MODEL PREDICTIVE CONTROL SYSTEM, INFORMATION PROCESSING DEVICE, PROGRAM, AND MODEL PREDICTIVE CONTROL METHOD

Abstract

To improve technology relating to the model prediction control of a process.SOLUTION: A model prediction control system comprises: storage means 131 for storing a process model; process control means 132 for carrying out the model prediction control of a process using the process model; accumulation means 133 for accumulating the detection values of process control output and control input as time-series data; update means 134 capable of executing an update process of updating the model parameters of the process model on the basis of the time-series data accumulated in a learning period; and determination means 135 for determining whether or not to execute the update process. The update means 134 executes the update process except when it is determined by the determination means 135 that the update process is not executed.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a model predictive control system, an information processing device, a program, and a model predictive control method. [Background technology]

[0002] Conventionally, model predictive control (MPC) using a mathematical model of a process to be controlled (hereinafter also referred to as a "process model") has been known as a process control method. In addition, a method of periodically updating a process model has been known to address the issue that the accuracy of model predictive control may decrease when the characteristics of the process change due to factors such as aging of equipment. For example, Patent Document 1 discloses a model predictive control method using online model identification, which discloses a technology for model re-identification in which a process dynamic model to be used is derived each time based on actual operation data when performing model predictive control calculation. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2011-198327 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional technology, model re-identification is performed periodically. However, in a configuration in which model re-identification is always performed in each period, the accuracy of model predictive control may not necessarily be improved. Therefore, there is room for improvement in technology related to model predictive control of processes.

[0005] In view of the above circumstances, an object of the present invention is to improve the technology relating to model predictive control of processes. [Means for solving the problem]

[0006] A model predictive control system according to an embodiment of the present invention includes: A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A model predictive control system comprising: A determination unit for determining whether or not to execute the update process is further provided, The update means executes the update process except when the decision means decides not to execute the update process.

[0007] An information processing device according to an embodiment of the present invention includes: A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; An information processing device comprising: A determination unit for determining whether or not to execute the update process is further provided, The update means executes the update process except when the decision means decides not to execute the update process.

[0008] An information processing device according to an embodiment of the present invention includes: A model predictive control system is provided that includes a storage means for storing a process model, a process control means for performing model predictive control of a process using the process model, a storage means for storing detection values ​​of a control output and a control input of the process as time-series data, an update means capable of executing an update process for updating a model parameter of the process model based on the time-series data stored during a learning period, and a decision means for deciding whether or not to execute the update process, and the update means executes the update process except when it is determined by the decision means not to execute the update process. The system includes at least one of the storage means, the process control means, the accumulation means, the update means, and the determination means.

[0009] A program according to an embodiment of the present invention includes: An information processing device, A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A program that functions as causing the information processing device to further function as a determination unit for determining whether or not to execute the update process; The update means executes the update process except when the decision means decides not to execute the update process.

[0010] A program according to an embodiment of the present invention includes: a process control means for performing model predictive control of a process using the process model; a storage means for storing a process model; a storage means for storing detection values ​​of a control output and a control input of the process as time-series data; an update means for executing an update process for updating a model parameter of the process model based on the time-series data accumulated during a learning period; and a decision means for deciding whether or not to execute the update process. In the model predictive control system, the update means executes the update process by selecting one of the information processing devices, except when the decision means determines not to execute the update process. The device functions as at least one of the storage means, the process control means, the accumulation means, the update means, and the determination means.

[0011] A model predictive control method according to an embodiment of the present invention includes: A model predictive control method for a process executed by an information processing device, comprising: storing the process model; performing model predictive control of a process using the process model; accumulating detection values ​​of control outputs and control inputs of the process as time series data; determining whether to execute an update process for updating model parameters of the process model based on the time-series data accumulated during a learning period; and performing the update process unless it is determined not to perform the update process. Effect of the Invention

[0012] According to one embodiment of the present invention, techniques for model predictive control of a process are improved. [Brief description of the drawings]

[0013] [Figure 1]1 is a diagram showing a schematic configuration of a model predictive control system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating an update period in model predictive control. [Diagram 3] 1 is a block diagram showing a schematic configuration of an information processing device; [Figure 4] FIG. 1 is a diagram showing time-series data stored in an information processing device. [Diagram 5] 11 is a flowchart showing a first operation of the information processing device. [Figure 6] 13 is a flowchart showing a second operation of the information processing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, an embodiment of the present invention will be described.

[0015] (Configuration of Model Predictive Control System) A model predictive control system 1 according to one embodiment of the present invention will be described with reference to Fig. 1. The model predictive control system 1 is a system used for controlling a process in any plant, such as a water treatment plant. The model predictive control system 1 includes an information processing device 10 and one or more plant facilities 20. Although Fig. 1 illustrates one information processing device 10 and three plant facilities 20, the model predictive control system 1 can include any number of information processing devices 10 and plant facilities 20.

[0016] The plant equipment 20 is any equipment used to carry out a process in a plant. For example, the plant equipment 20 used to carry out a water purification process in a water treatment plant includes field equipment such as a blower that blows air to the treated water (e.g., wastewater), a control device such as a PLC (Programmable Logic Controller) that controls the field equipment, a sensor (detection means) that detects process data such as the nitric acid concentration and / or ammonia concentration in the treated water, and an operation amount such as the blowing amount of air blown to the treated water or the driving power of the blower, and a communication device that communicates with the information processing device 10 via a network N such as the Internet or a LAN (Local Area Network). However, the configuration of the plant equipment 20 is not limited to this example, and it is also possible to further include a storage device that temporarily stores the detection value of the sensor.

[0017] The information processing device 10 is installed, for example, in a central control room of a plant, but is not limited thereto and may be installed in any location. The information processing device 10 is capable of communicating with plant equipment 20 installed in the plant via a network N.

[0018] The information processing device 10 is a device that performs model predictive control of an arbitrary process to be controlled. Specifically, the information processing device 10 predicts a future control output using a process model of the process to be controlled for each control cycle, and determines a control input so as to bring the actual control output closer to a target value. For example, when a water purification process in a water treatment plant is to be controlled, process data such as the nitric acid concentration and / or ammonia concentration in the treated water can be set as the control output, and an operation amount such as the amount of air blown to the treated water or the driving power of a blower can be set as the control input. However, the process to be controlled and the control output and control input of the process are not limited to this example and can be determined arbitrarily.

[0019] Furthermore, the information processing device 10 can execute a process of updating model parameters of the process model at each update period while the model predictive control is being performed (hereinafter, also referred to as "update process"). Specifically, while the model predictive control is being performed, the information processing device 10 accumulates the detection values ​​of the control output and control input of the process as time-series data at each control period. The information processing device 10 can execute an update process of updating model parameters of the process model based on the time-series data accumulated during a learning period (hereinafter, also referred to as "learned data"), which will be described later.

[0020] In this embodiment, the update process may be performed in a processing period to be described later. For example, FIG. 2 is a diagram showing the m-th and m+1-th update cycles. Each update cycle includes a learning period and a processing period following the learning period. The learning period is a period for accumulating learning data used in the update process of the model parameters. The processing period is a period for determining whether or not to execute the update process of the model parameters, and for executing the update process according to the result of the determination. In FIG. 2, T indicates the length of the update cycle, Ta indicates the length of the learning period, and Tb indicates the length of the processing period. Note that the length of the control cycle of the model predictive control is sufficiently short compared with the lengths T, Ta, and Tb of the update cycle, the learning period, and the processing period, respectively. For example, the model parameters may be updated in the processing period of the m-th update cycle based on the learning data accumulated in the learning period of the m-th update cycle. Then, when the model parameters are updated, the model predictive control is performed in the m+1-th update cycle using the updated model parameters. On the other hand, when the model parameters are not updated, the model predictive control is also performed in the m+1-th update cycle using the model parameters having the same values ​​as those in the m-th update cycle.

[0021] In this embodiment, the information processing device 10 determines whether to perform an update process for each update period, and performs the update process except when it is determined not to perform the update process. With this configuration, whether to perform an update process for the model parameters is automatically determined. Therefore, by appropriately determining whether to perform the update process, it is possible to update the model parameters while avoiding inappropriate situations, such as when an abnormality occurs in a sensor that detects the control output and the control input, thereby improving the technology related to model predictive control of a process.

[0022] (Hardware configuration of information processing device) The hardware configuration of the information processing device 10 will be described with reference to Fig. 3. The information processing device 10 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0023] The communication unit 11 is one or more communication interfaces that communicate with an external device wirelessly or via a wire. In this embodiment, the communication unit 11 includes a communication interface that communicates with the plant equipment 20 via a network N. The communication unit 11 also includes communication interfaces that communicate with each of the display device 30 and the input device 40. Here, the display device 30 is any display, such as a liquid crystal display or an OEL (Organic Electro-luminescence) display. The input device 40 is any input interface that accepts operations by a user, such as a keyboard or a mouse.

[0024] The storage unit 12 is one or more memories. In this embodiment, the "memory" is, for example, a semiconductor memory, a magnetic memory, an optical memory, or the like, but is not limited to these and may be any memory. Each memory included in the storage unit 12 functions as, for example, a main memory device, an auxiliary memory device, or a cache memory. The storage unit 12 is, for example, built into the information processing device 10, but may also be configured to be connected to the information processing device 10 from the outside via any interface.

[0025] The control unit 13 includes one or more processors. In this embodiment, the "processor" may be a general-purpose processor, a dedicated processor specialized for a specific process, or the like, but is not limited to these and may be any processor. The control unit 13 controls the operation of the entire information processing device 10. The operation of the information processing device 10 will be described in detail later.

[0026] (Software configuration of information processing device) The software configuration of the information processing device 10 will be described with reference to Fig. 3. One or more programs used to control the operation of the information processing device 10 are stored in the storage unit 12. When the one or more programs are loaded by the control unit 13, they cause the control unit 13 to function as a storage means 131, a process control means 132, an accumulation means 133, an update means 134, and a determination means 135.

[0027] The following provides an overview of each of the means 131 to 135. The storage means 131 is a means for storing a process model of a process to be controlled in the storage unit 12. The process model is a mathematical model having one or more model parameters. The process model further includes information on the initial value of each model parameter. The process model can be determined in advance, for example, by an experiment or a simulation.

[0028] The process control means 132 is a means for implementing model predictive control of the process using the process model stored in the storage unit 12. Specifically, the process control means 132 predicts a future control output (e.g., process data such as nitric acid concentration and / or ammonia concentration) for each control cycle based on the process model stored in the storage unit 12, determines a control input (e.g., an operation amount such as an air volume or a driving power of a blower) so as to bring an actual control output closer to a target value, and inputs a command value of the determined control input to the plant equipment 20. Note that the target value of the control output can be changed at any timing, in response to a user operation or automatically.

[0029] The accumulation means 133 is a means for accumulating the target value of the control output of the process, the detected value of the control output, and the detected value of the control input in the storage unit 12 as time-series data for each control cycle. For example, as shown in Fig. 4, a combination of time data, the target value of the control output, the detected value of the control output, and the detected value of the control input is accumulated in the storage unit 12 as time-series data. Note that the detected values ​​of the control output and the control input are numerical values ​​detected by a sensor of the plant equipment 20. Also, the detected value of the control input by the sensor of the plant equipment 20 does not necessarily have to match the command value of the control input inputted to the plant equipment 20 by the above-mentioned process control means 132.

[0030] The update means 134 is a means for executing an update process for updating the model parameters of the process model based on the time-series data (i.e., learning data) accumulated in the storage unit 12 during a learning period. As will be described later, the update means 134 executes the update process except when the decision means 135 decides not to execute the update process.

[0031] The determination means 135 is a means for determining whether or not to execute the update process for each update period. Here, any algorithm can be adopted for determining whether or not to execute the update process. A specific example of the algorithm will be described later.

[0032] In addition to the above-mentioned means 131 to 135, the control unit 13 can also be configured to further function as a means for performing any processing that can generally be executed by a processor (for example, a means for executing general arithmetic processing and timing processing, etc.).

[0033] (Operation of information processing device) The operation of the information processing device 10 will be described with reference to Figures 5 and 6. Figure 5 is a flowchart showing a first operation for implementing model predictive control of a process.

[0034] Step S100: The storage means 131 stores in the storage unit 12 a process model of a process to be controlled.

[0035] Step S101: The control unit 13 performs initial settings for model predictive control. Specifically, the control unit 13 sets the variable i to 1. The variable i is a variable indicating which update cycle the current time counted from the start of model predictive control belongs to. Assuming the current time counted from the start of model predictive control is t and the length of the update cycle is T, when (i - 1)T ≤ t < iT, the current time t belongs to the i-th update cycle. Also, the control unit 13 sets the model parameters of the process model to the initial values.

[0036] Step S102: The process control means 132 predicts future control outputs based on the process model and determines the control input so as to bring the actual control output closer to the target value. The process control means 132 inputs the command value of the determined control input to the plant facility 20 via the communication unit 11. Note that the process control means 132 can change the target value of the control output at an arbitrary timing in response to user operations or automatically as described above.

[0037] Step S103: When the accumulation means 133 acquires the detected values of the control output and control input of the process from the plant facility 20 via the communication unit 11, it accumulates the combination of the time data, the target value of the control output, the detected value of the control output, and the detected value of the control input as time-series data in the storage unit 12. Then, the operation returns to Step S102. Steps S102 and S103 are repeatedly executed for each control cycle.

[0038] Figure 6 is a flowchart showing the second operation for executing the update process of the model parameters. The second operation is performed within the processing period in each update period. Also, the second operation is performed in parallel with the process of repeating Steps S102 and S103 in the above-described first operation.

[0039] Step S200: The decision means 135 decides whether or not to execute the update process of the model parameters based on the learning data accumulated during the learning period of the i-th update cycle. A specific algorithm for deciding whether or not to execute the update process will be described later. If it is decided not to execute the update process (step S200-No), the operation proceeds to step S204. On the other hand, except for the case where it is decided not to execute the update process (step S200-Yes), the operation proceeds to step S201.

[0040] Step S201: The update means 134 estimates model parameters based on the learning data accumulated during the learning period of the i-th update cycle. Specifically, the update means 134 searches for estimated values ​​of the model parameters by machine learning using the learning data.

[0041] Step S202: The control unit 13 waits until the end of the i-th update cycle. When the i-th update cycle ends (step S202-Yes), the operation proceeds to step S203.

[0042] Step S203: The update means 134 updates the model parameters with the estimated values ​​found in step S201. After that, the operation proceeds to step S205.

[0043] Step S204: The control unit 13 waits until the end of the i-th update cycle. When the i-th update cycle ends (step S204-Yes), the operation proceeds to step S205.

[0044] Step S205: The control unit 13 increments the variable i, and then the operation ends.

[0045] (Operation of decision means) The operation of the decision means 135 for deciding whether or not to execute the update process of the model parameters will be specifically described. In this embodiment, there are a number of algorithms by which the decision means 135 decides not to execute the update process.

[0046] First, the first algorithm will be described. When a state in which the detection value of the control output or control input substantially matches the first reference value continues for a predetermined reference time during the learning period, the decision means 135 determines that there is an abnormality in the learning data and decides not to execute the update process. Here, the first reference value is the detection value of the control output or control input at an arbitrary reference time during the learning period, or the upper detection limit value or the lower detection limit value of a sensor that detects the control output or control input. In addition, the reference time may be determined as an absolute value or a relative value (for example, n% of the length Ta of the learning period, etc.).

[0047] In this embodiment, two values ​​being "substantially the same" means that the two values ​​are close enough to be considered to be the same in practice. For example, when the absolute value of the difference between the detection value of the control output or control input and the first reference value is less than a predetermined threshold, it can be determined that the detection value is substantially the same as the first reference value. The threshold may be determined as an absolute value or as a relative value (e.g., n% of the first reference value).

[0048] Here, in a situation different from the normal operation of the plant, an abnormality may occur in the detection value (i.e., learning data) of the control output or control input. For example, during calibration work of the plant equipment 20, the detection value may not change and may be a constant value. Also, for example, if a sensor breaks down or the amount of the object to be treated in the process (for example, the inflow amount of treated water in a water purification process) changes significantly compared to normal operation, the detection value may not change from the upper detection limit value or the lower detection limit value and may be a constant value. In contrast, according to the first algorithm, it is possible to update the model parameters while avoiding a situation where there is an abnormality in the learning data.

[0049] The second algorithm will be described. When the variance or standard deviation of the detection value of the control output or control input of the process is less than a predetermined reference value during a reference period that is at least a part of the learning period, the decision means 135 determines that the learning data is abnormal and decides not to execute the update process. Here, the length of the reference period is, for example, determined in advance. When the length of the reference period is shorter than the length Ta of the learning period, the decision means 135 sequentially determines whether the variance or standard deviation of the detection value of the control output or control input of the process is less than a predetermined reference value while shifting the start position of the reference period in the learning period. According to the second algorithm, like the above-mentioned first algorithm, it is possible to update the model parameters while avoiding a situation where the learning data is abnormal.

[0050] The third algorithm will now be described. If the total time during the learning period during which the detection value of the control output or control input of the process substantially matches a second reference value, which will be described later, is equal to or longer than a predetermined reference time, the decision means 135 determines that there is an abnormality in the learning data and decides not to execute the update process. Here, the second reference value is the upper or lower detection limit value of the sensor that detects the control output or control input. The reference time may be determined as an absolute value or a relative value (for example, n% of the length Ta of the learning period, etc.).

[0051] Here, in a situation different from the normal operation of the plant, the detection value of the control output or control input may not change from the upper or lower detection limit as described above. However, even in such a situation, the detection value may momentarily deviate from the upper or lower detection limit due to the influence of, for example, a disturbance. In contrast, according to the third algorithm, even if the detection value momentarily deviates from the upper or lower detection limit, it is possible to update the model parameters while avoiding a situation in which there is an abnormality in the learning data.

[0052] The fourth algorithm will be described. When the detection value of the control output of the process substantially matches the target value over the learning period, the decision means 135 judges that the model predictive control is being stably performed and decides not to execute the update process. According to the fourth algorithm, when the control output of the process accurately tracks the target value, the model parameters are not updated, so that overfitting in machine learning that searches for the estimated values ​​of the model parameters can be mitigated or prevented.

[0053] The fifth algorithm will be described. When the target value of the control output of the process changes within the learning period, if the amount of change in the target value of the control output of the process is less than a predetermined reference value, and the detected value of the control output of the process substantially matches the target value over the period of the learning period excluding the tracking period after the change in the target value, the determining means 135 determines that the model predictive control is being stably performed and determines not to execute the update process. Here, the reference value may be determined as an absolute value or a relative value (e.g., n% of the target value or detected value of the control output). The tracking period is a period during which the control output of the process tracks the changed target value, and can be determined by any method. For example, the tracking period can be a period from the time the target value is changed until a predetermined time (e.g., 4 hours) has elapsed, or a period during which a state in which a numerical derivative is calculated for a moving average of the detected value of the control output and the derivative value is equal to or greater than a predetermined reference value continues. According to the fifth algorithm, when the control output of the process accurately tracks the target value before and after the change, the model parameters are not updated, so that overfitting in machine learning that searches for estimated values ​​of the model parameters can be mitigated or prevented.

[0054] The sixth algorithm will be described. When the target value of the control output of the process changes within the learning period, if an evaluation value (for example, a tracking time or a numerical derivative of a moving average of a detection value of the control output) indicating the tracking ability of the control output of the process to the changed target value is within a predetermined reference range, the decision means 135 determines that the model predictive control is being stably performed and decides not to execute the update process. Here, the "reference range" may be a range that specifies only the upper limit of the evaluation value, or may be a range that specifies the upper and lower limits of the evaluation value. According to the sixth algorithm, when the control output of the process quickly tracks the change in the target value, the model parameters are not updated, so that overfitting in machine learning that searches for an estimated value of the model parameters can be mitigated or prevented.

[0055] The decision means 135 decides to execute the update process except when it is decided not to execute the update process by at least one of the above-mentioned first to sixth algorithms.

[0056] Although the present invention has been described based on the drawings and embodiments, it should be noted that those skilled in the art can easily make various modifications and corrections based on the present disclosure. Therefore, it should be noted that these modifications and corrections are included in the scope of the present invention. For example, the functions included in each means, each step, etc. can be rearranged so as not to be logically inconsistent, and multiple means, steps, etc. can be combined into one or divided into one.

[0057] For example, the model predictive control system 1 may be configured to include a plurality of information processing devices 10. Also, each component or each means of one information processing device 10 according to the embodiment may be distributed and arranged in a plurality of devices that can communicate with each other.

[0058] In the above-described embodiment, an example of the operation of the information processing device 10 has been described with reference to Fig. 5 and Fig. 6. However, a configuration in which some steps included in the above-described operation, or some operations included in one step, are omitted as long as it is not logically inconsistent is also possible. Also, a configuration in which the order of multiple steps included in the above-described operation is changed as long as it is not logically inconsistent is also possible.

[0059] In the above embodiment, each of the means realized by the control unit 13 has been described as a software configuration, but at least some of these means may be a concept including software resources and / or hardware resources. For example, the storage means 131 and the accumulation means 133 may include a memory.

[0060] Moreover, a general-purpose information processing device such as a computer can be used to function as the information processing device 10 according to the above-described embodiment. The information processing device can be realized by storing a program describing the processing contents for realizing each function of the information processing device 10 according to the embodiment in a memory of the information processing device, and reading and executing the program by a processor of the information processing device. [Explanation of symbols]

[0061] 1. Model predictive control system 10. Information processing device 11 Communications Department 12 Storage section 13 Control section 131 Memory means 132 Process Control Means 133 Means of storage 134 Update means 135 Decision-making procedures 20 Plant Facilities 30 Display device 40 Input Devices N Network

Claims

1. A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A model predictive control system comprising: A determination unit for determining whether or not to execute the update process is further provided, The determining means is configured to determine whether or not an anomaly exists in the time-series data, When the decision means determines that there is an abnormality in the time-series data, it decides not to execute the update process; The update means executes the update process except when the decision means decides not to execute the update process; The decision means decides not to execute the update process based on any one of the following (1) to (3). (1) the determination means determines not to execute the update process when a state in which a detection value of a control output or a control input of the process substantially matches a first reference value continues for a reference time during the learning period; The first reference value is a detected value of a control output or control input of the process, or an upper detection limit or lower detection limit value of a sensor that detects the control output or control input of the process, at a reference time within the learning period. (2) The decision means decides not to execute the update process if the variance or standard deviation of the detection value of the control output or control input of the process is less than a reference value during a reference period, which is at least a part of the learning period. (3) the decision means decides not to execute the update process when a total time during which the detection value of the control output or the control input of the process substantially matches a second reference value during the learning period is equal to or longer than a reference time; The second reference value is an upper or lower detection limit value of a sensor that detects a control output or a control input of the process.

2. A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A model predictive control system comprising: A determination unit for determining whether or not to execute the update process is further provided, The update means executes the update process except when the decision means decides not to execute the update process; the decision means decides not to execute the update process if, when the target value of the control output of the process changes within the learning period, the amount of change in the target value of the control output of the process is less than a reference value and the detection value of the control output of the process substantially matches the target value over a period of the learning period excluding a tracking period after the change in the target value.

3. A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A model predictive control system comprising: A determination unit for determining whether or not to execute the update process is further provided, The update means executes the update process except when the decision means decides not to execute the update process; the decision means decides not to execute the update process when, in a case where a target value of a control output of the process has changed within the learning period, an evaluation value indicating the tracking of the control output of the process to the changed target value is within a predetermined reference range.

4. A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; An information processing device comprising: A determination unit for determining whether or not to execute the update process is further provided, The determining means is configured to determine whether or not an anomaly exists in the time-series data, When the decision means determines that there is an abnormality in the time-series data, it decides not to execute the update process; The update means executes the update process except when the decision means decides not to execute the update process; The information processing device, wherein the decision means decides not to execute the update process based on any one of the following (1) to (3). (1) the determination means determines not to execute the update process when a state in which a detection value of a control output or a control input of the process substantially matches a first reference value continues for a reference time during the learning period; The first reference value is a detected value of a control output or control input of the process, or an upper detection limit or lower detection limit value of a sensor that detects the control output or control input of the process, at a reference time within the learning period. (2) The decision means decides not to execute the update process if the variance or standard deviation of the detection value of the control output or control input of the process is less than a reference value during a reference period, which is at least a part of the learning period. (3) the decision means decides not to execute the update process when a total time during which the detection value of the control output or the control input of the process substantially matches a second reference value during the learning period is equal to or longer than a reference time; The second reference value is an upper or lower detection limit value of a sensor that detects a control output or a control input of the process.

5. A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A model predictive control system comprising: A determination unit for determining whether or not to execute the update process is further provided, The update means executes the update process except when the decision means decides not to execute the update process; The decision means decides not to execute the update process if, when the target value of the control output of the process changes within the learning period, the amount of change in the target value of the control output of the process is less than a reference value, and the detection value of the control output of the process approximately matches the target value over a period of the learning period excluding a tracking period after the change in the target value.

6. A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A model predictive control system comprising: A determination unit for determining whether or not to execute the update process is further provided, The update means executes the update process except when the decision means decides not to execute the update process; The decision means, when a target value of the control output of the process changes during the learning period, decides not to execute the update process if an evaluation value indicating the tracking of the control output of the process to the changed target value is within a predetermined reference range.

7. a storage means for storing a process model, a process control means for performing model predictive control of a process using the process model, a storage means for storing detection values ​​of a control output and a control input of the process as time series data, an update means capable of executing an update process for updating a model parameter of the process model based on the time series data stored during a learning period, and a decision means for deciding whether or not to execute the update process, wherein the decision means is configured to determine the presence or absence of an abnormality in the time series data, and when the decision means determines that there is an abnormality in the time series data, the decision means decides not to execute the update process, and the update means executes the update process except when it has been determined by the decision means not to execute the update process, The method includes at least one of the storage means, the process control means, the accumulation means, the update means, and the determination means, The information processing device, wherein the decision means decides not to execute the update process based on any one of the following (1) to (3). (1) the determination means determines not to execute the update process when a state in which a detection value of a control output or a control input of the process substantially matches a first reference value continues for a reference time during the learning period; The first reference value is a detected value of a control output or control input of the process, or an upper detection limit or lower detection limit value of a sensor that detects the control output or control input of the process, at a reference time within the learning period. (2) The decision means decides not to execute the update process if the variance or standard deviation of the detection value of the control output or control input of the process is less than a reference value during a reference period, which is at least a part of the learning period. (3) the decision means decides not to execute the update process when a total time during which the detection value of the control output or the control input of the process substantially matches a second reference value during the learning period is equal to or longer than a reference time; The second reference value is an upper or lower detection limit value of a sensor that detects a control output or a control input of the process.

8. a storage means for storing a process model, a process control means for performing model predictive control of a process using the process model, a storage means for storing detection values ​​of a control output and a control input of the process as time series data, a storage means for storing detection values ​​of a control output and a control input of the process as time series data stored during a learning period, an update means capable of executing an update process for updating a model parameter of the process model based on the time series data stored during a learning period, and a decision means for deciding whether to execute the update process, wherein the update means executes the update process except when it has been determined by the decision means not to execute the update process, and the decision means decides not to execute the update process when a target value of a control output of the process has changed during the learning period, an amount of change in the target value of the control output of the process is less than a reference value, and the detection value of the control output of the process substantially matches the target value over a period of the learning period excluding a tracking period after the change in the target value, An information processing apparatus comprising at least one of the storage means, the process control means, the accumulation means, the update means, and the determination means.

9. a process control means for performing model predictive control of a process using the process model; a storage means for storing a process model; a storage means for storing detection values ​​of a control output and a control input of the process as time series data; an update means capable of executing an update process for updating a model parameter of the process model based on the time series data accumulated during a learning period; and a decision means for deciding whether to execute the update process, wherein the update means executes the update process except when it is determined by the decision means not to execute the update process, and the decision means decides not to execute the update process when a target value of a control output of the process has changed during the learning period and an evaluation value indicating the tracking ability of the control output of the process to the changed target value is within a predetermined reference range, An information processing apparatus comprising at least one of the storage means, the process control means, the accumulation means, the update means, and the determination means.

10. An information processing device, A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A program that functions as causing the information processing device to further function as a determination unit for determining whether or not to execute the update process; The determining means is configured to determine whether or not an anomaly exists in the time-series data, When the decision means determines that there is an abnormality in the time-series data, it decides not to execute the update process; The update means executes the update process except when the decision means decides not to execute the update process; The decision means decides not to execute the update process based on any one of the following (1) to (3). (1) the determination means determines not to execute the update process when a state in which a detection value of a control output or a control input of the process substantially matches a first reference value continues for a reference time during the learning period; The first reference value is a detected value of a control output or control input of the process, or an upper detection limit or lower detection limit value of a sensor that detects the control output or control input of the process, at a reference time within the learning period. (2) The decision means decides not to execute the update process if the variance or standard deviation of the detection value of the control output or control input of the process is less than a reference value during a reference period, which is at least a part of the learning period. (3) the decision means decides not to execute the update process when a total time during which the detection value of the control output or the control input of the process substantially matches a second reference value during the learning period is equal to or longer than a reference time; The second reference value is an upper or lower detection limit value of a sensor that detects a control output or a control input of the process.

11. An information processing device, A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A program that functions as causing the information processing device to further function as a determination unit for determining whether or not to execute the update process; The update means executes the update process except when the decision means decides not to execute the update process; The decision means decides not to execute the update process if, when the target value of the control output of the process changes within the learning period, the amount of change in the target value of the control output of the process is less than a reference value and the detection value of the control output of the process approximately matches the target value over a period of the learning period excluding a tracking period after the change in the target value.

12. An information processing device, A storage means for storing a process model; a process control means for performing model predictive control of a process using the process model; a storage means for storing detected values ​​of a control output and a control input of the process as time series data; an update means for executing an update process for updating model parameters of the process model based on the time series data accumulated during a learning period; A program that functions as The information processing device further functions as a determination unit that determines whether or not to execute the update process; The update means executes the update process except when the decision means decides not to execute the update process; The decision means, when a target value of the control output of the process changes within the learning period, decides not to execute the update process if an evaluation value indicating the tracking of the control output of the process to the changed target value is within a predetermined reference range.

13. a process control unit that performs model predictive control of a process using the process model; a storage unit that stores a process model; a storage unit that stores detection values ​​of a control output and a control input of the process as time series data; an update unit that is capable of executing an update process for updating a model parameter of the process model based on the time series data stored during a learning period; and a decision unit that decides whether or not to execute the update process, wherein the decision unit is configured to determine the presence or absence of an abnormality in the time series data, and when the decision unit determines that there is an abnormality in the time series data, the decision unit decides not to execute the update process, and the update unit executes the update process by selecting one of the information processing units among the plurality of information processing units, except when the decision unit has determined not to execute the update process. functioning as at least the determining means among the storage means, the process control means, the accumulation means, the update means, and the determining means; The decision means decides not to execute the update process based on any one of the following (1) to (3). (1) the determination means determines not to execute the update process when a state in which a detection value of a control output or a control input of the process substantially matches a first reference value continues for a reference time during the learning period; The first reference value is a detected value of a control output or control input of the process, or an upper detection limit or lower detection limit value of a sensor that detects the control output or control input of the process, at a reference time within the learning period. (2) The decision means decides not to execute the update process if the variance or standard deviation of the detection value of the control output or control input of the process is less than a reference value during a reference period, which is at least a part of the learning period. (3) the decision means decides not to execute the update process when a total time during which the detection value of the control output or the control input of the process substantially matches a second reference value during the learning period is equal to or longer than a reference time; The second reference value is an upper or lower detection limit value of a sensor that detects a control output or a control input of the process.

14. a process control unit that performs model predictive control of a process using the process model; a storage unit that stores a process model; a storage unit that stores detection values ​​of a control output and a control input of the process as time-series data; an update unit that is capable of executing an update process for updating a model parameter of the process model based on the time-series data stored during a learning period; and a decision unit that decides whether to execute the update process, wherein the update unit executes the update process except when it is determined by the decision unit not to execute the update process, and the decision unit decides not to execute the update process when a target value of the control output of the process changes within the learning period, an amount of change in the target value of the control output of the process is less than a reference value, and the detection value of the control output of the process substantially matches the target value over a period of the learning period excluding a tracking period after the change in the target value, A program that causes the device to function as at least one of the storage means, the process control means, the accumulation means, the update means, and the determination means.

15. a process control unit that performs model predictive control of a process using the process model; a storage unit that stores a process model; a storage unit that stores detection values ​​of a control output and a control input of the process as time-series data; an update unit that is capable of executing an update process for updating a model parameter of the process model based on the time-series data stored during a learning period; and a decision unit that decides whether to execute the update process, wherein the update unit executes the update process except when the decision unit decides not to execute the update process, and the decision unit decides not to execute the update process when a target value of a control output of the process has changed during the learning period and an evaluation value indicating the tracking ability of the control output of the process to the changed target value is within a predetermined reference range, A program that causes the device to function as at least one of the storage means, the process control means, the accumulation means, the update means, and the determination means.

16. A model predictive control method for a process executed by an information processing device, comprising: storing the process model; performing model predictive control of a process using the process model; accumulating detection values ​​of control outputs and control inputs of the process as time series data; determining whether or not there is an abnormality in the time series data; determining whether or not to execute an update process for updating model parameters of the process model based on the time-series data accumulated during a learning period, depending on the presence or absence of the abnormality; executing the update process except when it is determined that the abnormality exists and therefore it is determined not to execute the update process; Including, A model predictive control method comprising: determining not to execute the update process based on any one of the following (1) to (3): (1) determining not to execute the update process when a state in which a detection value of a control output or a control input of the process substantially matches a first reference value continues for a reference time during the learning period; The first reference value is a detected value of a control output or control input of the process, or an upper detection limit or lower detection limit value of a sensor that detects the control output or control input of the process, at a reference time within the learning period. (2) If the variance or standard deviation of the detected value of the control output or control input of the process is less than a reference value during a reference period, which is at least a part of the learning period, it is decided not to execute the update process. (3) determining not to execute the update process when a total time during which the detection value of the control output or the control input of the process substantially matches a second reference value during the learning period is equal to or longer than a reference time; The second reference value is an upper or lower detection limit value of a sensor that detects a control output or a control input of the process.

17. A model predictive control method for a process executed by an information processing device, comprising: storing the process model; performing model predictive control of a process using the process model; accumulating detection values ​​of control outputs and control inputs of the process as time series data; executing an update process for updating model parameters of the process model based on the time-series data accumulated during a learning period; determining whether to perform the update process; Including, Performing the update process unless it is determined not to perform the update process; a model prediction method comprising: determining not to execute the update process when a target value of a control output of the process changes within the learning period, if the amount of change in the target value of the control output of the process is less than a reference value, and if the detection value of the control output of the process substantially matches the target value over a period of the learning period excluding a tracking period after the change in the target value.

18. A model predictive control method for a process executed by an information processing device, comprising: storing the process model; performing model predictive control of a process using the process model; accumulating detection values ​​of control outputs and control inputs of the process as time series data; executing an update process for updating model parameters of the process model based on the time-series data accumulated during a learning period; determining whether to perform the update process; Including, Performing the update process unless it is determined not to perform the update process; a model prediction method in which, when a target value of a control output of the process changes within the learning period, a determination is made not to execute the update process if an evaluation value indicating the tracking of the control output of the process to the changed target value is within a predetermined reference range.

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