JPS62241005A - Auto-tuning controller - Google Patents

Abstract

PURPOSE:To attain the automatic control of various controlled systems without performing the identification of the controlled systems by storing previously the human experience or perception as an inference rule and then inferring the control parameters from the feature value of deviation between the test signal and the controlled variable based on said inference rule. CONSTITUTION:A feature value extractor 10 receives the deviation e(k) between a test signal T(k) and a controlled variable y(k) and outputs a feature value Si showing the characteristics of a controlled system 3. An inference rule storage device 11 stores an inference rule Rj obtained from the human experience or perception in order to optimize the control parameters. A position type fuzzy inference unit 12 receives the value Si and then outputs the optimum parameters, i.e., the gain KC, the integration action time TI and the derivative action time TD after inferring them based on the rule Rj. A PID controller 4 receives a target value signal r(k) or the deviation e(k) between the signal T(k) and the value y(k) via a deviation switch 8 to decide a manipulated variable u(k) according to those set parameters KC, TI and TD and delivers the value u(k) to the system 3.

Description

[Detailed Description of the Invention] [Field of Industrial Application] The present invention is directed to an auto-tuning controller used for example in process control, which is equipped with a function to automatically adjust control parameters according to the characteristics of a controlled object. The present invention relates to controllers, and in particular to automatic adjustment of their control parameters.

[Conventional technology]

Conventionally, as an auto-tuning controller, for example, one shown in FIG. 5 has been proposed. This is A
, B., Colivio, P., M., and I. Mumpkins “Industrial Applications of a Self-Tuning Feedback Control Algorithm”, l.
5A) Ranzakushins vol, 20.1981. Pages 3-10 (A, B, Corripio, P, ?1.
Tospkins, “Industrial Appl.
cation of a 5elf-↑uning
Feedback Control Algorithm
m”, ISA Transactions,
vol.

20. No. 2.1981. In figure 0, which was published in pp. 3-10), 1 is the target signal generator;
2 is an auto-tuning controller, 3 is a controlled object, 4 is a PID controller, 5 is a mathematical model calculator, 6 is an identifier, and 7 is an adjustment calculator.

Conventional auto-tuning configured as above
The operation of the controller will be explained.

The auto-tuning controller 2 inputs the target value signal r(kl given from the target value signal generator 1 and the control m ly (k) which is the output of the controlled object 3,
Operation 1 u (k), which is input to the controlled object 3, is output. Here, the values in parentheses represent discretized times at each sampling interval T.

At this time, the following operations are performed inside the auto-tuning controller 2.

First, the target value signal r (k) and the control amount y (t)
), the deviation e(k) is calculated.

e(kl=r(kl y(k)
...-(t) The PID controller 4 calculates the above deviation e.
(Input kl, calculate the manipulated variable u (k) according to the set control parameters, and output it. In the PID controller 4, the control parameters are gain Kc, integral time TI, differential time T, According to this parameter, the manipulated variable u (kl) is calculated as follows.

The manipulated variable u(k) becomes an input to the controlled object 3 and also serves as an input to the mathematical model calculator 5 and the identifier 6.

When the above-mentioned operation 1 u (k) is input, the mathematical model calculator 5 calculates the output V(k) using a mathematical model such as the following equation.

v(k)-a, v(k-1) +a, v(k-2
)+'b + u (ks-1) + b *
u (k-at-2) = (31 where m is 0
This is an integer greater than or equal to the above, and means the wasted time of the controlled object 3.

The identifier 6 includes the controlled object 3 and the mathematical model calculator 5.
The coefficients aI*at*b, lb of equation (3) are determined so that the input-output relationship of is equivalent, that is, so that the outputs y(k) and v(kl of both are equal).

For this purpose, the identifier 6 performs the above operation -! ! ku (k)
, the control amount y(k), and the output v(k) of the mathematical model are input. - In order to explain the operation of the identifier 6, the following vectors)((k), z(kl, φ) are defined here.

Xte (k-1) = (y(k-1), y(
k-2), u(k-1n-1).

u (k-m-2) ) ... (4)
′2 (k-1) −(v(k-1), v
(k-2), u(ks-1).

u (km-2))
...(5) φ(k) = (at + ax,
b+ + bz) ”・(6) Here,
The subscript T on the right shoulder of a vector means the transposition of the vector.

The identifier 6 executes the following algorithm.

G(k)=(1+z”(k)P(k)x(k))
−'z' (k)P(k) ...(7)φ(k
l1)-φ(k) + CF (kl1)-φ(kl
x (k) ) G (kl ”・(8)P(kl1)
-P(k)-P(k)x(k)G(k)
・(9) With this algorithm, the vector φ(t)), that is, the coefficient at l of the mathematical model equation (3)
ax 1 kll +bt is determined sequentially.

The vector φ (kl) obtained in the above manner is
The output from the identifier 6 is sent to the mathematical model calculator 5 and used to modify the mathematical model, and is also sent to the adjustment calculator 7 to calculate the control parameters, that is, gain Kt + integral time 711 differential time T0. Used to be wanted. In order to obtain these control parameters, the adjustment calculator 7 executes the following calculations.

Kc −(at +2 at) Q/bi
""αωHere, Q appearing in equations 01 and (6) is Q
=1-e-"" --・α1
Defined by where B is an adjustment parameter and is the desired time constant in the closed group.

Gain Kc and integral time 'r obtained as above
, , m minute time TI, is the PID controller 4
The deviation o (kl) is used again to calculate the manipulated variable u (kl) according to equation (2).

[Problem that the invention seeks to solve]

Since the conventional auto-tuning controller is configured as described above, it was necessary to identify the object to be controlled. However, this identification has the following problems.

(1) Calculations are complicated.

(2) The amount of calculation is large.

(3) It takes time to converge.

(4) Nonlinearity of the controlled object cannot be handled.

(5) Since the type of the mathematical model is limited to, for example, equation (3), it is inappropriate for identifying other types of controlled objects.

(6) In order to obtain the three control parameters of KP, Tt, and Tn, the aI + Z+ l) I +
There is no use in identifying the four coefficients of bl.

The above-mentioned identification problem has become a problem of its own in conventional auto-tuning controllers.

This invention was made to solve the above-mentioned problems. Instead of identifying the control target, human experience rules and intuition are memorized as inference rules, and this is used to control the system.
Deviation between I11 and test signal.

Another purpose is to obtain an auto-tuning controller that can infer optimal control parameters for a controlled object from control variables, test signals, and the like.

[Means for solving problems]

The auto-tuning controller according to the present invention, instead of having a mathematical model calculator, an identifier, and an adjustment calculator, uses the deviation between test No. 13 and the controlled variable as a deviation signal, and the difference between the target value signal and the controlled variable. A deviation switcher that selects one of the deviations, a test signal generator that generates the test signal, and a deviation between the test signal and the control 7Il quantity are manually input to generate a special quantity that indicates the characteristics of the controlled object. A feature extractor that outputs; an inference rule memory that stores inference rules for inferring optimal control parameters; and a positional fuzzy inference device that inputs the above feature quantities and infers optimal control parameters according to the above inference rules. It has been established that

[Effect]

In the auto-tuning controller of this invention, human experience and intuition are memorized as inference rules, and in accordance with this, optimal control parameters are phenomenologically inferred from waveforms such as deviations between test signals and control amounts. and perform automatic adjustment.

〔Example〕

An embodiment of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing an auto-tuning controller according to an embodiment of the present invention. In FIG. 1, 1 is a target value signal generator, and the target value signal r
(k) is generated. Reference numeral 2 denotes an auto-tuning controller, which inputs the target value signal r (k) and the control amount y(t)) which is the output of the controlled object 3, and outputs the input of the controlled object 3.

3 is the above control target, and the above auto tuning
The output of the controller 2 is input, and the control amount y) is output. As described above, this control amount 3F (k) is fed back to the auto-tuning controller 2.

Next, the internal configuration of the auto-tuning controller 2 will be explained. 4 is a controller, and in this embodiment a PID controller is used. This PID
The controller 4 inputs the deviation e(k) and adjusts the set control parameters, that is, the gain KC and the integration time Tr.
, the differential time T, and outputs the operation f1 u (k). 8 is a deviation switch, which selects the deviation TaK)-y(k) between the test signal TQt) and the control amount y('k) as the deviation e(g)), or selects the deviation TaK)-y(k) from the target value signal r(
The deviation r (k) −7(
k). A test signal generator 9 generates the test signal T(k). 10 is special ff1
f extractor, which inputs the deviation between the test signal and the controlled amount, the test signal, the controlled amount, etc., and extracts the feature amount si representing the characteristics of the controlled object: 1-L2+...3
Output n. 11 is an inference rule storage device which stores inference rules Rj;j-L2. for optimizing the control parameters of the controller 4; ....

The theory is remembered. Reference numeral 12 denotes a positional fuzzy inference machine, which inputs the above-mentioned feature 11si and calculates the optimum control parameter, that is, the gain Kc, according to the above-mentioned inference rule Rj.
The integral time T+++differential time T is inferred and output. The above Kc, 71, T is given to the above controller 4,
It is used to calculate the manipulated variable u(k).

FIG. 2 is a flowchart showing the operation of the auto-tuning controller.

This operation includes the first half, that is, the automatic adjustment mode in which the control parameters are automatically adjusted in steps 13 to 18, and the second half, that is, the control of the controlled object 3 according to the control parameters adjusted above, in steps 19 to 20. It is divided into two control modes:

First, in step 13, control parameters are initialized.

For example, the value is as follows. For the gain, take a smaller value IKco. Integral time T1 and differential time T0 are
Take infinity and zero respectively. Alternatively, set them to the maximum and minimum values, respectively.

In steps 14 to 16, the PID controller 4 determines the deviation e(k)− between the test signal T(k) and the control amount y(k),
T(k) −y(k) ・a.

will be entered. That is, the PID controller 4 controls the controlled object 3 according to the initially set control parameters according to the test signal T(g)).

In step 17, the feature extractor 10 manually calculates the deviation e(k) of Equation 041 or the above T(kl, Y(k), etc.) to calculate the feature S! representing the characteristics of the controlled object 3. and output.

In step 18, the positional fuzzy inference device 12 infers optimal control parameters from the feature amount St according to the inference rule R stored in the inference rule storage 11, and
It is given to the D controller 4.

This completes the i1 adjustment mode of automatic adjustment of control parameters.

In step 19, the auto-tuning controller enters a control mode for controlling the controlled object 3 according to the target value signal r (k).

Step 20 indicates the control mode described above.

Now, the feature amount Si output by the feature amount extractor 10,
Inference rules Rj stored in the inference rule storage device 11,
The positional fuzzy inference performed by the positional fuzzy inference unit 12 will be explained. For the sake of simplicity, only automatic gain adjustment will be described here.

FIG. 3 is a diagram showing an example of special 1'&mst. Assume that, for example, a pulse-like signal as shown in the upper graph of FIG. 3 is applied as the test signal T(g). In this case, the deviation e (k) between the test signal and the control amount according to the aQ equation is as shown in the lower graph of FIG. From the features of this waveform, the feature amount Si is determined as follows, for example.

Here, ・ePI+'J Pfu, ePff are the positive, negative, and positive peak values that appear after the test signal, respectively, and e(1), ..., e(N) are constant values after the test signal. This is the above deviation up to the time.

Next, the inference rule Rj will be explained. Inference rule R5
is a rule based on the empirical rules and intuition that humans use when adjusting control parameters. Above St, S
For example, the following is true for g.

R1: “If Sl is thick and St is small, K,
Set it small. ” Rz:r If S is small S! is also small, then Kc
Set it to medium, like this: #! Rule 1, R, has the form ``If..., then...''. The part that says "if..." is called the antecedent proposition, and the part that says "do..." is called the consequent proposition.

The form of this consequent proposition is, for example, the above-mentioned inference rule RI.

When the value itself is specified as "set to..." as in R2, this fuzzy inference is particularly called positional fuzzy inference. On the other hand, when the form of the consequent proposition specifies the amount of change in value, such as "increase by..." or "decrease by...", in this invention, the fuzzy inference is specifically called speed-type fuzzy inference. , a positional fuzzy inference unit 12 that executes positional fuzzy inference. Its operation will be explained.

FIG. 4 shows the mechanism of positional fuzzy inference.

Here, a case is shown in which S and St are selected as feature quantities and RI and R2 are used as inference rules.

First, it is evaluated to what extent the current state satisfies the conditions of the antecedent proposition of the fuzzy inference rule. Here, the straight lines of S, SR are actually 31-3t each.
” , St “St ”. These values are evaluated by the membership function. For example, regarding inference rule R3, as shown in the two left graphs in the upper part of ``is large'' and ``if St is small'' are evaluated to be satisfied by 0.75 and 0.5, respectively. Then, by taking the lower value of these values, it is assumed that the antecedent proposition of rule R1 is satisfied by 0.5.

Next, the membership function of ``Set rKc of the consequent proposition small'' is weighted according to the degree to which the antecedent proposition is satisfied. This situation is shown in the third graph from the left in the upper row of FIG.

Finally, the weighted consequent proposition membership functions of each rule, here R1 and R2, are superimposed and their centroids are calculated. The optimum gain is determined using the above center of gravity.

Similarly, inference is made regarding the integral time and the differential time, and the optimal control parameters are inferred by the position type fuzzy inference unit 12.

In addition, in the above embodiment, a PID controller is used as the controller, and its gain and integration time are determined.

Auto tuning that automatically adjusts the differential time.

Although a controller has been described, the invention is also applicable to other types of auto-tuning controllers. For example, it is effective to use a controller that combines 0N-OFF and a dead zone and apply it to an auto-tuning controller that automatically adjusts the width of the dead zone, or an optimal control controller based on modern control theory. 7, and it is ineffective even if it is applied to an auto-tuning controller that automatically adjusts the parameters of the evaluation function.

〔Effect of the invention〕

As described above, according to the present invention, human experience and intuition are memorized as inference rules, and test signals and controls are determined according to the inference rules. Since the control parameters are inferred using fuzzy inference from the characteristic quantities of the waveform, such as the deviation from l1lffi, there is no need for complicated identification that would limit the type of control target, and it is possible to calculate the control parameters using simple membership function calculations. , it is possible to automatically adjust control parameters based on human experience and intuition, and automatic adjustment is possible with a light calculation load and in a short time 1 Automatic adjustment of a wide variety of control objects is possible.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing an auto-tuning controller according to an embodiment of the present invention, FIG. 2 is a flowchart showing its operation, FIG. 3 is a diagram showing an example of evaluation using a membership function, and FIG. The figure shows the mechanism of fuzzy inference, and Figure 5 shows the conventional auto-tuning system.
It is a figure which shows an example of a cocitrol. - In the figure, 1 is a target value signal generator, 2 is an auto-tuning controller, 3 is a control object, 4 is a controller, and here, as an example, a PID controller, 8 is a deviation switch, and 9 is a test equipment. g generator,
10 is a feature extractor, 11 is an inference rule storage device, and 12 is a positional fuzzy inference device. Note that the same reference numerals in the figures indicate the same or equivalent parts.

Claims (1)

[Claims] (1) A controller that inputs the deviation and outputs the manipulated variable that is the input of the controlled object; A deviation between the controlled variable that is the output of the controlled object and the test signal; and a target value signal generator that generates the deviation. a deviation switch that selects either the deviation between the target value signal and the above-mentioned controlled variable; a test signal generator that generates the above-mentioned test signal; and a deviation between the controlled variable and the test signal, the above-mentioned controlled variable, or the above-mentioned test. a feature extractor that inputs a signal and outputs a feature representing the characteristics of a controlled object; an inference rule storage that stores an inference rule for inferring optimal control parameters for the controller; and an inference rule storage that inputs the feature. and a positional fuzzy inference device that infers optimal control parameters according to the inference rules and inputs them to the controller.