Observer-based multi-agent sliding mode control method and system under communication constraints

Abstract

This invention relates to the field of multi-agent system control. To address the consistency problem faced by networked multi-agent systems, it provides an observer-based multi-agent sliding mode control method and system under communication constraints. The observer-based multi-agent sliding mode control method under communication constraints includes establishing a model of a networked linear multi-agent system subject to external disturbances and with incompletely measurable states, and the communication topology between each agent; constructing distributed sliding mode observers for each agent; constructing an event-triggered mechanism based on exponential decay rate and a logarithmic quantizer model; and combining the distributed sliding mode observer, the event-triggered mechanism, and the logarithmic quantizer model to construct an observer-based event-triggered quantized integral sliding mode control law to control each agent, thereby achieving average consistency of the multi-agent system. It can effectively achieve average consistency of networked multi-agent systems under conditions of unmeasurable states, external disturbances, and limited communication bandwidth.

Description

Technical Field

[0001] This invention relates to the field of multi-agent system control, and more particularly to an observer-based multi-agent sliding mode control method and system under communication constraints. Background Technology

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

[0003] Networked multi-agent systems (MAS) have attracted significant attention due to their widespread applications in intelligent transportation, sensor networks, and drone swarms. MAS forms a typical networked cyber-physical system by deeply integrating communication, control, and computation. Consistency control is one of the core issues in this field, aiming to achieve state consistency among all agents through the design of distributed protocols. However, in practical applications, MAS typically face three major challenges: First, due to the cost limitations of sensor measurements and harsh operating environments, some state information of the agents is often difficult to obtain directly through measurement; second, system operation is inevitably affected by external interference such as wind resistance and load changes; and finally, agents must interact through digital communication networks, which inevitably imposes strict limitations on communication resources. Specifically, the bandwidth of actual communication networks is limited. As the system scales up, frequent exchanges of numerous signals can easily lead to channel congestion, and coupled with noise interference and long-distance transmission, data packet loss and communication delays are highly likely. Furthermore, the microprocessors and communication modules equipped on the agents are usually battery-powered, with extremely limited energy; continuous high-frequency communication can quickly deplete the system's energy. Therefore, if the problem of limited communication resources is not effectively resolved, it will severely reduce the performance of collaborative control and even lead to system instability.

[0004] To address the aforementioned problems, existing technologies have proposed several solutions from different perspectives, but each has its limitations. For example, for the problem of unmeasurable state, researchers typically use Romberg observers for state estimation; however, traditional linear observers are sensitive to external disturbances, and their estimation accuracy is difficult to guarantee in the presence of uncertainty. To enhance system robustness, sliding mode control is widely used due to its insensitivity to disturbances. For communication-constrained problems, event-triggered control and quantization techniques have become research hotspots. Event-triggered control aims to solve problems caused by bandwidth constraints and energy limitations. It sets a measurement error threshold, triggering communication only when the system state exceeds specified conditions, thereby effectively reducing communication frequency and saving computational and communication resources. Quantization techniques can map real-valued signals into a finite number of digital symbols for encoding and transmission, adapting to the characteristics of digital networks and possessing strong anti-interference capabilities. However, it also introduces quantization errors, which, if the controller is poorly designed, may affect system stability and control performance. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a multi-agent sliding mode control method and system based on observers under communication constraints, which can effectively achieve average consistency of networked multi-agent systems under conditions of unmeasurable states, external interference, and limited communication bandwidth.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides an observer-based multi-agent sliding mode control method under communication constraints.

[0007] In one or more embodiments, an observer-based multi-agent sliding mode control method under communication-constrained conditions is provided, comprising: Based on the relevant state information of the agents, a networked linear multi-agent system model and the communication topology between the agents are established, which are subject to external interference and whose states are not fully measurable. Based on a networked linear multi-agent system model, a distributed sliding mode observer is constructed for each agent to estimate the unmeasurable state of the system and suppress external disturbances. Based on the communication topology between the respective subjects, an event triggering mechanism based on exponential decay rate is constructed to determine the transmission time of the distributed sliding mode observer state; and a logarithmic quantizer model is constructed to quantize the triggered distributed sliding mode observer state signal. By combining a distributed sliding mode observer, an event-triggered mechanism, and a logarithmic quantizer model, an observer-based event-triggered quantization integral sliding mode control law is constructed to control each agent, thereby achieving average consistency of the multi-agent system.

[0008] As one implementation method, the distributed sliding mode observer of each subject is characterized as follows: ; in, For the first The sliding mode observer of an autonomous entity estimates the state. The output of the observer; For the self-subject number, To control the input, For bounded external disturbances, For measurement output; It is a constant matrix; The observer gain matrix is... This is a switching option used to suppress interference.

[0009] As one implementation method, a switching item for suppressing interference. Defined as: ; in, This represents the matrix that needs to be designed; This represents a constant matrix.

[0010] As one implementation method, the distributed sliding mode observers of each subject have distributed integral sliding surfaces, and their models are as follows: ; Furthermore, a distributed integral sliding mode control protocol is constructed: ; in, For distributed integral sliding surfaces; matrix ,satisfy and , To control the gain; For sliding mode observer gain; For the first The sliding mode observer of an autonomous entity estimates the state. For the first The sliding mode observer estimates the state at the initial moment of the subject; For the first The sliding mode observer of an autonomous entity estimates the state. The output of the observer; For the self-subject number, To control the input, For bounded external disturbances, For measurement output; It is a constant matrix; Let be the information connection weight from subject j to subject i; if subject i can receive information from subject j, then ,otherwise ; Where N is the required switching gain; N is the number of independent units; It is a symbolic function.

[0011] As one implementation method, the event triggering mechanism based on exponential decay rate is as follows: Define measurement error ; Next trigger time It is determined by the following conditions: ; At the triggering moment, the integral sliding surface is represented as: ; Among them, for , For the self The The triggering time of each; matrix ,satisfy and , To control the gain; It is a constant matrix; Initialization function; constant and ; For the first Individual self-subject moment The sliding mode observer estimates the state; For the first The sliding mode observer estimates the state at the initial moment of the subject; For the first Individual self-subject moment The sliding mode observer estimates the state; For the first Individual self-subject moment The sliding mode observer estimates the state; N is the number of agents; For the self The The triggering time of each Distributed integral sliding surface; For the self The moment Distributed integral sliding mode; Let be the information connection weight from subject j to subject i.

[0012] As one implementation method, the observer-based event-triggered quantization integral sliding mode control law is as follows: ; Among them, the triggering time ; For the self The The triggering time of each; For the self The +1 trigger time; The quantized trigger state, The sliding mode variable for the quantized trigger moment; The information connection weights from subject j to subject i; The required switching gain; It is a symbolic function; For the first Individual self-subject moment The sliding mode observer estimates the state; For the first Individual self-subject moment Sliding mode observer estimates state; matrix satisfy and , To control the gain; It is a constant matrix; N is the number of independent entities; For sliding mode observer gain; The output of the observer; For measurement output.

[0013] As one implementation method, the logarithmic quantizer model is set as follows: Its expression is: ; Its quantization error satisfies: ; in, For quantizer parameters, ; The input signal for the quantizer. For the self The output value of the quantizer; This represents the quantization error.

[0014] A second aspect of the present invention provides an observer-based multi-agent sliding mode control system under communication constraints.

[0015] In one or more embodiments, an observer-based multi-agent sliding mode control system under communication constraints includes: The system model and communication topology construction module is used to establish a networked linear multi-agent system model and the communication topology between each agent based on the agent's related state information, which is subject to external interference and whose state is not fully measurable. The distributed sliding mode observer building module is used to construct distributed sliding mode observers for each agent based on a networked linear multi-agent system model, in order to estimate the unmeasurable state of the system and suppress external disturbances. The triggering mechanism and logarithmic quantizer construction module are used to construct an event triggering mechanism based on exponential decay rate based on the communication topology between their respective subjects to determine the transmission time of the distributed sliding mode observer state; and to construct a logarithmic quantizer model to quantize the triggered distributed sliding mode observer state signal. The multi-agent average consistency control module combines a distributed sliding mode observer, an event-triggered mechanism, and a logarithmic quantizer model to construct an observer-based event-triggered quantization integral sliding mode control law to control each agent, thereby achieving average consistency of the multi-agent system.

[0016] A third aspect of the present invention provides a computer-readable storage medium.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the observer-based multi-agent sliding mode control method under communication constraints as described above.

[0018] A fourth aspect of the present invention provides an electronic device.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the observer-based multi-agent sliding mode control method under communication constraints as described above.

[0020] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a multi-agent sliding mode control method and system based on an observer under communication constraints. By designing a distributed sliding mode observer, the unmeasurable states of each agent system are accurately estimated, and external interference is effectively suppressed. To reduce steady-state errors, an integral sliding surface is designed. An event-triggered mechanism with exponential decay rate, driven by the sliding mode observer state, is established, saving significant communication resources for the control system. Based on a logarithmizer, the trigger state signal is quantized, further reducing the transmission burden on the digital network. Based on the quantized trigger signal, an observer-based event-triggered quantized integral sliding mode controller is designed, enabling the system to better balance theoretical performance and practical constraints, and operate reliably in real-world scenarios. The system systematically integrates a sliding mode observer, an event-triggered mechanism, a logarithmizer, and integral sliding mode control, effectively achieving average consistency in a networked multi-agent system under conditions of unmeasurable states, external interference, and limited communication bandwidth. Attached Figure Description

[0021] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0022] Figure 1 This is a flowchart of the observer-based multi-agent sliding mode control method under communication constraints according to an embodiment of the present invention; Figure 2 This is a topology diagram of the communication relationships between multiple self-agent systems in an embodiment of the present invention; Figure 3 This refers to the observer state of multiple autonomous entities in the embodiments of the present invention. The response curve as a function of time; Figure 4 This refers to the observer state of multiple autonomous entities in the embodiments of the present invention. The response curve as a function of time; Figure 5This refers to the observer state of multiple autonomous entities in the embodiments of the present invention. The response curve as a function of time; Figure 6 The observer error in the embodiments of the present invention Response curve over time; Figure 7 The observer error in the embodiments of the present invention The response curve as a function of time; Figure 8 The observer error in the embodiments of the present invention The response curve as a function of time; Figure 9 This is the integral sliding mode variable in the embodiments of the present invention. A curve that changes over time; Figure 10 This is the control signal of the closed-loop system in the embodiment of the present invention. curve; Figure 11 This refers to the number of samples taken by the event triggering mechanism in this embodiment of the invention. Figure 12 This is a schematic diagram illustrating the total number of event triggers for each subject in an embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] As can be seen from the background technology, a single technical means is insufficient to simultaneously address the three major challenges of unpredictable state, external interference, and limited communication. Therefore, there is an urgent need for a control strategy that can systematically and collaboratively design sliding mode observers, event triggering mechanisms, quantization techniques, and integral sliding mode control to solve the consistency problem faced by networked multi-agent systems.

[0027] Figure 1A schematic diagram of the observer-based multi-agent sliding mode control method under communication constraints according to an embodiment of the present invention is provided. Figure 1 The observer-based multi-agent sliding mode control method under communication constraints in this embodiment may include the following steps S101 to S104.

[0028] The specific implementation process of steps S101 to S104 is as follows: Step S101: Based on the relevant state information of the agents, establish a networked linear multi-agent system model that is subject to external interference and whose state is not fully measurable, as well as the communication topology between the agents, such as... Figure 2 As shown.

[0029] The networked linear multi-agent system model is as follows: ; in, For the self-subject number, Let be the state vector of a linear multi-agent system. To control the input, For bounded external disturbances, For measurement output; It is a constant matrix with appropriate dimensions; the communication topology between the respective entities is described using an undirected connected graph.

[0030] Step S102: Based on the networked linear multi-agent system model, construct distributed sliding mode observers for each agent to estimate the unmeasurable state of the system and suppress external disturbances.

[0031] The distributed sliding mode observer, representing each subject, is characterized as follows: ; in, For the first The sliding mode observer of an autonomous entity estimates the state. The output of the observer; For the self-subject number, To control the input, For bounded external disturbances, For measurement output; It is a constant matrix; The observer gain matrix is... This is a switching option used to suppress interference.

[0032] Switching items used to suppress interference Defined as: ; in, This represents the matrix that needs to be designed; This represents a constant matrix.

[0033] For the For each individual entity, the error between the actual state and the estimated state is caused by... Given this, the following error dynamic equation is derived: ; Furthermore, we can obtain: ; in, For N The identity matrix of N; For Kronecker product.

[0034] Step S103: Based on the communication topology between the respective subjects, construct an event triggering mechanism based on exponential decay rate to determine the transmission time of the distributed sliding mode observer state; and construct a logarithmic quantizer model to quantize the triggered distributed sliding mode observer state signal.

[0035] Each entity's distributed sliding mode observer has a distributed integral sliding surface, and its model is as follows: ; in, For distributed integral sliding surfaces; matrix ,satisfy and , To control the gain; For the first The sliding mode observer estimates the state at the initial moment of the subject; For the first The sliding mode observer of an autonomous entity estimates the state. For the first The sliding mode observer of an autonomous entity estimates the state. The output of the observer; For the self-subject number, To control the input, For bounded external disturbances, For measurement output; It is a constant matrix; Let be the information connection weight from subject j to subject i; if subject i can receive information from subject j, then ,otherwise N represents the number of independent entities. Furthermore, a distributed integral sliding mode control protocol is constructed: ; in, The required switching gain; It is a symbolic function.

[0036] The event triggering mechanism based on exponential decay is as follows: Define measurement error Among them, for , For the self The The trigger time of the first trigger; the next trigger time It is determined by the following conditions: ; Where, constant and ; For the first Individual self-subject moment The sliding mode observer estimates the state; For the first Individual self-subject moment The sliding mode observer estimates the state; For the self The The triggering time of each Distributed integral sliding surface; For the self The moment The distributed integral sliding mode; then, at the triggering time, the integral sliding surface is represented as: .

[0037] The logarithmic quantizer model is set as follows Its expression is: ; Its quantization error satisfies: ; in, For quantizer parameters, ; The input signal for the quantizer. For the self The output value of the quantizer; This represents the quantization error.

[0038] Step S104: Combining the distributed sliding mode observer, the event-triggered mechanism, and the logarithmic quantizer model, construct an observer-based event-triggered quantization integral sliding mode control law to control each agent, so as to achieve the average consistency of the multi-agent system.

[0039] The observer-based event-triggered quantized integral sliding mode control law is as follows: ; Among them, the triggering time ; For the self The The triggering time of each; For the self The +1 trigger time; The quantized trigger state, The sliding mode variable for the quantized trigger moment; The information connection weights from subject j to subject i; The required switching gain; It is a symbolic function; For the first Individual self-subject moment The sliding mode observer estimates the state; For the first Individual self-subject moment Sliding mode observer estimates state; matrix satisfy and , To control the gain; It is a constant matrix; N is the number of independent entities; For sliding mode observer gain; The output of the observer; For measurement output.

[0040] This invention proposes a collaborative design framework for sliding mode observers and integral sliding mode control. The sliding mode observer, while estimating the unmeasurable state of the system, effectively suppresses external disturbances, providing the controller with more accurate state information and improving the robustness of system estimation. It combines an event-triggered mechanism based on the observer's state with logarithmic quantization. This design ensures that state information is transmitted only when necessary, and the transmitted data is quantized and compressed, greatly saving network communication resources and making it suitable for bandwidth-constrained scenarios. It provides a complete systemic solution capable of simultaneously addressing the three major challenges of unmeasurable state, external disturbances, and communication limitations.

[0041] To further verify the effectiveness of the proposed method, a simulation is provided below. In this simulation, the control objective is to design an observer-based event-triggered quantized integral sliding mode control law that allows the system state trajectory to reach the actual sliding surface within a finite time, while ensuring the average consistency of the networked multi-agent system. The model parameters used in this example are as follows: System Matrix , , Set them to: ; Laplace matrix Set as ; The initial conditions for the networked multi-agent system and the initial conditions for the sliding mode observer are selected as follows: ; ; ; .

[0042] External interference settings: .

[0043] Sliding surface parameters Defined as: .

[0044] For each networked agent, the quantizer parameters and event triggering mechanism parameters are set as follows: .

[0045] Sliding mode observer gain and controller gain The calculations are as follows: ; ; The values ​​of other matrices and scalars are calculated as follows:

[0046] The stability analysis of the multi-agent system is as follows: The Lyapunov function is selected as follows: ; For Lyapunov functions Differentiation yields: ; Combining the event triggering rules and quantization error conditions, we can further deduce that: ; in, It's quantization error. When switching the gain coefficient... satisfy: ; have Therefore, the state trajectory of a networked multi-agent system can move to the actual sliding surface within a finite time. It is the network connectivity, taken from the i-th diagonal element of the Laplacian matrix L; It is the exponential decay rate in the event triggering mechanism; It is the upper limit of external disturbances.

[0047] Figures 3 to 5 The observer states of the four autonomous agents under the proposed control method are shown. The curve shows the change. As can be seen from the figure, despite the different initial states, the observer states of all autonomous entities converge to the same value within about 50 seconds, achieving average consistency. Figures 6 to 8 The response curve of the estimation error of the sliding mode observer is shown. Figure 9 Displaying integral sliding mode variables The motion trajectory was observed. All sliding mode variables converged rapidly and remained within a real sliding surface near zero, verifying the effectiveness of the sliding mode control. Figure 10 To control input The evolutionary process. Figure 11 The operation of the event triggering mechanism is illustrated, with the solid line representing the measurement error. The dashed line represents the dynamic threshold. Communication of the estimated state only occurs when the solid line reaches or exceeds the dashed line. Figure 12 The total number of observer state triggers for each subject is given as 2715, 2703, 2729, and 2659, respectively, which is far lower than the number of samples required by traditional periodic sampling, significantly saving communication resources. The results of this embodiment fully verify that the control method proposed in this invention can effectively achieve average consistency in networked multi-subject systems under conditions of unmeasurable states, external interference, and limited communication resources, demonstrating good control performance and practical value.

[0048] In one or more embodiments, a communication-constrained observer-based multi-agent sliding mode control system is also provided, which can be implemented in software. The communication-constrained observer-based multi-agent sliding mode control system includes the following software modules: The system model and communication topology construction module is used to establish a networked linear multi-agent system model and the communication topology between each agent based on the agent's related state information, which is subject to external interference and whose state is not fully measurable. The distributed sliding mode observer building module is used to construct distributed sliding mode observers for each agent based on a networked linear multi-agent system model, in order to estimate the unmeasurable state of the system and suppress external disturbances. The triggering mechanism and logarithmic quantizer construction module are used to construct an event triggering mechanism based on exponential decay rate based on the communication topology between their respective subjects to determine the transmission time of the distributed sliding mode observer state; and to construct a logarithmic quantizer model to quantize the triggered distributed sliding mode observer state signal. The multi-agent average consistency control module combines a distributed sliding mode observer, an event-triggered mechanism, and a logarithmic quantizer model to construct an observer-based event-triggered quantization integral sliding mode control law to control each agent, thereby achieving average consistency of the multi-agent system.

[0049] It should be noted that each module in the observer-based multi-agent sliding mode control system under communication constraints in this embodiment corresponds one-to-one with each step in the observer-based multi-agent sliding mode control method under communication constraints in the above embodiment, and their specific implementation processes are the same, so they will not be repeated here.

[0050] The structure of the electronic device according to embodiments of the present invention will be described in detail below. The electronic device provided in the embodiments of the present invention includes: at least one processor, a memory, a user interface, and at least one network interface. In a multi-agent sliding mode control system based on an observer under communication constraints, the various components are coupled together through a bus system. It can be understood that the bus system is used to realize the connection and communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus. The user interface may include a display, keyboard, mouse, trackball, click wheel, buttons, a touchpad, or a touch screen, etc.

[0051] It is understood that the memory can be volatile memory or non-volatile memory, or both. The memory in this embodiment of the invention is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0052] In some embodiments, the observer-based multi-agent sliding mode control system under communication constraints provided in this invention can be implemented using a combination of hardware and software. As an example, the observer-based multi-agent sliding mode control system under communication constraints provided in this invention can be a processor in the form of a hardware decoding processor, programmed to execute the observer-based multi-agent sliding mode control method under communication constraints provided in this invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0053] As an example, a processor can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where a general-purpose processor can be a microprocessor or any conventional processor, etc.

[0054] As an example of the hardware implementation of the observer-based multi-agent sliding mode control system under communication constraints provided in this embodiment of the invention, the device provided in this embodiment of the invention can be directly executed by a processor in the form of a hardware decoding processor. For example, it can be executed by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the observer-based multi-agent sliding mode control method under communication constraints provided in this embodiment of the invention.

[0055] The memory in this embodiment of the invention is used to store various types of data to support the operation of an observer-based multi-agent sliding mode control system under communication constraints, or to store data for execution. Figure 1The program code for the method shown. Examples of this data include: any executable instructions for operation on a communication-constrained observer-based multi-agent sliding mode control system, such as executable instructions that can be included in the executable instructions to implement the communication-constrained observer-based multi-agent sliding mode control method of the embodiments of the present invention.

[0056] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0057] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0058] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-agent sliding mode control method based on observers under communication constraints, characterized in that, include: Based on the relevant state information of the agents, a networked linear multi-agent system model and the communication topology between the agents are established, which are subject to external interference and whose states are not fully measurable. Based on a networked linear multi-agent system model, a distributed sliding mode observer is constructed for each agent to estimate the unmeasurable state of the system and suppress external disturbances. Based on the communication topology between the respective subjects, an event triggering mechanism based on exponential decay rate is constructed to determine the transmission time of the distributed sliding mode observer state; and a logarithmic quantizer model is constructed to quantize the triggered distributed sliding mode observer state signal. By combining a distributed sliding mode observer, an event-triggered mechanism, and a logarithmic quantizer model, an observer-based event-triggered quantization integral sliding mode control law is constructed to control each agent, thereby achieving average consistency of the multi-agent system.

2. The observer-based multi-agent sliding mode control method under communication constraints as described in claim 1, characterized in that, The distributed sliding mode observers of each subject are characterized as follows: ； in, For the first The sliding mode observer of an autonomous entity estimates the state. The output of the observer; For the self-subject number, To control the input, For bounded external disturbances, For measurement output; It is a constant matrix; The observer gain matrix is... This is a switching option used to suppress interference.

3. The observer-based multi-agent sliding mode control method under communication constraints as described in claim 2, characterized in that, Switching items used to suppress interference Defined as: ； in, This represents the matrix that needs to be designed; This represents a constant matrix.

4. The observer-based multi-agent sliding mode control method under communication constraints as described in claim 1, characterized in that, Each entity's distributed sliding mode observer has a distributed integral sliding surface, and its model is as follows: ； Furthermore, a distributed integral sliding mode control protocol is constructed: ； in, For distributed integral sliding surfaces; matrix ,satisfy and , To control the gain; For sliding mode observer gain; For the first The sliding mode observer estimates the state at the initial moment of the subject; For the first The sliding mode observer of an autonomous entity estimates the state. For the first The sliding mode observer of an autonomous entity estimates the state. The output of the observer; For the self-subject number, To control the input, For bounded external disturbances, For measurement output; It is a constant matrix; Let be the information connection weight from subject j to subject i; if subject i can receive information from subject j, then ,otherwise ; Where N is the required switching gain; N is the number of independent units; It is a symbolic function.

5. The observer-based multi-agent sliding mode control method under communication constraints as described in claim 1, characterized in that, The event triggering mechanism based on exponential decay is as follows: Define measurement error ; Next trigger time It is determined by the following conditions: ; At the triggering moment, the integral sliding surface is represented as: ； Among them, for , For the self The The triggering time of each; matrix ,satisfy and , To control the gain; It is a constant matrix; Initialization function; constant and ; For the first Individual self-subject moment The sliding mode observer estimates the state; For the first The sliding mode observer estimates the state at the initial moment of the subject; For the first Individual self-subject moment The sliding mode observer estimates the state; For the first Individual self-subject moment The sliding mode observer estimates the state; N is the number of agents; For the self The The triggering time of each Distributed integral sliding surface; For the self The moment Distributed integral sliding mode; Let be the information connection weight from subject j to subject i.

6. The observer-based multi-agent sliding mode control method under communication constraints as described in claim 1, characterized in that, The observer-based event-triggered quantized integral sliding mode control law is as follows: ； Among them, the triggering time ; For the self The The triggering time of each; For the self The +1 trigger time; The quantized trigger state, The sliding mode variable for the quantized trigger moment; The information connection weights from subject j to subject i; The required switching gain; It is a symbolic function; For the first Individual self-subject moment The sliding mode observer estimates the state; For the first Individual self-subject moment Sliding mode observer estimates state; matrix satisfy and , To control the gain; It is a constant matrix; N is the number of independent entities; For sliding mode observer gain; The output of the observer; For measurement output.

7. The observer-based multi-agent sliding mode control method under communication constraints as described in claim 1, characterized in that, The logarithmic quantizer model is set as follows Its expression is: ； Its quantization error satisfies: ； in, For quantizer parameters, ; The input signal for the quantizer. For the self The output value of the quantizer; This represents the quantization error.

8. A multi-agent sliding mode control system based on an observer under communication constraints, characterized in that, The observer-based multi-agent sliding mode control method under communication constraints as described in any one of claims 1-7 includes: The system model and communication topology construction module is used to establish a networked linear multi-agent system model and the communication topology between each agent based on the agent's related state information, which is subject to external interference and whose state is not fully measurable. The distributed sliding mode observer building module is used to construct distributed sliding mode observers for each agent based on a networked linear multi-agent system model, in order to estimate the unmeasurable state of the system and suppress external disturbances. The triggering mechanism and logarithmic quantizer construction module are used to construct an event triggering mechanism based on exponential decay rate based on the communication topology between their respective subjects to determine the transmission time of the distributed sliding mode observer state; and to construct a logarithmic quantizer model to quantize the triggered distributed sliding mode observer state signal. The multi-agent average consistency control module combines a distributed sliding mode observer, an event-triggered mechanism, and a logarithmic quantizer model to construct an observer-based event-triggered quantization integral sliding mode control law to control each agent, thereby achieving average consistency of the multi-agent system.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the observer-based multi-agent sliding mode control method under communication constraints as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the observer-based multi-agent sliding mode control method under communication constraints as described in any one of claims 1-7.

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