Networked multi-agent formation control method and system based on cloud computing

Abstract

The present invention belongs to the field of industrial Internet and automatic control technology, and provides a networked multi-agent formation control method and system based on cloud computing, including grouping the agents distributed in the hybrid network according to the network conditions, and assigning a cloud node to each group of agents; obtaining the input and output data of the agents and calculating the estimation of the pseudo-partial derivatives, and sending them to the corresponding cloud nodes; the cloud nodes generate the formation control protocol of the agents based on the data of the agents in the group obtained and the data sent from other cloud nodes; the cloud nodes generate the prediction control sequence based on the agent formation control protocol, and send it to the actuators of the agents to control the agents. The present disclosure overcomes the problems of real-time big data, communication delay, large amount of calculation and multi-task coordination existing in traditional networked multi-agent systems, and realizes the formation control of networked multi-agent systems.

Description

Technical Field

[0001] The present disclosure relates to the fields of industrial Internet and automatic control technology, and in particular to a networked multi-agent formation control method and system based on cloud computing. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] A networked multi-agent system is composed of a group of agents with perception, communication and computing capabilities. The agents and the environment communicate, negotiate and collaborate to solve problems that cannot be solved by a single agent. Compared with a single agent, a multi-agent system has a wider range of tasks and higher efficiency. Therefore, it has broad application prospects in the fields of intelligent transportation systems, the Internet of Things, cooperative control of drones, swarm robots, smart grids, etc. Multi-agent formation control refers to the process of coordinating and controlling multiple agents (or robots, drones, unmanned vehicles, etc.) to achieve specific tasks or achieve common goals. This control involves communication, coordination and behavior planning among multiple agents, as well as the formation and maintenance of the overall formation. At present, traditional networked multi-agent systems have problems such as real-time big data, communication delay, large computational workload and multi-task coordination.

[0004] Cloud computing is a technology that provides computing services through the Internet, providing computing resources (such as storage, servers, databases, networks, software, etc.) to users or enterprises so that they can obtain, manage and store data on demand without directly relying on local computers or local servers. The application of cloud computing in control systems presents important potential. It brings greater flexibility, scalability and efficiency to control systems. Cloud computing provides strong support for data storage and processing in control systems. The massive data generated by the control system can be collected, stored and analyzed through the cloud platform, and deeper data mining can be achieved using big data analysis and machine learning algorithms in the cloud, thereby providing smarter and more efficient decision support for the control system. Cloud computing also promotes resource sharing and collaborative work in control systems. Multiple devices or systems can share data and resources through the cloud, achieve collaborative work and information exchange, and improve the efficiency and coordination of the entire system. Summary of the invention

[0005] In response to the above-mentioned defects, the present invention proposes a networked multi-agent formation control method and system based on cloud computing. For the distributed networked multi-agent system under the hybrid network, the cloud computing system is used to establish communication channels for all agents, and a formation control protocol is designed to actively compensate for the random delays existing in the communication channels of the system, so as to realize the formation control of the networked multi-agent system.

[0006] In order to achieve the above objectives, the present disclosure adopts the following technical solutions:

[0007] The first aspect of the present disclosure provides a networked multi-agent formation control method based on cloud computing, comprising the following steps:

[0008] According to the network conditions, the agents distributed in the hybrid network are grouped and a cloud node is assigned to each group of agents;

[0009] Get the agent’s input and output data and calculate the estimate of the pseudo-partial derivative, and send it to the corresponding cloud node;

[0010] The cloud node generates a formation control protocol for the agents based on the data obtained from the agents in the group and the data sent from other cloud nodes;

[0011] The cloud node generates a predictive control sequence based on the intelligent agent formation control protocol and sends it to the actuator of the intelligent agent to control the intelligent agent.

[0012] As a further implementation, the communication topology between agents is fixed.

[0013] As a further implementation method, the input and output data of the agent are collected by sensors, and the input and output data of the agent are collected by sensors and the estimation of pseudo partial derivatives is calculated, specifically:

[0014]

[0015]

[0016] in, is an estimate of the pseudo partial derivative, whose initial value is and They represent adjustable parameters, is a normal number, is the output The change in k is the sampling time. is the control input The amount of change.

[0017] As a further implementation method, the intelligent agents in each group communicate with their respective cloud nodes through the network, and the cloud nodes communicate with each other through the network.

[0018] As a further implementation, sensors, actuators and controllers are all time driven and synchronized.

[0019] As a further implementation, the formation control protocol formula is:

[0020]

[0021] in,

[0022]

[0023]

[0024]

[0025] and are coordination factors, which are used to coordinate the formation tracking error and the error between agents. and is called the selection factor, and its value depends on the communication topology between agents. and These are controller parameters.

[0026] As a further implementation method, a prediction control sequence is generated based on the formation control protocol, specifically:

[0027]

[0028] At time k, at the actuator of agent ij, the control input can be described as:

[0029]

[0030] A second aspect of the present disclosure provides a networked multi-agent formation control system based on cloud computing, comprising:

[0031] The grouping module is configured to: group the agents distributed in the hybrid network according to network conditions, and assign a cloud node to each group of agents;

[0032] The parameter calculation module is configured to: obtain the input and output data of the agent and calculate the estimation of the pseudo partial derivative, and send it to the corresponding cloud node;

[0033] The control protocol generation module is configured to: the cloud node generates a formation control protocol for the intelligent agents based on the acquired data of the intelligent agents in the group and the data sent from other cloud nodes;

[0034] The execution module is configured as follows: the cloud node generates a predictive control sequence based on the intelligent agent formation control protocol, sends it to the actuator of the intelligent agent, and controls the intelligent agent.

[0035] The third aspect of the present disclosure provides a medium on which a program is stored. When the program is executed by a processor, the steps in the cloud computing-based networked multi-agent formation control method described in the first aspect of the present disclosure are implemented.

[0036] The fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the cloud computing-based networked multi-agent formation control method described in the first aspect of the present disclosure are implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The networked multi-agent formation control method and system based on cloud computing proposed in the present disclosure are applied to the distributed nonlinear multi-agent system under the hybrid network, using distributed cloud computing to establish the communication channel between the agents, and designing the formation control protocol to actively compensate for the random network delay in each communication channel of the system, thus overcoming the problems of real-time big data, communication delay, large amount of calculation and multi-task coordination in the traditional networked multi-agent system. The formation control of the networked multi-agent system is realized.

[0039] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0041] Figure 1 is a flow chart of the networked multi-agent formation control method based on cloud computing disclosed in the present invention;

[0042] Figure 2 It is a framework diagram of the networked multi-agent formation control system based on cloud computing disclosed in the present invention. DETAILED DESCRIPTION

[0043] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0045] In the absence of conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other.

[0046] Embodiment 1

[0047] like Figure 1 As shown, the first embodiment of the present disclosure provides a networked multi-agent formation control method based on cloud computing, comprising the following steps:

[0048] S1 groups the agents distributed in the hybrid network according to the network conditions and assigns a cloud node to each group of agents;

[0049] S2 obtains the input and output data of the agent and calculates the estimate of the pseudo-partial derivative, and sends it to the corresponding cloud node;

[0050] The S3 cloud node generates a formation control protocol for the agents based on the data of the agents in the group and the data sent from other cloud nodes;

[0051] The S4 cloud node generates a predictive control sequence based on the acquired intelligent agent formation control protocol and sends it to the actuator of the intelligent agent to control the intelligent agent.

[0052] The technical solution disclosed in this disclosure is based on the following assumptions:

[0053] 1. The network delay from cloud node f to cloud node i is The network delays of the agent ij feedback channel (from sensor to controller) and forward channel (from controller to actuator) are and They are all random but bounded, namely:

[0054]

[0055] in, Represents an upper bound on the delay.

[0056] 2. The communication topology between agents is fixed. Agents in each group communicate with their respective cloud nodes through the network, and cloud nodes communicate with each other through the network.

[0057] 3. Sensors, actuators, and controllers are all time-driven and synchronized.

[0058] The networked multi-agent system disclosed in this disclosure implements the definition of formation control:

[0059] For any given bounded initial state, a networked multi-agent system is said to achieve output grouping if the following conditions are met:

[0060]

[0061]

[0062] Among them, y d (k) is the given desired output, is the output of agent ij, and represents the preset formation, and k is the sampling time.

[0063] S1 is as follows: In reality, a large-scale multi-agent system is often distributed in a hybrid network. First, we divide the agents into different groups according to the network conditions. Then, each group of agents is assigned to a different cloud node. Therefore, the agents in the group can communicate directly with their respective cloud nodes through the network, and the cloud nodes can communicate with each other through the network. Assuming that the number of cloud nodes is m, and each cloud node is connected to n agents, the dynamic equation of the jth agent (called agent ij) under the i-th cloud node is expressed as:

[0064]

[0065] in, and represents the output and control input of agent ij at time k, represents the unknown nonlinear equation, n y 、n u Represent the agent output and control input The unknown order of , i = 1, 2, ..., m represents the number of cloud nodes, and j = 1, 2, ..., n represents the number of intelligent agents.

[0066] The dynamic equation of the above agent ij can be transformed into the following data model:

[0067]

[0068] in, is called the pseudo-partial-derivative (PPD), and is the control input The amount of change.

[0069] S2 is specifically:

[0070] For the agent ij, the sensor collects historical input and output data, and uses the following algorithm to calculate and estimate the pseudo partial derivative (PPD):

[0071]

[0072]

[0073] in, It is an estimate of the pseudo partial derivative, that is, the estimated PPD, and its initial value is They represent adjustable parameters, is a normal number, is the output The amount of change.

[0074] Then, the sensor of agent ij sends the calculated estimated PPD and input and output data to cloud node i at each sampling time.

[0075] S3 is specifically:

[0076] There are two computational tasks in cloud node i. In the first task, to obtain the output prediction of agent ij, based on the data from the sensors of agent ij and the deadline Given a known predicted control input, the following formula can be used to predict the output sequence of the agent if:

[0077]

[0078]

[0079] in, Represents agent ij based on time k 2 For time k 1 The output prediction of In the second task, similarly, to obtain the output prediction of agent f-1, based on the data packets sent from cloud node f, the following formula can be used to predict the output sequence of agent f-1:

[0080]

[0081]

[0082] in, is the network delay from cloud node f to cloud node i. Based on the output prediction sequence and estimated PPD of the agent obtained above, in order to actively compensate for the random network delay and achieve formation control of the multi-agent system, the following incremental formation control protocol is designed:

[0083]

[0084] in,

[0085]

[0086]

[0087]

[0088] in, and It is called coordination factor, which is used to coordinate the formation tracking error and the error between agents. and The selection factor, whose value depends on the communication topology between agents, and These are controller parameters.

[0089] S4 is specifically:

[0090] According to the agent formation control protocol designed in the previous step, the predicted control input of agent ij can be calculated as:

[0091]

[0092] Then, the following predictive control input sequence of agent ij is obtained and sent to the actuator at each sampling time:

[0093]

[0094] At time k, at the actuator of agent ij, the control input can be described as:

[0095]

[0096] Embodiment 2

[0097] Embodiment 2 of the present disclosure provides a networked multi-agent formation control system based on cloud computing, including:

[0098] The grouping module is configured to: group the agents distributed in the hybrid network according to network conditions, and assign a cloud node to each group of agents;

[0099] The parameter calculation module is configured to: obtain the input and output data of the agent and calculate the estimation of the pseudo partial derivative, and send it to the corresponding cloud node;

[0100] The control protocol generation module is configured to: the cloud node generates a formation control protocol for the intelligent agents based on the acquired data of the intelligent agents in the group and the data sent from other cloud nodes;

[0101] The execution module is configured as follows: the cloud node generates a predictive control sequence based on the intelligent agent formation control protocol, sends it to the actuator of the intelligent agent, and controls the intelligent agent.

[0102] The more detailed steps are the same as those in the first embodiment and will not be repeated here.

[0103] Embodiment 3

[0104] Embodiment 3 of the present disclosure provides a medium on which a program is stored. When the program is executed by a processor, the steps in the networked multi-agent formation control method based on cloud computing as described in Embodiment 1 of the present disclosure are implemented.

[0105] The more detailed steps are the same as those in the first embodiment and will not be repeated here.

[0106] Embodiment 4

[0107] Embodiment 4 of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the networked multi-agent formation control method based on cloud computing as described in Embodiment 1 of the present disclosure are implemented.

[0108] The more detailed steps are the same as those in the first embodiment and will not be repeated here.

[0109] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A networked multi-agent formation control method based on cloud computing, characterized in that: The following steps are involved: According to the network conditions, the agents distributed in the hybrid network are grouped and a cloud node is assigned to each group of agents; Get the agent’s input and output data and calculate the estimate of the pseudo-partial derivative, and send it to the corresponding cloud node; The cloud node generates a formation control protocol for the agents based on the data obtained from the agents in the group and the data sent from other cloud nodes; The cloud node generates a predictive control sequence based on the intelligent agent formation control protocol and sends it to the actuator of the intelligent agent to control the intelligent agent; The formation control protocol formula is: in, and are coordination factors, which are used to coordinate the formation tracking error and the error between agents. and is called the selection factor, and its value depends on the communication topology between agents. and are controller parameters. Based on Always Intelligent Agent at the Moment Control input variables predictions, For intelligent agents exist The estimate of the pseudo-partial derivative at time , For intelligent agents exist The formation tracking error at time, For intelligent agents Based on time About the moment The output prediction of For intelligent agents Based on time About the moment The output prediction of For intelligent agents Based on time About the moment The output prediction of for The expected output at time, and Agent and exist The preset formation at the moment, For Agent The network delay of the feedback channel, is the upper bound of the delay, For group leaders Formation error with other group leaders; For followers The formation error with other followers, is the number of cloud nodes, is the number of agents in a group, For intelligent agents exist Always The predicted output at time For intelligent agents exist The formation of the moment, For intelligent agents exist Always The predicted output at time For intelligent agents exist The formation of the moment, For intelligent agents exist Always The predicted output at time For intelligent agents exist The formation of the moment, For intelligent agents exist The formation of the moment, For slave cloud nodes To the cloud node network latency.

2. The networked multi-agent formation control method based on cloud computing as claimed in claim 1, characterized in that: The communication topology between agents is fixed.

3. The networked multi-agent formation control method based on cloud computing as claimed in claim 1, characterized in that: The input and output data of the agent are collected by sensors, and the input and output data of the agent are collected by sensors and the estimation of pseudo partial derivatives is calculated, specifically: in, is an estimate of the pseudo partial derivative, whose initial value is , They represent adjustable parameters, is a normal number, is the output The amount of change, is the sampling time, is the control input The amount of change.

4. The networked multi-agent formation control method based on cloud computing as claimed in claim 1, characterized in that: The agents in each group communicate with their respective cloud nodes through the network, and the cloud nodes communicate with each other through the network.

5. The networked multi-agent formation control method based on cloud computing as claimed in claim 3, characterized in that: Sensors, actuators, and controllers are all time-driven and synchronized.

6. The networked multi-agent formation control method based on cloud computing as claimed in claim 1, characterized in that: Generate a predictive control sequence based on the formation control protocol, specifically: At time k, in the agent The control input of the actuator can be described as: 。 7. A networked multi-agent formation control system based on cloud computing, characterized in that: include: The grouping module is configured to: group the agents distributed in the hybrid network according to network conditions, and assign a cloud node to each group of agents; The parameter calculation module is configured to: obtain the input and output data of the agent and calculate the estimation of the pseudo partial derivative, and send it to the corresponding cloud node; The control protocol generation module is configured to: the cloud node generates a formation control protocol for the intelligent agents based on the acquired data of the intelligent agents in the group and the data sent from other cloud nodes; The execution module is configured as follows: the cloud node generates a prediction control sequence based on the intelligent agent formation control protocol, sends it to the actuator of the intelligent agent, and controls the intelligent agent; The formation control protocol formula is: in, and are coordination factors, which are used to coordinate the formation tracking error and the error between agents. and is called the selection factor, and its value depends on the communication topology between agents. and are controller parameters. Based on Always Intelligent Agent at the Moment Control input variables predictions, For intelligent agents exist The estimate of the pseudo-partial derivative at time , For intelligent agents exist The formation tracking error at time, For intelligent agents Based on time About the moment The output prediction of For intelligent agents Based on time About the moment The output prediction of For intelligent agents Based on time About the moment The output prediction of for The expected output at time, and Agent and exist The preset formation at the moment, For Agent The network delay of the feedback channel, is the upper bound of the delay, For group leaders Formation error with other group leaders; For followers The formation error with other followers, is the number of cloud nodes, is the number of agents in a group, For intelligent agents exist Always The predicted output at time For intelligent agents exist The formation of the moment, For intelligent agents exist Always The predicted output at time For intelligent agents exist The formation of the moment, For intelligent agents exist Always The predicted output at time For intelligent agents exist The formation of the moment, For intelligent agents exist The formation of the moment, For slave cloud nodes To the cloud node network latency.

8. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the cloud computing based networked multi-agent formation control method as described in any one of claims 1-6 are implemented.

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