utilizing tampered signals to obtain normal information transmitted within the system, and an adaptive attack compensation mechanism is built to compensate for actuator and sensor signals subjected to spoofed data injection attacks. This invention is applicable to path tracking control of multi-AGV clusters, especially in unreliable network environments with spoofed data injection attacks, ensuring secure path tracking of AGVs, and has significant practical value and broad application prospects.

[0007] To achieve the above objectives, the multi-AGV event-triggered secure path tracking method for resisting false data injection attacks researched in this invention includes the following steps:

[0008] S1: Construct a two-degree-of-freedom dynamic model of the AGV considering uncertainties, and give the desired tracking trajectory signal;

[0009] S2: Model the fake data injection attack and establish the communication topology between the multi-AGV cluster and the reference trajectory;

[0010] S3: Use fuzzy logic to approximate uncertain dynamics, and design a state estimator and an adaptive attack compensation mechanism;

[0011] S4: Provide the designed control strategy and dynamic event triggering mechanism, and verify its trajectory tracking and attack resistance capabilities.

[0012] The multi-AGV event-triggered secure path tracing method for resisting fake data injection attacks, as described above, is characterized in that the operation of S1 specifically includes:

[0013] The two-degree-of-freedom dynamic model of the AGV is established as follows:

[0014]

[0015] Where v x and v y These represent the lateral and longitudinal speeds of the AGV, respectively. z The yaw rate represents the AGV's speed. and They represent v respectively y and w z The time derivative, F1 and F2 represent the generalized tire lateral forces on the front and rear axles respectively, m represents the mass of the AGV, h1 and h2 represent the lengths of the front and rear axles respectively, Iz The moment of inertia represents the yaw direction; considering the influence of unknown nonlinear factors such as modeling uncertainties and disturbances on AGV dynamics, the dynamics of the i-th vehicle are re-expressed as:

[0016]

[0017] Where i = 1, 2, ..., N represents the label of the AGV, x i and u i These represent the AGV's status and control input, respectively. Represents x i The time derivative, where A and B are the system matrix and input matrix, respectively, f i (x i (t) is an unknown nonlinear function satisfying the Lipschitz condition, where t represents the time variable; the dynamics of the desired reference path or trajectory are described as follows:

[0018]

[0019] Where x0 represents the leader's state, and f0(x0,t) represents a bounded nonlinear function set as a reference signal.

[0020] The multi-AGV event-triggered secure path tracing method for resisting fake data injection attacks, as described above, is characterized in that the operation of S2 specifically includes:

[0021] Fake data injection attacks can damage the signals received by actuators and sensors. The affected actuators and sensors can be modeled as follows:

[0022]

[0023] Where μ i Represents the original actuator input signal. This represents the disturbance signal injected into the actuator. x represents the actual control signal available to the actuator. i Represents the original sensor measurement signal. This represents the disturbance signal injected into the sensor. This represents the altered sensor output signal; based on topology graph theory, the communication within a multi-AGV cluster is modeled, and when there is information transmission from vehicle j to vehicle i, a communication weight 'a' is defined. ij >0, otherwise a ij =0; the Laplace matrix is ​​defined as The element is defined as l ij =-a ij i≠j and N represents the total number of AGVs in the topology graph; when the i-th AGV can directly receive the leader's reference path information, the leader's communication weight b is defined. i >0, otherwise b i =0.

[0024] The multi-AGV event-triggered secure path tracing method for resisting fake data injection attacks, as described above, is characterized in that the operation of S3 specifically includes:

[0025] By approximating the unknown nonlinear function in the system using fuzzy logic, we can obtain:

[0026]

[0027] Where f i (r i (t) represents the unknown nonlinear function after variable transformation, r i Represents the system state x i The estimate, Θ i Φ represents the unknown ideal fuzzy weight matrix. i (r i ) represents the fuzzy basis function, ε i (r i ) represents the fuzzy logic approximation error; to obtain the unusable state x i Based on the estimated value, an adaptive state estimator is designed as follows:

[0028]

[0029] in This represents an attack signal on the actuator. The estimate, Represents the ideal fuzzy weight matrix Θ i The estimate, ξ i Represents the coupling strength of the estimator gain. Representative is defined as The estimator coupling error vector, r i a Represents a signal attacking the sensor. The estimation and fuzzy weight update law are designed as follows:

[0030]

[0031] in represent Time derivative, κ represents the weight update parameter. 1,i Represents the fuzzy weight gain constant; the adaptive attack compensation mechanism is designed as follows:

[0032]

[0033] in and Represent and r i a Time derivative, and κ represents the weight update parameter. 2,i and κ 3,i This represents the compensation gain constant.

[0034] The multi-AGV event-triggered secure path tracing method for resisting fake data injection attacks, as described above, is characterized in that the operation of S4 specifically includes:

[0035] Based on the designed adaptive state estimator and attack compensation mechanism, the secure path tracing control strategy is designed as follows:

[0036]

[0037] Where τ i (t i,k ) represents the function τ i At time t i,k The value of τ i Represents a continuous control function, t i,k Represents the moment the event is triggered, ζ i z represents the controller gain coupling strength. i Represents distributed tracking error; the event triggering conditions are designed as follows:

[0038]

[0039] Where inf{} represents the infimum function, t i,k+1 Represents t i,k The next event trigger time, Defined as Represents measurement error, θ i β represents the event-triggered design parameter. i Represents dynamic threshold weight. Represents a dynamic threshold function that satisfies:

[0040]

[0041] in represent The time derivative, ρ i and This represents updating the weight parameters according to the corresponding threshold.

[0042] Finally, a stability analysis of the designed control strategy is conducted based on the Lyapunov method to ensure the feasibility of the designed path tracking control strategy and its resistance to spoofed data injection attacks.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] 1) This invention recovers tampered sensor signals by designing an adaptive state estimator and an adaptive attack compensation mechanism to effectively suppress the impact of false data injection attacks on the system, ensuring that the multi-AGV cluster system can still achieve the desired path tracking task in an unreliable network environment and maintain stable and secure operation.

[0045] 2) By introducing a dynamic event triggering mechanism, this invention updates the control input only when needed, reducing unnecessary data transmission in the system, thereby reducing the system's energy consumption and communication burden, and making the system run more efficiently;

[0046] 3) By introducing an adaptive mechanism, this invention can adaptively adjust the control strategy according to the attack situation of the system, which improves the decision-making and adaptability of the multi-AGV cluster system and ensures that the AGV can still accurately track the target path in complex environments.

[0047] 4) This invention is not only applicable to path tracking control of multi-AGV clusters, but can also be extended to other control systems affected by false data injection attacks, which has great application innovation and practicality.

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, embodiments of the present invention are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a control framework diagram of a multi-AGV cluster system in the embodiment.

[0051] Figure 2 This is an example diagram of the AGV path tracking control task in the embodiment.

[0052] Figure 3 This is a model diagram of the drive wheel of the AGV in the embodiment.

[0053] Figure 4This is a communication topology diagram of a multi-AGV cluster in the embodiment.

[0054] Figure 5 This is a diagram showing the evolution of the state error between the follower AGV and the leader in the embodiment.

[0055] Figure 6 This is a diagram illustrating the evolution of the state estimation error of the AGV's state estimator in the embodiment.

[0056] Figure 7 This is a schematic diagram of the event triggering sequence distribution of the AGV in the embodiment. Detailed Implementation

[0057] The present invention will now be described in further detail with reference to the accompanying drawings.

[0058] First, the control framework of the multi-AGV cluster system in this invention is as follows: Figure 1 As shown, an example of the desired AGV path tracking control task is given as follows. Figure 2 As shown in the diagram. Specifically, this refers to multiple AGVs working collaboratively in a cooperative environment, coordinating their respective motion trajectories to jointly track a pre-set path or dynamic target while maintaining the desired tracking accuracy. For ease of analysis, we assume all AGVs operate on the same horizontal plane, i.e., we do not consider the velocity component in the vertical direction. The drive wheel model of the AGV is given as follows. Figure 3 As shown, the two front wheels are responsible for steering, and the two rear wheels provide driving force. When driving, the front wheels have a smaller wheel angle, the AGV has a smaller sideslip angle, the heading error is relatively small, and the longitudinal speed can be regarded as a stable constant.

[0059] This application provides a method for secure path tracing triggered by multiple AGV events to resist fake data injection attacks, including the following steps:

[0060] S1: Construct a two-degree-of-freedom dynamic model of the AGV considering uncertainties, and give the desired tracking trajectory signal;

[0061] S2: Model the fake data injection attack and establish the communication topology between the multi-AGV cluster and the reference trajectory;

[0062] S3: Use fuzzy logic to approximate uncertain dynamics, and design a state estimator and an adaptive attack compensation mechanism;

[0063] S4: Provide the designed control strategy and dynamic event triggering mechanism, and verify its trajectory tracking and attack resistance capabilities.

[0064] S1: Construct a two-degree-of-freedom dynamic model of the AGV considering uncertainties, and give the desired tracking trajectory signal. The specific steps are as follows:

[0065] Step 1-1: Provide the drive wheel model of the AGV as follows: Figure 3 As shown, the two-degree-of-freedom dynamic model of the AGV is established as follows:

[0066]

[0067] Where v x and v y These represent the lateral and longitudinal speeds of the AGV, respectively. z The yaw rate represents the AGV's speed. and They represent v respectively y and w z The time derivative, F1 and F2 represent the generalized tire lateral forces on the front and rear axles respectively, m represents the mass of the AGV, h1 and h2 represent the lengths of the front and rear axles respectively, I z The moment of inertia representing the yaw direction; the generalized tire lateral forces F1 and F2 satisfy the following relationship:

[0068]

[0069] Where C1 and C2 represent the lateral stiffness of the front and rear wheels, respectively, and φ = v x / v y The horizontal and vertical speed ratio of the AGV represents the path tracking model of the AGV, which can be described as:

[0070]

[0071] Where σ represents the lateral offset of the AGV, and ψ represents the heading angle error. The curvature representing the path. and Let σ and ψ represent the time derivatives of σ and ψ, respectively. Based on the above analysis, the two-degree-of-freedom model of AGV dynamics can be described as follows:

[0072]

[0073] Let x = [ω] z ,φ] T Representing the system state, u = δ represents the system input. The AGV dynamics in state-space form can be rewritten as:

[0074]

[0075] Where A and B are the system matrix and the input matrix, respectively, and their specific expressions are as follows:

[0076]

[0077] Step 1-2: Considering the influence of nonlinear factors such as modeling uncertainties and disturbances on AGV dynamics, the dynamics of the i-th vehicle are re-expressed as:

[0078]

[0079] Where i = 1, 2, ..., N represents the label of the AGV, x i and u i These represent the AGV's status and control input, respectively. Represents x i The time derivative, where A and B are the system matrix and input matrix, respectively, f i (x i (t) is an unknown nonlinear function satisfying the Lipschitz condition, where t represents the time variable; the dynamics of the desired reference path or trajectory are described as follows:

[0080]

[0081] Where x0 represents the leader's state, and f0(x0,t) represents a bounded nonlinear function set as a reference signal.

[0082] S2: Model the fake data injection attack and establish the communication topology between the multi-AGV cluster and the reference trajectory. The specific steps are as follows:

[0083] Step 2-1: Considering the widespread presence of spoofed data injection attacks in unreliable networks, the AGV's sensor and actuator networks, once affected, can only acquire the corrupted signals, which severely impacts the achievement of the intended control objectives. The affected actuators and sensors are modeled as follows:

[0084]

[0085] Where μ i Represents the original actuator input signal. This represents the disturbance signal injected into the actuator. x represents the actual control signal available to the actuator. i Represents the original sensor measurement signal. This represents the disturbance signal injected into the sensor. This represents the altered sensor output signal. Due to the existence of communication networks, the impact of malicious attacks is significantly amplified; even if only one vehicle is attacked, it can affect the entire multi-AGV cluster through network communication. In reality, attackers usually have limited energy; they cannot continuously inject false signals into the network, and the rate of change of the attack signal is bounded. Therefore, it is reasonable to assume that the attack injects a signal... and and its derivative and Both are bounded.

[0086] Step 2-2: Information transmission between multiple AGV clusters is achieved through a topology diagram. To indicate, among which It is a set of vertices, where each vertex υ i Each represents a vehicle, and N represents the total number of AGVs; It is a set of edges, where each edge (υ) i ,ν j () represents information exchange between two different AGVs; It is an adjacency matrix. When there is information transmission from vehicle j to vehicle i, we have a ij >0, otherwise a ij =0; AGV does not have self-to-self information transmission, i.e., a ii =0; the Laplace matrix is ​​defined as Among them l ij =-a ij i≠j and Add a path reference trajectory as the leader to the topology graph, and then modify the communication topology graph. Extend to G, define a diagonal matrix. For the interaction weight matrix related to the path reference, when the i-th vehicle can directly receive the path reference signal, b i >0, otherwise b i ≠0; the information interaction matrix is ​​represented as The topology G in a multi-AGV cluster is connected, and there exists a spanning tree with the path reference signal as the root node.

[0087] Steps 2-3: The desired control objectives are described as follows: i) Develop an adaptive controller to enable a multi-AGV cluster to achieve the desired path tracking under the influence of uncertain nonlinear terms; ii) Construct a secure control protocol that can resist the impact of spoofed data injection attacks on actuators and sensors; iii) Design an effective dynamic event triggering strategy to alleviate bandwidth limitations in network transmission; the desired control task is to enable a cluster system consisting of N AGVs to track the desired path reference trajectory through information interaction when the actuators and sensors are subjected to spoofed data injection attacks, that is, to synchronize the system state with the path reference trajectory, which can be expressed as:

[0088] lim t→∞ ||x i -x0‖≤∈ (21)

[0089] Where ∈ represents an arbitrarily small constant within the tracking error range.

[0090] S3: Fuzzy logic is used to approximate uncertain dynamics, and a state estimator and adaptive attack compensation mechanism are designed. The specific steps are as follows:

[0091] Step 3-1: Due to the impact of the fake data injection attack, directly use the signal affected by the attack. Designing a control strategy based solely on this approach will fail to achieve the intended control objectives. Therefore, it is necessary to analyze the impact of attacks on system stability from the attacker's perspective. The actuator attack coupling error vector is defined as follows:

[0092]

[0093] in The vector r represents the actuator attack coupling error vector. i and r i a Representing system state x respectively i and sensor attack signals The estimated value; after taking into account the impact of fake data injection attacks on the actuator, the dynamics of the i-th vehicle can be reformulated as:

[0094]

[0095] Where f i (r i ,t) represents the unknown nonlinear function after variable transformation. This represents the error of the transformation of a nonlinear function; note that the function f i (r i The uncertain nonlinearity of t makes the precise calculation of its value complex. Therefore, fuzzy logic is used to approximate the unknown nonlinear function in the system, resulting in:

[0096]

[0097] Where f i (r i (t) represents the unknown nonlinear function after variable transformation, r i Represents the system state x i The estimate, Θ i φ represents the unknown ideal fuzzy weight matrix. i (r i ) represents the fuzzy basis function, ε i (r i ) represents the fuzzy logic approximation error.

[0098] Step 3-2: To eliminate the impact of fake data injection attacks on multi-AGV clusters, a state estimator and attack compensation mechanism need to be designed to restore the normal state and suppress the attack's impact; to obtain the unavailable state xi Based on the estimated value, an adaptive state estimator is designed as follows:

[0099]

[0100] in This represents an attack signal on the actuator. The estimate, Represents the ideal fuzzy weight matrix Θ i The estimate, ξ i >0 represents the coupling strength of the estimator gain. Representative is defined as The estimator coupling error vector, r i a Represents a signal attacking the sensor. The estimation and fuzzy weight update law are designed as follows:

[0101]

[0102] in represent Time derivative, The parameter k represents the weight update parameter. 1,i >0 represents the fuzzy weight gain constant; the adaptive attack compensation mechanism is designed as follows:

[0103]

[0104] in and Represent and r i a Time derivative, and κ represents the weight update parameter. 2,i >0 and κ 3,i >0 represents the compensation gain constant; according to the expression, it is easy to know that and r i a They are all consistent and ultimately bounded.

[0105] S4: Describe the designed control strategy and dynamic event triggering mechanism, and verify its trajectory tracking and attack resistance capabilities. The specific steps are as follows:

[0106] Step 4-1: Define the distributed error as follows:

[0107]

[0108] Where z i a represents the distributed tracking error of the system. ijb represents the communication weight between vehicle i and vehicle j. i r represents the communication weight between the i-th vehicle and the reference path trajectory. i and r j Let represent the system state vectors of vehicle i and vehicle j, respectively. Based on the designed adaptive state estimator and attack compensation mechanism, the safe path tracking control strategy is designed as follows:

[0109]

[0110] Where τ i (t i,k ) represents the function τ i At time t i,k The value of τ i Represents a continuous control function, t i,k Represents the moment the event is triggered, ζ i >0 represents the controller gain coupling strength; to construct an appropriate dynamic event triggering mechanism, define... Let's define the measurement error of the i-th vehicle, and then design the event triggering conditions as follows:

[0111]

[0112] Where inf{} represents the infimum function, t i,k+1 Represents t i,k The next event trigger time, Defined as Represents measurement error, θ i β represents the event-triggered design parameter. i Represents dynamic threshold weight. Represents a dynamic threshold function that satisfies:

[0113]

[0114] in represent Time derivative of the function initial value satisfy ρ i and This represents the corresponding threshold update weight parameter; based on the event triggering condition and the dynamic threshold parameter, we can know... Further, based on differential theory, we can obtain In other words, the dynamic threshold parameter is non-negative.

[0115] Step 4-2: To verify the path tracking performance, the tracking error is defined as e. i =r i-x0, further, the error dynamics can be obtained as follows:

[0116]

[0117] Calculate the coupling error vector The time derivative can be obtained as follows:

[0118]

[0119] in This represents the sensor attack estimation error. This represents the error in estimating the executor attack. The Lyapunov function represents the estimation error of the fuzzy weight matrix; the design is as follows:

[0120]

[0121] Where Tr() represents finding the trace of the matrix; calculating the time derivative of the Lyapunov function V yields the following results:

[0122]

[0123] After sorting, we can obtain:

[0124]

[0125] Where γ 1,i ,γ 2,i ,γ 3,i ,γ 4,i ,γ 5,i ,γ 6,i ,Δ i These are all intermediate variables in the derivation process, and we can further obtain:

[0126]

[0127] in Then we can get:

[0128]

[0129] Where e represents the natural constant, and V0 represents the initial value of the function V; it is not difficult to see that lim t→∞ ||x i -x0||≤∈, where ∈ is a very small constant representing the expected error bound; this means that expected path tracking control of multiple AGV clusters can be achieved.

[0130] Step 4-3: The construction of the safe path tracking control strategy considers a dynamic event triggering mechanism, therefore it is necessary to eliminate the possibility of Zeno's phenomenon. Zeno's phenomenon is the occurrence of an infinite number of events within a finite time, which can cause the designed control scheme to fail. To verify that the designed controller does not exhibit Zeno's phenomenon, the measurement error norm is... Taking the time derivative, we can obtain:

[0131]

[0132] in In addition, we can obtain:

[0133]

[0134] in Represents intermediate variables in the derivation process; in the time interval [t] i,k ,t i,k+1 Within ) we can obtain:

[0135]

[0136] Therefore, the minimum transmission interval satisfies:

[0137]

[0138] in It is a time constant greater than zero. Representative function At time t i,k+1 The value at the specified location; therefore, the designed dynamic event triggering condition can guarantee that there is a minimum transmission interval between any two consecutive triggering times, which indicates that the Zeno phenomenon can be strictly excluded.

[0139] The present invention will be further described below using a preferred embodiment.

[0140] Consider a multi-AGV cluster consisting of four follower AGVs and one leader AGV, with the following topology: Figure 4 As shown; relevant parameters are as follows: l1 = 1.18m, l2 = 1.77m, C1 = C2 = 80000N / rad, v x =30m / s, m=1832kg, I z =2488kg·m 2 Based on the provided AGV parameters, the system matrix can be calculated as follows:

[0141]

[0142] The initial state of the leader and four follower AGVs is set as: x0(0) = [2,2] Tx1(0) = [-3, 2] T x2(0) = [2, -2] T x3(0) = [-2, -1] T x3(0) = [4, -4] T The uncertain nonlinear term in the leader and four follower AGVs is set as: f0(x0,t) = 0.05sin(x0,t) 01 +x 02 )+0.02cos(-x 01 x 02 ) and f i (x i ,t)=0.05sin(x i1 +x i2 )+0.02cos(-x i1 x i2 ), i = 1, 2, 3, 4; the spoofed data injection attacks suffered by the actuators and sensors are modeled as follows: and The fuzzy membership function is selected as follows:

[0143]

[0144] Where ι1 = -4, ι2 = -3, ι3 = -2, ι4 = -1, ι5 = 0, ι6 = 1, ι7 = 2, ι8 = 3, ι9 = 4. The control parameters are selected as follows: κ 1,i =5,κ 2,i =0.8, κ 3,i =2.5, ξ i =100, ζ i =45, β i =0.9, θ i =0.8, w i =0.4, ρ i =1.5; The initial value of the adaptive state estimator is set to: r i (0) = [0,0] T , i = 1, 2, 3, 4.

[0145] The system was simulated under the above simulation conditions to verify its formation performance and obstacle avoidance capabilities.

[0146] Figure 1 This invention presents a control framework for a multi-AGV cluster system under the influence of false data injection attacks. Figure 2 This is an example diagram of the AGV path tracking control task in this invention; Figure 3This is a model diagram of the drive wheel of the AGV in this invention; Figure 4 This is a communication topology diagram of the multi-AGV cluster in this invention; Figure 5 The diagram shows the state error evolution between the four follower AGVs and the leader. It can be seen that under the proposed control strategy, the state of all follower AGVs can be synchronized with the leader's reference signal, that is, even if the actuators and sensors are attacked by false data injection, the expected path following task can be completed. Figure 6 The diagram shows the evolution of the state estimation error of the AGV state estimator. It can be seen that the designed adaptive state estimator has a good estimation ability for the normal state before the fake data injection attack, and can recover the original state information from the attack signal. Figure 7 The distribution of the event triggering sequence for the AGV demonstrates its bandwidth-saving capability and verifies that the Zeno phenomenon can be avoided.

[0147] The above results demonstrate that the event-triggered secure path tracking control method for a multi-AGV cluster system proposed in this invention, which can resist false data injection attacks, enables the multi-AGV cluster to achieve the desired secure path tracking under the influence of false data injection attacks.

[0148] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for secure path tracing triggered by multiple AGV events to resist spoofed data injection attacks, characterized in that, Includes the following steps: S1: Construct a two-degree-of-freedom dynamic model of the AGV considering uncertainties, and give the desired tracking trajectory signal; S2: Model the fake data injection attack and establish the communication topology between the multi-AGV cluster and the reference trajectory; S3: Use fuzzy logic to approximate uncertain dynamics, and design a state estimator and an adaptive attack compensation mechanism; S4: Provide the designed control strategy and dynamic event triggering mechanism, and verify its trajectory tracking and attack resistance capabilities; The specific operation of S3 is as follows: By approximating the unknown nonlinear function in the system using fuzzy logic, we can obtain: (1) in This represents the unknown nonlinear function after variable transformation. Represents the system state The estimate, The ideal fuzzy weight matrix represents the unknown. Represents fuzzy basis functions. Represents the approximation error of fuzzy logic; used to obtain unavailable states. Based on the estimated value, an adaptive state estimator is designed as follows: (2) in and These are the system matrix and the input matrix, respectively. Represents the original actuator input signal. This represents an attack signal on the actuator. The estimate, Represents the ideal fuzzy weight matrix The estimate, Represents the coupling strength of the estimator gain. Representative is defined as The estimator coupling error vector, Represents a signal attacking the sensor. The estimate, The fuzzy weight update law is designed as follows: (This represents the altered sensor output signal.) (3) in represent Time derivative, This represents the weight update parameter. Represents the fuzzy weight gain constant; the adaptive attack compensation mechanism is designed as follows: (4) in and Represent and Time derivative, and This represents the weight update parameter. and This represents the compensation gain constant.

2. The multi-AGV event-triggered secure path tracking method for resisting fake data injection attacks according to claim 1, characterized in that, The operation of S1 is specifically as follows: The two-degree-of-freedom dynamic model of the AGV is established as follows: (5) in and These represent the lateral and longitudinal speeds of the AGV, respectively. The yaw rate represents the AGV's speed. and Represent and Time derivative, and These represent the generalized tire lateral forces on the front and rear axles, respectively. Represents the quality of the AGV. and These represent the lengths of the front and rear axles, respectively. Moment of inertia representing the direction of yaw; Considering the influence of unknown nonlinear factors such as modeling uncertainties and disturbances on AGV dynamics, the first... The dynamics of the vehicle are rewritten as: (6) in The label representing the AGV. and These represent the AGV's status and control input, respectively. represent Time derivative, and These are the system matrix and the input matrix, respectively. It is an unknown nonlinear function that satisfies the Lipsitz condition. Represents a time variable; The dynamics of the desired reference path or trajectory are described below: (7) in Represents the leader's state. This represents a bounded nonlinear function set as a reference signal.

3. The multi-AGV event-triggered secure path tracking method for resisting fake data injection attacks according to claim 1, characterized in that, The specific operation of S2 is as follows: Fake data injection attacks can damage the signals received by actuators and sensors. The affected actuators and sensors can be modeled as follows: (8) in Represents the original actuator input signal. This represents the disturbance signal injected into the actuator. This represents the control signals that are actually available to the actuator. Represents the original sensor measurement signal. This represents the disturbance signal injected into the sensor. This represents the tampered sensor output signal; based on topology graph theory, the communication within a multi-AGV cluster is modeled, when there is a signal from the first... The car arrived at the When transmitting vehicle information, define communication weights. ,otherwise Define the Laplace matrix as The elements therein are defined as and , Let be the total number of AGVs in the topology graph; when the When a vehicle can directly receive the leader's reference path information, the leader's communication weight is defined. ,otherwise .

4. The multi-AGV event-triggered secure path tracking method for resisting fake data injection attacks according to claim 1, characterized in that, The specific operation of S4 is as follows: Based on the designed adaptive state estimator and attack compensation mechanism, the secure path tracing control strategy is designed as follows: (9) in Representative function At the point of time The value of , Represents a continuous control function. Represents the moment the event was triggered. Represents the coupling strength of the controller gain. Represents distributed tracking error; the event triggering conditions are designed as follows: (10) in Represents the infimum function. represent The next event trigger time, Defined as Represents measurement error. Represents the event triggering design parameters. Represents dynamic threshold weight. Represents a dynamic threshold function that satisfies: (11) in represent Time derivative, and This represents updating the weight parameters according to the corresponding threshold. Finally, a stability analysis of the designed control strategy is conducted based on the Lyapunov method to ensure the feasibility of the designed path tracking control strategy and its resistance to spoofed data injection attacks.

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