JPS63239508A - Plant simulation device - Google Patents

Abstract

PURPOSE:To omit re-adjustment at the time of setting to a real plant by correcting the model of the plant with the real data of said plant and executing the design, etc., of a control device to it equal to the fact that they are executed with the real plant as an object. CONSTITUTION:A plant 1 is controlled so that a desired output is obtained by a control device 2. An input S1, a manipulated variable S2, etc., of the device 2 are stored through a data collecting device 3 to a memory 11 of a plant simulator 10 and the manipulated variable S2 is inputted to a model means 12. The output of the means 2 is compared with a plant output S4 by a comparator 13, the error is obtained and sent to a parameter changing means 14. When an expert changes the parameter so that the error can be the allowable value or below with the means 14, a block element means 15 is controlled with the means 14, the comparator 13, etc., so as to be equal to the device 2. As the result, the responsiveness equal to the device 2 and the plant 1 is obtained by the simulator 10 and it is not necessary to re-adjust after the system design.

Description

[Detailed description of the invention] [Industrial application field] The present invention relates to a plant simulation device.

In particular, the present invention relates to a plant simulation device suitable for creating a model necessary for designing a multivariable control system and analyzing that model.

[Conventional technology]

Conventional plant simulation equipment is, as discussed in ``Tanuma, Morooka, and Takano, ``Interactive CAD System for Control Systems,'' Hitachi Hyoron Section Vol. 65, No. 3, March 1983'', It was proposed as an analysis system that could be expressed using linear ordinary differential equations such as delay elements, quadratic lag elements, and dead time elements.

By the way, it is extremely difficult to express rolling phenomena and the like using only elements that can be analyzed, such as those employed in the above-mentioned plant simulation apparatus. For this reason, models for designing control systems have traditionally been used in computer languages (
For example, what was expressed in FORTRAN procedures was created by approximating it with linear elements.

The control system was then designed using the model created in this way. However, a system designed using a model created by linear approximation in this way has errors in the model, and thus differs from the actual operating state of the system. Therefore, after a system designed using this model is installed on-site, the constants of the control device are adjusted to obtain the desired system response. However, in the case of a system such as a steel rolling plant, a large amount of material is required to perform trial rolling to adjust the constant, so it is not possible to perform trial rolling that changes the constant widely. For this reason, once a certain degree of accuracy is achieved, adjustments are made by changing the constants of the control device within a range that does not significantly affect the rolling results, that is, within a range that allows the rolled material to be shipped as a product. It takes a lot of time to adjust.

[Problem that the invention seeks to solve]

The above conventional technology has the problem that the model does not accurately reflect the input/output relationship of the controlled object, so the control system must be designed using the model and the i1 adjustment must be performed again after the system is installed. .

An object of the present invention is to provide a plant simulation device that accurately describes a model of a controlled object and eliminates the need for readjustment after system installation.

[Means for solving problems]

The present invention, which has achieved the above object, provides a plant simulation device for realizing the operation of a plant, which includes a storage means for capturing and storing information on the plant and a control device for operating the plant, and a model means for performing an operation similar to the operation of the plant. a comparing means for obtaining an error signal by comparing the output from the model means with the plant output information stored in the storing means when plant input information is inputted to the model means from the storing means; and the comparing means and changing means for changing the constants of the model means so that the error signal from the model means is minimized.

[Effect]

A model that operates in the same way as No. 43 representing the input/output relationship of the plant and its operating state is created and analyzed in combination with the control device model. This allows the number of rooms in the control device to be determined in advance in accordance with the operation of the plant, making it unnecessary to readjust the system after setting it up.

〔Example〕

An embodiment of the present invention will be described below based on the drawings.

Figure 1 is a block diagram showing an embodiment of the present invention. In Figure 1, 1 is a plant, 2 is a control device 1, 3 is a data acquisition device, and 4 is an analog/digital (A/D) conversion 10 is a plant simulator. The plant simulator 10 includes a memory 11 as a storage means, a model means 12, a comparator 13, and a parameter changing means 1.
4 and block element means 15.

The operation of the embodiment having the above configuration will be explained.

A plant 1 such as a steel rolling mill is controlled by a control device 2 so as to obtain a desired output.

That is, when the input Si is input to the control device 2, an output of the manipulated variable Sz is obtained from the control device 2.

When the manipulated variable S2 is input to the plant 1, it affects the state S8 of the plant 1 and the output S4 of the plant 1. These inputs S1. Operation amount S2. The state S8° output S4 is input to the data collection device 3. The data acquisition device 3 may be a simple level converter, or may be of a type such as a data recorder that records data on a magnetic tape and can be used at a later date.

If the signal from the data collecting device 3 is an analog signal, the output of the data collecting device 3 is converted into a digital signal by the A/D converter 4 and stored in the memory 11 of the plant simulator 1o.

Further, if the output of the data collection bag rR3 is a digital signal, it is directly stored in the memory 11.

Among the data stored in the memory 11, data corresponding to the manipulated variable S4 of the plant 1 is input to the model means 12.

The model means 12 is a simulation of the plant 1, and the output of the model means 12 is input to a comparator 13. Information corresponding to the output S4 of the plant 1 stored in the memory 11 is input to the comparator 13. This comparator 13 obtains an error by comparing the information corresponding to the output S4 with the output from the model means 12, and transmits the error signal to the parameter changing means 1.
4 is input. The parameter changing means 14 is for a person familiar with the plant 1 to change the parameters of the model means 12 based on the error signal. Here, a person familiar with the plant 1 (referred to as an expert) uses the parameter changing means 14 to change the parameters of the model means 12 so that the error signal becomes less than the allowable error. When the error signal falls below the allowable error, a signal stored in the memory 11 and corresponding to the input Sz of the control device 2 is input to the block element means 15. The output of the block element means 15 and the operation amount of the memory 11 are input to the comparator 13, and the error signal which is the output signal of the comparator 13 is input to the parameter changing means 1.
4, and the parameter changing means 14 changes the parameters of the block element means 15 so that the error signal becomes zero.

As a result, the plant 1 has a model means 12 and a control device 2.
is equivalent to the block element means 15. Here, the control device 2 is usually a linear element such as a PID control device, a limiter,
It can be expressed by a combination of simple nonlinear elements such as switches, corresponds to the block element means of the simulator 10, and the parameters usually have the same values.

Said block element means 15. When the adjustment of the parameters of the model means 12 is completed, the input signal of the memory 11 is inputted to the block element means 15.

The output from the block element means 15 (in the case of an actual plant, a signal corresponding to the manipulated variable S2) is input to the model means 12. The model means 12 is as follows.

The receiver receives a signal corresponding to the manipulated variable S2, and outputs a signal corresponding to the output S4 to the comparator 13. Furthermore, comparator 1
3 compares the output from the model means 12 with the plant output stored in the memory 11, and also compares the output from the block element means 15 with a signal corresponding to the manipulated variable stored in the memory 11. Then, an error signal resulting from the comparison is output to the parameter changing means 14. Parameter changing means 14 so that each error signal becomes less than the allowable error.
is used to change the parameters of the block element means 15 and model means 12. As a result, the control device 2. Response characteristics equivalent to those of the plant 1 can be obtained with the plant simulator 10.

The simulator 10, which has undergone parameter adjustment, operates simultaneously with the control device 2 and the plant 1, so it inputs external inputs So into the memory 11 in accordance with various operating conditions, including signals that cannot actually be input in the actual plant. Various simulations can be executed by inputting signals instead of signals.

Therefore, according to this embodiment, there is no need to design the system and readjust it after the system is installed.

Next, regarding the specific processing of the plant simulator 10,
This will be explained using FIG. 2 as an example.

As an example of a plant, in a steel rolling mill system, the speed control system that controls the speed of the rolls and the rolling system that controls the gap between the rolls are continuous with respect to time, and the control device 2 uses a primary lag element or Linear control element of second-order delay element and limiter.

It can be represented by nonlinear elements such as switches.

These elements represent differential equations.

For example, in the case of a first-order lag element as shown in FIG. 2, tT1 = f (xzt xzt −)
"It can be expressed by the differential equation shown in (1). At the same time, the second-order lag element can also be described as a differential method.

Generally, the control device 2 is a PID control device that can be described using first-order lag elements, second-order lag elements, etc. as described above. As a result, it can be described using a block diagram.

On the other hand, the plant 1 to be controlled is 1 in the case of a rolling mill.
It depends on the Hill's rolling equation as shown in the following equation (2).

P l= b ”k, t・K1・D p i・J shoulder π plane Tko ``...(2) However, Dpt: 1.08-
1.02riμ: Friction coefficient In this case, since the above equation (2) is expressed nonlinearly, it is difficult to analytically find a solution by combining the above linear elements.

On the other hand, in multi-high rolling systems, etc., the speed control system and the downward system are controlled using analog quantities, but in multi-high rolling mills, speed commands and gauge commands for each stand are issued from a digital computer or the like. This is often done with a so-called discrete value control system configuration.

FIG. 3 shows a process for determining the time response of a system including linear elements, nonlinear elements, and discrete elements.

The process shown in FIG.
-Kutta) formula is applied.

In other words, when −th order lag elements, etc. are expressed in a professional suspension diagram, they can be mechanically expressed as a differential equation (simultaneous 11'! ordinary differential equations) as in equation (1), and f (
Perform continuous system calculations to obtain (XI, K2...) (Kyu step 100). Of the model of the plant 1, etc., a part that can be expressed as a continuous system, for example, a model system (continuous) that calculates the HILL formula such as equation (2), is calculated (step 110. Steps 100, 110).

Runge-Kutt asking for the next cut time
A calculation (RK calculation) is performed (step 120).

It is determined in step 130 whether or not the RK calculation has been performed four times. If it is less than four times, the process moves to step 100, and if the RK calculation is completed, the process moves to step 140.

When the time corresponding to the sampling period of the discrete value system has come, the process moves to step 150, but otherwise, step 100 is executed (step 40).

The discrete value system is calculated (step 150), and then the layer number part of the model system is calculated (step 160).

Then, it is determined whether or not the process is finished (step 170).

The calculation step 150 for a discrete value system will now be explained using an example.

for example.

x(k+1)−ΦX(k)+”ψ”U(k)−(3
)y (k) = CX (k) + D U (
k ) ... (4) A discrete value system expressed by the state equation (4) is a state X at the kth step.
(k) and the input U (k) are stored in memory, and the output is X (k).

Obtained by multiplying U (k) by the output matrix [CD] from the right,
The state of the k+1 step is obtained using the 9 calculations on the right side of equation (3), and instead of X (k), the state of the k+1 step
).

Through these operations, the state of the next step is determined from the current state and input.

On the other hand, the parameter changing means 14 controls the HI of equation (4).
H, V, S, which is the input of the rolling equation of L, h of the output
Other items can be set as parameters.

For example, when writing equation (4) in the FOIITRAN program, this is the initial value. Therefore, this can be realized by adding a program that sets this initial value.

With the above-mentioned configuration, steel plants can calculate various coefficients and accurately control rolling phenomena in the high-speed range, but in the low-speed range of about 1/100 of the normal operating speed. This will reduce accuracy.

This is because the iron produced when operating at high speeds has high precision, so it can be shipped as a product and data collection is easy. Therefore, it is possible to adjust the parameters while collecting day and night data.

However, in the low speed range of about 1/100, parameters such as friction coefficient and oil film thickness vary widely, making it impossible to produce iron products with enough precision to ship as products. was collecting data.

The data is stored in the memory of the simulator 10, the difference between the actual model and the model is determined, and the parameters are slightly varied to observe changes in response. As a result, the error signal output from the comparator 13 fluctuates. The parameters are corrected so that the deflection is maximum and the value of the error signal is minimum. Adjust the system by repeating this process.

Once Model 12 is able to faithfully simulate the actual system, we begin designing the control device, configuring block elements, etc. to obtain the desired response, and modifying it while obtaining that response. .

As a result, the results of modifying the control device can be simulated without using a local rolling mill, making it possible to significantly shorten the development period. .

FIG. 4 is a block diagram showing a modification of the present invention. In the embodiment of the present invention, the optimum countermeasure for changing parameters is determined using the knowledge of a plant expert.

That is, the difference between the embodiment shown in FIG. 4 and the embodiment shown in FIG. , the parameter to be changed is instructed to the parameter changing means 14.

An example of the knowledge base 17 is shown in FIG. For example, in thick rolling of iron plates, when the rolling speed decreases, the friction coefficient decreases as a parameter, and the resulting plate thickness increases as a phenomenon.

Using such knowledge, this embodiment executes the inference program shown in FIG. 6. That is, the condition part of the rule is extracted (step 200). Next, a condition part matching the starting state is found (step 210).

Furthermore, the matching condition part is removed and replaced with the conclusion part (step 220). It is determined whether the state matches the target state (step 230).

By operating in this way, when the rolling speed decreases and the resulting plate thickness becomes thicker, when the phenomenon of rolling speed decrease occurs, the condition of the phenomenon is removed from the state where the speed decreases, and the generated plate thickness increases. Replaces the phenomenon of thickening. A phenomenon in which the error signal generation plate thickness increases occurs, which is consistent with the inferred phenomenon. As a result, the parameters of the shaking part are changed to reduce the friction coefficient.

In this way, by incorporating the knowledge of experts in qualitative expression, it is possible to make the model more accurate.

Incidentally, the inference means 16 in FIG. Instead of knowledge base 17,
An identification mechanism 18 as shown in FIG. 7 may be provided.

The processing algorithm in this case is ``Setsuo Sagara et al., ``System Identification'', Institute of Instrumentation and Control Engineers, February 1980, 1st
It can be determined definitively by using the method described on pages 84 to 259. In this case, unlike the inference method, the information is not ambiguous, so the parameters can be changed quantitatively. [Effects of the Invention] As described above, according to the present invention, a plant model is modified using actual data of the plant, and the design of a control device, etc., is performed in the same way as if it were done for an actual plant. This has the effect of omitting readjustment when setting it up in an actual plant.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing an embodiment of the present invention, FIG. 2 is a block diagram showing a display example of a first-order delay system, FIG. 3 is a flowchart showing the operation of an embodiment of the present invention, and FIG. FIG. 3 is a configuration diagram showing another embodiment of the present invention. FIG. 5 is an explanatory diagram showing an example of a knowledge base, FIG. 6 is a flowchart showing an inference algorithm, and FIG. 7 is a configuration diagram showing another embodiment of the present invention. 11...Memory, 12...Model means, 14...
Parameter changing means, 15...Block element means, 1
6... Reasoning means, 17... Knowledge base, 18...
Identification mechanism.

Claims (1)

[Claims] 1. In a plant simulation device that realizes the operation of a plant, a storage means that captures and stores information about the plant and a control device that operates the plant, and performs an operation similar to the operation of the plant. a model means; a comparison means for obtaining the difference signal by comparing an output from the model means when inputting plant input information from the storage means to the model means with plant output information stored in the storage means; A plant simulation apparatus comprising: changing means for changing constants of said model means so that an error signal from said comparing means is minimized. 2. In claim 1, the changing means:
A plant simulation device comprising a fixing mechanism that automatically determines plant parameters from the input information of the plant stored in a storage means and the output information of the plant. 3. In claim 1, the changing means:
Optimization is achieved by providing a knowledge base that describes a set of phenomena and parameters to be changed for the output of the memorandum comparison means, and an inference mechanism that performs inference based on the knowledge base, and operating the inference mechanism using the output of the comparison means. A plant simulation device characterized in that it is configured to determine parameters of the plant.