PLC platform embedded LPV prediction control method and device and computer equipment

Abstract

The invention relates to a PLC platform embedded LPV prediction control method and device and computer equipment, and the method comprises the steps: collecting the scheduling parameters and output measurement values of process equipment in real time; determining a corresponding discrete matrix according to the scheduling parameters and a pre-constructed structured data table; determining an adjusting signal according to the output measurement value and the discrete matrix; collecting an input signal and output response data of the process equipment; constructing a continuous system model corresponding to predefined scheduling parameters according to the input signal and the output response data; performing discretization calculation on the continuous system model to obtain a target discrete matrix set; and constructing a structured data table according to predefined scheduling parameters and the target discrete matrix set. According to the method and the device, the problem of performance reduction caused by discrete matrix online calculation in related technologies is solved, and the performance of the prediction control method is improved in a computing power limited scene.

Description

Technical Field

[0001] This application relates to the field of industrial process control, and in particular to PLC platform embedded LPV predictive control methods, devices and computer equipment. Background Technology

[0002] In recent years, with the continuous improvement of industrial automation, higher requirements have been placed on the control precision, adaptability, and energy efficiency management of production processes in process industries such as tobacco processing, food processing, and pharmaceuticals. A predictive control method that can accurately adapt to changes in operating conditions and operate stably in industrial field control equipment will directly determine the consistency of product quality and production efficiency under complex process conditions.

[0003] The current mainstream predictive control method combines the Linear Parameter Variation (LPV) system model with the Generalized Predictive Control (GPC) algorithm, giving the controller stronger adaptability and generalization ability. However, when the predictive control method combining the LPV model and the GPC algorithm is applied to the Programmable Logic Controller (PLC), its drawback is that the online calculation of the discrete matrix of the LPV model generates a huge computational load, far exceeding the limited CPU processing power and memory resources of the PLC controller. As a result, the performance of the predictive control method cannot be effectively utilized in industrial settings.

[0004] There is currently no effective solution to the problem of performance degradation caused by online discrete matrix calculation in related technologies. Summary of the Invention

[0005] This embodiment provides a PLC platform embedded LPV predictive control method, device, and computer equipment to solve the problem of performance degradation caused by online discrete matrix calculation in related technologies.

[0006] Firstly, this embodiment provides a PLC platform embedded LPV predictive control method, applicable to a PLC embedded LPV predictive control system. The system includes process equipment, a PLC platform connected to the process equipment, and a simulation platform connected to the PLC platform. The method is applicable to the PLC platform side. The method includes:

[0007] Real-time acquisition of scheduling parameters and output measurement values ​​of the process equipment;

[0008] Based on the scheduling parameters and the pre-built structured data table, determine the corresponding discrete matrix;

[0009] Based on the output measurement value and the discrete matrix, an adjustment signal is determined; the adjustment signal is used to control the operation of the process equipment.

[0010] The offline construction of the structured data table on the simulation platform is as follows:

[0011] Collect the input signals and output response data of the process equipment;

[0012] Based on the input signal and the output response data, construct a continuous system model corresponding to the predefined scheduling parameters;

[0013] The continuous system model is discretized to obtain the target set of discrete matrices;

[0014] The structured data table is constructed based on the predefined scheduling parameters and the target discrete matrix set.

[0015] In some embodiments, determining the corresponding discrete matrix based on the scheduling parameters and a pre-built structured data table includes:

[0016] If the scheduling parameter is equal to the predefined scheduling parameter, then the corresponding discrete matrix is ​​directly looked up in the structured data table;

[0017] If the scheduling parameters are not equal to the predefined scheduling parameters, then an LPV model with time-varying parameters is constructed based on the scheduling parameters and the structured data table; the discrete matrix of the LPV model is used as the discrete matrix.

[0018] In some embodiments, determining the adjustment signal based on the output measurement and the discrete matrix includes:

[0019] Based on the output measurement value and the discrete matrix, state estimation is performed to obtain the state vector;

[0020] The adjustment signal is determined based on the state vector and the pre-calculated gain matrix.

[0021] In some embodiments, the predefined scheduling parameters include:

[0022] Based on different typical operating conditions, the scheduling parameters under the typical operating conditions are defined; the scheduling parameters include incoming material temperature, incoming material humidity, air volume and steam flow rate.

[0023] In some embodiments, the method further includes, depending on different typical operating conditions:

[0024] The typical operating conditions include the first operating condition, the second operating condition, and the third operating condition;

[0025] In the first operating condition stage, the incoming material temperature is higher than a preset temperature threshold, and the incoming material humidity is higher than a preset humidity threshold;

[0026] In the second operating condition stage, the incoming material temperature is equal to the temperature threshold, and the incoming material humidity is equal to the humidity threshold;

[0027] In the third operating condition stage, the incoming material temperature is lower than the temperature threshold, and the incoming material humidity is lower than the humidity threshold.

[0028] In some embodiments, the method further includes:

[0029] After constructing the structured data table, the singular value decomposition method is used to reduce the order of the data stored in the structured data table to obtain a reduced-order discrete matrix.

[0030] Based on the reduced-order discrete matrix, the gain matrix is ​​pre-calculated offline.

[0031] In some embodiments, the method further includes:

[0032] After calculating the gain matrix, the gain matrix and the structured data table are combined to form a complete scheduling mapping table.

[0033] Secondly, this embodiment provides a PLC platform embedded LPV predictive control device, including: an acquisition module, a control module, and a construction module;

[0034] The acquisition module is used to collect the scheduling parameters and output measurement values ​​of the process equipment in real time;

[0035] The control module is used to determine the corresponding discrete matrix based on the scheduling parameters and a pre-built structured data table; and to determine an adjustment signal based on the output measurement value and the discrete matrix; the adjustment signal is used to control the operation of the process equipment.

[0036] The construction module is used to offline construct the structured data table on the simulation platform. The process includes: collecting input signals and output response data of the process equipment; constructing a continuous system model corresponding to predefined scheduling parameters based on the input signals and output response data; discretizing the continuous system model to obtain a target discrete matrix set; and constructing the structured data table based on the predefined scheduling parameters and the target discrete matrix set.

[0037] Thirdly, this embodiment provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the PLC embedded LPV predictive control method described in the first aspect above.

[0038] Fourthly, this embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the PLC embedded LPV predictive control method described in the first aspect above.

[0039] Compared with related technologies, the PLC-embedded LPV predictive control method, device, and computer equipment provided in this embodiment acquire the scheduling parameters and output measurement values ​​of the process equipment in real time; determine the corresponding discrete matrix based on the scheduling parameters and a pre-built structured data table; determine the adjustment signal based on the output measurement values ​​and the discrete matrix; and use the adjustment signal to control the operation of the process equipment. The structured data table is constructed offline on the simulation platform, the process of which is as follows: acquiring the input signals and output response data of the process equipment; constructing a continuous system model corresponding to the predefined scheduling parameters based on the input signals and output response data; discretizing the continuous system model to obtain a target discrete matrix set; and constructing a structured data table based on the predefined scheduling parameters and the target discrete matrix set. This solves the problem of performance degradation caused by online discrete matrix calculation in related technologies. By discretizing the model and using a lookup mechanism based on the structured data table, the performance of the predictive control method is improved in scenarios with limited computing power.

[0040] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0042] Figure 1 This is a hardware structure block diagram of the terminal device of the PLC platform embedded LPV predictive control method provided in one embodiment of this application;

[0043] Figure 2 This is a flowchart of an embodiment of the embedded LPV predictive control method for a PLC platform provided in this application;

[0044] Figure 3 This is a flowchart illustrating the online acquisition of an offline matrix according to an embodiment of this application;

[0045] Figure 4 This is a schematic diagram of the process for obtaining an offline matrix online according to an embodiment of this application;

[0046] Figure 5 This is a flowchart of an embodiment of the present application providing an offline pre-calculation of the gain matrix;

[0047] Figure 6 This is a flowchart illustrating the offline pre-calculation of the gain matrix provided in one embodiment of this application;

[0048] Figure 7 This is a structural block diagram of an embedded LPV predictive control device on a PLC platform provided in one embodiment of this application.

[0049] In the diagram: 102, process equipment; 104, PLC platform; 106, simulation platform; 210, acquisition module; 220, control module; 230, construction module. Detailed Implementation

[0050] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0051] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0052] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a hardware structure block diagram of the terminal of the PLC platform embedded LPV predictive control method in this embodiment. (See diagram for example.) Figure 1 As shown, a terminal may include one or more ( Figure 1 (Only one is shown) A process device 102 and a PLC platform 104 for controlling the operation of the process device. The process device 102 provides device data to the PLC platform 104 via sensors; the PLC platform 104 transmits data to a simulation platform 106 and calculates adjustment signals; the simulation platform 106 calculates relevant parameters offline and transmits the parameters to the PLC platform 104 for storage. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0053] This embodiment provides an embedded LPV predictive control method for a PLC platform. Figure 2 This is a flowchart of the embedded LPV predictive control method on the PLC platform in this embodiment, as shown below. Figure 2 As shown, the process includes the following steps:

[0054] Step S210: Real-time acquisition of scheduling parameters and output measurement values ​​of process equipment.

[0055] Specifically, the scheduling parameters and output measurements of process equipment 102 are obtained through industrial sensors. During the data processing stage, each scheduling parameter is filtered to eliminate measurement noise and abnormal fluctuations, and then normalized to eliminate the influence of dimensional differences on subsequent calculations.

[0056] Step S220: Determine the corresponding discrete matrix based on the scheduling parameters and the pre-built structured data table; determine the adjustment signal based on the output measurement value and the discrete matrix; the adjustment signal is used to control the operation of the process equipment.

[0057] Specifically, after preprocessing the real-time acquired scheduling parameters, a segmented lookup or hash matching is performed in the structured data table stored on the PLC platform 104 to locate the scheduling parameters, and the corresponding discrete matrix is ​​read. Based on the acquired discrete matrix and the real-time output measurement value, the adjustment signal is determined. The adjustment signal includes, but is not limited to, the steam control valve position adjustment signal, the blower frequency adjustment signal, or the feed valve adjustment signal; there are no restrictions on the type of adjustment signal.

[0058] Step S230 involves offline construction of the structured data table on the simulation platform. The process includes: collecting input signals and output response data of the process equipment; constructing a continuous system model corresponding to predefined scheduling parameters based on the input signals and output response data; discretizing the continuous system model to obtain a target discrete matrix set; and constructing a structured data table based on the predefined scheduling parameters and the target discrete matrix set.

[0059] Specifically, firstly, input and output data of the process equipment 102 are collected under various preset operating conditions, and corresponding continuous system models are established accordingly. Then, these continuous system models are discretized to form a set of discrete matrices containing the system's dynamic characteristics. Finally, these discrete matrices are associated and integrated with the corresponding scheduling parameters to construct a complete structured data table and store it on the PLC platform 104, providing plug-and-play model support for online control.

[0060] Through the above steps, the problem of performance degradation caused by online discrete matrix calculation in related technologies has been solved, and the performance of predictive control methods has been improved in scenarios with limited computing power.

[0061] The above steps are explained in detail below:

[0062] In some embodiments, the PLC platform embeds an LPV predictive control method, such as Figure 3 As shown, it also includes the following steps:

[0063] Step S221: If the scheduling parameter is equal to the predefined scheduling parameter, then directly look up the corresponding discrete matrix in the structured data table;

[0064] Step S222: If the scheduling parameters are not equal to the predefined scheduling parameters, then construct an LPV model with time-varying parameters based on the scheduling parameters and the structured data table; use the discrete matrix of the LPV model as the discrete matrix.

[0065] Specifically, such as Figure 4 As shown, when the real-time acquired scheduling parameters completely match a predefined scheduling parameter in the structured data table stored on the PLC platform, the corresponding data record is directly located, and the pre-calculated discrete matrix is ​​read as the model parameter for the current control cycle.

[0066] When the scheduling parameters are between predefined operating points but not equal to the predefined scheduling parameters, an LPV model with time-varying parameters is constructed. First, the current scheduling parameters are obtained. There are multiple ways to obtain the Euclidean distance from the predefined scheduling parameter Euclidean distance. If the formulaic approach is used, the Euclidean distance calculation formula is as follows:

[0067] ;

[0068] in, Let Euclidean distance be the distance between the current scheduling parameter and the predefined i-th scheduling parameter. For the current scheduling parameters, This is the predefined i-th scheduling parameter.

[0069] Next, calculate the weighting coefficients. The formula for calculating the weighting coefficients is as follows:

[0070] ;

[0071] in, These are weighting coefficients. This represents the summation of the reciprocals of the calculated Euclidean distances.

[0072] Finally, the LPV model is generated based on the weighted coefficients, and the formula for calculating the discrete matrix of the model is as follows:

[0073] ;

[0074] in, This is a discrete matrix corresponding to predefined scheduling parameters.

[0075] This embodiment constructs a dual model scheduling mechanism that includes precise matching and intelligent interpolation. The precise matching mechanism ensures optimal control performance at typical operating points, while the distance-weighted interpolation mechanism enables smooth model transitions under atypical operating conditions, effectively avoiding jumps in controller parameters.

[0076] In some embodiments, the PLC platform embedded LPV predictive control method further includes the following steps:

[0077] Based on the output measurements and the discrete matrix, state estimation is performed to obtain the state vector;

[0078] The adjustment signal is determined based on the state vector and the pre-calculated gain matrix.

[0079] Specifically, the PLC platform receives the output measurements from the process equipment. Based on the discrete matrix corresponding to the current operating condition obtained from the structured data table, the PLC platform constructs a reduced-order state observer. The real-time acquired output measurements are input to the observer, which performs a state prediction and correction loop based on the system dynamic model, and finally obtains the reduced-order state vector through projection transformation.

[0080] Read the pre-calculated gain matrix corresponding to the current scheduling parameters from the structured data table. Calculate the adjustment signal using the gain matrix and the state vector. The formula for calculating the adjustment signal is:

[0081] ;

[0082] in, The adjustment signal at the current moment, The adjustment signal from the previous moment. To control the increment, Here is the gain matrix. This is the state vector.

[0083] This embodiment constructs a complete state estimation and feedback control closed loop. The introduction of the state observer solves the engineering problem that the internal state of the system cannot be directly measured, providing the necessary state information for predictive control; while the state feedback based on the pre-calculated gain matrix significantly reduces computational complexity while ensuring control performance through efficient matrix multiplication operations.

[0084] In some embodiments, the PLC platform embedded LPV predictive control method further includes the following steps:

[0085] Based on different typical operating conditions, the scheduling parameters under typical operating conditions are defined; the scheduling parameters include incoming material temperature, incoming material humidity, air volume and steam flow rate.

[0086] Specifically, based on the dynamic characteristics of the tobacco processing process, four key process variables are selected to construct a scheduling vector.

[0087] ;

[0088] in, The temperature of the incoming material. The moisture content of the incoming material. For air volume, This represents the steam flow rate.

[0089] Incoming material temperature and the moisture content of incoming materials It reflects the initial state of the material being processed and is a key factor affecting the heat and mass transfer balance during the process; air volume and steam flow These represent the main operational variables, directly affecting the dynamic response characteristics of the system. These four scheduling parameters together constitute a complete feature set describing the system's working state, which can comprehensively reflect the changes in the system's dynamic characteristics under different working conditions during the yarn-making process. The scheduling parameters can also be the bobbin speed, motor load, or energy consumption index; there are no restrictions on the type of scheduling parameters.

[0090] Each scheduling parameter is processed by a 5th-order moving average filter, and then normalized to the [0,1] interval by a linear transformation. After the scheduling parameters under typical operating conditions are defined, the input signals and output response data of the process equipment are collected for each predefined scheduling parameter operating point, and a continuous system model corresponding to the predefined scheduling parameters is constructed. The constructed model is as follows:

[0091] ;

[0092] ;

[0093] in, Let x be the derivative of the state variable. State variables include, but are not limited to, cylinder temperature, rate of change of tobacco leaf moisture content, and air flow hysteresis state. There are no restrictions on the type of state variables here. To control input variables, including but not limited to steam valve position and fan frequency, the type of control input variable is not restricted here; The output response quantities include, but are not limited to, the actual measured cylinder temperature and outlet humidity. There are no restrictions on the type of output response quantities. It is a parameter matrix.

[0094] Secondly, the continuous system model is discretized using the zero-order preservation method to obtain the target set of discrete matrices, which are in the following form:

[0095] ;

[0096] in, The sampling period (e.g., 1 second). It is a discrete matrix.

[0097] Finally, each group The corresponding predefined scheduling parameters are stored in a structured data table, and the structured data table is then stored on the PLC platform.

[0098] In this embodiment, four key parameters capable of comprehensively characterizing the dynamic characteristics of the tobacco processing process were selected as scheduling variables. A multi-dimensional scheduling parameter definition method was used to more accurately describe the system's multi-dimensional operating states. The construction of a structured data table enabled the mapping between scheduling parameters and model parameters. Complex model parameters were organized and stored in a lookup table format, avoiding model mismatch caused by incomplete operating condition representation and ensuring the accuracy and reliability of operating condition identification and model switching.

[0099] In some embodiments, the PLC platform embedded LPV predictive control method further includes the following steps:

[0100] Typical operating conditions include the first operating condition, the second operating condition, and the third operating condition;

[0101] In the first operating condition stage, the incoming material temperature is higher than the preset temperature threshold and the incoming material humidity is higher than the preset humidity threshold.

[0102] In the second operating condition stage, the incoming material temperature is equal to the temperature threshold and the incoming material humidity is equal to the humidity threshold.

[0103] In the third operating condition stage, the incoming material temperature is lower than the temperature threshold and the incoming material humidity is lower than the humidity threshold.

[0104] Specifically, the threshold is usually set as the ideal working position required by the process. Its function is to distinguish the system in different process stages. The temperature threshold can be 70℃ or 75℃, and the humidity threshold can be 20% or 25%. There is no limitation on the value of the threshold.

[0105] For example, in the humidification and drying processes of tobacco processing, the temperature threshold is set to 70℃ and the humidity threshold is set to 20%. When the incoming material temperature is >70℃ and the incoming material humidity is >20%, the system is in the first operating condition, corresponding to the strong humidification stage; when the incoming material temperature is =70℃ and the incoming material humidity is =20%, the system is in the second operating condition, corresponding to the steady-state adjustment stage; when the incoming material temperature is <70℃ and the incoming material humidity is <20%, the system is in the third operating condition, corresponding to the drying stage. For these three typical operating conditions, corresponding input and output data are collected respectively, and continuous system models are established for each condition through system identification.

[0106] This embodiment defines three typical operating conditions covering the entire production process based on the physical characteristics of tobacco processing. This classification method ensures the completeness of the operating condition classification, covering the entire process range from strong humidification to drying, while maintaining the distinguishability of each operating condition interval by setting reasonable thresholds.

[0107] In some embodiments, the PLC platform embeds an LPV predictive control method, such as Figure 5 As shown, it also includes the following steps:

[0108] Step S231: After constructing the structured data table, the singular value decomposition method is used to reduce the order of the data stored in the structured data table to obtain a reduced-order discrete matrix.

[0109] Step S232: Based on the reduced-order discrete matrix, pre-calculate the gain matrix offline.

[0110] Specifically, such as Figure 6 As shown, firstly, a discrete matrix is ​​extracted from the structured data table. Then, singular value decomposition is performed on the discrete matrix, retaining the top 95% of the cumulative energy percentage. Construct a projection matrix from the eigenvectors.

[0111] Secondly, the projection matrix is ​​used to reduce the order of the discrete matrix for each working condition, and the corresponding reduced-order discrete matrix is ​​calculated. The formula for calculating the reduced-order discrete matrix is ​​as follows:

[0112] ;

[0113] in, For the projection matrix, It is the transpose matrix. It is a reduced-order discrete matrix.

[0114] Simultaneously, the state vector is reduced in order using a projection matrix. The formula for calculating the reduced-order state vector is as follows:

[0115] ;

[0116] Where X is the original state vector; Xr is the state vector after dimensionality reduction.

[0117] Therefore, the dimension reduction model after projection is:

[0118] ;

[0119] ;

[0120] Set prediction step size Control step size The optimized output objects include outlet humidity and cylinder temperature; therefore, the output expression is:

[0121] ;

[0122] Where Y is the output prediction vector. This is the state prediction matrix. For the reduced-dimensional state vector, To control the incremental prediction matrix, To control the increment vector.

[0123] Construct the cost function; the formula for calculating the cost function is as follows:

[0124] ;

[0125] in, , .

[0126] Finally, for each scheduling model, the gain matrix is ​​pre-calculated. The formula for calculating the gain matrix is:

[0127] ;

[0128] In this embodiment, singular value decomposition is used on the simulation platform to reduce the order of the discrete matrix of the model, and the gain matrix is ​​calculated offline based on the reduced-order discrete matrix. This series of offline processing significantly reduces the amount of matrix multiplication operations required for the PLC platform during online operation, thereby further reducing the computational burden on the PLC and improving the response speed of the control system while ensuring control performance.

[0129] In some embodiments, the PLC platform embedded LPV predictive control method further includes the following steps:

[0130] After calculating the gain matrix, a complete scheduling mapping table is formed by combining the gain matrix and the structured data table.

[0131] Specifically, the predefined scheduling parameters, the corresponding discrete matrix, the projection matrix, and the gain matrix obtained offline are associated and integrated with the control parameters to form a complete mapping record:

[0132] ;

[0133] in, For predefined scheduling parameters, The discrete matrix corresponding to the predefined scheduling parameters. For the projection matrix, Here is the gain matrix. To predict the step size, To control the step size.

[0134] By collecting and storing the mapping records for all typical operating conditions, a complete scheduling mapping table is formed. This scheduling mapping table is then downloaded to the PLC platform, serving as the sole basis for parameter scheduling during online PLC operation.

[0135] This embodiment integrates the model parameters characterizing the object's dynamics with the core parameters determining control behavior in an offline phase, and unifies them by indexing the scheduling parameters. This allows the PLC platform to perform online control without complex matrix operations; a single lookup using the scheduling parameters is sufficient to simultaneously obtain all necessary matrices and control parameters for the current operating condition. This ensures the stability and versatility of implementing complex LPV predictive control algorithms on resource-constrained PLC platforms.

[0136] This embodiment also provides an embedded LPV predictive control device for a PLC platform, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that implement a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0137] Figure 7 This is a structural block diagram of the PLC platform embedded LPV predictive control device in this embodiment, as shown below. Figure 7 As shown, the device includes:

[0138] The acquisition module 210 is used to collect the scheduling parameters and output measurement values ​​of the process equipment in real time.

[0139] The control module 220 is used to determine the corresponding discrete matrix based on the scheduling parameters and the pre-built structured data table; and to determine the adjustment signal based on the output measurement value and the discrete matrix; the adjustment signal is used to control the operation of the process equipment.

[0140] The construction module 230 is used to offline construct a structured data table on the simulation platform. The process is as follows: collecting input signals and output response data of the process equipment; constructing a continuous system model corresponding to predefined scheduling parameters based on the input signals and output response data; discretizing the continuous system model to obtain a target discrete matrix set; and constructing a structured data table based on the predefined scheduling parameters and the target discrete matrix set.

[0141] The apparatus provided in this embodiment solves the problem of performance degradation caused by online discrete matrix calculation in related technologies, and improves the performance of predictive control methods in scenarios with limited computing power.

[0142] In some embodiments, the control module 220 is also configured to directly look up the corresponding discrete matrix in the structured data table if the scheduling parameter is equal to a predefined scheduling parameter.

[0143] If the scheduling parameters are not equal to the predefined scheduling parameters, then an LPV model with time-varying parameters is constructed based on the scheduling parameters and the structured data table; the discrete matrix of the LPV model is used as the discrete matrix.

[0144] In some embodiments, the control module 220 is further configured to perform state estimation based on the output measurement value and the discrete matrix to obtain a state vector;

[0145] The adjustment signal is determined based on the state vector and the pre-calculated gain matrix.

[0146] In some embodiments, the construction module 230 is also used to define scheduling parameters under different typical operating conditions; the scheduling parameters include incoming material temperature, incoming material humidity, air volume and steam flow rate.

[0147] In some of these embodiments, the construction module 230 is also used for typical operating conditions including a first operating condition, a second operating condition, and a third operating condition;

[0148] In the first operating condition stage, the incoming material temperature is higher than the preset temperature threshold and the incoming material humidity is higher than the preset humidity threshold.

[0149] In the second operating condition stage, the incoming material temperature is equal to the temperature threshold and the incoming material humidity is equal to the humidity threshold.

[0150] In the third operating condition stage, the incoming material temperature is lower than the temperature threshold and the incoming material humidity is lower than the humidity threshold.

[0151] In some embodiments, the construction module 230 is also used to reduce the order of the data stored in the structured data table by using the singular value decomposition method after the structured data table is constructed, so as to obtain a reduced-order discrete matrix.

[0152] The gain matrix is ​​pre-calculated offline based on the reduced-order discrete matrix.

[0153] In some embodiments, the construction module 230 is also used to combine the gain matrix and the structured data table to form a complete scheduling mapping table after the gain matrix is ​​calculated.

[0154] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0155] This embodiment also provides a computer device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0156] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0157] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0158] S1 collects the scheduling parameters and output measurements of the process equipment in real time.

[0159] S2 determines the corresponding discrete matrix based on the scheduling parameters and the pre-built structured data table; determines the adjustment signal based on the output measurement value and the discrete matrix; the adjustment signal is used to control the operation of the process equipment.

[0160] S3, in which the structured data table is constructed offline on the simulation platform, the process is as follows: collect the input signals and output response data of the process equipment; construct a continuous system model corresponding to the predefined scheduling parameters based on the input signals and output response data; discretize the continuous system model to obtain the target discrete matrix set; construct the structured data table based on the predefined scheduling parameters and the target discrete matrix set.

[0161] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0162] Furthermore, in conjunction with the PLC platform embedded LPV predictive control method provided in the above embodiments, this embodiment can also provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the PLC platform embedded LPV predictive control methods described in the above embodiments.

[0163] It should be noted that all information and data involved in this application are authorized by the user or fully authorized by all parties and will be used legally.

[0164] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0165] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0166] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0167] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A PLC platform embedded LPV predictive control method, characterized in that, A PLC-embedded LPV predictive control system is applicable, the system comprising process equipment, a PLC platform connected to the process equipment, and a simulation platform connected to the PLC platform; The method is applicable to the PLC platform side; the method includes: Real-time acquisition of scheduling parameters and output measurement values ​​of the process equipment; Based on the scheduling parameters and the pre-built structured data table, determine the corresponding discrete matrix; Based on the output measurement value and the discrete matrix, an adjustment signal is determined; the adjustment signal is used to control the operation of the process equipment. The offline construction of the structured data table on the simulation platform is as follows: Collect the input signals and output response data of the process equipment; Based on the input signal and the output response data, construct a continuous system model corresponding to the predefined scheduling parameters; The continuous system model is discretized to obtain the target set of discrete matrices; The structured data table is constructed based on the predefined scheduling parameters and the target discrete matrix set.

2. The PLC platform embedded LPV predictive control method according to claim 1, characterized in that, The step of determining the corresponding discrete matrix based on the scheduling parameters and the pre-constructed structured data table includes: If the scheduling parameter is equal to the predefined scheduling parameter, then the corresponding discrete matrix is ​​directly looked up in the structured data table; If the scheduling parameters are not equal to the predefined scheduling parameters, then an LPV model with time-varying parameters is constructed based on the scheduling parameters and the structured data table; the discrete matrix of the LPV model is used as the discrete matrix.

3. The PLC platform embedded LPV predictive control method according to claim 1, characterized in that, Determining the adjustment signal based on the output measurement value and the discrete matrix includes: Based on the output measurement value and the discrete matrix, state estimation is performed to obtain the state vector; The adjustment signal is determined based on the state vector and the pre-calculated gain matrix.

4. The PLC platform embedded LPV predictive control method according to claim 1, characterized in that, The predefined scheduling parameters include: Based on different typical operating conditions, the scheduling parameters under the typical operating conditions are defined; the scheduling parameters include incoming material temperature, incoming material humidity, air volume and steam flow rate.

5. The PLC platform embedded LPV predictive control method according to claim 4, characterized in that, According to different typical working conditions, it also includes: The typical operating conditions include the first operating condition, the second operating condition, and the third operating condition; In the first operating condition stage, the incoming material temperature is higher than a preset temperature threshold, and the incoming material humidity is higher than a preset humidity threshold; In the second operating condition stage, the incoming material temperature is equal to the temperature threshold, and the incoming material humidity is equal to the humidity threshold; In the third operating condition stage, the incoming material temperature is lower than the temperature threshold, and the incoming material humidity is lower than the humidity threshold.

6. The PLC platform embedded LPV predictive control method according to claim 1, characterized in that, The method further includes: After constructing the structured data table, the singular value decomposition method is used to reduce the order of the data stored in the structured data table to obtain a reduced-order discrete matrix. Based on the reduced-order discrete matrix, the gain matrix is ​​pre-calculated offline.

7. The PLC platform embedded LPV predictive control method according to claim 6, characterized in that, The method further includes: After calculating the gain matrix, the gain matrix and the structured data table are combined to form a complete scheduling mapping table.

8. A PLC platform embedded LPV predictive control device, characterized in that, include: Acquisition module, control module, and construction module; The acquisition module is used to collect the scheduling parameters and output measurement values ​​of the process equipment in real time; The control module is used to determine the corresponding discrete matrix based on the scheduling parameters and a pre-built structured data table; and to determine an adjustment signal based on the output measurement value and the discrete matrix; the adjustment signal is used to control the operation of the process equipment. The construction module is used to offline construct the structured data table on a simulation platform. The process includes: collecting input signals and output response data of the process equipment; constructing a continuous system model corresponding to predefined scheduling parameters based on the input signals and output response data; discretizing the continuous system model to obtain a target discrete matrix set; and constructing the structured data table based on the predefined scheduling parameters and the target discrete matrix set.

9. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the steps of the PLC platform embedded LPV predictive control method according to any one of claims 1 to 7.

10. A 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 PLC platform embedded LPV predictive control method as described in any one of claims 1 to 7.

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