Pre-set time fuzzy control method for re-entrant manufacturing system under intermittent state feedback

Abstract

The application provides a preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback, comprising the following steps: constructing a nonlinear error dynamics model and adopting a fuzzy modeling technology to represent the system as a combination of multiple linear subsystems; introducing a time scaling function, transforming an error state of the global fuzzy model by using the time scaling function to obtain a scaled error state; designing an event-triggered mechanism to realize intermittent state feedback to reduce communication cost; based on Lyapunov stability theory, deriving a sufficient condition for the system to be stable within a preset time, and obtaining controller gains by solving a linear matrix inequality of the sufficient condition, so that the system still has robustness under external disturbance, thereby realizing agile response and low communication cost operation of the reentrant manufacturing system within the preset time.

Description

Technical Field

[0001] This invention relates to the fields of control science and engineering technology, and in particular to a preset-time fuzzy control method for a reentrant manufacturing system under intermittent state feedback. Background Technology

[0002] In modern manufacturing, reentrant manufacturing systems are widely used in semiconductor manufacturing, printed circuit board production and other fields. The characteristic that work-in-process needs to repeatedly access the same workstation makes system analysis and capacity control extremely challenging.

[0003] Currently, existing technologies for modeling reentrant manufacturing systems mostly employ discrete event models, which are prone to the "curse of dimensionality" when dealing with large-scale complex systems. While hyperbolic partial differential equation continuous flow models based on the law of mass conservation can effectively capture system characteristics, existing control strategies based on this model still have significant drawbacks: On the one hand, existing strategies are mostly based on asymptotic stability theory, which can only guarantee that the system approaches the production target in an infinite time, and cannot meet the rigid requirements of strict delivery deadlines for sudden and urgent orders, which can easily lead to delivery delays and increased product holding costs. On the other hand, existing control generally relies on continuous state feedback mechanisms, but manufacturing systems have long operating cycles and low state update frequencies. Continuous high-frequency data transmission will generate massive amounts of redundant information, resulting in wasted communication resources, increased sensor network load and failure risks, and problems of poor economy and low security.

[0004] Therefore, how to achieve agile response and low communication cost operation of reentrant manufacturing systems within a preset time is a technical problem that technical personnel urgently need to solve. Summary of the Invention

[0005] This invention provides a preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback, which enables the reentrant manufacturing system to achieve agile response and low communication cost operation within a preset time.

[0006] On one hand, the present invention provides a preset-time fuzzy control method for a reentrant manufacturing system under intermittent state feedback, which includes:

[0007] Construct a nonlinear error dynamics model to characterize the dynamic properties of reentrant manufacturing systems;

[0008] Based on fuzzy modeling technology, the nonlinear error dynamics model is represented as a convex combination of multiple local linear subsystems, resulting in a global fuzzy model;

[0009] A time scaling function is constructed that decays from an initial value to zero within a preset time, and the error state of the global fuzzy model is transformed using the time scaling function to obtain the error scaling state;

[0010] Based on the time scaling function and the acquired intermittent measurement state signal, a fuzzy state feedback controller is designed for the global fuzzy model;

[0011] Based on the aforementioned error scaling state, a Lyapunov stability function is constructed, and sufficient conditions for the reentrant manufacturing system to satisfy a preset time stability are derived; wherein, the sufficient conditions include multiple linear matrix inequalities;

[0012] Solving the multiple linear matrix inequalities yields the controller gain matrix, enabling the reentrant manufacturing system to converge within the preset time under the control of the fuzzy state feedback controller.

[0013] The present invention provides a pre-set time fuzzy control method for reentrant manufacturing systems under intermittent state feedback. This method constructs a nonlinear error dynamics model and employs fuzzy modeling technology to represent the system as a combination of multiple linear subsystems. A time scaling function is introduced to achieve system state convergence to zero within a pre-set time. An event triggering mechanism is designed to realize intermittent state feedback and reduce communication costs. Based on Lyapunov stability theory, sufficient conditions for system stability within the pre-set time are derived, and the controller gain is obtained by solving linear matrix inequalities, ensuring the system remains robust under external disturbances. Thus, the reentrant manufacturing system achieves agile response and low communication cost operation within the pre-set time. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in this invention 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 this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating the preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback provided in an embodiment of the present invention.

[0016] Figure 2 It is an error variable Response surfaces in time and space;

[0017] Figure 3 It is an error variable Response surfaces in time and space;

[0018] Figure 4 Error variables under different preset times The response curve;

[0019] Figure 5 Error variables under different preset times The response curve;

[0020] Figure 6 It is a time sequence of event trigger intervals;

[0021] Figure 7 This is a schematic diagram of the pre-set time fuzzy control system for a reentrant manufacturing system under intermittent state feedback provided in an embodiment of the present invention;

[0022] Figure 8 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] Figure 1 This is a flowchart illustrating the preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback provided in an embodiment of the present invention.

[0026] like Figure 1 As shown in the embodiments of the present invention, the preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback mainly includes the following steps:

[0027] 101. Construct a nonlinear error dynamics model to characterize the dynamic properties of reentrant manufacturing systems;

[0028] Specifically, this step can be implemented as follows:

[0029] a1. Based on the law of conservation of mass, establish a nonlinear continuous evolution model of the reentrant manufacturing system;

[0030] Specifically, the flow and transformation patterns of materials in reentrant manufacturing systems can be analyzed, taking into account material exchange, control input channels, and potential nonlinear factors between multiple production lines. A nonlinear continuous evolution model can be constructed with product completion and time as variables and work-in-process quantity as a state index. This model can accurately capture the dynamic characteristics of the system in the time and space dimensions, avoiding the "curse of dimensionality" problem of discrete event models.

[0031] The nonlinear continuous evolution model can be found in equation (1):

[0032] (1)

[0033] in It indicates the degree of product completion during the manufacturing process. Indicates raw materials, Indicates finished product; This represents the average speed at which products move in the production process of a reentrant manufacturing system. Indicates completion level and given time Quantity of products at any time, each portion To achieve a certain level of completion and given time Production line Product quantity; Indicates control input; This indicates matching external disturbances; This represents a nonlinear external term, used to characterize dynamic characteristics such as material flow between different production lines.

[0034] a2. Obtain the expected production target vector driven by market demand, and define the deviation between the state vector and the expected production target vector as the error vector;

[0035] Specifically, based on market order demand and customer delivery requirements, a target production vector driven by market demand can be determined. This vector clarifies the production status that the system needs to achieve at different time points (such as the target quantity of work-in-process on each production line and the output rate of finished products). The difference between the current state vector of the system (obtained in real time by sensors) and the target production vector is calculated, and the resulting deviation is the error vector. This vector intuitively reflects the gap between the current state of the system and the production target.

[0036] The error vector can be represented as: ,in This represents the expected production target vector driven by market demand.

[0037] a3. Substitute the error vector into the nonlinear continuous evolution model to obtain a nonlinear error dynamics model with the error vector as the state variable.

[0038] Specifically, the defined error vector can be substituted into the nonlinear continuous evolution model established in the first step. Through variable substitution and mathematical rearrangement, the original state variables can be eliminated to obtain a nonlinear error dynamics model with the error vector as the core state variable. This model directly focuses on the dynamic changes of "system deviation" and provides a clear control object for subsequent controller design.

[0039] The nonlinear error dynamics model can be found in equation (2):

[0040] (2)

[0041] The initial condition is: Boundary conditions are ; This represents the transformed nonlinear function. This represents the control input after the transformation.

[0042] 102. Based on fuzzy modeling technology, the nonlinear error dynamics model is represented as a convex combination of multiple local linear subsystems to obtain a global fuzzy model;

[0043] In a specific implementation process, considering the high complexity of control design for nonlinear models, Takagi-Sugeno (TS) fuzzy modeling technology can be adopted. By defining fuzzy rules, membership functions and premise variables that adapt to the system characteristics, the nonlinear error dynamics model is decomposed into multiple easily analyzable local linear subsystems, and then integrated into a global fuzzy model through convex combination. This not only preserves the essence of the nonlinear characteristics of the system, but also reduces the difficulty of controller design.

[0044] The global fuzzy model can be found in equation (3):

[0045] (3)

[0046] in To determine the number of fuzzy rules, express A vector consisting of 10 premise variables , It is a system matrix with appropriate dimensions. It is an uncertain approximation error. Let represent the state matrix of the local linear subsystem described by the i-th fuzzy rule. Normalized membership function. Conditions met: .

[0047] 103. Construct a time scaling function that decays from the initial value to zero within a preset time, and use the time scaling function to transform the error state of the global fuzzy model to obtain the error scaling state;

[0048] In a specific implementation, to achieve stable control within a preset time period, a piecewise time scaling function is constructed: During the time interval from the initial moment to the moment preceding the preset time, the value of the time scaling function monotonically increases as time approaches the preset time, ensuring that the error scaling state obtained through its transformation gradually decays to zero; from the moment the preset time is reached or exceeded, the value of the time scaling function remains greater than 1; this constant is used to ensure that the system maintains stable operation after the preset time has elapsed. Simultaneously, an event-triggered mechanism is used to acquire intermittent measurement status signals, avoiding resource waste from continuous data transmission.

[0049] In a specific implementation, the process of acquiring the intermittent measurement state signal includes:

[0050] b1. Determine the state measurement error between the current system state value and the system state value at the previous trigger time;

[0051] Specifically, sensors can be deployed at key monitoring nodes in a reentrant manufacturing system to collect system status values ​​(such as work-in-process inventory, production progress, etc.) in real time, while also recording the system status value and corresponding trigger time at the time of the last trigger signal transmission. The difference between the current system status value and the status value at the previous trigger time is calculated, and this difference is the status measurement error, the magnitude of which directly reflects the magnitude of the change in system status.

[0052] b2. When the state measurement error is greater than the preset trigger threshold, the current time is determined as the new trigger time, and the system state value at the current time is used as the intermittent measurement state signal.

[0053] The calculated state measurement error can be compared with the trigger threshold in real time: if the state measurement error is greater than the trigger threshold, it indicates that the system state has undergone a significant change that is sufficient to affect the control effect, and the feedback information needs to be updated in time. At this time, the current moment is determined as the new trigger moment, and the current state value is transmitted to the controller as an intermittent measurement state signal; if the state measurement error does not exceed the trigger threshold, it indicates that the system state change is gradual, and the control accuracy can still be guaranteed by using the previously transmitted state signal. At this time, no signal transmission is triggered, and communication remains silent.

[0054] The above methods enable non-periodic state monitoring and signal transmission, providing reliable state input for the fuzzy state feedback controller.

[0055] In one specific implementation, the trigger threshold includes a given trigger threshold parameter and a switching item that changes over time; the switching item remains positive before a preset time and decays to zero over time; after the preset time is exceeded, the switching item is set to a constant; the constant is used to prevent the occurrence of subsequent trigger events.

[0056] Specifically, trigger threshold parameters can be pre-set based on the control accuracy requirements, communication resource status, and system disturbance characteristics of the reentrant manufacturing system. These parameters provide a baseline for trigger judgment, ensuring the initial rationality of the triggering mechanism. A switching term that dynamically changes over time can be designed, its variation closely related to the aforementioned preset time: before the preset time, the switching term remains strictly positive, and as time approaches the preset time, the switching term gradually decays to zero. This design, in conjunction with the aforementioned time scaling function, dynamically reduces the overall level of the trigger threshold during the system's convergence towards the preset target, ensuring that even minute state changes near the preset time can be captured, guaranteeing convergence accuracy.

[0057] Once the preset time has elapsed and the system has stabilized in the desired state, the switching item is set to a sufficiently large fixed constant. The value of this constant needs to be verified through simulation to ensure that the overall trigger threshold, when combined with the trigger threshold parameter, can effectively filter out minor disturbances after the system stabilizes, avoid unnecessary triggering events, and prevent the waste of communication resources and system fluctuations caused by frequent triggering.

[0058] Specifically, the instructions in this embodiment Indicates the first The event triggering mechanism is designed as follows:

[0059] (4)

[0060] in For the given trigger threshold parameter; This represents the state measurement error; a switching term is introduced into the formula. The aim is to achieve two main objectives: firstly, It was designed to maintain a strictly positive value throughout the control process, thus effectively avoiding the Zeno phenomenon; secondly, when , Defined as It can be seen that, with time Approaching Toggle item Gradually decaying to 0, this helps achieve the system's preset time stability. Once Toggle item Set it to a sufficiently large constant to prevent subsequent triggering events from occurring. When the state measurement error... When the trigger threshold condition is exceeded, the current time is marked as... and the sampling status Transmitted to the controller.

[0061] 104. Based on the time scaling function and the acquired intermittent measurement state signal, design a fuzzy state feedback controller for the global fuzzy model;

[0062] In a specific implementation, the output of the fuzzy state feedback controller is obtained by weighted summation of the local controller outputs corresponding to all fuzzy rules; wherein, the local controller output corresponding to each fuzzy rule is calculated by multiplying the membership function of its respective fuzzy rule, its respective controller gain matrix, the gain adjustment factor after adding one to the time scaling function, and the intermittent measurement state signal.

[0063] Specifically, for each time interval Utilizing the latest sampled state, i.e., the intermittently measured state signal Construct the following fuzzy state feedback controller:

[0064] (5)

[0065] in, It is the controller gain matrix.

[0066] 105. Based on the error scaling state, construct a Lyapunov stability function and derive sufficient conditions for the reentrant manufacturing system to satisfy a preset time stability; wherein, the sufficient conditions include multiple linear matrix inequalities;

[0067] Specifically, the Lyapunov stability function can be obtained by integrating the error scaling state over its entire spatial domain; wherein the integrand is formed by multiplying the transpose of the error scaling state, a set symmetric positive definite matrix, and the error scaling state.

[0068] The Lyapunov stability function can be found in equation (6):

[0069] (6)

[0070] in yes A symmetric positive definite matrix.

[0071] In a specific implementation process, the sufficient conditions include the preset time stability condition inequality, the event triggering feasibility condition inequality, and the controller gain solution transformation condition inequality.

[0072] We can differentiate the Lyapunov stability function and then let... , obtain Sufficient conditions. Among them, when there is no external interference ( When ), the preset time stability condition inequality is given by equation (7):

[0073] (7)

[0074] The inequality for the feasibility condition of the event triggering is shown in equation (8):

[0075] (8)

[0076] The transformation condition inequality for solving the controller gain is given in equation (9):

[0077] (9)

[0078] in, , , , , , These are design parameters;

[0079]

[0080] in, Describes a symmetric positive definite matrix. Describes the inverse of a symmetric positive definite matrix. This represents the controller gain matrix for the i-th fuzzy rule. This represents the gain auxiliary matrix of the fuzzy controller. This represents the preset time convergence performance matrix. This represents the event-triggered robustness matrix. Indicates the scaling factor. Indicates the trigger threshold. This represents the convergence rate adjustment parameter. This parameter indicates measures to prevent the phenomenon of triggering infinitely frequently. This represents the state matrix of the local linear subsystem described by the i-th fuzzy rule. This represents the control matrix of the local linear subsystem of the i-th fuzzy rule. Represents the identity matrix.

[0081] When there is external interference ( When ), the preset time stability condition inequality is given by equation (10):

[0082] (10)

[0083] The inequality for the feasibility of triggering the event is given in equation (8).

[0084] The transformation condition inequality for solving the controller gain is given in equation (11):

[0085] (11)

[0086] It should be noted that the aforementioned formula contains " The core meaning of "" is a simplified representation of a symmetric matrix, specifically referring to "the transpose of corresponding elements".

[0087] 106. Solve the multiple linear matrix inequalities of the sufficient condition to obtain the controller gain matrix, so that the reentrant manufacturing system converges within the preset time under the control of the fuzzy state feedback controller.

[0088] In a specific implementation, when there is no interference, apply Schur complement to equations (7) and (9) and multiply both sides by the matrix. (8) ride together on the left and right We can obtain the condition that makes the nonlinear error dynamics model stable at a preset time, which indicates that the system is stable at a preset time. It accurately meets market demands.

[0089] When interference is present, apply Schur complement to equations (10) and (11) and multiply both sides by the matrix. (8) ride together on the left and right We can obtain the conditions for the nonlinear error dynamics model to satisfy the constructed anti-interference performance index, which indicates that the system accurately meets market demands within the constructed anti-interference performance index. Here, the constructed anti-interference performance index is the performance that is stable over infinite time after a preset time.

[0090] In a specific implementation process, the anti-interference performance index can be found in equation (12):

[0091] (12)

[0092] in, Indicates the preset time. Indicates the level of interference suppression. Indicates the error scaling state. Indicates external interference. Represents a time variable.

[0093] This embodiment presents a pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback. By constructing a nonlinear error dynamics model and employing fuzzy modeling techniques, the system is represented as a combination of multiple linear subsystems. A time scaling function is introduced to ensure the system state converges to zero within a pre-set time. An event-triggered mechanism is designed to achieve intermittent state feedback and reduce communication costs. Based on Lyapunov stability theory, sufficient conditions for system stability within the pre-set time are derived, and the controller gain is obtained by solving linear matrix inequalities, ensuring the system remains robust under external disturbances. Thus, the reentrant manufacturing system achieves agile response and low communication cost operation within the pre-set time.

[0094] In one specific implementation process, the technical solution of this invention was used to conduct an experiment on a reentrant manufacturing system with two production lines, and the following experimental results were obtained:

[0095] Figure 2 It is an error variable Response surfaces in time and space, Figure 3 It is an error variable The response surface in time and space. (By...) Figures 2 to 3 It can be seen that the error variable converges to 0 within the preset time, indicating that the system has responded to market demand within the preset time.

[0096] Figure 4 Error variables under different preset times The response curve, Figure 5 Error variables under different preset times The response curve. (From) Figures 4 to 5 As can be seen from both curves, the error gradually approaches zero as time approaches the preset time, indicating that the system has achieved tracking of market demand within the preset time.

[0097] Figure 6 It is a time sequence of event trigger intervals. Figure 6 It can be seen that by using an event-triggered mechanism, continuous monitoring is transformed into on-demand communication, which is in line with the actual characteristics of slow changes in the state of the manufacturing system. In this way, while ensuring convergence within the preset time, the amount of data transmission is significantly reduced, and the practicality and engineering feasibility of the system are improved.

[0098] Based on the same general inventive concept, this invention also protects a preset time fuzzy control system for a reentrant manufacturing system under intermittent state feedback. The preset time fuzzy control system for a reentrant manufacturing system under intermittent state feedback provided by this invention will be described below. The preset time fuzzy control system for a reentrant manufacturing system under intermittent state feedback described below can be referred to in correspondence with the preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback described above.

[0099] Figure 7 This is a schematic diagram of the pre-time fuzzy control system for a reentrant manufacturing system under intermittent state feedback provided in an embodiment of the present invention, as shown below. Figure 7 As shown, the preset time fuzzy control system of the reentrant manufacturing system under intermittent state feedback in this embodiment includes a first construction module 71, a transformation module 72, a second construction module 73, a control module 74, a third construction module 75, and a solution module 76.

[0100] The first building module 71 is used to build a nonlinear error dynamics model to characterize the dynamics of a reentrant manufacturing system.

[0101] Transformation module 72 is used to represent the nonlinear error dynamics model as a convex combination of multiple local linear subsystems based on fuzzy modeling technology, so as to obtain a global fuzzy model;

[0102] The second construction module 73 is used to construct a time scaling function that decays from an initial value to zero within a preset time, and to use the time scaling function to transform the error state of the global fuzzy model to obtain the error scaling state.

[0103] Control module 74 is used to design a fuzzy state feedback controller for the global fuzzy model based on the time scaling function and the acquired intermittent measurement state signal;

[0104] The third construction module 75 is used to construct a Lyapunov stability function based on the error scaling state, and derive sufficient conditions for the reentrant manufacturing system to satisfy a preset time stability; wherein, the sufficient conditions include multiple linear matrix inequalities;

[0105] The solver module 76 is used to solve the plurality of linear matrix inequalities to obtain the controller gain matrix, so that the reentrant manufacturing system converges within the preset time under the control of the fuzzy state feedback controller.

[0106] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback.

[0107] Furthermore, the logical instructions in the aforementioned memory 830 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, 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.

[0108] It should be noted that all relevant information that may be involved in the various embodiments of the present invention is processed in strict accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, based on the reasonable purpose of the business scenario, and is information that users actively provide or generate during the use of the product / service, as well as information obtained with user authorization.

[0109] The information processed by this invention may vary depending on the specific product / service scenario and should be based on the specific scenario in which the user uses the product / service. This may involve user account information, device information, or other related information. This invention will treat the relevant information and its processing with the utmost diligence.

[0110] This invention places great emphasis on the security of relevant information and has adopted reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent unauthorized access, public disclosure, use, modification, damage or loss of relevant information.

[0111] 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.

[0112] 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.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A preset-time fuzzy control method for a reentrant manufacturing system under intermittent state feedback, characterized in that, include: Construct a nonlinear error dynamics model to characterize the dynamic properties of reentrant manufacturing systems; Based on fuzzy modeling technology, the nonlinear error dynamics model is represented as a convex combination of multiple local linear subsystems, resulting in a global fuzzy model; A time scaling function is constructed that decays from an initial value to zero within a preset time, and the error state of the global fuzzy model is transformed using the time scaling function to obtain the error scaling state; Based on the time scaling function and the acquired intermittent measurement state signal, a fuzzy state feedback controller is designed for the global fuzzy model; Based on the aforementioned error scaling state, a Lyapunov stability function is constructed, and sufficient conditions for the reentrant manufacturing system to satisfy a preset time stability are derived; wherein, the sufficient conditions include multiple linear matrix inequalities; Solving the multiple linear matrix inequalities yields the controller gain matrix, enabling the reentrant manufacturing system to converge within the preset time under the control of the fuzzy state feedback controller.

2. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback as described in claim 1, characterized in that, The process of acquiring the intermittent measurement status signal includes: Determine the state measurement error between the current system state value and the system state value at the previous trigger time; When the state measurement error is greater than the preset trigger threshold, the current time is determined as the new trigger time, and the system state value at the current time is used as the intermittent measurement state signal.

3. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback as described in claim 2, characterized in that, The trigger threshold includes a given trigger threshold parameter and a switching item that changes over time; the switching item remains positive before a preset time and decays to zero over time; after the preset time has elapsed, the switching item is set to a constant; the constant is used to prevent the occurrence of subsequent trigger events.

4. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback as described in claim 1, characterized in that, The output of the fuzzy state feedback controller is obtained by weighted summation of the outputs of the local controllers corresponding to all fuzzy rules; The local controller output corresponding to each fuzzy rule is calculated by multiplying the membership function of the respective fuzzy rule, the respective controller gain matrix, the gain adjustment factor after adding one to the time scaling function, and the intermittent measurement state signal.

5. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback according to claim 1, characterized in that, The construction of the Lyapunov stability function based on the error scaling state includes: The Lyapunov stability function is obtained by integrating the error scaling state over its entire spatial domain; wherein the integrand is formed by multiplying the transpose of the error scaling state, a set symmetric positive definite matrix, and the error scaling state.

6. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback as described in claim 5, characterized in that, The sufficient conditions include the preset time stability condition inequality, the event triggering feasibility condition inequality, and the controller gain solution transformation condition inequality; When there is no external interference, the preset time stability condition inequality is: The feasibility inequality for triggering the event is as follows: The transformation condition inequality for solving the controller gain is: in, , , , , , These are design parameters; in, Describes a symmetric positive definite matrix. Describes the inverse of a symmetric positive definite matrix. This represents the controller gain matrix for the i-th fuzzy rule. This represents the gain auxiliary matrix of the fuzzy controller. This represents the convergence performance matrix at a preset time, where T represents the preset time. This represents the event-triggered robustness matrix. Indicates the scaling factor. Indicates the trigger threshold. This represents the convergence rate adjustment parameter. This parameter indicates measures to prevent the phenomenon of triggering infinitely frequently. This represents the state matrix of the local linear subsystem described by the i-th fuzzy rule. This represents the control matrix of the local linear subsystem of the i-th fuzzy rule. Represents the identity matrix.

7. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback according to claim 6, characterized in that, When external disturbances are present, the preset time stability condition inequality is: The feasibility inequality for triggering the event is as follows: The transformation condition inequality for solving the controller gain is: 。 8. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback according to claim 1, characterized in that, A nonlinear error dynamics model is constructed to characterize the dynamic properties of reentrant manufacturing systems, including: Based on the law of conservation of mass, a nonlinear continuous evolution model of the reentrant manufacturing system is established. Obtain the expected production target vector driven by market demand, and define the deviation between the state vector and the expected production target vector as the error vector; Substituting the error vector into the nonlinear continuous evolution model yields a nonlinear error dynamics model with the error vector as the state variable.

9. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback according to claim 1, characterized in that, The time scaling function is constructed as a piecewise function; In the time interval from the initial moment to the moment before the preset time, the value of the time scaling function increases monotonically as time approaches the preset time. From the time the preset time is reached or exceeded, the value of the time scaling function remains a constant greater than 1; the constant is used to ensure that the system maintains stable operation after the preset time has been exceeded.

10. The pre-set time fuzzy control method for a reentrant manufacturing system under intermittent state feedback according to claim 1, characterized in that, Also includes: Construct anti-interference performance indicators; The anti-interference performance indicators are as follows: in, Indicates the preset time. Indicates the level of interference suppression. Indicates the error scaling state. Indicates external interference. Represents a time variable.

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