JP6813416B2 - Plant control device and its control method, rolling mill control device and its control method and program - Google Patents

Description

The present invention relates to a plant control device and its control method, a rolling mill control device and its control method, and a program in real-time feedback control performed by using an artificial intelligence technique such as a neural network.

Conventionally, in various plants, plant control based on various control theories has been carried out in order to obtain a desired control result by the control.

As an example of a plant, for example, in rolling mill control, fuzzy control and neuro-fuzzy control have been applied as control theories for shape control for controlling the wavy state of a plate as an example of control. Fuzzy control is applied to shape control using coolant, and neuro-fuzzy control is applied to shape control of a sentimia rolling mill. Of these, the shape control to which the neuro-fuzzy control is applied is similar to the difference between the actual shape pattern detected by the shape detector and the target shape pattern and the preset reference shape pattern, as shown in Patent Document 1. The ratio is obtained, and the control output amount for the operation end is obtained from the similar ratio according to the control rule expressed by the control operation end operation amount for the reference shape pattern set in advance. Hereinafter, as a conventional technique, shape control of a sendimia rolling mill using neuro-fuzzy control will be used.

FIG. 5 shows the shape control of the Sendimia rolling mill described in FIG. 1 of Patent Document 1. Neuro-fuzzy control is used to control the shape of the Sendimia rolling mill. In this example, the pattern recognition mechanism 51 recognizes the shape pattern from the actual shape detected by the shape detector 52, and calculates which of the preset reference shape patterns the actual shape is closest to. The control calculation mechanism 53 executes control using a control rule composed of a control operation end operation amount for a preset shape pattern as shown in FIG. More specifically, FIG. 6 shows that in the pattern recognition mechanism 51, the difference (Δε) between the actual shape detected by the shape detector 52 and the target shape (εref) is the shape pattern (ε) of 1 to 8. The closest control method is calculated, and the control calculation mechanism 53 selects and executes any of the control methods 1 to 8.

However, in the method of Patent Document 1, in order to verify the control rule, the operator may perform a manual operation during rolling to verify the control rule, but the shape may change unexpectedly. That is, there may be a case where the control rule determined as described above does not conform to the reality. This is due to insufficient examination of mechanical characteristics and changes in the operating conditions and machine conditions of the rolling mill, but it should be considered to verify whether the preset control rules are the best rules one by one. There are many conditions and it is difficult. Therefore, once the control rule is set, it is often left as it is unless there is a problem.

When the control rules do not conform to the reality due to changes in operating conditions or the like, the control rules are fixed, and it becomes difficult to obtain control accuracy above a certain level. Further, once the shape control is operated, the operator does not perform the manual operation (it becomes a disturbance for the control), so that it is difficult to find a new control rule by the manual intervention of the operator. Furthermore, when rolling a rolled material of a new standard, it is difficult to set control rules according to the material.

As described above, in the conventional shape control, since the control is performed by using the preset control rule, there is a problem that it is difficult to modify the control rule.

In order to solve this problem, as shown in Patent Document 2, the control rule is randomly changed while performing shape control, and the rule for improving the shape is learned.
1) Discover new control rules while controlling the shape during rolling.
2) New control rules are not predictable in advance, and control rules that could not be predicted at all may be optimal. Therefore, operate the control operation end at random and find it while looking at the control results for it. I will go.
I have realized that.

Patent No. 2804161 Patent No. 4000733

In the above-mentioned prior art, a representative shape is set in advance as a reference shape pattern, and control is performed based on a control rule indicating a relationship with a control operation end operation amount with respect to the reference waveform pattern. Also in learning the control rule, it is related to the control operation end operation amount with respect to the reference waveform pattern, and the typical reference shape pattern defined in advance is used as it is. Therefore, there is a problem that the shape control reacts only to a specific shape pattern.

The standard shape pattern is determined by human beings in advance based on their knowledge of the target rolling mill and the experience of accumulating shape results and manual intervention operations, but all shapes generated in the target rolling mill and the material to be rolled It is difficult to cover. Therefore, when a shape different from the reference shape pattern is generated, the control by the shape control is not performed and the shape deviation remains unsuppressed, or it is erroneously recognized as a similar reference shape pattern and erroneously controlled. In some cases, the shape may be deteriorated by performing an operation.

As described above, in the conventional shape control, since the control rule is learned and controlled by using the preset reference shape pattern and the control rule for it, there is a problem that there is a limit to the improvement of the control accuracy. There was.

From the above, in the present invention, "a plant control device that recognizes a pattern of combination of actual data of a controlled plant with respect to the controlled plant and performs control.
It is equipped with a control method learning device that learns the combination of the actual data of the controlled plant and the control operation, and a control execution device that controls the controlled plant according to the combination of the learned actual data and the control operation.
The control execution device determines whether or not the control rule execution unit that gives control output according to the specified combination of the actual data of the controlled plant and the control operation and the control output output by the control rule execution unit, and the actual data When the control output judgment unit that notifies the control method learning device that the control operation is incorrect and the control output judgment unit output the control output to the control target plant, it is determined that the actual data of the control target plant deteriorates. Is equipped with a control output suppression unit that prevents the control output from being output to the controlled plant.
In the control method learning device, when the control execution device actually outputs the control output to the controlled plant, the actual data becomes better than before the control after a time delay until the control effect appears in the actual data. A control result quality judgment unit that determines the quality of the control result as to whether it has become worse or worse, a learning data creation unit that obtains teacher data using the control output, and a training data creation unit that obtains teacher data using the control result quality judgment unit. It is equipped with a control rule learning unit that learns the teacher data as learning data, and by learning by the control method learning device, it is possible to obtain separate actual data and control operations for multiple control targets according to the state of the controlled plant. A plant control device characterized in that a combination is obtained and the combination of the obtained actual data and the control operation is used as a defined combination of the actual data of the controlled target plant and the control operation in the control rule execution unit. ".

Further, the present invention is characterized in that "a rolling mill control device to which a plant control device is applied, the controlled plant is a rolling mill, and the actual data is the shape of the outside of the rolling mill. . ".

Further, the present invention is a plant control method for recognizing a pattern of combination of actual data of a controlled plant with respect to the controlled plant and performing control.
It is equipped with a control method learning device that learns the combination of the actual data of the controlled plant and the control operation, and a control execution device that controls the controlled plant according to the combination of the learned actual data and the control operation.
The control execution device gives a control output according to a predetermined combination of the actual data of the controlled plant and the control operation, determines whether or not the control output is possible, and determines that the actual data and the control operation are incorrect. If it is determined that the actual data of the controlled plant will deteriorate when the control output is output to the controlled plant, the control output is prevented from being output to the controlled plant.
In the control method learning device, when the control execution device actually outputs the control output to the controlled plant, the actual data becomes better than before the control after a time delay until the control effect appears in the actual data. By judging the quality of the control result as to whether it is bad or bad, obtaining the teacher data using the control result and the control output, and learning the actual data and the teacher data as training data, the state of the controlled plant. Separate performance data and control operation combinations are obtained for multiple control targets according to the above, and the combination of the obtained performance data and control operation is determined by the control target plant performance data and control operation in the control rule execution unit. A plant control method characterized in that it is used as a combination. ".

Further, the present invention is "a rolling mill control method to which a plant control method is applied, wherein the controlled plant is a rolling mill, and the actual data is the shape of the outside of the rolling mill. Method. "

Further, the present invention is a program for realizing a plant control device that recognizes a pattern of a combination of actual data of a controlled plant for a controlled plant and executes control by a computer system.
The computer system includes a control method learning device that learns the combination of the actual data of the controlled plant and the control operation, and a control execution device that controls the controlled plant according to the combination of the learned actual data and the control operation. ,
The program
A control rule execution program that gives control output according to a predetermined combination of control target plant performance data and control operations to achieve the processing of the control execution device, and determines whether or not the control output output by the control rule execution program is possible. At the same time, when the control output determination program that notifies the control method learning device that the actual data and the control operation are incorrect and the control output determination program outputs the control output to the control target plant, the control target plant It is a control output suppression program that prevents the control output from being output to the controlled plant when it is determined that the actual data deteriorates.
When the control execution device actually outputs the control output to the controlled plant in order to achieve the processing of the control method learning device, the actual data is controlled after a time delay until the control effect appears in the actual data. A control result quality determination program for achieving the control result quality determination process for determining whether the control result is good or bad compared to the previous one, and the control result quality in the control result quality determination program. , A learning data creation program that obtains teacher data using control output, and a control rule learning program that learns the actual data and the teacher data as training data.
By learning the control method learning device, separate performance data and control operation combinations are obtained for a plurality of control targets according to the state of the control target plant, and the obtained performance data and control operation combination is described above. A program characterized in that it is used as a defined combination of actual data of a controlled plant and control operations in a control rule execution program. ".

By using the present invention, it is possible to automatically modify the control rules of the shape pattern and the operation method used in the shape control during the control to obtain the optimum one. Therefore, there are effects such as improvement of control accuracy, shortening of the start-up period of the control unit, and ability to respond to aging.

The figure which shows the outline of the plant control apparatus which concerns on embodiment of this invention. The figure which shows the specific configuration example of the control rule execution part 10 which concerns on embodiment of this invention. The figure which shows the specific configuration example of the control rule learning part 11 which concerns on embodiment of this invention. The figure which shows the neural network composition when this invention is used for the shape control of a Sendimia rolling mill. The figure which shows the shape control of the Sendimia rolling mill described in FIG. 1 of Patent Document 1. FIG. The figure which shows the control rule in the shape control of the Sendimia rolling mill described in FIG. 1 of Patent Document 1. FIG. The figure which shows the outline of the control input data creation part 2. The figure which shows the outline of the control output calculation unit 3. The figure which shows the outline of the control output determination part 5. The figure which shows the shape deviation and the control method. The figure which shows the outline of the control quality determination part 6. The figure which organizes and shows the relationship of each part data and a symbol in a control output calculation part 3. The figure which shows the processing stage and processing contents in a learning data creation part 7. The figure which shows the example of data stored in the learning data database DB2. The figure which shows the example of the neural network management table TB. The figure which shows the example of the learning data database DB2.

Hereinafter, examples of the present invention will be described in detail with reference to the drawings, but before that, the findings of the present invention and the background to the present invention will be described by taking shape control of a rolling mill as an example.

First, in order to solve the above problems in the present invention,
1) Rather than setting the reference shape pattern and the control operation for it separately in advance and learning the control operation method, the combination of the shape pattern and the control operation is learned and the control operation is performed using it. ..
2) New control rules are not predictable in advance, and control rules that could not be predicted at all may be optimal. Therefore, operate the control operation end at random and find it while looking at the control results for it. I will go.
Is required.

In order to realize this, it is necessary to change the combination of the shape pattern used for the shape control and the control operation, and change the control operation so that the control result is improved. For that purpose, a neural network that can learn the combination of the shape pattern and the control operation is constructed, and the output of the control operation of the neural network for the shape pattern generated by the rolling mill is changed according to the quality of the control result. Things are needed.

If the above is performed while controlling the shape of the rolling mill in operation, an erroneous control output may be output, so that the shape may deteriorate and an operation abnormality such as plate breakage may occur. .. When a plate break occurs, it takes time to replace the roll used in the rolling mill, and the material to be rolled during rolling is wasted, resulting in great damage. Therefore, it is necessary not to output an erroneous control output to the rolling mill as much as possible.

From the above, in the present invention, in order to realize this, the quality of the control operation output by the neural network is verified by using, for example, a simple model of a rolling mill, and the output that is considered to have a clearly deteriorated shape is obtained. The output is not output to the control operation end of the rolling mill to prevent shape deterioration. At this time, regarding the neural network, learning is performed assuming that the control operation for the shape pattern is incorrect.

Since there is a possibility that the verification method itself for the quality of the control operation is incorrect, it is assumed that the control operation output of the neural network, which is determined to be incorrect with a certain probability, is also output to the control operation end of the rolling mill. It is possible to learn about the combination of the outer shape pattern and the control operation.

FIG. 1 shows an outline of a plant control device according to an embodiment of the present invention. The plant control device of FIG. 1 inputs the control target plant 1 and the actual data Si from the control target plant 1 and controls the control operation amount output SO determined according to the control rule (neural network) as illustrated in FIG. Control method learning in which the control execution device 20 that is given to and controlled by the target plant 1 and the actual data Si from the control target plant 1 are input and learned, and the learned control rules are reflected in the control rules in the control execution device 20. It is composed of an apparatus 21, a plurality of database DBs (DB1 to DB3), and a management table TB of the database DB.

The control execution device 20 is composed of a control input data creation unit 2, a control rule execution unit 10, a control output calculation unit 3, a control output suppression unit 4, a control output determination unit 5, and a control operation disturbance generation unit 16 as main elements. There is.

Of these, in the control execution device 20, first, the input data S1 of the control rule execution unit 10 is created by using the control input data creation unit 2 from the actual data Si of the rolling mill which is the control target plant 1. The control rule execution unit 10 creates a control operation end operation command S2 from the control target actual data Si by using a neural network (control rule) expressing the relationship between the control target actual data Si and the control operation end operation command S2. To do. The control output calculation unit 3 calculates the control operation amount S3 to the control operation end based on the control operation end operation command S2. As a result, the control operation amount S3 is created using the neural network according to the actual data Si of the controlled plant 1.

Further, in the control output determination unit 5 in the control execution device 20, whether or not the control operation amount can be output to the control operation end by using the actual data Si from the controlled target plant 1 and the control operation amount S3 from the control output calculation unit 3. The data S4 is determined. The control output suppression unit 4 determines whether or not the control operation amount S3 can be output to the control operation end according to the control operation amount output availability data S4, and gives the permitted control operation amount S3 to the controlled target plant 1. Control operation amount output Output as SO. As a result, the control operation amount S3 determined to be abnormal is not output to the controlled plant 1. The control operation disturbance generation unit 16 generates a disturbance and gives it to the controlled plant 1 for the purpose of verifying the plant control device.

The control execution device 20 configured as described above refers to the control rule database DB1 and the output determination database DB3 for the purpose of executing the processing, as will be described later. The control rule database DB 1 is accessiblely connected to both the control rule execution unit 10 in the control execution device 20 and the control rule learning unit 11 in the control method learning device 21 described later. The control rule (neural network) as a learning result in the control rule learning unit 11 is stored in the control rule database DB1, and the control rule execution unit 10 refers to the control rule stored in the control rule database DB1. The output determination database DB 3 is accessibly connected to the control output determination unit 5 in the control execution device 20.

FIG. 2 shows a specific configuration example of the control rule execution unit 10 according to the embodiment of the present invention. The control rule execution unit 10 inputs the input data S1 created by the control input data creation unit 2 and gives the control operation end operation command S2 to the control output calculation unit 3. The control rule execution unit 10 includes a neural network 101, and the neural network 101 basically defines a control operation end operation command S2 by the method of Patent Document 1 as illustrated in FIG. In the present invention, the control rule execution unit 10 further includes a neural network selection unit 102, and by referring to the control rules stored in the control rule database DB1, the optimum control rules as the control rules in the neural network 101 Select and execute. As described above, in the control rule execution unit 10 of FIG. 2, a necessary neural network is selected and used from a plurality of neural networks divided by an operator group and a control purpose. It is preferable that the control rule database DB1 also includes the actual data (data of the operation team, etc.) Si that allows the selection of the neural network and the quality judgment criteria as the data from the controlled plant 1. It should be noted that, since there is a relationship that when a neural network is executed, it becomes a control rule, in this specification, the neural network and the control rule are not distinguished and are used in the same meaning.

Returning to FIG. 1, the control method learning device 21 learns the neural network 101 used by the control execution device 20. When the control execution device 20 outputs the control operation amount output SO to the controlled target plant 1, it takes time for the control effect to actually appear as a change in the actual data Si. Therefore, learning is performed using data that is delayed by that time. In FIG. 1, Z ^-1 represents an appropriate time delay function for each data.

The control method learning device 21 is configured with a control result quality determination unit 6, a learning data creation unit 7, a control rule learning unit 11, and a quality determination database DB 4 as main elements.

Of these, the control result pass / fail judgment unit 6 improves the actual data Si by using the actual data Si from the controlled plant 1, the actual data previous value Si0, and the pass / fail judgment data S5 stored in the pass / fail judgment database DB4. It is determined whether the change is in the direction or the change in the worse direction, and the control result good / bad data S6 is output.

In the learning data creation unit 7 in the control method learning device 21, input data such as the control operation end operation command S2, the control operation amount S3, and the control operation amount output availability data S4 created by the control execution device 20 are input for the same time. Using the data delayed by the time and the control result quality data S6 from the control result quality determination unit 6, new teacher data S7a used for learning the neural net is created and given to the control rule learning unit 11. The teacher data S7a corresponds to the control operation end operation command S2 output by the control rule execution unit 10, and the learning data creation unit 7 uses the control result quality data S6 given by the control result quality determination unit 6. It can be said that the data obtained by estimating the control operation end operation command S2 output by the control rule execution unit 10 is obtained as new teacher data S7a.

FIG. 3 shows a specific configuration example of the control rule learning unit 11 according to the embodiment of the present invention. The control rule learning unit 11 includes an input data creation unit 114, a teacher data creation unit 115, a neural network processing unit 110, and a neural network selection unit 113 as main components. Further, the control rule learning unit 11 obtains and controls new teacher data S7a from the learning data creation unit 7 from the data S8a in which the input data S1 from the control input data creation unit 2 is delayed in time as an external input. Refer to the data accumulated in the rule database DB1 and the learning data database DB2 .

In the control rule learning unit 11, the input data S1 is taken into the neural network processing unit 110 via the input data creation unit 114 after appropriate time delay compensation.

Further, in the control rule learning unit 11, the new teacher data S7a from the learning data creation unit 7 is the total teacher data S7c including the past teacher data S7b stored in the learning data database DB2 in the teacher data creation unit 115. Is given to the neural net processing unit 110. These teacher data S7a and S7b are appropriately stored in the learning data database DB2 and used.

Similarly, the input data S8a from the control input data creation unit 2 is the total input data S8c including the past input data S8b stored in the training data database DB2 in the input data creation unit 114, and is the neural net processing unit. Given to 110. These input data S8a and S8b are appropriately stored in the learning data database DB2 and used.

The neural network processing unit 110 is composed of a neural network 111 and a neural network learning control unit 112, and the neural network 111 includes input data S8c from the input data creation device 114, teacher data S7c from the teacher data creation unit 115, and the like. The control rule (neural network) selected by the neural network selection unit 113 is taken in, and the finally determined neural network is stored in the control rule database DB1.

The neural network learning control unit 112 controls the input data creation device 114, the teacher data creation unit 115, and the neural network selection unit 113 at appropriate timings, obtains the input of the neural network 111, and outputs the processing result. Control rule Control is performed so that it is stored in the database DB1.

Here, the neural network 101 in the control execution device 20 of FIG. 2 and the neural network 111 in the control method learning device 21 of FIG. 3 are both neural networks of the same concept, but they are based on the basic concept of use. The difference is as follows. First, the neural network 101 in the control execution device 20 is a neural network having predetermined contents, and obtains a control operation end operation command S2 as a corresponding output when input data S1 is given, so to speak, in one direction. It is a neural network used for the processing of. On the other hand, the neural network 111 in the control method learning device 21 satisfies this input / output relationship when the input data S1 and the input data S8c and the teacher data S7c for the control operation end operation command S2 are set as learning data. This is for finding a neural network by learning.

The concept of basic processing in the control method learning device 21 configured as described above is as follows. First, when the content of the control operation amount output availability data S4 is "OK", the control operation amount output SO is output to the controlled target plant 1, and the content of the control result quality data S6 is "good" (actual data Si is improved). In the case of (change in direction), it is determined that the control operation end operation command S2 output by the control rule execution unit 10 is correct, and learning data is created so that the output of the neural network becomes the control operation end operation command S2.

On the other hand, the content of the control operation amount output availability data S4 is "No", or the content of the control operation amount output SO is output to the controlled target plant 1 and the content of the control result quality data S6 is "No" (actual data Si becomes worse). In the case of (change in direction), it is determined that the control operation end operation command S2 output by the control rule execution unit 10 is incorrect, and learning data is created so that the output of the neural network is not output. At this time, the neural network output is configured so that two types of outputs, + direction and-direction, are output to the same control operation end as the control output, and the control operation end operation command S2 on the output side is not output. Create training data as in.

Further, in the control rule learning unit 11 illustrated in FIG. 3, as a result of data processing by the neural network learning control unit 112, the processing is performed as follows. Here, first, the control rule execution unit 10 uses learning data that is a combination of S8c in which the input data S1 to the control execution device 20 is delayed by time and the teacher data S7c created by the teacher data creation unit 115. The learning of the neural network 101 used is carried out. Actually, the same neural network 111 as the neural network 101 of the control rule execution unit 10 is provided in the control rule learning unit 11, and the operation test is performed under various conditions to learn the response at that time, and the result of learning is better. The control rule that has been confirmed to occur is obtained. Since it is necessary to perform learning using a plurality of learning data, a plurality of past learning data are extracted from the learning data database DB2 that stores the learning data created in the past, and training is performed. At the same time, the training data of this time is stored in the training data database DB2. Further, the learned neural network is stored in the control rule database DB1 for use by the control rule execution unit 10.

In the learning of the neural net, each time new learning data is created, the past learning data may be used together for learning, or after the learning data is accumulated to some extent (for example, 100 pieces), the past learning You may learn by using the data together.

In addition, the control result quality determination unit 6 performs quality determination based on the quality determination criteria from the quality determination database DB4. Since the judgment result of the control result is different depending on the control purpose, it is necessary to create a plurality of neural networks according to a plurality of control purposes, and even if the input data is the same, create and learn teacher data for each control purpose. So, by creating multiple teacher data for one input data and using it for learning the neural network corresponding to each teacher data, we will learn the neural network corresponding to multiple control purposes at the same time. Is possible. Here, the plurality of control objectives are, for example, in the case of shape control, a plurality of control target items (for example, a plurality of control target items (for example, a plate end portion, a center portion, an asymmetric portion, etc.)) to be preferentially controlled in the plate width direction. Which of the plate thickness, tension, rolling load, etc.) should be preferentially controlled, etc.

With the above configuration, once the neural network 101 used in the control rule execution unit 10 has learned, a new control operation will not be executed. Therefore, the control operation disturbance generation unit 16 randomly generates a new operation method in a timely manner, and executes the control operation in addition to the control operation amount S3 to learn the new control method.

Hereinafter, the details of the present plant control method will be described for shape control in the Sendimia rolling mill as shown in Patent Document 1. The shape control will be described assuming that the following specifications A and B are adopted.

Specification A is a specification for priority, and has information on priority in the plate width direction. For example, in shape control, it is often difficult to control the target value over the entire plate width direction due to mechanical characteristics. Therefore, specifications A1 and A2 for the following two priorities are provided in the plate width direction. Of these, the specification A1 for priority is "priority is given to the plate edge", and the specification A2 for priority is "priority is given to the central part", and control is performed according to two priorities, A1 and A2. .. When performing control, consider either specifications A1 or A2 for priority.

Specification B is a specification for dealing with a condition that is known in advance. As an example, since the relationship between the shape pattern and the control method changes under various conditions, it may be necessary to classify the specifications B1 as the plate width and the specifications B2 as the steel type. By changing each of the above, the degree of influence on the shape of the shape operation end changes.

In this example, the controlled plant 1 is a sendmia rolling mill, and the actual data is the actual shape. The Sendimia rolling mill is a rolling mill having a cluster roll for cold rolling a hard material such as stainless steel. In the Zendimia rolling mill, a work roll having a small diameter is used for the purpose of applying strong rolling pressure to a hard material. Therefore, it is difficult to obtain a flat steel plate. As a countermeasure, the structure of the cluster roll and various shape control units are adopted. In general, a sendimia rolling mill has upper and lower first intermediate rolls having a single taper so that they can be shifted, and also has six split rolls and two rolls called AS-U on the upper and lower sides. In the example described below, the shape detector detection data is used as the shape actual data Si, and the shape deviation, which is the difference from the target shape, is used as the input data S1. The control operation amount S3 is the AS-U of # 1 to #n and the roll shift amount of the upper and lower first intermediate rolls.

FIG. 4 shows a neural network configuration when used for shape control of a Sendimia rolling mill. Here, the neural network is the neural network 101 for the control rule execution unit 10, and the neural network shown in the neural network 111 for the control rule learning unit 11, but the structure is the same. is there.

In the case of shape control of the Sendimia rolling mill shown in FIG. 4, the actual data Si from the controlled plant 1 is the data of the shape detector (here, the shape deviation which is the difference between the actual shape and the target shape is output. This is the actual data of the Sendimia rolling mill including the above), and the control input data creation unit 2 obtains the standardized shape deviation 201 and the shape deviation step 202 as the input data S1. As a result, the input layers of the neural networks 101 and 111 are composed of the normalized shape deviation 201 and the shape deviation step 202. In FIG. 4, the shape deviation step 202 is input to the neural network input layer, but the neural network may be switched according to the step.

Further, the output layer is composed of an AS-U operating degree 301 and a first intermediate operating degree 302 in accordance with the AS-U and the first intermediate roll, which are the shape control operating ends of the Sendimia rolling mill. For AS-U, the degree of operation is the AS-U opening direction (the direction in which the roll gap (distance between the upper and lower working rolls of the rolling mill) opens) and the AS-U closing direction (the direction in which the roll gap closes). I have about AS-U. Regarding the first intermediate roll, the first intermediate roll opening direction (the direction in which the first intermediate roll operates outward from the center of the rolling mill) and the first intermediate roll closing direction (the first intermediate roll is on the center side of the rolling mill). The direction of movement toward the upper and lower first intermediate rolls). For example, if the shape detector has 20 zones and the shape deviation stage 202 is set to 3 stages (large, medium, small), the input layer has 23 inputs. Further, assuming that the AS-U saddles are 7 and the upper and lower first intermediate rolls can be shifted in the plate width direction, the output layer has 14 AS-U operation degrees 301 and 4 1 intermediate operation degrees, for a total of 18 pieces. It becomes. The number of layers in the middle layer and the number of neurons in each layer are set in a timely manner. As will be described later with reference to FIG. 8, for the shape control operation end of the sendimia rolling mill, which is the output layer, a neural network output is performed so that two types of outputs, + direction and-direction, are output to each control operation end. Consists of.

FIG. 10 shows the shape deviation and the control method. Here, the upper part of FIG. 10 shows the control method when the shape deviation is large, and the lower part of FIG. 10 shows the control method when the shape deviation is small. The height direction is the magnitude of the shape deviation, the horizontal axis direction is the plate width direction, and both sides of the plate width represent the plate end and the center represents the plate center. As shown in the upper part of FIG. 10, when the shape deviation is large, the correction of the overall shape is prioritized over the local shape deviation in the plate width direction. On the other hand, as shown in the lower part of FIG. 10, when the shape deviation is small, priority is given to reducing the local shape deviation.

In this way, since it is necessary to change the control method according to the magnitude of the shape deviation, the shape deviation step 202 is provided to the neural networks 101 and 111 as shown in FIG. 4, and the magnitude of the shape deviation is determined. .. Regarding the shape deviation, regardless of the magnitude of the shape deviation, it is preferable to use one standardized to 0 to 1, for example. This is just an example, and it is conceivable to input the shape deviation to the input layer of the neural network as it is without standardizing it, or to change the neural network itself according to the magnitude of the shape deviation (for example, two neural networks). It is also conceivable to prepare a net and separate the neural network used when the shape deviation is large and the neural network used when the shape deviation is small).

The neural networks 101 and 111 having the configuration shown in FIG. 4 described above are trained in the operation method for the shape pattern, and the shape control is performed using the trained neural network. Even neural networks with the same configuration have different characteristics depending on the learning conditions, and different control outputs can be output for the same shape pattern.

Therefore, by properly using a plurality of neural networks according to other conditions of the shape record, it is possible to configure optimum control for various conditions. This is a response to specification B. The configuration of FIG. 2 described above shows a specific example in the case of making such a specification. In the configuration example of FIG. 2, the neural network 101 used in the control rule execution unit 10 is prepared as a separate neural network according to the rolling results, the name of the rolling mill operator, the steel type of the material to be rolled, the plate width, etc., and the control rule database is prepared. Register in DB1. The neural network selection unit 102 selects a neural network that matches the conditions at that time, and sets it in the neural network 101 of the control rule execution unit 10. As a condition at that time in the neural network selection unit 102, it is preferable to take in the plate width data from the actual data Si in the controlled plant 1 and select the neural network accordingly. Further, if the plurality of neural networks used here have an input layer and an output layer as shown in FIG. 4, the number of layers of the intermediate layer and the number of units of each layer may be different.

FIG. 7 shows an outline of the control input data creation unit 2 that creates data S1 (normalized shape deviation 201, shape deviation step 202) for inputting to the input layers of the neural networks 101 and 111. Here, as the actual data Si, the shape detector data of the shape detector that detects the plate shape at the time of rolling in the Sendimia rolling mill which is the controlled plant 1 is input, and first, each shape deviation PP value calculation unit 210 is used. The shape deviation PP value (Peak To Peak value) SPP, which is the difference between the maximum value and the minimum value of the detection result of the shape detector zone, is obtained. The shape deviation stage calculation unit 211 classifies the shape deviation into three stages of large, medium, and small according to the shape deviation PP value SPP. The shape is the distribution of the elongation rate of the material to be rolled in the plate width direction, and I-UNIT, which expresses the elongation rate in 10-5 units, is used as a unit. For example, it is classified as follows.

Here, the shape deviation stage is set to (large = 1, medium = 0, small = 0) by the establishment of equation (1), and the shape deviation stage is (large = 0, medium = 1, small) by the establishment of equation (2). = 0), and the shape deviation stage is classified as (large = 0, medium = 0, small = 1) by the establishment of equation (3). Here, the shape deviation of each zone is standardized using S _PM with S _PM = S _PP .

As described above, the normalized shape deviation 201 and the shape deviation step 202, which are the input data to the neural network 101, are created. The standardized shape deviation 201 and the shape deviation step 202 are the input data S1 of the control rule execution unit 10.

FIG. 8 shows an outline of the control output calculation unit 3. The control output calculation unit 3 is a control operation end operation command S2 (in the case of shape control of the Sendimia rolling mill, AS-U operation degree 301, first intermediate) which is an output from the neural network 101 in the control rule execution unit 10. From the operation degree 302 corresponds to this), the control operation amount S3 which is an operation command to each shape control operation end is created. Here, one data example is shown for each of the AS-U operation degree 301 and the first intermediate operation degree 302 in which a plurality of numbers exist, and each data is composed of a pair of data of the opening direction degree and the closing direction degree. Has been done.

In the control output calculation unit 3, the input AS-U operation degree 301 has outputs in the open direction and the closed direction of each AS-U. _Therefore, by multiplying the difference between them by the conversion gain G _ASU , each AS- Outputs an operation command to U. Since the control output to each AS-U is the AS-U position change amount (unit is length), the conversion gain G _ASU is the conversion gain from the degree to the position change amount.

Further, since the input first intermediate operation degree 302 has outputs of the first intermediate outer side and the inner side, the operation command to each first intermediate roll shift is output by multiplying the difference between them by the conversion gain G _1ST. To do. The conversion gain G _1ST is a conversion gain from the degree to the position change amount because the control output to each first intermediate roll is the first intermediate roll shift position change amount (unit is length).

From the above, the control operation amount S3 can be calculated. The control operation amount S3 is composed of # 1 to # nAS-U position change amount (n depends on the number of saddles of the AS-U roll), the upper first intermediate shift position change amount, and the lower first intermediate shift position change amount. ing. Note that FIG. 8 shows a system for adding disturbance data from the control operation disturbance generation unit 16 to the control operation end operation command S2.

FIG. 9 shows an outline of the control output determination unit 5. The control output determination unit 5 is composed of a rolling phenomenon model 501 and a shape correction pass / fail determination unit 502, and includes actual data Si from the controlled plant 1, control operation amount S3 from the control output calculation unit 3, and an output determination database. The information of DB3 is obtained, and the control operation amount output availability data S4 is given to the control operation end. With this configuration, in the control output determination unit 5, the change in shape when the control operation amount S3 calculated by the control output calculation unit 3 is output to the rolling mill, which is the control target plant 1, is reported to the known control target plant 1. Predict by inputting to the model (rolling phenomenon model 501 in the case of the embodiment of FIG. 9), and if the shape is expected to deteriorate, the control operation amount output SO is suppressed to prevent the shape from deteriorating significantly. To do.

More specifically, the control operation amount S3 is input to the rolling phenomenon model 501, the shape change due to the control operation amount S3 is predicted, and the shape deviation correction amount prediction data 503 is calculated. On the other hand, the shape deviation prediction data 505 is obtained by adding the shape deviation correction amount prediction data 503 to the shape detector data Si (current shape deviation actual data 504) from the controlled plant 1, and the shape deviation prediction data 505 is obtained. By evaluating, it is possible to predict how the shape will change when the control operation amount S3 is output to the controlled plant 1. From the current shape deviation actual data 504 and shape deviation prediction data 505, the shape correction pass / fail judgment unit 502 determines whether the shape changes in the direction of improvement or deterioration, and the control operation amount output availability data. Get S4.

Specifically, the shape correction quality determination unit 502 determines the quality of the shape correction as follows. First, as shown in the specifications A1 and A2 regarding the priority of shape control, in order to consider the control priority in the plate width direction, the weight coefficient w (i) in the plate width direction is specified in the output determination database DB3. Set for each specification of A1 and specification A2. Using it, for example, the quality of the shape change is judged by using the evaluation function J as shown in the following equation (4). In the equation (4), w (i) is a weighting coefficient, εfb (i) is a shape deviation actual 504, εest (i) is a shape deviation prediction 505, i is a shape detector zone, and land is a random number term.

When the evaluation function J of the equation (4) is used, the evaluation function J is positive when the shape is good, and the evaluation function J is negative when the shape is bad. Further, land is a random number term, and the evaluation result of the evaluation function J is randomly changed. As a result, even if the shape deteriorates, the evaluation function J may be positive. Therefore, even if the rolling phenomenon model 501 is incorrect, the relationship between the shape pattern and the control method should be learned. Is possible. Here, the land is timely set to 0 when the model of the controlled plant 1 is uncertain, as in the beginning of the test run, and 0 when the control method is learned to some extent and stable control is to be performed. change.

The shape correction pass / fail judgment unit 502 calculates the evaluation function J, sets the control operation amount output availability data S4 = 1 (possible) when J ≧ 0, and controls operation amount output availability data S4 = 0 (possible) when J <0. The control operation amount output availability data S4 is output as in (No).

The control output suppression unit 4 determines whether or not the control operation amount output SO is output to the controlled target plant 1 according to the control operation amount output availability data S4, which is the determination result of the control output determination unit 5. The control operation amount output availability data S4 is # 1 to # nAS-U position change amount output, upper first intermediate shift position change amount output, and lower first intermediate shift position change amount output.
IF (control operation amount output availability data S4 = 0) THEN
# 1 to # nAS-U Position change amount output = 0
Upper 1st intermediate shift position change amount output = 0
Lower 1st intermediate shift position change amount output = 0
ELSE
# 1 to # nAS-U position change amount output = # 1 to # nAS-U position change amount Upper 1st intermediate shift position change amount output = Upper 1st intermediate shift position change amount Lower 1st intermediate shift position change amount output = Lower 1st intermediate shift position change amount ENDIF
Is determined by.

In the control execution device 20, shape control is performed by executing the above calculation from the actual data Si from the controlled target plant 1 (rolling machine) and outputting the control operation amount output SO to the controlled target plant 1 (rolling machine). To carry out.

Next, an outline of the operation of the control method learning device 21 will be described. In the control method learning device 21, the time delay data of the data used in the control execution device 20 is used. Time delay ^{Z -1 means} the ^{e -TS,} indicating that delaying preset time T. Since the controlled plant 1 has a time response, there is a time delay until the actual data changes due to the control operation amount output SO. Therefore, the learning is performed using the actual data at the time when the delay time T has elapsed after the control operation is executed. In shape control, it takes several seconds for the shape meter to detect the shape change after the operation command is output to the AS-U and the first intermediate roll, so it is better to set T = 2 to 3 seconds (of the shape detector). Since the delay time changes depending on the type and rolling speed, the optimum time until the change of the control operation end becomes the shape change may be set as T).

FIG. 11 shows an outline of the operation of the control quality determination unit 6. In the shape change pass / fail judgment unit 602, the pass / fail judgment evaluation function _JC as shown in the following equation is used.

In the equation (5), εfb (i) is the shape deviation actual data included in the actual data Si, εlast (i) is the previous value of the shape deviation actual data, and wC (i) is the plate width direction weight for pass / fail judgment. It is a coefficient. Here, the weight coefficient wC (i) for pass / fail judgment is set from the pass / fail judgment database DB4 according to the specifications A1 and A2 regarding the priority of control. The quality of the control result is judged by the quality judgment evaluation function Jc. Also, when the control operation amount output availability data S4, which is the determination result of the control output determination unit 5, is 0 (control output is not possible), the control operation amount output to the controlled target plant 1 is actually 0, but the shape is Judge that it has become worse.

Here, when the control operation amount output availability data S4 = 0, the control result quality data S6 = -1 is set. Further, the threshold upper limit LCU and the threshold lower limit LCL are set in advance under the threshold condition (LCU ≧ 0 ≧ LCL). At this time, if the result of comparison with the pass / fail judgment evaluation function Jc is Jc> LCU, the control result pass / fail data S6 = -1 (the shape has deteriorated), and if LCU ≧ Jc ≧ 0, control is performed. If the result good / bad data S6 = 0 (change in the direction of worsening the shape) and 0> Jc ≧ LCL, the control result good / bad data S6 = 1 (change in the direction of improving the shape) and Jc <LCL. , It is assumed that the control result quality data S6 = 0 (the shape has improved).

Here, since the shape of the control result good / bad data S6 = -1 has deteriorated, when the output control output is suppressed, the control result good / bad data S6 = 0 is output because there is no shape change or the shape has improved. When the control output is held, the control result quality data S6 = 1 has changed in the direction of improving the shape, but there is a possibility that the shape will be further improved. Therefore, this is a case where the output control amount is increased.

As described above, since the weighting coefficient wC (i) in the plate width direction changes according to the specifications A1 and A2 regarding the priority of control, the quality determination evaluation function Jc is different. Therefore, it is conceivable that the determination result of the control result quality data S6 is also different. Therefore, in the control method learning device 21, the control result quality data S6 is determined for the two types of specifications A1 and A2 regarding the priority of control.

Next, the outline of the learning data creation unit 7 will be described. As shown in FIG. 1, in the learning data creation unit 7, the control operation end operation command S2, the control operation amount S3, based on the determination result (control result quality data S6) from the control result quality determination unit 6. From the determination result of the control output suppression unit (control operation amount output availability data S4), the teacher data S7a for the neural network 111 used by the control rule learning unit 11 is created.

The teacher data S7a in this case has an AS-U operation degree 301 and an intermediate operation degree 302, which are outputs from the output layer of the neural network 111 shown in FIG. The learning data creation unit 7 includes a control operation end operation command S2 (AS-U operation degree 301, 1 intermediate operation degree 302 ) which is an output of the neural network 101 and # 1 to # nAS-U which is a control operation amount output SO. The teacher data S7a for the neural network 111 used in the control rule learning unit 11 is created by using the position change amount output, the upper first intermediate shift position change amount output, and the lower first intermediate shift position change amount output.

In explaining the outline of the operation of the learning data creation unit 7, the relationship between the data and symbols of each unit in the control output calculation unit 3 of FIG. 8 is organized in FIG. Here, the AS-U operation degree 301 is typically shown for the control operation end operation command S2 which is the output of the neural network 101, the data on the operation degree positive side is OPref, the data on the operation degree negative side is OMref, and control. The operation degree generated randomly from the operation disturbance generation unit 16 will be described as an operation degree random number Olef, the conversion gain will be G, and the control operation amount output SO will be Clef. As described above, here, for the sake of simplicity, the output from the output layer of the neural network 101 of the control rule execution unit 10 is randomly generated from the operation degree positive side and the operation degree negative side, and the control operation disturbance generation unit 16. The degree of operation to be performed is a random number of degree of operation. Further, the control operation amount output SO for the control operation end is set as the operation command value.

FIG. 13 shows a processing stage and processing contents in the learning data creation unit 7. Here, to explain according to the promise of the symbols in FIG. 12, in the first processing step 71, the operation command value Clef is obtained by the equation (6).

In the next processing step 72, the operation command value Clef is modified to be C'ref according to the control result quality data S6. Specifically, the operation command is based on the equation (7) when the control result quality data S6 = -1, the equation (8) when the control result quality data S6 = 0, and the equation (9) when the control result quality data S6 = 1. Let the modified value C'ref of the value Clef.

In the processing step 73, the operation degree correction amount ΔOref is obtained from the corrected operation command value C'ref by the equations (10) and (11).

In the processing step 74, the teacher data OP'ref and OM'ref to the neural network 111 are obtained by the equation (12).

As described above, in the learning data creation unit 7, as shown in FIG. 12, the operation command value Cref actually output to the controlled target plant 1 is the control result good / bad data S6 which is the judgment result in the control result good / bad judgment unit 6. The operation command value correction value C'ref is calculated according to the above. Specifically, when the control result quality data S6 = 1, the control direction is OK, but when it is determined that the control output is insufficient, the operation command value is increased by ΔClef in the same direction. To. On the contrary, when the control result quality data S6 = -1, the operation command value is reduced by ΔClef in the opposite direction when it is determined that the control direction is wrong. Since the conversion gain G is known because it is set in advance, it is possible to obtain the correction amount ΔOref if the values on the positive operation degree side and the negative operation degree side are known. Here, ΔClef is set by obtaining an appropriate value in advance by simulation or the like. By the above procedure, the teacher data OP'ref and OM'ref used in the control rule learning unit 11 can be obtained by the above equation (12).

Although the explanation is given in FIG. 13 with a simple example, in reality, the AS-U operation degree 301 for # 1 to # nAS-U, the upper first intermediate roll shift, and the lower first intermediate roll shift are the first. All of the 1 intermediate operation degree 302 is carried out, and it is used as the teacher data (AS-U operation degree teacher data, 1 intermediate operation degree teacher data) of the neural network 111 used in the control rule learning unit 11.

FIG. 14 shows an example of data stored in the learning data database DB2. In order to learn the neural network 111, a combination of a large number of input data S8a and teacher data S7a is required. Therefore, the teacher data S7a (AS-U operation degree teacher data, first intermediate operation degree) created by the learning data creation unit 7 is the input data S1 (standard) input to the control rule execution unit 10 by the control execution device 20. It is stored in the training data database DB2 as a set of training data in combination with the time delay data S8a of the shape deviation 201 and the shape deviation step 202 ).

In the plant control device of FIG. 1, various databases DB1, DB2, DB3, and DB4 are used, but in FIG. 15 , a neural for connecting and operating each of the databases DB1, DB2, DB3, and DB4 is shown. The configuration of the net management table TB is shown. The management table TB includes a specification management table. Specifically, the management table TB is classified according to specifications A1 and A2 regarding (B1) plate width, (B2) steel grade, and control priority. (B1) As the plate width, for example, 4 divisions of 3 feet width, meter width, 4 feet width, and 5 feet width are used, and as the steel grade, about 10 divisions of steel grades (1) to (10) are used. Further, regarding the specification A regarding the priority of control, there are two types, A1 and A2. In this case, there are 80 divisions, and 80 neural networks are used properly according to the rolling conditions.

The neural network learning control unit 112 transfers the learning data, which is a combination of the input data and the teacher data, as shown in FIG. 14, according to the neural network management table TB of FIG. Is stored in the learning data database DB2 as shown in FIG.

Each time the control execution device 20 executes shape control on the controlled target plant 1, two sets of learning data are created. This is because two types of teacher data are created for the same input data and control output because the control result quality judgment is performed using the two evaluation criteria of the specification A1 and the specification A2 regarding the control priority. Is. When the teacher data is accumulated to some extent (for example, 200 sets) or newly accumulated in the learning data database DB2, the neural network learning control unit 112 instructs the learning of the neural network 111.

A plurality of neural networks are stored in the control rule database DB1 according to the management table TB as shown in FIG. 15, and in the neural network learning control unit 112, the neural network No. that requires learning. Is specified, the neural network selection unit 113 extracts the neural network from the control rule database DB1 and sets it in the neural network 111. The neural network learning control unit 112 instructs the input data creation unit 114 and the teacher data creation unit 115 to take out the input data and the teacher data corresponding to the neural network from the learning data database DB2, and uses them to make a neural network. The learning of the net 111 is carried out. Various methods have been proposed for learning the neural network, and any method may be used.

When the learning of the neural network 111 is completed, the neural network learning control unit 112 sends the neural network 111, which is the learning result, to the neural network No. of the control rule database DB1. Learning is completed by writing back to the position of.

The learning may be performed simultaneously for all the neural networks defined in FIG. 15 at regular time intervals (for example, every day), or the neural network in which new learning data is accumulated to some extent (for example, 100 sets). No. Only the neural network of the above may be trained at that time.

As a result, the shape of the rolling mill, which is the controlled plant 1, is not significantly disturbed.
1) Rather than setting the reference shape pattern and the control operation for it separately in advance and learning the control operation method, the combination of the shape pattern and the control operation is learned and the control operation is performed using it. ..
2) New control rules are not predictable in advance, and control rules that could not be predicted at all may be optimal. Therefore, operate the control operation end at random and find it while looking at the control results for it. I will go.
Things can be realized.

The control rule database DB1 stores the neural network used by the control execution device 20, but if the stored neural network is only the initial processing performed with random numbers, the learning of the neural network proceeds. However, it takes time until some control becomes possible. Therefore, when the control unit is constructed for the controlled target plant 1, the control rules are learned in advance by simulation based on the control model of the controlled target plant 1 known at that time, and the simulator is used. By storing the learned neural network in the database, it is possible to control the performance to some extent from the beginning of the controlled plant.

The plant control device of the present invention is actually realized as a computer system, but in this case, a plurality of program groups are formed in the computer system.

These programs are, for example,
A control rule execution program that gives control output according to a defined combination of control target plant performance data and control operations to achieve the processing of the control execution device, and whether or not the control output output by the control rule execution program is possible is determined. , When the control output determination program and the control output determination program that notify the control method learning device that the actual data and the control operation are incorrect output the control output to the controlled target plant, the actual data of the controlled target plant. It is a control output suppression program that prevents the control output from being output to the controlled plant when it is determined that
When the control execution device actually outputs the control output to the controlled plant in order to achieve the processing of the control method learning device, the actual data is displayed before the control after a time delay until the control effect appears in the actual data. A control result quality determination program for achieving the control result quality determination process for determining whether the control result is good or bad, and the control result quality in the control result quality determination program. A learning data creation program that obtains teacher data using control output, and a control rule learning program that learns the actual data and the teacher data as training data.
Then, by learning by the control method learning device, different combinations of actual data and control operations are obtained for a plurality of control targets according to the state of the controlled target plant, and the obtained actual data and the combination of control operations are combined. Is used as a defined combination of the actual data of the controlled plant and the control operation in the control rule execution program.

In applying the apparatus of the present invention to an actual plant, it is necessary to determine the initial value of the neural network. In this regard, the combination of the actual data and the control operation is controlled before the controlled plant is controlled. It is preferable to create by simulation using the control model of the controlled plant and shorten the learning period of the combination of the actual data and the control operation in the controlled plant.

The present invention relates to, for example, a control method and a part of a rolling mill, which is one of rolling equipment, and there is no particular problem in actual application.

1: Control target plant 2: Control input data creation unit 3: Control output calculation unit 4: Control output suppression unit 5: Control output judgment unit 6: Control result quality judgment unit 7: Learning data creation unit 10: Control rule execution unit 11 : Control rule learning unit 20: Control execution device 21: Control method learning device DB1: Control rule database
DB2: Learning data database
DB3: Output judgment database Si: Actual data SO: Control operation amount output S1: Input data S2: Control operation end operation command S3: Control operation amount S4: Control operation amount output availability data S5: Good / bad judgment data S6: Control result good / bad data S7a, S7b, S7c: Teacher data S8a, S8b, S8c: Input data (for control rule learning unit)

Claims (11)

It is a plant control device that recognizes the pattern of combination of actual data of the controlled plant and executes control for the controlled plant.
It is equipped with a control method learning device that learns the combination of the actual data of the controlled plant and the control operation, and a control execution device that controls the controlled plant according to the combination of the learned actual data and the control operation.
The control execution device determines whether or not the control rule execution unit that gives control output according to a predetermined combination of the actual data of the controlled plant and the control operation and the control output output by the control rule execution unit are possible, and the actual result. When the control output determination unit that notifies the control method learning device that the data and the control operation are incorrect and the control output determination unit output the control output to the control target plant, the actual result of the control target plant. When it is determined that the data deteriorates, it is provided with a control output suppression unit that prevents the control output from being output to the controlled plant.
In the control method learning device, when the control execution device actually outputs the control output to the controlled plant, the actual data may be compared before the control after a time delay until the control effect appears in the actual data. A control result quality determination unit that determines the quality of the control result as to whether the control result has become worse or worse, a learning data creation unit that obtains teacher data using the control output and the quality of the control result in the control result quality judgment unit. A control rule learning unit that learns the actual data and the teacher data as learning data is provided, and the control method learning device learns the data separately for a plurality of control targets according to the state of the controlled plant. The feature is that a combination of the actual data and the control operation is obtained, and the combination of the obtained actual data and the control operation is used as a defined combination of the actual data of the controlled plant and the control operation in the control rule execution unit. Plant controller. The plant control device according to claim 1.
In order to change the combination of the actual data and the control operation according to the size of the actual data of the controlled plant, the actual data is used by using the information on the size of the actual data and the information that standardizes the actual data and facilitates pattern recognition. A plant control device characterized by learning and controlling a combination of control operations. The plant control device according to claim 1 or 2.
The control rule execution unit holds a defined combination of the actual data of the controlled plant and the control operation as the first neural network, and the control rule learning unit holds the combination of the actual data and the control operation as the second neural network. A plant control device that is held as a net and uses a second neural network obtained as a result of learning in the control method learning device as the first neural network in the control rule execution unit. The plant control device according to any one of claims 1 to 3.
The control execution device includes a control operation disturbance generating unit that gives a disturbance to the control output, and the control method learning device is a plant control device that learns including when a disturbance is applied. The plant control device according to any one of claims 1 to 4.
The control method learning device has obtained a plurality of combinations of actual data and control operations by learning under a plurality of predetermined specifications, and the control execution device has a plurality of actual data and control operations. A plant control device characterized in that one combination of actual data and control operation is selected from the combinations according to the operating state of the controlled plant and the control output is given. The plant control device according to claim 3.
A plant control device characterized in that a neural network for learning a combination of actual data to be used and an operation method is changed according to the size of actual data. The plant control device according to any one of claims 1 to 6 .
The combination of the actual data and the control operation is created by simulation using the control model of the controlled plant before the control is performed in the controlled plant, and the combination of the actual data and the control operation in the controlled plant is learned. A plant control device characterized by shortening the period. A rolling mill control device to which the plant control device according to any one of claims 1 to 7 is applied.
The rolling mill control device is characterized in that the controlled plant is a rolling mill, and the actual data is the shape of the outside of the rolling mill. It is a plant control method that recognizes the combination pattern of the actual data of the controlled plant and executes the control for the controlled plant.
It is equipped with a control method learning device that learns the combination of the actual data of the controlled plant and the control operation, and a control execution device that controls the controlled plant according to the combination of the learned actual data and the control operation.
The control execution device gives a control output according to a predetermined combination of the actual data of the controlled plant and the control operation, determines whether or not the control output is possible, and controls that the actual data and the control operation are incorrect. If the method learning device is notified and it is determined that the actual data of the controlled target plant deteriorates when the control output is output to the controlled target plant, the control output is prevented from being output to the controlled target plant.
In the control method learning device, when the control execution device actually outputs the control output to the controlled plant, the actual data may be compared before the control after a time delay until the control effect appears in the actual data. The quality of the control result as to whether the control result has become worse or worse is determined, the teacher data is obtained by using the control result and the control output, and the actual data and the teacher data are learned and learned as learning data. As a result, separate performance data and control operation combinations are obtained for a plurality of control targets according to the state of the control target plant, and the obtained performance data and control operation combination is used as the control target plant in the control execution device. A plant control method characterized in that it is used as a defined combination of actual data and control operations. A rolling mill control method to which the plant control method according to claim 9 is applied.
The rolling mill control method, wherein the controlled plant is a rolling mill, and the actual data is the shape of the outside of the rolling mill. It is a program when a computer system realizes a plant control device that recognizes a combination pattern of actual data of a controlled plant and executes control for the controlled plant.
The computer system includes a control method learning device that learns the combination of the actual data of the controlled plant and the control operation, and a control execution device that controls the controlled plant according to the combination of the learned actual data and the control operation. ,
The program
A control rule execution program that gives control output according to a predetermined combination of control target plant performance data and control operations to achieve the processing of the control execution device, and determines whether or not the control output output by the control rule execution program is possible. At the same time, when the control output determination program that notifies the control method learning device that the actual data and the control operation are incorrect and the control output determination program outputs the control output to the control target plant, the control target plant It is a control output suppression program that prevents the control output from being output to the controlled plant when it is determined that the actual data deteriorates.
When the control execution device actually outputs the control output to the controlled plant in order to achieve the processing of the control method learning device, the actual data is before control after a time delay until the control effect appears in the actual data. A control result good / bad judgment program for achieving the control result good / bad judgment process for determining whether the control result is good or bad, and the good / bad of the control result in the control result good / bad judgment program. It is a learning data creation program that obtains teacher data using control output, and a control rule learning program that learns the actual data and the teacher data as training data.
By learning the control method learning device, different combinations of actual data and control operations are obtained for a plurality of control targets according to the state of the controlled plant, and the obtained combinations of actual data and control operations are obtained. A program characterized in that it is used as a defined combination of actual data of a controlled plant and a control operation in a control rule execution program.

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