A state decomposition-based distributed privacy protection platoon control method and system

Abstract

The application belongs to the technical field of distributed cluster cooperative control, and specifically discloses a distributed privacy protection formation control method and system based on state decomposition, which divides a multi-unmanned vehicle system into an information layer and a physical layer, the information layer is responsible for running a dynamic average consistency algorithm, and the target position of each unmanned vehicle, i.e., formation geometric center estimation, is calculated, and the physical layer utilizes the target position calculated by the information layer to further calculate the required running speed of the unmanned vehicle according to the actual position. The application comprises the following steps: decomposing and configuring the initial state of the unmanned vehicle; designing an auxiliary cooperative variable for distributed iterative calculation, which significantly improves the anti-interference ability of the system in a complex communication environment; and converting the cooperative calculation result into a linear speed control instruction and an angular speed control instruction to realize accurate formation control of the multi-unmanned vehicle system. The application solves the problem that traditional cooperative control cannot simultaneously consider state information leakage and noise robustness in the cooperation of the multi-unmanned vehicle system.

Description

Technical Field

[0001] This invention belongs to the field of distributed cluster collaborative control technology, specifically relating to a distributed privacy-preserving formation control method and system based on state decomposition. Background Technology

[0002] Distributed multi-agent cooperative control technology is a core method for realizing swarm intelligence systems such as unmanned vehicle swarms and drone formations. Its goal is to converge the states of all agents to a common objective through local information exchange. The dynamic average consensus algorithm, as a fundamental control paradigm, requires agents to continuously exchange state information through neighbor communication, ultimately tracking the average position of a time-varying moving target. Traditional methods (through direct transmission of the complete state) While achieving coordination can guarantee convergence, it poses a serious risk of privacy breaches: attackers can use intercepted state sequences to deduce the location of the moving target. This allows for the acquisition of mission intent or sensitive environmental information. To protect privacy, existing research mainly employs two types of schemes: one is encrypted communication, such as homomorphic encryption or key sharing, but this requires a large amount of computational resources; the other is noise injection, such as differential privacy, which can mask the true state, but the accumulation of noise will amplify the convergence error. Summary of the Invention

[0003] The purpose of this invention is to address the problem that traditional cooperative control in multi-unmanned vehicle systems struggles to balance state information leakage and noise robustness. It proposes a distributed privacy-preserving formation control method and system based on state decomposition, which is applicable to multi-unmanned vehicle formation control scenarios with information security risks and communication noise interference.

[0004] The technical solution of the present invention is as follows: Firstly, a distributed privacy-preserving formation control method based on state decomposition, comprising the following steps:

[0005] Initialize the information layer: obtain the moving target position and the estimated geometric center of the moving target of the local unmanned vehicle, decompose the moving target position into an interaction component and a privacy component, and decompose the moving target geometric center estimate into the first sub-state information corresponding to the interaction component and the second sub-state information corresponding to the privacy component;

[0006] The second sub-state information is stored locally and is not visible to the outside world. Based on the communication topology of the multi-unmanned vehicle system, the first sub-state information is sent to neighboring unmanned vehicles through each unmanned vehicle.

[0007] In response to the first sub-state information of all neighboring autonomous vehicles, the first sub-state information, second sub-state information, and auxiliary variables of the local autonomous vehicle are updated using a dynamic average consensus algorithm based on state decomposition, and the updated first sub-state information is sent to the neighboring autonomous vehicles.

[0008] Based on the updated first sub-state information, second sub-state information and auxiliary variables, the expected heading angle and expected speed of each unmanned vehicle are calculated at the physical layer. The position error is solved, and time-varying auxiliary variables are introduced to generate linear velocity control commands and angular velocity control commands.

[0009] The linear velocity control command and angular velocity control command are iteratively updated to update the position of the unmanned vehicle until the termination condition is met, thus completing the distributed privacy-preserving formation control of the multi-unmanned vehicle system.

[0010] As a preferred option, acquire driverless vehicles The moving target location and estimation of the geometric center of the moving target The target location will be moved. Decomposed into interactive components and privacy The geometric center estimation of the moving target is decomposed into interactive components. The corresponding first sub-state information and privacy Corresponding second sub-state information ;in, Represents the real number field. Indicates driverless car The moving target location Axial components, Indicates driverless car The moving target location Axial components, Indicates driverless car Interactive components Axial components, Indicates driverless car Interactive components Axial components, Indicates driverless car Privacy weight Axial components, Indicates driverless car Privacy weight Axial components, superscript Indicates transpose. Indicates driverless car Geometric center of the estimated position of the moving target Axial components, Indicates driverless car Geometric center of target location estimation Axial components, Indicates the second sub-state information Axial components, Indicates the second sub-state information Axial components;

[0011] First sub-state information initial value Set as the initial value of the interactive component. Second sub-state information initial value Set as privacy component initial value ;have:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] in, express The derivative of This represents a mask signal used for protection. Privacy and security express The derivative of Represents interactive components The derivative of Indicates privacy component The derivative of .

[0019] As a preferred option, the communication topology of multiple autonomous vehicle systems for:

[0020]

[0021] in, This represents a set of autonomous vehicle nodes, where each node corresponds one-to-one with each autonomous vehicle in a multi-autonomous vehicle system. This represents the set of all edges in the communication topology graph. If the edges between autonomous vehicle nodes are undirected, then the communication topology of the multi-autonomous vehicle system is as follows: It is an undirected graph, meaning that for any autonomous vehicle node... and driverless vehicle nodes If it exists This indicates the existence of driverless vehicle nodes. and driverless vehicle nodes Directly connected communication links are used to complete the autonomous vehicle nodes. and driverless vehicle nodes Two-way information exchange between them.

[0022] Preferably, in response to the first sub-state information of all neighboring autonomous vehicles, the local autonomous vehicle's first sub-state information, second sub-state information, and auxiliary variables are updated using a dynamic average consensus algorithm based on state decomposition, and the updated first sub-state information is sent to the neighboring autonomous vehicles. This specifically includes the following steps:

[0023] In response to the first sub-state information of the neighboring autonomous vehicles, calculate the weighted sum of the state differences between the local autonomous vehicle and the neighboring autonomous vehicles;

[0024] Define auxiliary variables as and Set the initial value of the auxiliary variable to . Based on the weighted sum of the state differences between the autonomous vehicle and its neighbors, and the first sub-state information of the local autonomous vehicle... Second sub-state information Update auxiliary variables derivative and auxiliary variables derivative ;in, Represents the real number field, superscript Indicates transpose. Representing auxiliary variables of Axial components, Representing auxiliary variables of Axial components, Representing auxiliary variables of Axial components, Representing auxiliary variables of Axial components;

[0025] Based on the derivatives of the updated auxiliary variables, update the first and second sub-state information of the autonomous vehicle to obtain the updated first and second sub-state information.

[0026] The updated first sub-state information is sent to the neighboring unmanned vehicles.

[0027] As a preferred option, the formula for calculating the weighted sum of the state differences between the local autonomous vehicle and its neighboring autonomous vehicles is as follows:

[0028]

[0029] in, Indicates driverless car The weighted sum of the state differences with its neighbors, Represents autonomous vehicle nodes The set consisting of all neighboring nodes Represents autonomous vehicle nodes The first sub-state information, Representing the adjacency matrix The elements in Represents autonomous vehicle nodes and driverless vehicle nodes There are directly connected links between them. Represents autonomous vehicle nodes and driverless vehicle nodes There is no direct link between them;

[0030] Auxiliary variables derivative and derivative The update formula is:

[0031]

[0032] in, Indicates uniformity gain;

[0033] Auxiliary variables derivative The update formula is:

[0034]

[0035] in, Indicates uniformity gain;

[0036] First sub-state information Second sub-state information The specific update formula is as follows:

[0037]

[0038]

[0039] in, Indicates auxiliary parameters, Represents interactive components The derivative of Indicates privacy component The derivative of .

[0040] Preferably, the goal of the physical layer of each autonomous vehicle is to enable the autonomous vehicle to move to its actual position. In the position, It represents the formation shape of the multi-unmanned vehicles obtained from the information layer, that is, the relative position of the time-varying geometric center of the moving target;

[0041] The process of calculating the desired heading angle and desired velocity of each unmanned vehicle at the physical layer based on the updated first sub-state information, second sub-state information, and auxiliary variables, solving for the position error, and introducing time-varying signals and auxiliary variables to generate linear velocity control commands and angular velocity control commands specifically includes the following steps:

[0042] Calculate the desired heading angle and desired linear velocity;

[0043] Calculate the position error based on the desired heading angle;

[0044] Introduce time-varying signals to calculate auxiliary errors;

[0045] Based on the desired linear velocity, position error, time-varying signal, and auxiliary error, the linear velocity control command and angular velocity control command are calculated.

[0046] As a preferred option, the desired heading angle and expected linear velocity The calculation formula is:

[0047]

[0048]

[0049] in, Represents the arctangent function. Indicates driverless car Geometric center of the estimated position of the moving target Axial component derivative, Indicates driverless car Geometric center of the estimated position of the moving target Axial component derivatives;

[0050] The position error includes , and The specific calculation formula is as follows:

[0051]

[0052]

[0053] in, Indicates driverless car of Shaft position error components, Indicates driverless car of Shaft position error components, Indicates driverless car The heading angle error, Indicates driverless car The actual location, Indicates driverless car actual location Axial components, Indicates driverless car actual location Axial components; Indicates driverless car Regarding the relative position of the time-varying geometric center of the moving target Indicates driverless car Relative position based on time-varying geometric center Axial components, Indicates driverless car Relative position based on time-varying geometric center Axial components; Represents the sine function. Represents the cosine function; Indicates the heading angle, and ;

[0054] The formula for calculating the auxiliary error is:

[0055]

[0056] in, The formula for calculating a time-varying signal is:

[0057]

[0058] in, This represents the motion-related attenuation factor, the value of which varies with the motion intensity of the autonomous vehicle. The cumulative increase and decrease are used in time-varying signals. Adaptive amplitude adjustment is achieved in the middle, with , , , This represents the initial control parameters obtained from the information layer. Represents the hyperbolic tangent function. This represents the natural exponential function. Indicates the integral symbol, Represents the integral variable. Indicates time;

[0059] Linear speed control command and angular velocity control commands The formula for generating it is:

[0060]

[0061]

[0062] in, , , and Indicates positive control gain. Represents time-varying signals The derivative of .

[0063] Preferably, the termination condition is:

[0064] Position error , and All tend to 0, when , and When all values ​​approach zero, driverless cars The actual location reached The location.

[0065] The beneficial effects of this invention are:

[0066] 1. By setting the initial value of the first sub-state information to the initial value of the interaction component and the initial value of the second sub-state information to the initial value of the privacy component, the initial state values ​​of each autonomous vehicle are configured and the state is decomposed so that the state components used for information interaction can be sent to neighboring autonomous vehicles in the future.

[0067] 2. This invention decomposes the autonomous vehicle's state into interactive and local components, communicating only the interactive components to ensure that the vehicle's target location information cannot be reconstructed. Even if an attacker intercepts all communication data, they cannot infer the vehicle's true target location, reducing the risk of privacy leakage. A distributed iterative mechanism using auxiliary variables effectively suppresses channel noise accumulation. Complex encryption calculations are eliminated, reducing communication overhead; and the distributed architecture, without a central node, avoids single-point-of-failure risks, improving system scalability. Progressive heading adjustments at the physical layer further reduce autonomous vehicle formation control errors. This invention solves the dilemma of privacy and robustness in cooperative control, significantly improving control accuracy in complex communication environments while ensuring state information security, providing reliable technical support for highly sensitive scenarios.

[0068] In a second aspect, a state-decomposition-based distributed privacy-preserving formation control system is provided, the system comprising a processor for executing the state-decomposition-based distributed privacy-preserving formation control method as described in the first aspect.

[0069] Thirdly, a computer-readable storage medium stores computer instructions, and in response to a computer reading the computer instructions in the storage medium, the computer executes the state-decomposition-based distributed privacy-preserving formation control method as described in the first aspect. Attached Figure Description

[0070] Figure 1 The diagram shows a flowchart of a distributed privacy-preserving formation control method based on state decomposition.

[0071] Figure 2 The diagram shows the communication network topology of six unmanned vehicles.

[0072] Figure 3 The image shown is a result where the real state cannot be distinguished under privacy protection during state decomposition.

[0073] Figure 4 The image shown illustrates the different results of corresponding privacy information when the actual situation cannot be distinguished.

[0074] Figure 5 The diagram shows the changes in the Alpha state variable information of the autonomous vehicle system.

[0075] Figure 6 The diagram shows the changes in the Beta state variables of the autonomous vehicle system.

[0076] Figure 7 The figure shown is a graph illustrating the changes in the Alpha state variable information of an autonomous vehicle system under noise conditions.

[0077] Figure 8 The figure shows the changes in Beta state variables of the autonomous vehicle system under noise conditions.

[0078] Figure 9 The diagram shown is a result of the formation control of a multi-unmanned vehicle system.

[0079] Figure 10 The figure shown is a diagram of the position error results for the formation control of a multi-unmanned vehicle system. Detailed Implementation

[0080] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, and are not intended to limit the scope of the invention.

[0081] Example 1:

[0082] like Figure 1 As shown, a distributed privacy-preserving formation control method based on state decomposition is proposed, considering the motion plane... A driverless car and Given a moving target, each autonomous vehicle (V2V) possesses local positioning capabilities and can perceive the position of its corresponding moving target. Formation control requires the V2Vs to collaboratively estimate the time-varying geometric center of the moving target using information received from the communication network, and then drive each V2V to move to the relative position of that time-varying geometric center. However, since each V2V can only perceive one moving target, it needs to cooperate with its neighbors to calculate the geometric center, which poses a risk of infringing on the privacy of other moving targets. The state decomposition-based distributed privacy-preserving formation control method includes the following steps:

[0083] S1. Initialize the information layer: Obtain the moving target position and the estimated geometric center of the moving target of the local unmanned vehicle, decompose the moving target position into an interaction component and a privacy component, and decompose the moving target geometric center estimate into the first sub-state information corresponding to the interaction component and the second sub-state information corresponding to the privacy component;

[0084] S2. Store the second sub-state information locally and make it invisible to the outside world, and send the first sub-state information to neighboring unmanned vehicles through each unmanned vehicle based on the communication topology of the multi-unmanned vehicle system;

[0085] S3. In response to the first sub-state information of all neighboring autonomous vehicles, update the first sub-state information, second sub-state information, and auxiliary variables of the local autonomous vehicle using a dynamic average consensus algorithm based on state decomposition, and send the updated first sub-state information to the neighboring autonomous vehicles.

[0086] S4. Calculate the desired heading angle and desired speed of each unmanned vehicle at the physical layer based on the updated first sub-state information, second sub-state information and auxiliary variables, solve for the position error, and introduce time-varying auxiliary variables to generate linear velocity control commands and angular velocity control commands. Iteratively update the linear velocity control commands and angular velocity control commands, and then update the position of the unmanned vehicle.

[0087] S5. Determine if the formation system has met the termination condition. If yes, the process ends; otherwise, return to step S2.

[0088] In this embodiment, the information layer is initialized using a dynamic average consensus algorithm to obtain the information of the unmanned vehicle. The moving target location and estimation of the geometric center of the moving target The target location will be moved. Decomposed into interactive components and privacy And estimate the geometric center of the moving target Decomposed into interactive components The corresponding first sub-state information and privacy Corresponding second sub-state information First sub-state information Part of it is used to exchange information with neighboring autonomous vehicles, the second sub-state information The local autonomous vehicle information is not visible to the outside world. Initializing the autonomous vehicle model parameters includes: initial position. Initial heading angle Given formation shape and control parameters , , , , , and .in, Represents the real number field. Indicates driverless car The moving target location Axial components, Indicates driverless car The moving target location Axial components, Indicates driverless car Interactive components Axial components, Indicates driverless car Interactive components Axial components, Indicates driverless car Privacy weight Axial components, Indicates driverless car Privacy weight Axial components, superscript Indicates transpose. Indicates driverless car Geometric center of the estimated position of the moving target Axial components, Indicates driverless car Geometric center of target location estimation Axial components, Indicates the second sub-state information Axial components, Indicates the second sub-state information Axial components.

[0089] First sub-state information initial value Set as interactive component initial value Second sub-state information initial value Set as privacy component initial value ;have:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] in, Indicates driverless car The location of a detectable moving target. express The derivative of This represents a mask signal used for protection. Privacy and security express The derivative of Represents interactive components The derivative of Indicates privacy component The derivative of .

[0097] For any autonomous vehicle node Its moving target location and the derivative of the target position All are bounded, that is, they satisfy... , In particular, and its derivative satisfy , , and It is a constant.

[0098] In this embodiment, the communication network topology diagram of the six unmanned vehicles is as follows: Figure 2 As shown, the communication topology for:

[0099]

[0100] in, This represents a set of nodes, where each node corresponds one-to-one with each agent in a multi-agent system. This represents the set of all edges (i.e., the set of communication links) in a communication topology graph. When the edges between nodes are undirected, the graph... It is an undirected graph, meaning that for any node... and If it exists This indicates that a node exists. and Directly connected communication links allow for node-to-node communication. and Two-way information exchange between them.

[0101] The first sub-state information corresponding to the interaction component Used for interaction with neighboring nodes, the second sub-state information corresponding to the privacy component. Updating locally means that eavesdroppers cannot obtain information from all nodes. The value of is therefore not accurate enough to reconstruct the moving target position of each node, such as Figure 3 As shown, even if the interactive portion is intercepted by an eavesdropper, the eavesdropper cannot accurately obtain the node's state or distinguish the true state, thus protecting the node's privacy information. Figure 4 .

[0102] In this embodiment, step S3 specifically includes the following sub-steps:

[0103] In response to information from neighboring autonomous vehicles, the state difference between the local autonomous vehicle and its neighbors is summed using a weighted average:

[0104]

[0105] in, Indicates driverless car The weighted sum of the state differences with its neighbors, Represents autonomous vehicle nodes The set consisting of all neighboring nodes Represents autonomous vehicle nodes The first sub-state information, Representing the adjacency matrix The elements in Represents autonomous vehicle nodes and driverless vehicle nodes There are directly connected links between them. Represents autonomous vehicle nodes and driverless vehicle nodes There is no direct link between them;

[0106] Define auxiliary variables as and Set the initial value of the auxiliary variable to . Based on the weighted sum of the state differences between the autonomous vehicle and its neighbors, and the first sub-state information of the local autonomous vehicle... Second sub-state information Update auxiliary variables derivative and derivative The update formulas are as follows:

[0107]

[0108]

[0109] in, Indicates uniformity gain;

[0110] Based on the updated derivatives of the auxiliary variables, the first and second sub-state information of the autonomous vehicle are updated to obtain the updated first sub-state information. Second sub-state information The specific update formula is as follows:

[0111]

[0112]

[0113] in, Indicates auxiliary parameters, Represents interactive components The derivative of Indicates privacy component The derivative of. For example... Figure 5 and Figure 6 As shown, all state values ​​evolve in a common direction, namely the direction of the average position of the moving target of the autonomous vehicle system, and all states tend to be consistent. Even in the presence of noise in the channel, the values ​​of all nodes change in a common direction, namely the direction of the average position of the moving target of the system, and eventually the states of all nodes tend to be consistent, as shown in the figure. Figure 7 and Figure 8 As shown.

[0114] In this embodiment, step S4 specifically includes the following sub-steps:

[0115] Obtain autonomous vehicles from the information layer Geometric center estimation of the moving target position Formation shape The physical layer, through control law design, drives the autonomous vehicle to a certain relative position of the time-varying geometric center, enabling the autonomous vehicle to... actual location Exercise The target formation is formed based on the position of the target; there are many control algorithms that can achieve this step, and the following algorithm is used in this embodiment of the invention:

[0116] Calculate the desired heading angle and expected linear velocity :

[0117]

[0118]

[0119] Calculate position error , and :

[0120]

[0121]

[0122] Among them, driverless cars actual location , Indicates driverless car actual location Axial components, Indicates driverless car actual location Axis components; geometric center for estimating the moving target position of an autonomous vehicle. , Indicates driverless car Geometric center of the estimated position of the moving target Axial components, Indicates driverless car Geometric center of target location estimation Axial components; autonomous vehicles Regarding the relative position of the time-varying geometric center of the moving target , Indicates driverless car Relative position based on time-varying geometric center Axial components, Indicates driverless car Relative position based on time-varying geometric center Axial components; Represents the sine function. Represents the cosine function; Indicates the heading angle, and .

[0123] when , and When all values ​​approach zero, the driverless car arrives. The location.

[0124] Introducing time-varying auxiliary variables to aid in controller design and defining auxiliary errors. for:

[0125]

[0126] in, The formula for calculating a time-varying signal is:

[0127]

[0128] in, This represents the motion-related attenuation factor, the value of which varies with the motion intensity of the autonomous vehicle. The cumulative increase and decrease are used in time-varying signals. Adaptive amplitude adjustment is achieved in the middle, with ;

[0129] According to position error , and The inputs for calculating the linear velocity and angular velocity are:

[0130]

[0131]

[0132] in, Indicates positive control gain. Represents time-varying signals The derivative of .

[0133] In this embodiment, in step S5, the position error is determined. , and Do they all tend to 0 when the position error is zero? , and When all values ​​approach zero, the driverless car arrives. If the position is not specified, return to step S2 for repeated iterations; if it is specified, end the iteration and complete the dynamic average consistency formation control of the multi-unmanned vehicle system. The results are as follows: Figure 9 As shown. Calculate the deviation between the actual position and the theoretically calculated position of the autonomous vehicle, such as... Figure 10 As shown, the error value continuously decreases during the algorithm's execution.

[0134] Example 2:

[0135] Based on Embodiment 1, this embodiment of the invention provides a distributed privacy-preserving formation control system based on state decomposition, used to configure and execute a distributed privacy-preserving formation control method based on state decomposition in Embodiment 1.

[0136] In this embodiment, the system may be an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. The processor executes the program to implement some or all of the steps of the state decomposition-based distributed privacy-preserving formation control method as described in Embodiment 1.

[0137] In this embodiment, the electronic device may include: a processor, a memory, a bus, and a communication interface. The processor, the communication interface, and the memory are connected via the bus. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes some or all of the steps of the distributed privacy-preserving formation control method based on state decomposition provided in Embodiment 1 of this application.

[0138] The system in this embodiment of the invention may also be a computer-readable storage medium storing a computer program that, when executed, implements some or all of the steps of the state-decomposition-based distributed privacy-preserving formation control method as described in Embodiment 1.

[0139] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0140] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0141] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0142] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0143] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0144] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A distributed privacy-preserving formation control method based on state decomposition, characterized in that, Includes the following steps: Initialize the information layer: obtain the location of the moving target and the estimated geometric center of the moving target of the local unmanned vehicle, decompose the location of the moving target into an interaction component and a privacy component, and decompose the estimated geometric center of the moving target into the first sub-state information corresponding to the interaction component and the second sub-state information corresponding to the privacy component; The second sub-state information is stored locally and is not visible to the outside world. Based on the communication topology of the multi-unmanned vehicle system, the first sub-state information is sent to neighboring unmanned vehicles through each unmanned vehicle. In response to the first sub-state information of all neighboring autonomous vehicles, the first sub-state information, second sub-state information, and auxiliary variables of the local autonomous vehicle are updated using a dynamic average consensus algorithm based on state decomposition, and the updated first sub-state information is sent to the neighboring autonomous vehicles. Based on the updated first sub-state information, second sub-state information and auxiliary variables, the expected heading angle and expected speed of each unmanned vehicle are calculated at the physical layer. The position error is solved, and time-varying auxiliary variables are introduced to generate linear velocity control commands and angular velocity control commands. Iteratively update the linear velocity control command and angular velocity control command, and then update the position of the unmanned vehicle until the termination condition is met, thus completing the distributed privacy-preserving formation control of the multi-unmanned vehicle system; The information layer is initialized as follows: Acquiring driverless cars The moving target location and estimation of the geometric center of the moving target The target location will be moved. Decomposed into interactive components and privacy The geometric center estimation of the moving target is decomposed into interactive components. The corresponding first sub-state information and privacy Corresponding second sub-state information ;in, Represents the real number field. Indicates driverless car The moving target location Axial components, Indicates driverless car The moving target location Axial components, Indicates driverless car Interactive components Axial components, Indicates driverless car Interactive components Axial components, Indicates driverless car Privacy weight Axial components, Indicates driverless car Privacy weight Axial components, superscript Indicates transpose. Indicates driverless car Geometric center of the estimated position of the moving target Axial components, Indicates driverless car Geometric center of target location estimation Axial components, Indicates the second sub-state information Axial components, Indicates the second sub-state information Axial components; First sub-state information initial value Set as the initial value of the interactive component. Second sub-state information initial value Set as privacy component initial value ;have: in, express The derivative of This represents a mask signal used for protection. Privacy and security express The derivative of Represents interactive components The derivative of Indicates privacy component The derivative of .

2. The distributed privacy-preserving formation control method based on state decomposition according to claim 1, characterized in that, Communication topology of multi-unmanned vehicle system for: in, This represents a set of autonomous vehicle nodes, where each node corresponds one-to-one with each autonomous vehicle in a multi-autonomous vehicle system. This represents the set of all edges in the communication topology graph. If the edges between autonomous vehicle nodes are undirected, then the communication topology of the multi-autonomous vehicle system is as follows: It is an undirected graph, meaning that for any autonomous vehicle node... and driverless vehicle nodes If it exists This indicates the existence of driverless vehicle nodes. and driverless vehicle nodes Directly connected communication links are used to complete the autonomous vehicle nodes. and driverless vehicle nodes Two-way information exchange between them.

3. The distributed privacy-preserving formation control method based on state decomposition according to claim 1, characterized in that, The first sub-state information of the local autonomous vehicle is updated using a dynamic average consensus algorithm based on state decomposition, along with its second sub-state information and auxiliary variables. The updated first sub-state information is then sent to the neighboring autonomous vehicles. This process includes the following steps: In response to the first sub-state information of the neighboring autonomous vehicles, calculate the weighted sum of the state differences between the local autonomous vehicle and the neighboring autonomous vehicles; Define auxiliary variables as and Set the initial value of the auxiliary variable to . Based on the weighted sum of the state differences between the autonomous vehicle and its neighbors, and the first sub-state information of the local autonomous vehicle... Second sub-state information Update auxiliary variables derivative and auxiliary variables derivative ;in, Represents the real number field, superscript Indicates transpose. Representing auxiliary variables of Axial components, Representing auxiliary variables of Axial components, Representing auxiliary variables of Axial components, Representing auxiliary variables of Axial components; Based on the derivatives of the updated auxiliary variables, update the first and second sub-state information of the autonomous vehicle to obtain the updated first and second sub-state information. The updated first sub-state information is sent to the neighboring unmanned vehicles.

4. The distributed privacy-preserving formation control method based on state decomposition according to claim 1, characterized in that, The goal of the physical layer of each autonomous vehicle is to enable the autonomous vehicle to move to its actual position. In the position, It represents the formation shape of the multi-unmanned vehicles obtained from the information layer, that is, the relative position of the time-varying geometric center of the moving target; The process of calculating the desired heading angle and desired velocity of each unmanned vehicle at the physical layer based on the updated first sub-state information, second sub-state information, and auxiliary variables, solving for the position error, and introducing time-varying signals and auxiliary variables to generate linear velocity control commands and angular velocity control commands specifically includes the following steps: Calculate the desired heading angle and desired linear velocity; Calculate the position error based on the desired heading angle; Introduce time-varying signals to calculate auxiliary errors; Based on the desired linear velocity, position error, time-varying signal, and auxiliary error, the linear velocity control command and angular velocity control command are calculated.

5. The distributed privacy-preserving formation control method based on state decomposition according to claim 4, characterized in that, Desired heading angle and expected linear velocity The calculation formula is: in, Represents the arctangent function. Indicates driverless car Geometric center of the estimated position of the moving target Axial component derivative, Indicates driverless car Geometric center of the estimated position of the moving target Axial component derivatives; The position error includes , and The specific calculation formula is as follows: in, Indicates driverless car of Shaft position error components, Indicates driverless car of Shaft position error components, Indicates driverless car The heading angle error, Indicates driverless car The actual location, Indicates driverless car actual location Axial components, Indicates driverless car actual location Axial components; Indicates driverless car Regarding the relative position of the time-varying geometric center of the moving target Indicates driverless car Relative position based on time-varying geometric center Axial components, Indicates driverless car Relative position based on time-varying geometric center Axial components; Represents the sine function. Represents the cosine function; Indicates the heading angle, and ; The formula for calculating the auxiliary error is: in, The formula for calculating a time-varying signal is: in, This represents the motion-related attenuation factor, the value of which varies with the motion intensity of the autonomous vehicle. The cumulative increase and decrease are used in time-varying signals. Adaptive amplitude adjustment is achieved in the middle, with , , , This represents the initial control parameters obtained from the information layer. Represents the hyperbolic tangent function. This represents the natural exponential function. Indicates the integral symbol, Represents the integral variable. Indicates time; Linear speed control command and angular velocity control commands The formula for generating it is: in, , , and Indicates positive control gain. Represents time-varying signals The derivative of .

6. The distributed privacy-preserving formation control method based on state decomposition according to claim 5, characterized in that, The termination condition is: Position error , and All tend to 0, when , and When all values ​​approach zero, driverless cars The actual location reached The location.

7. A distributed privacy-preserving formation control system based on state decomposition, characterized in that, The system includes a processor for executing the state-decomposition-based distributed privacy-preserving formation control method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. In response to the computer reading the computer instructions from the storage medium, the computer executes the distributed privacy-preserving formation control method based on state decomposition as described in any one of claims 1-6.

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