Event-triggered component assembly control method, device and medium

Abstract

This application discloses an event-triggered component assembly control method, device, and medium, relating to the field of component assembly technology. The method includes: creating a multi-agent mathematical model and a master agent mathematical model; determining an agent control protocol based on the multi-agent mathematical model and the master agent mathematical model; and, upon receiving a cycle command, performing component assembly operations based on the control protocol and the operating states of each agent and the states of the components. This application aims to solve the problems of poor product accuracy, high assembly defect rate, and high product rework rate that occur in existing multi-station component assembly operations, thereby improving product accuracy and reducing assembly defect rate and product rework rate.

Description

Technical Field

[0001] This application relates to the field of component assembly technology, and in particular to a component assembly control method, equipment and medium based on event triggering. Background Technology

[0002] Multi-station assembly lines for automotive parts (such as engine block assembly and transmission housing assembly) are core processes in automobile manufacturing. Their assembly precision and cycle time efficiency directly determine the overall vehicle quality and production capacity. This scenario is suited to a "multi-leader static grouping" architecture, which includes: the assembly line is divided into fixed stations according to processes (such as bolt tightening stations, component positioning stations, and seal inspection stations). Each station is equipped with a main robotic arm as the "leader," responsible for setting the assembly benchmark and issuing tasks for that station. Several auxiliary robotic arms act as "followers," responsible for performing collaborative actions such as grasping and moving. The grouping boundaries are fixed, the station spacing is 3-5 meters, there is no need for dynamic reorganization across stations, and each station leader must synchronously respond to the cycle time instructions of the Manufacturing Execution System (MES) to ensure full-line collaboration.

[0003] However, during the operation of each workstation, due to the fact that each station unilaterally receives the cycle instructions from the MES to carry out the assembly operation, there may be deviations in the actions, which will lead to a larger synchronization error between different workstations. As a result, the assembled products may not meet the accuracy requirements, resulting in an increase in the assembly defect rate and the product rework rate. Summary of the Invention

[0004] In response to the aforementioned problems and technical requirements, the applicant proposes an event-triggered component assembly control method, equipment, and medium to address the issues of poor product accuracy, high assembly defect rate, and high product rework rate that occur in existing technologies when performing multi-station component assembly operations. This aims to improve product accuracy and reduce assembly defect rate and product rework rate.

[0005] This application provides an event-triggered component assembly control method applied to a component assembly system. The component assembly system includes multiple assembly stations and a generation execution system. Each assembly station includes multiple intelligent agents and components to be assembled. The multiple intelligent agents include at least one main intelligent agent and at least one auxiliary intelligent agent. The multiple assembly stations perform component assembly operations based on the cycle instructions of the generation execution system. The method includes:

[0006] Based on the system state parameters of the component assembly system, the control input parameters of each agent, and the assembly disturbance parameters, a multi-agent mathematical model is created. Based on the functional requirements of each assembly station and the operational requirements of each agent, multiple agents are grouped and the corresponding mathematical model of the main agent of each group is obtained. Based on the multi-agent mathematical model and the master agent mathematical model, an agent control protocol is determined. The agent control protocol is used to determine the control adjustment amount of the auxiliary agent, the control input parameters corresponding to the control adjustment amount under saturation constraints, and the event triggering parameters of the auxiliary agent. The event triggering parameters are used to adjust the triggering time of the assembly operation of the auxiliary agent. Upon receiving a cycle command, the assembly operation of components is performed based on the control protocol and the operating status of each intelligent agent and the status of the components.

[0007] According to the event-triggered component assembly control provided in the embodiments of this application, the multi-agent mathematical model includes: ; in, express The derivative of Indicates system state parameters, , This represents a predefined coefficient matrix. Indicates the control input parameters. This represents the input saturation constraint function. This represents the control input parameters after applying saturation constraints. Indicates assembly interference parameters. This indicates the number of intelligent agents.

[0008] According to the event-triggered component assembly control provided in the embodiments of this application, multiple intelligent agents are grouped and a mathematical model of the main intelligent agent corresponding to each group is obtained, including: Multiple agents are divided into y clusters, where the i-th agent belongs to the y-th cluster. ; Among them, for clusters The mathematical model of the main agent corresponding to this cluster includes: ; in, express The derivative of This represents the first running state of the main agent in the cluster to which the i-th auxiliary agent belongs at time t; Among them, the total amount of information interaction of auxiliary agents within a cluster is balanced, satisfying the group consistency condition; Among them, the group consistency conditions include: ; in, This represents the second operating state of the i-th auxiliary agent at time t. This represents the second operating state of the j-th auxiliary agent at time t.

[0009] According to the event-triggered component assembly control provided in the embodiments of this application, a first control sub-protocol for determining the control adjustment amount of the auxiliary intelligent agent includes: ; in, This represents the control adjustment amount of the i-th auxiliary agent. Represents the neighbor cluster of the auxiliary agent. This indicates the communication strength between adjacent agents. This indicates the communication strength between the main intelligent agent and the auxiliary intelligent agent within the assembly station. The i-th assembly station is adjacent to the j-th assembly station.

[0010] According to the event-triggered component assembly control provided in the embodiments of this application, a second control sub-protocol for determining the control input parameters corresponding to the control adjustment amount under saturation constraints includes: ; in, Indicates the preset coefficient. This represents the k-th trigger time of the i-th auxiliary agent. This represents the control gain matrix.

[0011] According to the event-triggered component assembly control provided in the embodiments of this application, a third control sub-protocol for determining the event triggering parameters of the auxiliary intelligent agent includes: ; in, This represents the error signal of the i-th auxiliary agent within the neighboring cluster. This represents the k-th trigger time of the i-th auxiliary agent. Indicates the event triggering parameters, .

[0012] According to the event-triggered component assembly control provided in the embodiments of this application, component assembly operations are performed based on the control protocol and the operating states of each intelligent agent and the states of the components, including: Acquire the first running state of the main intelligent agent, the second running state of the auxiliary intelligent agent, and the state of the parts to be assembled at each assembly station when performing component assembly operations. The following control process is executed for each assembly station: Based on the first operating state, the second operating state, the state of the parts to be assembled, and the preset intelligent agent collaborative operation requirements, the control adjustment amount of the auxiliary intelligent agent is determined; and when it is determined that the control adjustment amount meets the preset event triggering mechanism, the control input corresponding to the control adjustment amount under the saturation constraint corresponding to the auxiliary intelligent agent is calculated, so as to adjust the second operating state of the auxiliary intelligent agent through the control input.

[0013] According to the event-triggered component assembly control provided in the embodiments of this application, before calculating the control input corresponding to the control adjustment amount under the saturation constraint corresponding to the auxiliary intelligent agent, after determining that the control adjustment amount satisfies the preset event triggering mechanism, the method further includes: The timing of the assembly operation of the auxiliary agent is adjusted based on the obtained event triggering parameters.

[0014] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the event-triggered component assembly control method as described above.

[0015] This application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the event-triggered component assembly control method as described above.

[0016] This application provides an event-triggered component assembly control method, device, and medium. The method is applied to a component assembly system, which includes multiple assembly stations and a generation execution system. Each assembly station includes multiple intelligent agents and components to be assembled. The multiple intelligent agents include at least one main intelligent agent and at least one auxiliary intelligent agent. The multiple assembly stations perform component assembly operations based on the cycle instructions of the generation execution system. The method creates a multi-agent mathematical model based on the system state parameters of the component assembly system, the control input parameters of each intelligent agent, and assembly interference parameters. Based on the functional requirements of each assembly station and the operational requirements of each intelligent agent, the method controls the multiple intelligent agents... The system is grouped to obtain the corresponding main agent mathematical model for each group. Then, based on the multi-agent mathematical model and the main agent mathematical model, the agent control protocol is determined. The agent control protocol is used to determine the control adjustment amount of the auxiliary agent, the control input parameters corresponding to the control adjustment amount under saturation constraints, and the event triggering parameters of the auxiliary agent. The event triggering parameters are used to adjust the triggering time of the assembly operation of the auxiliary agent. When assembling parts at the assembly station, the system uses the control protocol and the running status of each agent and the status of the parts to perform the assembly operation based on the cycle instructions, thereby improving the product accuracy and production efficiency of the parts and reducing the assembly defect rate and product rework rate. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts of the event-triggered component assembly control method provided in the embodiments of this application; Figure 2 This is a communication topology diagram of the component assembly system provided in the embodiments of this application; Figure 3 This is one of the convergence state diagrams of the component assembly system provided in the embodiments of this application; Figure 4 This is the second schematic diagram of the convergence state of the component assembly system provided in the embodiments of this application; Figure 5 This is a schematic diagram of the control input curve provided in an embodiment of this application; Figure 6 This is a schematic diagram of the triggering time of the control input provided in the embodiments of this application; Figure 7This is the second flowchart of the event-triggered component assembly control method provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] To clearly illustrate this application, the assembly of components is further described as follows: In a production line, both the leader and the follower robotic arms are subject to coupling constraints. For example, the maximum output torque of the main robotic arm when tightening bolts is limited by the rated power of the motor, such as a rated torque of less than or equal to 85N. The maximum speed of the assisted robotic arm during transport is constrained by the load capacity of the reducer; for example, the maximum linear speed is less than or equal to 1.2 m / s. If the control command exceeds this physical limit, a hardware protection mechanism will be triggered, resulting in motion interruption or accuracy deviation.

[0021] Each workstation's main robotic arm relies on an independent control unit, synchronized via MES cycle commands. This lack of real-time status interaction makes it prone to motion deviations. Furthermore, when the main robotic arm needs to rapidly adjust its posture to correct deviations, control commands can easily exceed corresponding saturation limits for torque, speed, etc., triggering a protective shutdown. This further amplifies the leader error between different workstations (e.g., positioning and tightening), leading to reduced product accuracy, increased assembly defect rates, higher product rework rates, and ultimately, lost production capacity.

[0022] Furthermore, the existing assembly system suffers from communication network bandwidth congestion. For example, the system uses a 100ms time-triggered communication cycle, with bandwidth utilization consistently maintained at 75%-85% (under 100Mbps industrial Ethernet conditions). Input saturation-induced motion interruptions require additional transmission of fault reset commands, further consuming bandwidth. Communication latency increases further when the MES issues process adjustment commands. Additionally, input saturation limitations lead to amplified cycle time delays. The auxiliary robotic arm, using a fixed trigger threshold (e.g., triggering when the position deviation is greater than 0.05mm), experiences increased communication congestion due to high-frequency triggering. Moreover, to keep up with the main robotic arm's state, speed commands from the auxiliary robotic arm easily exceed saturation limits, causing deceleration and shutdown; torque commands exceeding saturation limits cause motion lag. Both of these situations extend the workstation cycle time and reduce the daily output of the entire production line.

[0023] To address the aforementioned problems, this application provides an event-triggered component assembly control method. This method utilizes a component assembly system, which includes multiple assembly stations and a generation execution system. Each assembly station includes multiple intelligent agents and components to be assembled. The multiple intelligent agents include at least one main intelligent agent and at least one auxiliary intelligent agent. The multiple assembly stations perform component assembly operations based on the cycle instructions of the generation execution system. The specific implementation of this method is as follows... Figure 1 As shown: Step 101: Based on the system state parameters of the component assembly system, the control input parameters of each agent, and the assembly disturbance parameters, create a multi-agent mathematical model.

[0024] Among them, the system status parameters are the operating status parameters of each assembly station when assembling parts, including: the operating status of the intelligent agent (e.g., motor speed, robotic arm posture, etc.) and the status of the parts (e.g., the position, quantity, and posture of the parts to be assembled, etc.).

[0025] The control input parameters include: motor speed, motor torque, etc.

[0026] Among them, the assembly interference parameters are nonlinear terms, including: nonlinear changes in assembly resistance caused by equipment wear and material tolerances during the assembly process of parts, time delays and jams in material transmission between multiple workstations, and nonlinear effects of environmental factors (such as temperature and vibration) on assembly accuracy.

[0027] Step 102: Based on the functional requirements of each assembly station and the operational requirements of each agent, the multiple agents are grouped and the corresponding main agent mathematical model is obtained for each group.

[0028] Step 103: Determine the agent control protocol based on the multi-agent mathematical model and the master agent mathematical model.

[0029] Among them, the agent control protocol is used to determine the control adjustment amount of the auxiliary agent, the control input parameters corresponding to the control adjustment amount under saturation constraints, and the event triggering parameters of the auxiliary agent.

[0030] Among them, the event triggering parameter is used to adjust the triggering time of the assembly operation of the auxiliary intelligent agent.

[0031] Among them, constraint saturation refers to the limitation of system input or state by physical limits, causing the control signal to be unable to increase indefinitely, which belongs to nonlinear constraints. It includes input constraints, which refer to the limitation of the controller output command due to the physical limits of the actuator.

[0032] Step 104: Upon receiving the clock signal, perform component assembly operations based on the control protocol and the operating status of each agent and the status of the components.

[0033] The event-triggered component assembly control method provided in this application is applied to a component assembly system. The component assembly system includes multiple assembly stations and a generation execution system. Each assembly station includes multiple intelligent agents and components to be assembled. The multiple intelligent agents include at least one main intelligent agent and at least one auxiliary intelligent agent. The multiple assembly stations perform component assembly operations based on the cycle instructions of the generation execution system. This method creates a multi-agent mathematical model based on the system state parameters of the component assembly system, the control input parameters of each intelligent agent, and assembly interference parameters. Based on the functional requirements of each assembly station and the operational requirements of each intelligent agent, the multiple intelligent agents are grouped and... The mathematical model of the main agent for each group is obtained. Then, based on the multi-agent mathematical model and the main agent mathematical model, the agent control protocol is determined. The agent control protocol is used to determine the control adjustment amount of the auxiliary agent, the control input parameters corresponding to the control adjustment amount under saturation constraints, and the event triggering parameters of the auxiliary agent. The event triggering parameters are used to adjust the triggering time of the assembly operation of the auxiliary agent. When assembling parts at the assembly station, the assembly operation of the parts is performed based on the cycle instruction using the control protocol and the running status of each agent and the status of the parts. This improves the product accuracy and production efficiency of the parts and reduces the assembly defect rate and product rework rate.

[0034] In one specific embodiment, the component system is abstracted into a multi-agent mathematical model in mathematical form, as shown in formula (1): …(1) in, express The derivative of Indicates system state parameters, , This represents a predefined coefficient matrix. Indicates the control input parameters. This represents the input saturation constraint function. This represents the control input parameters after applying saturation constraints. Indicates assembly interference parameters. This indicates the number of intelligent agents.

[0035] in, , where n represents the dimension of the system state parameters, which is determined based on the functional requirements of the assembly station; , m represents the dimension controlling the input parameters. .

[0036] in, , Indicates the status of components.

[0037] In one specific embodiment, the specific implementation of grouping multiple agents and obtaining the corresponding master agent mathematical model for each group includes: To ensure consistency among the agents, multiple agents are divided into y clusters. .

[0038] in, , ,and , .

[0039] Wherein, for the i-th agent, the cluster to which it belongs is .

[0040] Where the i-th agent and the j-th agent belong to the same cluster, i.e., both are... .

[0041] Among them, for clusters The mathematical model of the main agent corresponding to this cluster is shown in formula (2): ………………(2) in, express The derivative of This represents the first running state of the master agent in the cluster to which the i-th auxiliary agent belongs.

[0042] In one specific embodiment, the total amount of information interaction between auxiliary agents within a cluster is balanced to satisfy the group consistency condition.

[0043] For a multi-agent mathematical model, the parameters in its corresponding Laplacian matrix satisfy the condition. .

[0044] The physical meaning of this condition is that the total amount of information interaction within the cluster to which the auxiliary agent belongs is balanced, that is, the information received and output by each assembly station in the cluster cancel each other out, ensuring stable interaction of states within the cluster.

[0045] in, The elements in the Laplacian matrix represent the information interaction weights between workstation i and workstation j (positive weights indicate positive information transmission, while negative weights indicate feedback adjustment).

[0046] Specifically, based on the multi-agent mathematical model, the Laplacian matrix is ​​reconstructed to obtain formula (3): ……………………(3) in, The block submatrices of the Laplacian matrix include Let represent the information interaction matrix between assembly stations in cluster r and assembly work in cluster t. When r=t, let represent the information interaction matrix between different assembly stations in the same cluster.

[0047] in, The sum of the rows is zero. , This indicates two scenarios: one is that the agent has no connection with agents in other groups, reflected in the Lplacian matrix where all out-of-group elements in that row are 0; the other is that the out-of-group elements in that row of the Lplacian matrix are both positive and negative, indicating that the information interaction between the agent and agents in other groups has both positive and negative effects (e.g., the positive and negative directions when tightening a screw). A single block submatrix constitutes one group, or a cluster constitutes one group.

[0048] Ultimately, this means that for members of a cluster Agent i and agent j ( Communication between them is balanced.

[0049] For any auxiliary agent i, if there are control input parameters The grouping consistency condition of formula (4) must be met: ………………(4) The group consistency condition indicates that the assembly stations within the same cluster eventually have completely consistent operating states, while the assembly stations in different clusters maintain different operating states.

[0050] In one specific embodiment, determining the control protocol is equivalent to determining the control adjustment amount of the auxiliary agent, the control input parameters corresponding to the control adjustment amount under saturation constraints, and the event triggering parameters of the auxiliary agent.

[0051] In one specific embodiment, the first control sub-protocol used to determine the control adjustment amount of the auxiliary intelligent agent is shown in formula (5): …(5) in, This represents the control adjustment amount of the i-th auxiliary agent. Represents the neighbor cluster of the auxiliary agent. This indicates the communication strength between adjacent agents. This indicates the communication strength between the main intelligent agent and the auxiliary intelligent agent within the assembly station.

[0052] The i-th assembly station is adjacent to the j-th assembly station.

[0053] in, This indicates that the i-th auxiliary agent can receive signals from the master agent, and vice versa. .

[0054] In one specific embodiment, the second control sub-protocol used to determine the control input parameters corresponding to the control adjustment amount under saturation constraints is shown in formula (6): ... (6) in, Indicates the preset coefficient. This represents the k-th trigger time of the i-th auxiliary agent. This represents the control gain matrix.

[0055] In one specific embodiment, the third control sub-protocol used to determine the event triggering parameters of the auxiliary agent is shown in formula (7): …………………………(7) in, This represents the error signal of the auxiliary agent i within the neighboring cluster. Indicates the event triggering parameters, .

[0056] Specifically, after determining the event triggering parameters, the event triggering mechanism represented by formula (7) is used for judgment, and the control input parameters are updated at the triggering time corresponding to the condition of the event triggering mechanism.

[0057] in, For real-time signals, This is the signal corresponding to the trigger moment.

[0058] Specifically, the Laplacian matrix is ​​extended, and an extended matrix is ​​defined. , Representing a diagonal matrix, the Laplacian matrix is ​​extended to... .

[0059] Specifically, the control gain matrix K is solved by solving linear inequalities.

[0060] According to Lyapunov's stability theorem, if there exist matrices P > 0, K, H, ,for The conditions of formulas (8) and (9) must be met: ………………(8) ………………(9) in, , , , , , , , , , , , , , , .

[0061] Among them, subscript Represents the first element of the corresponding matrix OK.

[0062] in, express and The sum of the transposes of, here Used to illustrate a matrix This represents the calculation of the Kronecker product between matrices. It is an N-dimensional identity matrix. Let n be the identity matrix. Let B be a diagonal matrix with diagonal elements of 1 or 0, and let its subscript p be determined by the number of columns in matrix B. , and H represents the preset scaling factor, and H, Q, and K are matrices to be solved, which can be obtained through control theory. As a preset value, , These are preset coefficients.

[0063] in, Greater than zero, , Greater than zero, which is the preset value.

[0064] Among them, formulas (8) and (9) are the matrix equations for solving K, and the remaining matrices are intermediate derived variables in the control theory analysis. These variables are used to transform the stability adjustment of the multi-agent group consensus into a matrix inequality form that can be solved numerically for the calculation of control gain variables.

[0065] If formulas (8) and (9) have solutions, K can be obtained, and then the grouping consistency can be achieved.

[0066] Formula (4) is the judgment criterion for determining whether the design of this application is feasible.

[0067] Specifically, the obtained K is input into the control protocol to obtain a defined control protocol, and the assembly of parts is carried out based on the defined control protocol.

[0068] The following is an explanation of this application through specific examples: For the three workstations of bolt tightening, component positioning, and seal inspection on the multi-station assembly line of automobile engine cylinder block, they are transformed into a specific multi-agent mathematical model, as shown in formula (10): ……………………(10) Where M represents the mass of the object, q represents the displacement of the object, c represents the damping viscous friction coefficient, w represents the elastic coefficient, and u represents the tension.

[0069] Transform formula (10) into a state equation and define the state variables of the system as follows: The expression for the state equation is shown in formula (11): …………………………(11) Where f represents the external nonlinear disturbance present in the system.

[0070] Below, through Figure 2 A schematic diagram of the communication topology of a component assembly system.

[0071] In the topology diagram, the three dashed groups correspond to the three workstations for engine assembly, and each agent is an industrial robotic arm sensing and execution unit. Communication between agents is indicated by arrows. The number 1 represents the sending of data by the agent, and the number -1 represents the feedback of data by the agent.

[0072] The main division of labor among the nodes is shown in Table 1.

[0073]

[0074] Table 1 Group Node Function Table Among them, with Figure 2 Here's an example of an extended Laplacian matrix: .

[0075] For example, parameters , , , The nonlinear functions corresponding to the agents in the three groups Defined respectively , , By solving for K, the correlation matrix is ​​obtained, as shown in formula (12): ………………(12) Here, G is an intermediate quantity involved in the calculation of K.

[0076] The obtained K is then used in the control protocol for verification, as follows: Figure 3 The results are shown.

[0077] Specifically, through Figure 3 and Figure 4 It can be seen that, under the control protocol and event triggering mechanism, the assembly line system (parts assembly system) eventually converges to three different states, and satisfies the grouping requirements of the system. Each node realizes the state following of the leader in its own group.

[0078] in, Figure 3 The curves represent the changes in state 1 for each agent, where state 1 represents displacement. Figure 4 The curves representing the changes in state 2 for each agent are shown, where state 2 represents the derivative of the displacement.

[0079] Figure 3 and Figure 4 In the notation, s represents the primary agent and x represents the auxiliary agent. and The number i in the range 1-3 represents Figure 2 The three main intelligent agents shown are 1-7. Figure 2 The illustration shows 7 auxiliary agents. The second number 1 represents state 1, and the second number 2 represents state 2.

[0080] Specifically, through Figure 5 This illustrates the control input curve. (Through...) Figure 5 The control protocol of this application simultaneously meets the requirements of amplitude not exceeding the saturation threshold and event-triggered control, which conforms to the characteristics of phased changes rather than continuous changes, thus saving the use of communication resources.

[0081] Figure 5 The mark in In the middle, i represents 1-7. Figure 2 The diagram illustrates the control input parameters for the seven auxiliary intelligent agents.

[0082] Specifically, through Figure 6 The triggering time of the control input is shown in the diagram. Figure 2 This illustration uses four auxiliary agents as an example. Each marker represents an event triggered at that moment, signifying that the control input needs to be updated.

[0083] Specifically, parameters are introduced into the event triggering mechanism. To further explore the characteristics of event triggering mechanisms, different... The impact of the selected value on the number of triggers for each agent. As shown in Table 2, when The larger the value of , the fewer times the agent is triggered, which conforms to the definition of a triggering mechanism. Because when The larger the value of the setting, the more difficult it is to satisfy the trigger function, and thus the number of triggers will decrease, which can more effectively save the system's communication resources.

[0084]

[0085] Table 2. The impact of event triggering mechanism parameter selection on the number of agent triggers. Below, through Figure 7 This application will be described in detail as follows: Step 701: Create a multi-agent mathematical model.

[0086] Step 702: Group the multi-agent system to determine the master agent and the auxiliary agents.

[0087] Step 703: Design the intelligent agent control protocol.

[0088] Step 704: Determine whether the control gain matrix can be obtained by solving the linear matrix inequality. If the control gain matrix can be obtained, proceed to step 705; otherwise, proceed to step 706.

[0089] Step 705: Input the control gain matrix into the agent control protocol for verification, and determine the control adjustment amount, control input parameters, and event trigger parameters.

[0090] Step 706: Modify the event triggering parameters and return to step 704.

[0091] This application addresses the issue of leader coordination deviation coupled with input saturation. While ensuring the main robotic arm's control commands do not exceed physical limits, it achieves real-time status synchronization of leaders at each workstation, ensuring that positional deviations between leaders meet requirements. This reduces the actual assembly defect rate and improves production efficiency. It also solves the problem of communication bandwidth congestion coupled with input saturation by constructing an event-triggered control protocol adapted to input saturation constraints. Based on this protocol, while maintaining control accuracy, it reduces communication volume between robotic arms and industrial Ethernet bandwidth occupancy, mitigating command delays caused by bandwidth congestion.

[0092] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8 As shown, the electronic device may include a processor 801, a communications interface 802, a memory 803, and a communication bus 804. The processor 801, communications interface 802, and memory 803 communicate with each other via the communication bus 804. The processor 801 can call logical instructions from the memory 803 to execute an event-triggered component assembly control method.

[0093] Furthermore, the logical instructions in the aforementioned memory 803 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to execute the event-triggered component assembly control method provided by the above methods.

[0095] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the event-triggered component assembly control method provided in the above embodiments.

[0096] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0097] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0098] Finally, it should be noted that the above descriptions are merely preferred embodiments of this application, and this application is not limited to the above embodiments. It is understood that other improvements and variations directly derived or conceived by those skilled in the art without departing from the spirit and concept of this application should be considered to be included within the protection scope of this application.

Claims

1. An event-triggered based control method for assembly of parts, characterized in that, The method is applied to a component assembly system, which includes multiple assembly stations and a generation execution system. Each assembly station includes multiple intelligent agents and components to be assembled. The multiple intelligent agents include at least one main intelligent agent and at least one auxiliary intelligent agent. The multiple assembly stations perform component assembly operations based on the cycle instructions of the generation execution system. Based on the system state parameters of the component assembly system, the control input parameters of each agent, and the assembly disturbance parameters, a multi-agent mathematical model is created. Based on the functional requirements of each assembly station and the operational requirements of each agent, multiple agents are grouped and the corresponding mathematical model of the main agent of each group is obtained. Based on the multi-agent mathematical model and the master agent mathematical model, an agent control protocol is determined. The agent control protocol is used to determine the control adjustment amount of the auxiliary agent, the control input parameters corresponding to the control adjustment amount under saturation constraints, and the event triggering parameters of the auxiliary agent. The event triggering parameters are used to adjust the triggering time of the assembly operation of the auxiliary agent. Upon receiving a cycle command, the assembly operation of components is performed based on the control protocol and the operating status of each intelligent agent and the status of the components.

2. The event-triggered based component assembly control method according to claim 1, wherein, Multi-agent mathematical models include: ； wherein, denotes derivative of denotes system state parameters, , denotes a preset coefficient matrix, denotes control input parameters, denotes an input saturation constraint function, denotes control input parameters after saturation constraint, denotes assembly disturbance parameters, denotes the number of agents.

3. The event-triggered based component assembly control method according to claim 2, wherein, Grouping multiple agents and obtaining the corresponding mathematical model of the main agent for each group, including: The plurality of agents are divided into y clusters, wherein for the i-th agent the cluster to which it belongs is ; wherein, for the cluster the main agent mathematical model corresponding to the cluster comprises: ； wherein, denotes the derivative of denotes the first operating state of the master agent in the cluster to which the ith auxiliary agent belongs at time t. Among them, the total amount of information interaction of auxiliary agents within a cluster is balanced, satisfying the group consistency condition; Among them, the group consistency conditions include: ； wherein, denotes the second running state of the i-th auxiliary agent at time t, denotes the second running state of the j-th auxiliary agent at time t.

4. The event-triggered component assembly control method according to claim 3, characterized in that, The first control sub-protocol for determining the control adjustment quantities of the auxiliary intelligent agent includes: ； in, This represents the control adjustment amount of the i-th auxiliary agent. Represents the neighbor cluster of the auxiliary agent. This indicates the communication strength between adjacent agents. This indicates the communication strength between the main intelligent agent and the auxiliary intelligent agent within the assembly station. The i-th assembly station is adjacent to the j-th assembly station.

5. The event-triggered component assembly control method according to claim 3, characterized in that, The second control sub-protocol, which determines the control input parameters corresponding to the control adjustment quantity under saturation constraints, includes: ； in, Indicates the preset coefficient. This represents the k-th trigger time of the i-th auxiliary agent. This represents the control gain matrix.

6. The event-triggered component assembly control method according to claim 3, characterized in that, The third control sub-protocol for determining the event triggering parameters of the auxiliary agent includes: ； in, This represents the error signal of the i-th auxiliary agent within the neighboring cluster. This represents the k-th trigger time of the i-th auxiliary agent. Indicates the event triggering parameters, .

7. The event-triggered component assembly control method according to any one of claims 1-6, characterized in that, The assembly of components is performed based on the control protocol and the operating states of each intelligent agent and the states of the components, including: Acquire the first running state of the main intelligent agent, the second running state of the auxiliary intelligent agent, and the state of the parts to be assembled at each assembly station when performing component assembly operations. The following control process is executed for each assembly station: Based on the first operating state, the second operating state, the state of the parts to be assembled, and the preset intelligent agent collaborative operation requirements, the control adjustment amount of the auxiliary intelligent agent is determined; and when it is determined that the control adjustment amount meets the preset event triggering mechanism, the control input corresponding to the control adjustment amount under the saturation constraint corresponding to the auxiliary intelligent agent is calculated, so as to adjust the second operating state of the auxiliary intelligent agent through the control input.

8. The event-triggered component assembly control method according to claim 7, characterized in that, And before calculating the control input corresponding to the control adjustment amount under the saturation constraint corresponding to the auxiliary agent, given that the control adjustment amount satisfies the preset event triggering mechanism, the method further includes: The timing of the assembly operation of the auxiliary agent is adjusted based on the obtained event triggering parameters.

9. 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 of the event-triggered component assembly control method as described in any one of claims 1 to 8.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the event-triggered component assembly control method as described in any one of claims 1 to 8.

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