Finite time formation obstacle avoidance control method for multiple automatic guided vehicle systems

Abstract

The invention discloses a finite time formation obstacle avoidance control method for multiple automatic guided vehicle systems, and relates to the technical field of automatic guided vehicle system safety control. A finite time formation control method is provided, so that a plurality of automatic guided vehicle systems can realize an expected formation in finite time; considering that the output of an actuator in an actual system is usually limited and is easily influenced by false data injection attacks, an event triggering mechanism and an attack compensation strategy are fused in the design of the control method; because the prior map information of the operation environment does not exist, an obstacle avoidance mechanism is embedded in the control method by using the artificial potential field technology so as to ensure the safe operation of the multi-automated guided vehicle system. According to the method, the artificial potential field technology is effectively utilized to construct the virtual potential field, under the condition that the actuator is attacked, it is guaranteed that the multiple automatic guided vehicle systems can achieve expected formation within limited time and avoid collision with obstacles, and the response speed and safety of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety control of an automated guided vehicle system, and particularly to a finite-time formation obstacle avoidance control method for a multi-automated guided vehicle system. Background Art

[0002] Automated guided vehicles have broad application prospects in fields such as industrial logistics, intelligent warehousing, and unmanned distribution. With the rapid development of intelligent technologies, a multi-automated guided vehicle system, as an automated transportation tool without human intervention, can achieve functions such as autonomous navigation, automatic obstacle avoidance, and automatic task allocation. However, when multiple automated guided vehicles work collaboratively in a complex environment, how to achieve efficient and safe formation control remains a challenge.

[0003] When an automated guided vehicle executes a formation task, it usually has no prior knowledge of the map, which means it may be affected by obstacles during operation. To ensure the safe operation of the formation, it is necessary to consider avoiding collisions with obstacles. The artificial potential field method has been widely studied due to its convenience, high efficiency, and strong real-time performance. By simulating potential fields such as gravitational fields and electromagnetic fields, each obstacle is set as a high electric potential point, and then a virtual potential field function is constructed. When an automated guided vehicle enters the potential field, a repulsive force will be generated to make it bypass the obstacle.

[0004] In practical applications, actuators are usually affected by external cyber attacks, which will have an adverse impact on system performance and even hinder the completion of control tasks. Therefore, it is very necessary to consider the situation of actuator attacks in multi-automated guided vehicle formation control.

[0005] The convergence speed of formation error is one of the key indicators for evaluating its performance. In the obstacle avoidance problem and actual formation construction, the convergence speed of infinite time cannot guarantee good transient performance. To improve the convergence speed of formation error, scholars have developed finite-time control methods, which can improve system robustness and convergence speed.

[0006] Finite-time formation control combined with an event-triggered mechanism effectively reduces the communication burden while meeting the requirements of formation stability and efficiency. This control method has important application value in modern intelligent logistics and automated production, especially when facing large-scale multi-automated guided vehicle systems and dynamic environments, it can provide a more efficient and stable solution.

[0007] In summary, it is necessary to develop a formation control and obstacle avoidance strategy with finite-time stability for a multi-automated guided vehicle system under the influence of actuator attacks. Summary of the Invention

[0008] The main objective of the present invention is to provide a finite-time formation obstacle avoidance control method for a multi-autonomous guided vehicle system, which utilizes the efficient and convenient advantages of the artificial potential field method to construct a virtual potential field function to achieve the desired obstacle avoidance behavior. In the case of false data injection attacks on the actuators, the finite-time formation of the multi-autonomous guided vehicles is achieved, and the system response speed and robustness are improved.

[0009] To achieve the above objective, the present invention provides a finite-time formation obstacle avoidance control method for a multi-autonomous guided vehicle system, including the following steps:

[0010] S1: Based on the kinematic model of the multi-autonomous guided vehicle system, construct the state space expression of the multi-autonomous guided vehicle system with unknown non-linear terms, and give the desired leader trajectory signal;

[0011] S2: Establish the communication relationship between the follower autonomous guided vehicle and the leader signal by means of the topological graph theory, and define the desired formation control objective based on the constructed state space expression of the multi-autonomous guided vehicle system;

[0012] S3: Model the actuators in the actual scenario, consider the actuator model under the influence of false data injection attacks, and construct a virtual potential field obstacle avoidance function and obstacle avoidance repulsive force based on the artificial potential field method;

[0013] S4: Design a Lyapunov function and approximate the non-linear terms in the dynamics using a fuzzy logic system;

[0014] S5: Give the designed control strategy and adaptive update law to achieve the finite-time convergence of the system error and ensure the obstacle avoidance ability of the designed formation.

[0015] For the finite-time formation obstacle avoidance control method of the multi-autonomous guided vehicle system as described above, the operation of S1 is specifically as follows:

[0016] Establish the kinematic equation of the multi-autonomous guided vehicle to obtain the state space expression of the multi-autonomous guided vehicle system as follows:

[0017]

[0018] where p i (t), v i (t), u i (t) represent the position, velocity and control input of the i-th autonomous guided vehicle respectively; f i (p i (t), v i (t)) represents a non-linear vector-valued function with uncertainty. The reference trajectory or path of the leader is given as follows:

[0019]

[0020] where p 0 (t) and v 0 (t) are the position and velocity vectors of the leader respectively, and f 0 (t) represents a bounded function set as the reference signal.

[0021] For the finite-time formation obstacle avoidance control method of the multi-AGV system as described above, the operation of S2 is specifically as follows:

[0022] Express the communication relationship between the follower AGV and the leader signal as a topological graph G, which consists of a vertex set an edge set and an adjacency matrix composed. The degree matrix related to the leader reference signal is defined as B = diag{b 1 , b 2 ,..., b N}, and the Laplacian matrix is defined as L = D - A. Where D = diag{d 1 , d 2 ,..., d N}. The information interaction matrix is expressed as H = L + B. There exists a spanning tree with the leader as the root node in the graph G. If the vertex set can be decomposed into two vertex subsets and satisfying and then the topological graph G is structurally balanced.

[0023] For the finite-time formation obstacle avoidance control method of the multi-AGV system as described above, the operation of S3 is specifically as follows:

[0024] Express the actual actuator output as:

[0025]

[0026] where μ i (t) is the original control input signal; represents the attack signal injected into the actuator. α i takes a value of 0 or 1, where 0 indicates that the actuator is not attacked and 1 indicates that the actuator is attacked. The occurrence of the attack can be monitored, so the value of α i is known. Apply the artificial potential field method, the position of the l-th obstacle is expressed as o l , and the relative displacement vector between the i-th AGV and the l-th obstacle is expressed as:

[0027] z il (t) = p i (t) - ol (4)

[0028] Define the repulsive potential function generated by the \(l\)-th obstacle on the \(i\)-th automated guided vehicle approaching it as a non-negative differentiable function \(\varPsi\) l (||z il (t)||), and define the repulsive force function of the obstacle on the automated guided vehicle as the negative gradient along the potential function \(\varPsi\) l (||z il (t)||), that is The repulsive potential function \(\varPsi\) l (||z il (t)||) satisfies: i) when ||z il ||→d l , the repulsive force gradually increases, that is, \(\varPsi\) l (||z il ||)→+∞, where d l is the minimum obstacle avoidance distance of the \(l\)-th obstacle; ii) when , the repulsive force gradually weakens to the minimum value, where is the threshold of the maximum action distance of the repulsive force.

[0029] For the finite-time formation obstacle avoidance control method of the multi-automated guided vehicle system as described above, the operation of S4 is specifically as follows:

[0030] Define the formation error as e p,i (t)=p i -p 0 -h i , e i (t)=e p,i (t)+e v,i (t).

[0031] Record:

[0032]

[0033] According to the defined formation error, design the Lyapunov function as shown below:

[0034]

[0035] where and satisfy θ=e p +e v . Use the fuzzy logic system to approximate the non-linear function, and there is f i (p i ,v i )=W i *T s i (p i ,vi ) + ε i (p i , v i ).

[0036] For the finite - time formation obstacle - avoidance control method of the multi - automated guided vehicle system as described above, the operation of S5 is specifically as follows:

[0037] To achieve the desired finite - time formation control and obstacle - avoidance ability, the adaptive control strategy is designed as follows:

[0038]

[0039] where 0 < l < 2, λ max (·) represents the maximum eigenvalue of the matrix; k 1 > 0 and are control gains; is the actuator attack compensation term, which satisfies r 2 > 1; To update the parameter the adaptive law is given as:

[0040]

[0041] where r 1 > 0 is the design parameter, and ξ is used as the gain term to adjust the amplitude of the update rule. The obstacle - avoidance term is given as where ι is the number of obstacles, and β k is the obstacle - avoidance repulsive force coefficient. An event - triggered mechanism is proposed The event - trigger function is designed as where ρ > 0 is a given constant, is the measurement error of the i - th automated guided vehicle.

[0042] Based on the control strategy designed by the Lyapunov function, the finite - time stability is verified, and it is ensured that the designed formation has the obstacle - avoidance ability.

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

[0044] 1) A finite - time formation control strategy is constructed for the multi - automated guided vehicle system, realizing rapid formation construction and improving the convergence speed of the formation control error.

[0045] 2) The obstacle - avoidance strategy based on the repulsive potential field is embedded in the finite - time formation control strategy, ensuring the safe operation of the automated guided vehicle in the obstacle environment.

[0046] 3) An adaptive attack compensation mechanism is proposed to defend against actuator false data injection attacks, ensuring the security of the multi - automated guided vehicle system.

[0047] To make the above objects, features, and advantages of the present invention more obvious and understandable, specific embodiments of the present invention are hereinafter given, and in conjunction with the accompanying drawings, the following detailed description is provided. Description of the Drawings

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use 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 limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a flowchart of the control method in the embodiment.

[0050] Figure 2 It is a schematic structural diagram of the automated guided vehicle in the embodiment.

[0051] Figure 3 It is a communication topology diagram inside the multi-automated guided vehicle system in the embodiment.

[0052] Figure 4 It is an effect diagram of the formation of the multi-automated guided vehicle system in the embodiment, where sub-diagram (a) is a snapshot of the movement trajectory and position at 0s, (b) is a snapshot of the movement trajectory and position at 5s, (c) is a snapshot of the movement trajectory and position at 15s, and (d) is a snapshot of the movement trajectory and position at 25s.

[0053] Figure 5 It is the running trajectory of the automated guided vehicle when approaching an obstacle in the embodiment, where sub-diagram (a) is the movement trajectory of automated guided vehicle 1, (b) is the movement trajectory of automated guided vehicle 2, (c) is the movement trajectory of automated guided vehicle 3, and (d) is the movement trajectory of automated guided vehicle 4.

[0054] Figure 6 It is the evolution law of the position error of the automated guided vehicle in the embodiment, where sub-diagram (a) is the evolution law of the position error of automated guided vehicle 1, (b) is the evolution law of the position error of automated guided vehicle 2, (c) is the evolution law of the position error of automated guided vehicle 3, and (d) is the evolution law of the position error of automated guided vehicle 4.

[0055] Figure 7 It is the evolution law of the speed error of the automated guided vehicle in the embodiment, where sub-diagram (a) is the evolution law of the speed error of automated guided vehicle 1, (b) is the evolution law of the speed error of automated guided vehicle 2, (c) is the evolution law of the speed error of automated guided vehicle 3, and (d) is the evolution law of the speed error of automated guided vehicle 4. Detailed Embodiment

[0056] The present invention will be further described in detail below with reference to the accompanying drawings.

[0057] Taking a wheeled automated guided vehicle as an example, as Figure 2 shown, the vehicle includes four moving wheels, two front wheels are driven wheels, and two rear wheels are driving wheels. (x i , y i ) is the center point position coordinate between the two driving wheels, R i is the wheel radius, L i is half of the distance between the two driving wheels, (p x,i , p y,i ) represents the center point position coordinate of the automated guided vehicle, is the yaw angle.

[0058] The embodiment of the present application provides a finite-time formation obstacle avoidance control method for a multi-automated guided vehicle system, including the following steps:

[0059] S1: Based on the kinematic model of the multi-automated guided vehicle system, construct a state space expression of the multi-automated guided vehicle system with unknown nonlinear terms, and give the desired leader trajectory signal;

[0060] S2: Establish the communication relationship between the follower automated guided vehicle and the leader signal by means of the topological graph theory, and define the desired formation control objective based on the constructed state space expression of the multi-automated guided vehicle system;

[0061] S3: Model the actuator in the actual scenario, consider the actuator model under the influence of false data injection attack, and construct a virtual potential field obstacle avoidance function and obstacle avoidance repulsive force based on the artificial potential field method;

[0062] S4: Design a Lyapunov function and approximate the nonlinear terms in the dynamics by using a fuzzy logic system;

[0063] S5: Give the designed control strategy and adaptive update law to achieve the finite-time convergence of the system error and ensure the obstacle avoidance ability of the designed formation.

[0064] S1: Based on the motion model of the multi-automated guided vehicle, construct a state space expression of the multi-automated guided vehicle system with unknown nonlinear terms, and give the desired leader trajectory signal. The specific steps are as follows.

[0065] Give the structural schematic diagram of the wheeled mobile automated guided vehicle as Figure 2 shown, establish the kinematic equation of the multi-automated guided vehicle, and obtain the state space expression of the multi-automated guided vehicle system as follows:

[0066]

[0067] Where pi (t), v i (t), u i (t) represent the position, velocity, and control input of the i-th automated guided vehicle (AGV); f i (p i (t), v i (t)) represents a non-linear vector-valued function with uncertainties. The reference trajectory or path of the leader is given as follows:

[0068]

[0069] where p 0 (t) and v 0 (t) are the position and velocity vectors of the leader respectively, and f 0 (t) represents a bounded function set as the reference signal.

[0070] S2: Establish the communication relationship between the follower AGVs and the leader signal by means of the topological graph theory, and define the desired formation control objective based on the constructed state space expression of the mobile AGV. The specific steps are as follows.

[0071] Step 2-1: Express the communication relationship between the follower AGVs and the leader signal as a topological graph G, which consists of a vertex set an edge set and an adjacency matrix . The degree matrix related to the leader reference signal is defined as B = diag{b 1 , b 2 ,..., b N}, and the Laplacian matrix is defined as L = D - A. Where D = diag{d 1 , d 2 ,..., d N}. The information interaction matrix is expressed as H = L + B. There exists a spanning tree with the leader as the root node in the graph G. If the vertex set can be decomposed into two vertex subsets and that satisfy and then the topological graph G is structurally balanced.

[0072] Step 2-2: The desired control objective is to design a distributed control strategy such that: i) all follower AGVs can achieve the desired formation based on the leader's reference trajectory within a finite time; ii) the impact of the actuator suffering from false data injection attacks on the system can be eliminated; iii) and collisions between each AGV and any obstacles in the operating environment can be prevented.

[0073] S3: Model the actuator in the actual scenario, consider the actuator model under false data injection attack, and construct a virtual potential field obstacle avoidance function and obstacle avoidance repulsive force based on the artificial potential field method. The specific steps are as follows.

[0074] Step 3-1: Represent the actual actuator output as:

[0075]

[0076] where μ i (t) is the original control input signal; represents the attack signal injected into the actuator. α i takes values of 0 or 1, where 0 means the actuator is not attacked and 1 means the actuator is attacked. The occurrence of the attack can be monitored, so the value of α i is known.

[0077] Step 3-2: Apply the artificial potential field method. Consider all obstacles in the operating environment as high-potential points. If the automated guided vehicle operates near an obstacle, it will be affected by the repulsive force to move away from the obstacle. The position of the l-th obstacle is denoted as o l , and the relative displacement vector between the i-th automated guided vehicle and the l-th obstacle is represented as:

[0078] z il (t) = p i (t) - o l (4)

[0079] The sensor device installed on the automated guided vehicle can obtain the position information of the obstacle, but only knows the specific position of the obstacle when the automated guided vehicle is close enough to the obstacle.

[0080] Step 3-3: Define the repulsive potential function generated by the l-th obstacle on the i-th automated guided vehicle approaching it as a non-negative differentiable function Ψ l (||z il (t)||), and define the repulsive force function of the obstacle on the automated guided vehicle as the negative gradient along the potential function Ψ l (||z il (t)||), that is, ψ il (t) = -▽ zil Ψ l (||z il ||). The repulsive potential function Ψ l (||z il (t)||) satisfies: i) when ||z il || → d l , the repulsive force gradually increases, that is, Ψ l (||z il||) → +∞, where d l is the minimum obstacle avoidance distance of the l-th obstacle; ii) when , the repulsive force gradually weakens to the minimum value, where is the maximum action distance threshold of the repulsive force.

[0081] S4: Design a Lyapunov function and use a fuzzy logic system to approximate the non-linear terms in the dynamics;

[0082] Step 4-1: Define the formation error as e p,i (t) = p i - p 0 - h i , e i (t) = e p,i (t) + e v,i (t). The distributed consensus error is defined as:

[0083]

[0084] According to the defined formation error, design the Lyapunov function as shown below:

[0085]

[0086] where and satisfies θ = e p + e v .

[0087] Step 4-2: Use a radial basis function neural network to approximate the non-linear function, with f i (p i , v i ) = W i *T s i (p i , v i ) + ε i (p i , v i .

[0088] S5: Give the designed control strategy and adaptive update law to achieve the finite-time convergence of the system and ensure the obstacle avoidance ability of the designed formation.

[0089] Step 5-1: To achieve the desired finite-time formation control and obstacle avoidance ability, the adaptive control strategy is designed as follows:

[0090]

[0091] where 0 < l < 2, λ max(·) represents the maximum eigenvalue of the matrix; k 1 > 0 and is the control gain; is the actuator attack compensation term, which satisfies r 2 > 1; To update the parameter the adaptation law is given as:

[0092]

[0093] where r 1 > 0 is the design parameter, and ξ is used as a gain term to adjust the amplitude of the update rule. The obstacle avoidance term is given as where ι is the number of obstacles, and β k is the obstacle avoidance repulsive force coefficient. An event-triggered mechanism is proposed The event-triggering function is designed as where ρ > 0 is a given constant, is the measurement error of the i-th automated guided vehicle.

[0094] Step 5-2: Calculating the derivative of V(t) gives:

[0095]

[0096] By using Young's inequality, we can obtain:

[0097]

[0098] According to the event-triggering condition, we can obtain:

[0099]

[0100] where ρ > 0 is a given constant, is the measurement error of the i-th automated guided vehicle. In addition, there is also which holds.

[0101] Step 5-3: The following lemma is given to handle the power terms.

[0102] Lemma 1: For any two real variables a 1 and a 2 , the following inequality holds:

[0103]

[0104] where are all positive design constants.

[0105] Lemma 2: For real numbers c i , i = 1, 2,..., N and a constant 0 < χ ≤ 1, the following inequality can be established

[0106]

[0107] Applying Lemma 1, the following inequality holds:

[0108]

[0109] The following results can be obtained:

[0110]

[0111] where

[0112]

[0113] Let Set k 1 > 0, r 1 > 0, r 2 > 1 and 0 < l < 2, then o > 0 can be obtained. According to Lemma 2, it can be obtained that:

[0114]

[0115] Lemma 3: Consider a class of systems Suppose there exists a positive function and several parameters η > 0, 0 < γ < 1, such that:

[0116]

[0117] Then the system is semi-globally practically finite-time stable. The settling time satisfies:

[0118]

[0119] where is the initial value of the system.

[0120] Combining Lemma 3, under the designed controller, the multi-AGV system achieves the expected finite-time formation control. The settling time T(t) satisfies:

[0121]

[0122] Step 5-4: When the AGV approaches the obstacle, that is, when it reaches the repulsive force range, according to the defined relative displacement vector, the energy function between the i-th AGV and the l-th obstacle is designed as:

[0123]

[0124] Taking the derivative of it, we can obtain:

[0125]

[0126] It can be seen that the signal τ i , and f i are all bounded. When the i-th automated guided vehicle approaches the l-th obstacle along the gradient direction of the repulsive potential, that is, when ||z il || → d l , we can obtain:

[0127]

[0128] Therefore, when the i-th automated guided vehicle is close enough to the l-th obstacle, the following result holds:

[0129]

[0130] Combining equations (27) and (28), we can obtain:

[0131]

[0132] Integrating the above inequality from t to t 0 gives:

[0133]

[0134] This means the following result holds:

[0135]

[0136] That is to say, once the automated guided vehicle approaches the obstacle within a certain range, the designed obstacle avoidance strategy can make the automated guided vehicle move away from the obstacle.

[0137] The following uses a preferred embodiment to further illustrate the present invention.

[0138] Figure 2 For the multi-automated guided vehicle system shown, its parameters are as follows: R i = 0.15m, L i = 0.75m, d i = 0.2m, m 1,i = 0.4, m 2,i = 0.02, c i = 1.2, d 1,i = d 2,i = 5. The leader reference signal is set as The initial position of each automated guided vehicle is given as , and the initial speed , where i = 1, 2, 3, 4. The initial position of the leader is set to , and the initial speed of the leader is set to . The positions of the obstacles in the operating environment are set to . The formation offset vectors of the automated guided vehicles numbered 1, 2, 3, and 4 are given as:

[0139]

[0140] The influence of the actuator attack on the automated guided vehicle is given as:

[0141]

[0142] The artificial potential field function is set to:

[0143]

[0144] The repulsive force function is given as:

[0145]

[0146] To verify the finite-time formation obstacle avoidance control strategy proposed in the present invention, the parameters are set as k 1 = 60, k 2 = 50, ρ = 0.8, r 1 = 3, r 2 = 7, l = 1.4.

[0147] According to the above simulation conditions, the system is simulated to verify the formation performance and obstacle avoidance ability of the system.

[0148] Figure 1 is the flowchart of the control method of the present invention. Figure 2 is the schematic diagram of the structure of the wheeled automated guided vehicle for the verification algorithm of the present invention. Figure 3 is the communication topology diagram inside the multi-automated guided vehicle system of the present invention. Figure 4 are the motion trajectories of the four follower automated guided vehicles and the leader from 0 s to 30 s. Subfigures (a), (b), (c), and (d) are the position snapshots of the automated guided vehicle at 0 s, 5 s, 15 s, and 25 s under finite-time formation control respectively. It can be seen that the four follower automated guided vehicles can complete their respective formation tasks within a finite time, can achieve a diamond formation according to the designed formation offset vector, and can avoid obstacles. Figure 5This is the running trajectory of the automated guided vehicle (AGV) when approaching an obstacle. When the AGV is at a certain distance from the obstacle, the embedded artificial potential field obstacle avoidance mechanism generates a repulsive force to make the AGV move away from the obstacle. Sub-figures (a), (b), (c), and (d) are the obstacle avoidance effect diagrams of four follower AGVs respectively. Figure 6 Among them, sub-figures (a), (b), (c), and (d) are the evolution laws of the position errors of the four follower AGVs respectively. Figure 7 Among them, sub-figures (a), (b), (c), and (d) are the evolution laws of the velocity errors of the four follower AGVs respectively. Due to the obstacle avoidance behavior, there is still a sudden change in the error after it stabilizes. However, when the obstacle avoidance behavior is completed, the velocity errors of all followers finally stabilize near the origin.

[0149] The above results show that the finite-time formation obstacle avoidance control method for the multi-AGV system proposed by the present invention can enable the AGV to operate safely in an obstacle environment without colliding with the obstacles, and can achieve finite-time stability, with good transient performance, and has strong robustness against actuator attacks and system uncertainties, etc.

[0150] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions, or substitutions made by those skilled in the art within the substantial scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A finite-time formation obstacle avoidance control method for a multi-automatic guided vehicle system, characterized in that: The following steps are involved: S1: Based on the kinematic model of the multi-automatic guided vehicle system, the state space expression of the multi-automatic guided vehicle system with unknown nonlinear terms is constructed, and the expected leader trajectory signal is given; S2: Establish the communication relationship between the follower AGV and the leader signal with the help of topological graph theory, and define the desired formation control goal based on the constructed multi-AGV system state space expression; S3: Model the actuator in the actual scenario, consider the actuator model under the influence of false data injection attack, and construct the virtual potential field obstacle avoidance function and obstacle avoidance repulsion based on the artificial potential field method; S4: Design Lyapunov functions and use fuzzy logic systems to approximate nonlinear terms in dynamics; S5: The designed control strategy and adaptive update law are given to achieve finite time convergence of the system error and ensure the obstacle avoidance capability of the designed formation.

2. The finite-time formation obstacle avoidance control method for multiple automated guided vehicle systems according to claim 1, characterized in that: The operation of S1 is specifically as follows: The kinematic equations of multiple automated guided vehicles are established, and the state space expression of the multiple automated guided vehicle system is obtained as follows: where p i (t), v i (t),u i (t) represent the position, velocity and control input of the i-th AGV respectively; f i (p i (t),v i (t)) represents a nonlinear vector-valued function with uncertainty; the reference trajectory or path of the leader is given as follows: where p0(t) and v0(t) are the position and velocity vectors of the leader, respectively, and f0(t) represents the bounded reference signal of the setup.

3. The finite-time formation obstacle avoidance control method for multiple automated guided vehicle systems according to claim 1, characterized in that: The operation of S2 is specifically as follows: The communication relationship between the follower AGV and the leader signal is represented as a topological graph G, which consists of a vertex set Edge Collection and the adjacency matrix The degree matrix associated with the leader reference signal is defined as B = diag{b1, b2, ..., b N }, the Laplace matrix is ​​defined as L = DA, where D = diag{d1, d2, ..., d N }; The information interaction matrix is ​​expressed as H = L + B; There is a spanning tree with the leader as the root node in the graph G; If the vertex set can be decomposed into satisfying and Two subsets of vertices and Then the topological graph G is structurally balanced.

4. The finite-time formation obstacle avoidance control method for multiple automated guided vehicle systems according to claim 1, characterized in that: The operation of S3 is specifically as follows: The actual actuator output is represented as: where μ i (t) is the original control input signal; represents the attack signal injected into the actuator; α i The value is 0 or 1, where 0 means the actuator is not attacked and 1 means the actuator is attacked; The occurrence of the attack can be detected, so α i The value of is known; using the artificial potential field method, the position of the lth obstacle is expressed as o l , the relative displacement vector between the i-th automated guided vehicle and the l-th obstacle is expressed as: z il (t)=p i (t)-o l (4) The repulsive potential function generated by the lth obstacle on the i-th AGV approaching it is defined as a non-negative differentiable function Ψ l (||z il (t)||), the repulsive force function of the obstacle on the AGV is defined as the potential function Ψ l (||z il The negative gradient of (t)||), that is, Repulsive potential function Ψ l (||z il (t)||) satisfies: i) when ||z il ||→d l When , the repulsive force gradually increases, that is, Ψ l (||z il ||)→+∞, where d l is the minimum obstacle avoidance distance of the lth obstacle; ii) when When , the repulsive force gradually weakens to the minimum value, is the maximum distance threshold of the repulsive force.

5. The finite-time formation obstacle avoidance control method for multiple automated guided vehicle systems according to claim 1, characterized in that: The operation of S4 is specifically as follows: Define the formation error as e p,i (t) = p i -p0-h i , e i (t) = e p,i (t)+e v,i (t); remember: According to the defined formation error, the Lyapunov function is designed as follows: in And satisfy θ=e p +e v ; Use fuzzy logic system to approximate nonlinear functions, there are f i (p i ,v i )=W i *T s i (p i ,v i )+ε i (p i ,v i ).

6. The finite-time formation obstacle avoidance control method for multiple automated guided vehicle systems according to claim 1, characterized in that: The operation of S5 is specifically as follows: In order to achieve the desired finite-time formation control and obstacle avoidance capabilities, the adaptive control strategy is designed as follows: Where 0 <l<2,λ max (·) represents the largest eigenvalue of the matrix; k1>0 and is the control gain; is the actuator attack compensation term, which satisfies r2>1; in order to update the parameters The adaptive law is given as: Among them, r1>0 is the parameter to be designed, ξ i As a gain term, adjust the amplitude of the update rule; the obstacle avoidance term is given by Where ι is the number of obstacles, β k To avoid obstacles, repulsion coefficient is proposed; event trigger mechanism is proposed The event trigger function is designed as Where ρ>0 is a given constant, is the measurement error of the i-th automated guided vehicle.

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