State prediction method for periodic event-triggered control system based on informative analysis
By constructing a discrete-time linear model and dividing the state region, the problem of state prediction for a periodic event-triggered control system under limited wireless communication was solved, achieving accurate state prediction and improved stability in an uncertain environment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING UNIV OF TECH
- Filing Date
- 2026-02-24
- Publication Date
- 2026-06-12
AI Technical Summary
Existing technologies, under the influence of limited and uncertain wireless communication, struggle to accurately predict state changes in control systems triggered by periodic events, leading to strained communication resources and system stability issues.
By acquiring system parameters, a set of parameter matrix pairs for a discrete-time linear model is constructed. Using conditional homogenization decision matrices and region homogenization constraint matrices, the event triggering time interval is predicted, thereby achieving state region division and target state prediction, thus avoiding dependence on a precise mathematical model of the system.
In the case of unknown system parameters, it improves the accuracy of control system state prediction, optimizes communication scheduling and resource allocation, and enhances system stability.
Smart Images
Figure CN122194944A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of system state prediction, specifically to a state prediction method for a periodic event-triggered control system based on informational analysis. Background Technology
[0002] With the deep integration of wireless communication technology and control theory, wireless network-based control systems have been widely used in industrial automation, intelligent transportation, and energy management. In these systems, sensors, controllers, and actuators form a closed-loop feedback control structure via a wireless network. While these systems offer advantages such as flexible deployment and low maintenance costs, they also face challenges including limited wireless channel bandwidth, susceptibility to communication link interference, and unavoidable network-induced latency, leading to strain on system communication and computing resources.
[0003] In traditional periodic sampling control strategies, systems typically perform data acquisition and control updates at fixed time intervals, which can easily generate a large amount of redundant communication data. This not only increases the load on the wireless network but may also affect the overall system performance. To reduce unnecessary communication overhead, periodic event-triggered control methods have gradually gained attention. This method detects event triggering conditions within a fixed sampling period and only performs data transmission and control updates when the triggering conditions are met. This reduces the communication burden to some extent while retaining the scheduling regularity of periodic sampling, making it suitable for implementation in digital control platforms and wireless network environments.
[0004] However, when multiple control loops share the same wireless communication channel, the aperiodic communication behavior introduced by periodic event-triggered control may lead to channel contention and communication conflicts, thereby affecting the system's real-time performance and stability. Therefore, modeling and predicting the system's event-triggered behavior, especially accurately estimating the event triggering time interval, is of great significance for communication scheduling optimization, resource allocation, and system stability analysis.
[0005] To address the aforementioned issues, existing research often employs system model-based methods to abstractly model periodically event-triggered control systems. This includes methods such as establishing communication flow models, quantized control models, decentralized abstract models, or symbolic models to describe the system's reachable states and event-triggered behaviors. However, these methods typically rely on the precise acquisition of the system dynamics model. In practical engineering applications, the system model may be partially unknown or affected by uncertainties and external disturbances, thus limiting the applicability and reliability of precise model-based methods and making it impossible to accurately predict system state changes. Summary of the Invention
[0006] The purpose of this application is to overcome the shortcomings and deficiencies in the prior art and provide a state prediction method for a periodic event-triggered control system based on informational analysis, which can accurately predict the state changes of the periodic event-triggered control system.
[0007] A first aspect of this application provides a state prediction method for a periodic event-triggered control system based on informational analysis, comprising: Multiple sets of system parameters are acquired for the periodic event-triggered control system; each set of system parameters includes first state data acquired in a first acquisition count, input data acquired in a first acquisition count, and second state data acquired in a second acquisition count; wherein, the second acquisition count is the first acquisition count plus one; Based on the multiple sets of system parameters, a set of parameter matrix pairs for the discrete-time linear model of the control system is obtained; the set of parameter matrix pairs includes the model parameters of the discrete-time linear model. Based on the model parameters, obtain the condition homogenization decision matrix of the periodic event triggering conditions of the control system; Based on the state data in the multiple sets of system parameters, a state space is constructed, and the state space is divided into regions according to the preset constraint matrix to obtain multiple state regions and corresponding region homogenization constraint matrices. Based on the homogenization decision matrix and the region homogenization constraint matrix, predict the event triggering time interval for each state region; Based on the parameter matrix pair set, the current state region of the control system, the current input of the control system, and the event triggering time interval corresponding to the state region, the target state region to which the control system transitions within the time interval is obtained.
[0008] As one implementation, the step of obtaining the set of parameter matrix pairs of the discrete-time linear model of the control system based on the multiple sets of system parameters includes: The set of parameter matrix pairs is obtained using the following formula: in, For including system parameters , The set of parameter matrix pairs, This is the second state data. For data perturbed by an upper bound constraint function in the form of a chinopolygon matrix, This is the first state data. For input data, Represents the generalized inverse operation of a matrix.
[0009] In one implementation, the model parameters include a first model parameter and a second model parameter; The step of obtaining the condition homogenization decision matrix of the periodic event triggering conditions of the control system based on the model parameters includes: The state transition coefficients are obtained based on the first and second parameters of the model and the number of data collections. The state perturbation coefficients are obtained based on the first parameter of the model and the random variable parameters; The conditional homogenization decision matrix is obtained based on the state transition coefficients and the state perturbation coefficients.
[0010] As one implementation method, the step of obtaining the state transition coefficients based on the first parameters of the model, the second parameters of the model, and the number of data acquisitions includes: The state transition coefficients can be obtained using the following formula: in, For the corresponding number State transition coefficients of the second data collection. The first parameter of the model, The second parameter of the model. To control the gain.
[0011] As one implementation method, the step of obtaining the state perturbation coefficients based on the first parameters of the model and the random variable parameters includes: The state disturbance coefficients can be obtained using the following formula: in, For the corresponding number The state disturbance coefficient of the second data collection. The first parameter of the model, For random variable parameters.
[0012] As one implementation method, the step of obtaining the conditional homogenization decision matrix based on the state transition coefficients and the state perturbation coefficients includes: The conditional homogenization decision matrix can be obtained using the following formula: in, To conditionally homogenize the decision matrix, The state transition coefficient matrix is... Here is the state perturbation coefficient matrix. For known event trigger parameters, This indicates transpose.
[0013] As one implementation, the event triggering time interval includes a lower bound and an upper bound; The step of predicting the event triggering time interval for each state region based on the homogenized decision matrix and the region homogenization constraint matrix includes: Based on the preset lower bound constraint function, the lower bound of the event triggering time interval is predicted by the homogenization decision matrix and the region homogenization constraint matrix. Based on the preset upper bound constraint function, the upper bound of the event triggering time interval is predicted by the homogenization decision matrix and the region homogenization constraint matrix.
[0014] As one implementation method, the lower bound constraint function is as follows: in, To conditionally homogenize the decision matrix, For the region The region homogenization constraint matrix, For the nonnegative scalar multipliers corresponding to the lower bound of the interval, For the region The maximum number of sampling times that the corresponding lower bound constraint function holds. This represents the lower bound of the time interval for triggering events.
[0015] As one implementation method, the upper bound constraint function is as follows: in, To conditionally homogenize the decision matrix, For the region The region homogenization constraint matrix, For the non-negative scalar multipliers corresponding to the upper bound of the interval, For the region The minimum number of data collections required for the corresponding upper bound constraint function to hold. The upper bound of the interval representing the time interval between event triggering. This represents the maximum number of samples.
[0016] As one implementation method, the step of obtaining the target state region that the control system transitions to within the time interval, based on the parameter matrix pair set, the current state region of the control system, the current input of the control system, and the event triggering time interval corresponding to the state region, includes: The target state region is obtained using the following formula: in, For the target state region, For the set of parameter matrix pairs, This represents the current state region corresponding to the control system. The set of currently available control inputs. It is a perturbation set in the form of a chinopolygon.
[0017] Compared to existing technologies, the state prediction method for a periodic event-triggered control system based on informational analysis in this application obtains a set of parameter matrix pairs for the discrete-time linear model of the control system based on multiple sets of system parameters. This set of parameter matrix pairs includes the model parameters of the discrete-time linear model. Then, based on the model parameters, a conditional homogenization decision matrix for the periodic event triggering conditions of the control system is obtained. Furthermore, a state space is constructed based on the state data from the multiple sets of system parameters. The state space is then divided into regions according to a preset constraint matrix, resulting in multiple state regions and corresponding region homogenization constraint matrices. Next, based on the homogenization decision matrix and the region homogenization constraint matrix, the event triggering time interval for each state region is predicted. Finally, based on the set of parameter matrix pairs, the current state region of the control system, the current input of the control system, and the event triggering time interval corresponding to the state region, the target state region that the control system transitions to within the time interval is obtained. This method can estimate unknown system parameters using system operating data without requiring a precise mathematical model of the system. It can be used to predict event triggering time intervals and the achievable target state of the control system, thus improving the accuracy of control system state prediction.
[0018] To provide a clearer understanding of this application, the specific embodiments of this application will be described below in conjunction with the accompanying drawings. Attached Figure Description
[0019] Figure 1 This is a flowchart illustrating a state prediction method for a periodic event-triggered control system based on informational analysis, according to one embodiment of this application.
[0020] Figure 2 This is a schematic diagram of the state space of a state prediction method for a periodic event-triggered control system based on informational analysis, according to an embodiment of this application.
[0021] Figure 3 This is a schematic diagram of the lower bound of the interval calculated by the state prediction method of a periodic event-triggered control system based on informational analysis according to an embodiment of this application.
[0022] Figure 4 This is a schematic diagram of the calculated upper bound of the interval in a state prediction method for a periodic event-triggered control system based on informational analysis, according to an embodiment of this application.
[0023] Figure 5 This is a schematic diagram showing the mapping of the lower and upper interval bounds of a state prediction method for a periodic event-triggered control system based on informational analysis, according to an embodiment of this application, to the corresponding state regions.
[0024] Figure 6 This is a schematic diagram showing the target state region reached by each state region in the state space of the state prediction method of the periodic event-triggered control system based on informational analysis according to an embodiment of this application. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0026] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.
[0027] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The singular forms "a," "the," and "the" used in this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" as used herein can be interpreted as "when," "when," or "in response to determination."
[0028] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0029] Please see Figure 1 , Figure 1 This is a flowchart of a state prediction method for a periodic event-triggered control system based on informational analysis, according to the first embodiment of this application. The method is applied to a periodic event-triggered control (PETC) system and includes the following steps: S1: Acquire multiple sets of system parameters from the periodic event-triggered control system; each set of system parameters includes first state data acquired in a first acquisition count, input data acquired in a first acquisition count, and second state data acquired in a second acquisition count; wherein, the second acquisition count is the first acquisition count plus one; S2: Based on the multiple sets of system parameters, obtain the set of parameter matrix pairs for the discrete-time linear model of the control system; the set of parameter matrix pairs includes the model parameters of the discrete-time linear model; The set of parameter matrix pairs is obtained using the following formula: in, For including system parameters , The set of parameter matrix pairs, This is the second state data. For perturbation data in the form of a matrix chinopolygon with bounded upper bound constraint functions, This is the first state data. For input data, Represents the generalized inverse operation of a matrix.
[0030] S3: Based on the model parameters, obtain the condition homogenization decision matrix of the periodic event triggering conditions of the control system; S4: Based on the state data in the multiple sets of system parameters, construct a state space, and divide the state space into regions according to the preset constraint matrix to obtain multiple state regions and corresponding region homogenization constraint matrices. Please see Figure 2 The state space is as follows: Figure 2 As shown, the constraint matrix is an inequality. Taking two dimensions as an example, it is in the form of... The box region is defined by the constraint matrix. The constraint structure matrix describes how to combine system states. To form a boundary, To constrain the boundary vector, the coordinates of the center point are used. With half the length of the region composition.
[0031] Transforming the inequality into a quadratic form, the partitioning method is as follows: in, This is the state region. Indicates the system status. This is a region index used to identify the row and column indices of the sub-region within the state space partition. It is the region homogenization constraint matrix. It is an identity matrix of appropriate dimensions.
[0032] For example, the state space is divided into Block area, The region index is mapped to a one-dimensional label Num: therefore, The area corresponding to label 1, The area corresponding to number 2, and so on.
[0033] By using the above method, the state space of the control system is divided into a finite number of non-overlapping regions, and the union of these regions covers the entire state space of the system. States located within the same state region are similar in system behavior and event triggering characteristics, and each region is considered an abstract state in the abstract model.
[0034] S5: Based on the homogenization decision matrix and the region homogenization constraint matrix, predict the event triggering time interval for each state region; S6: Based on the parameter matrix pair set, the current state region of the control system, the current input of the control system, and the event triggering time interval corresponding to the state region, obtain the target state region to which the control system transitions within the time interval.
[0035] In one feasible embodiment, the model parameters include a first model parameter and a second model parameter; S3: The step of obtaining the condition homogenization decision matrix of the periodic event triggering conditions of the control system based on the model parameters includes: S31: Obtain the state transition coefficients based on the first parameter of the model, the second parameter of the model, and the number of data acquisitions; The state transition coefficients can be obtained using the following formula: in, For the corresponding number State transition coefficients of the second data collection. The first parameter of the model, The second parameter of the model. To control the gain.
[0036] S32: Obtain the state disturbance coefficients based on the first parameter of the model and the random variable parameters; The state disturbance coefficients can be obtained using the following formula: in, For the corresponding number The state disturbance coefficient of the second data collection. The first parameter of the model, For random variable parameters.
[0037] S33: Obtain the conditional homogenization decision matrix based on the state transition coefficients and the state perturbation coefficients.
[0038] The conditional homogenization decision matrix can be obtained using the following formula: in, To conditionally homogenize the decision matrix, The state transition coefficient matrix is... Here is the state perturbation coefficient matrix. For known event trigger parameters, This indicates transpose.
[0039] In a feasible embodiment, the event triggering time interval includes a lower bound and an upper bound; the step of predicting the event triggering time interval for each state region based on the homogenized decision matrix and the region homogenization constraint matrix includes: Based on the preset lower bound constraint function, the lower bound of the event triggering time interval is predicted by the homogenization decision matrix and the region homogenization constraint matrix. The lower bound constraint function is as follows: in, To conditionally homogenize the decision matrix, For the region The region homogenization constraint matrix, For the nonnegative scalar multipliers corresponding to the lower bound of the interval, For the region The maximum number of samples required for the corresponding lower bound constraint function to hold, and the lower bound of the event trigger time interval are... ,in, The sampling period.
[0040] Specifically, the sampling count is gradually increased from 0 within each region, starting from the first region and proceeding to the last. This continues until the data corresponding to the first sampling count fails to meet the lower bound constraint function. The maximum number of sampling counts required for the lower bound constraint function to hold in that region is then obtained. Based on this maximum number of sampling counts, the lower bound of the event trigger time interval is determined. .
[0041] Based on the preset upper bound constraint function, the upper bound of the event triggering time interval is predicted by the homogenization decision matrix and the region homogenization constraint matrix. The upper bound constraint function is as follows: in, To conditionally homogenize the decision matrix, For the region The region homogenization constraint matrix, For the non-negative scalar multipliers corresponding to the upper bound of the interval, For the region The minimum number of samples required for the corresponding upper bound constraint function to hold, and the upper bound of the event trigger time interval are... ,in, The sampling period is This represents the maximum number of samples.
[0042] Specifically, traversing from the first region to the last region, within each region, the number of samples is reduced from the preset maximum number of samples. Gradually reduce the number of samples until the first instance occurs where the data corresponding to the number of samples does not conform to the upper bound constraint function. This yields the minimum number of samples required for the upper bound constraint function to hold in that region. The upper bound of the event trigger time interval is then determined based on this minimum number of samples. .
[0043] The index steps corresponding to the lower bound of the interval and the index steps corresponding to the upper bound of the interval calculated through this implementation are as follows: Figure 3 and Figure 4 As shown, after mapping the corresponding index steps of the lower and upper bounds of the interval to the corresponding state regions, the result is as follows. Figure 5 As shown.
[0044] In a feasible embodiment, the step of obtaining the target state region that the control system transitions to within the time interval, based on the parameter matrix pair set, the current state region of the control system, the current input of the control system, and the event triggering time interval corresponding to the state region, includes: The target state region is obtained using the following formula: in, For the target state region, For the set of parameter matrix pairs, This represents the current state region corresponding to the control system. The set of currently available control inputs. It is a perturbation set in the form of a chinopolygon.
[0045] The target state region is calculated using a recursive method. Starting from each state region in the state space, the reachable target state regions are as follows: Figure 6 As shown.
[0046] In summary, compared with the prior art, the state prediction method for a periodic event-triggered control system based on informational analysis in this application embodiment obtains a set of parameter matrix pairs of the discrete-time linear model of the control system based on multiple sets of system parameters of the periodic event-triggered control system; the set of parameter matrix pairs includes the model parameters of the discrete-time linear model; then, based on the model parameters, it obtains the conditional homogenization decision matrix of the periodic event triggering conditions of the control system; furthermore, it constructs a state space based on the state data in the multiple sets of system parameters, divides the state space into regions according to a preset constraint matrix, and obtains multiple state regions and corresponding region homogenization constraint matrices; then, based on the homogenization decision matrix and the region homogenization constraint matrix, it predicts the event triggering time interval of each state region; and finally, based on the set of parameter matrix pairs, the current corresponding state region of the control system, the current input of the control system, and the event triggering time interval corresponding to the state region, it obtains the target state region that the control system transitions to within the time interval. This method can estimate unknown system parameters using system operating data without needing to obtain a precise mathematical model of the system, and can be used to predict event triggering time intervals and the achievable target state of the control system, thus improving the accuracy of control system state prediction.
[0047] The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and 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 application according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.
[0048] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0049] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and 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 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function selected in one or more boxes.
[0050] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function selected in one or more boxes.
[0051] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0052] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0053] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0054] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0055] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A state prediction method for a periodic event-triggered control system based on informational analysis, characterized in that, include: Acquire multiple sets of system parameters for a periodically event-triggered control system; The system parameters described in each group include first state data collected in the first number of acquisitions, input data collected in the first number of acquisitions, and second state data collected in the second number of acquisitions; wherein, the second number of acquisitions is the first number of acquisitions plus one; Based on the multiple sets of system parameters, a set of parameter matrix pairs for the discrete-time linear model of the control system is obtained; the set of parameter matrix pairs includes the model parameters of the discrete-time linear model. Based on the model parameters, obtain the condition homogenization decision matrix of the periodic event triggering conditions of the control system; Based on the state data in the multiple sets of system parameters, a state space is constructed, and the state space is divided into regions according to the preset constraint matrix to obtain multiple state regions and corresponding region homogenization constraint matrices. Based on the homogenization decision matrix and the region homogenization constraint matrix, predict the event triggering time interval for each state region; Based on the parameter matrix pair set, the current state region of the control system, the current input of the control system, and the event triggering time interval corresponding to the state region, the target state region to which the control system transitions within the time interval is obtained.
2. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 1, characterized in that, The step of obtaining the set of parameter matrix pairs of the discrete-time linear model of the control system based on the multiple sets of system parameters includes: The set of parameter matrix pairs is obtained using the following formula: in, For including system parameters , The set of parameter matrix pairs, This is the second state data. For bounded perturbation data in the form of a matrix chinopolygon, This is the first state data. For input data, Represents the generalized inverse operation of a matrix.
3. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 1, characterized in that, The model parameters include the first model parameter and the second model parameter; The step of obtaining the condition homogenization decision matrix of the periodic event triggering conditions of the control system based on the model parameters includes: The state transition coefficients are obtained based on the first and second parameters of the model and the number of data collections. The state perturbation coefficients are obtained based on the first parameter of the model and the random variable parameters; The conditional homogenization decision matrix is obtained based on the state transition coefficients and the state perturbation coefficients.
4. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 3, characterized in that, The steps for obtaining the state transition coefficients based on the first and second parameters of the model and the number of data acquisitions include: The state transition coefficients can be obtained using the following formula: in, For the corresponding number State transition coefficients of the second data collection. The first parameter of the model, express of Power of 1 The second parameter of the model. To control the gain.
5. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 3, characterized in that, The steps for obtaining the state perturbation coefficients based on the first parameter of the model and the random variable parameters include: The state disturbance coefficients can be obtained using the following formula: in, For the corresponding number The state disturbance coefficient of the second data collection. The first parameter of the model, The parameter is a bounded random variable.
6. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 3, characterized in that, The step of obtaining the conditional homogenization decision matrix based on the state transition coefficients and the state perturbation coefficients includes: The conditional homogenization decision matrix can be obtained using the following formula: in, To conditionally homogenize the decision matrix, The state transition coefficient matrix is... Here is the state perturbation coefficient matrix. For known event trigger parameters, This indicates transpose.
7. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 1, characterized in that, The event triggering time interval includes a lower bound and an upper bound; The step of predicting the event triggering time interval for each state region based on the homogenized decision matrix and the region homogenization constraint matrix includes: Based on the preset lower bound constraint function, the lower bound of the event triggering time interval is predicted by the homogenization decision matrix and the region homogenization constraint matrix. Based on the preset upper bound constraint function, the upper bound of the event triggering time interval is predicted by the homogenization decision matrix and the region homogenization constraint matrix.
8. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 7, characterized in that, The lower bound constraint function is as follows: in, To conditionally homogenize the decision matrix, For the region The region homogenization constraint matrix, For the nonnegative scalar multipliers corresponding to the lower bound of the interval, For the region The maximum number of sampling times that the corresponding lower bound constraint function holds.
9. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 7, characterized in that, The upper bound constraint function is as follows: in, To conditionally homogenize the decision matrix, For the region The region homogenization constraint matrix, For the non-negative scalar multipliers corresponding to the upper bound of the interval, For the region The minimum number of data collections required for the corresponding upper bound constraint function to hold. This represents the maximum number of samples.
10. The state prediction method for a periodic event-triggered control system based on informational analysis according to claim 1, characterized in that, The step of obtaining the target state region that the control system transitions to within the time interval, based on the parameter matrix pair set, the current state region of the control system, the current input of the control system, and the event triggering time interval corresponding to the state region, includes: The target state region is obtained using the following formula: in, For the target state region, For the set of parameter matrix pairs, This represents the current state region corresponding to the control system. The set of currently available control inputs. It is a perturbation set in the form of a chinopolygon.