JPS6252881B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a process control device having a function of automatically adjusting control constants of a controller that controls a process to optimal values.

In order to control a process to be controlled under optimal conditions, a procedure is required to know the dynamic characteristics of the process. This procedure is called identification of its dynamic characteristics. This identification can be carried out either by separating the controller that controls the process and performing open-loop operation, or by connecting the controller to the process and performing identification while controlling the process. From the viewpoint of economy, quality control, or safety, it is desirable that this identification can be performed quickly during closed-loop operation so as to be able to respond to changes in the dynamic characteristics of the process during operation.

This type of control device includes an analog type control device that handles data signals as analog quantities, and a digital type control device that controls data signals while sampling them.The control constants of the above-mentioned control devices can be determined. In reality, there are only analog methods (for example, the adaptive control method described in "Introduction to Optimal Control" by Masubuchi, published by Ohm Publishing, p. 284), and in order to calculate the control constants, large-scale This required the introduction of several computers, which was extremely uneconomical. In addition, as a digital control device, especially a process control device that identifies a controlled object during closed-loop operation, for example,
There is one that uses a limit cycle as described in Publication No. 32031. This device generates a limit cycle by superimposing an on/off signal on the output of the PI controller that controls the controlled object, and uses the period and amplitude of this limit cycle to determine the dead time and optimal proportional gain of the controlled object.
This is done by setting the control constants of the PI controller using Nichols' optimal adjustment method.

However, with this process control device, in order to obtain a sufficient S/N ratio in consideration of the process noise that enters the feedback loop, it is necessary to use a considerably large limit cycle.
As a result, there are drawbacks such as the control amount for the controlled object may vary greatly.

The present invention has improved the above-mentioned conventional drawbacks, and its purpose is to calculate the deviation between the feedback signal that feeds back the control amount of the controlled object and the target value signal that commands the target value. In process control that has a controller, that is, a control calculation device that performs control calculations from signals according to control constants at regular intervals to obtain operation signals to be input to the control object, identification is performed to identify the dynamic characteristics of the control object. an M-sequence identification signal generation section that generates a signal; an addition section that adds the identification signal generated by the generation section to the operation signal; and a first filter that receives the feedback signal and generates a first variable. , a second filter that receives the identification signal to generate the second variable; a third filter that receives the deviation signal and generates the third variable; and the first to third filters. an identification calculation unit that sequentially identifies the coefficients of the ARMA model of the controlled object based on the time series data of the first to third variables generated in the above, and calculates control constants of the controlled object from the results calculated by this identification calculation unit. By including a control constant calculation unit that controls the controller, it is possible to calculate the control constants without being limited to the sampling period, for example, by increasing the sampling period and using a simple calculator. A process control device is constructed that does not degrade the control performance of the controller, and is capable of controlling the dynamic characteristics of a controlled object during closed-loop control in a short time without significantly changing the control amount even in the presence of process noise. It is an object of the present invention to provide a process control device that can identify and, as a result, automatically adjust the control constants of a controller optimally.

The present invention applies a signal obtained by adding an M-sequence signal to an operation signal for operating the controlled object to the controlled object,
The controlled object is controlled, the controlled object is expressed by a pulse transfer function, and then the target value signal in the closed loop,
A new variable is generated from the identification signal and the feedback signal using a filter determined by the configuration of the controller, the coefficients of the pulse transfer function described above are calculated using the obtained variable sequence, and the controlled object is determined from the identification result. The configuration is such that each control constant of the controller to be controlled can be optimally adjusted.

Examples of the present invention will be described in detail below.
The figure is a block diagram showing one embodiment of a process control device according to the present invention. This process control device is configured so that when controlling the process 1 to be controlled, the control constants of the controller can be adjusted so that the response of the controlled variable is optimal. A deviation signal ek is obtained from the target value signal rk from the value signal generator 2 and the feedback signal yk from the process 1, and this signal ek
is input to a controller 3, for example, a PID control calculation device, control calculations are performed using control constants of the controller 3, and the process 1 is operated using an operation signal uk.

At this time, the operation signal uk is an identification signal vk consisting of a binary M-sequence signal generated by the identification signal generator 4.
is added. Deviation signal ek, identification signal vk and feedback signal obtained in this way
yk is input to filter 6, filter 7, and filter 8, respectively. The outputs of filter 6 and filter 7 are added and input to MA identification model section 9, and the output of filter 8 is input to AR identification model section 10. The outputs of the MA and AR identification model units 9 and 10 are subtracted and input to an identification calculation unit 11, and the output of this identification calculation unit 11 is configured to be input to a control constant calculation unit 12. This control constant calculation section 12 obtains the control constant d ₁ of the control calculation device 3 as its output signal. This signal with the control constant _d1 is used as a control constant of the control calculation device 3 to perform closed-loop control, so that the dynamic characteristics of the controlled object can be identified during the closed-loop operation, and the control constant can be adjusted. It's summery.

Note that the identification signal generation section 4, the identification calculation section 11, and the control constant calculation section 12 operate under the control of the central control section 5.

To explain the above configuration in more detail, the process 1 to be controlled is operated by inputting the operation signal uk every sampling period τ, and this process 1 outputs the control amount x(t). Here, the operation signal uk is a signal held only during the sampling period τ time, as shown in the first equation.

u _k =u(t) . . . 1st equation However, k·τ≦t<(k+1)·τ.

The control amount x(t) is detected by a not-shown detector provided within the process, and sampled at every sampling period τ time to become a feedback signal _yk . This feedback signal y _k contains unmeasurable observation noise ζ including sampling error.

The target value r _k of the controlled variable u _k is the target value signal generator 2
is generated and input to the controller 3. Target value at this time
The deviation ek between rk and the feedback signal yk is shown in the second equation.

e _k =r _k −y _k ...Second equation The controller 3 is configured to perform PID (proportional, integral, differential) control calculations based on the deviation ek. The calculation output signal u _k ' outputted by the controller 3 is as shown in the third equation, where Kc is the proportional gain, T ₁ is the integral time constant,
Td is the differential time constant.

The third equation described above becomes the fourth equation when the time transition operator is Z (however, Z ^-1 f _k =f _k-1 ).

u' _k =D(Z ^-1 )/C(Z ^-1 )・e _k ...4th
Formula: C(Z ^-1 )=1-Z ^-1 D(Z ^-1 )=d ₀ +d ₁ Z ^-1 +d ₂ Z ^-2 d ₀ =Kc(1+τ/2Ti+T _d /τ) d ₁ =- Kc (1-τ/2Ti+2T _d /τ) d ₂ =KcT _d /τ Furthermore, if the sample output of the controlled variable at each sample period τ time is the controlled variable x _k , the dynamic characteristic of the controlled object (1) is the fifth It can be expressed by the ARMA model (autoregressive moving average model), a so-called pulse transfer function, as shown in the equation.

x _k =B(Z ^-1 )/A(Z ^-1 )u _k =G(Z ^-1 )u _k
...Equation 5 However, A(Z ^-1 ) = 1 + a ₁ Z ^-1 + a ₂ Z ^-2
+...+a _n Z ^-m B (Z ^-1 ) = b ₁ Z ^-1 +b ₂ Z ^-2 +...b _n Z ^-m Controlled object 1 is the PID control calculation output from controller 3
Identification is performed by superimposing the identification signal v _k generated by the identification signal generating section 4 on uk'. The operation signal u _k at this time is as shown in equation 6.

u _k =u' _k +v _k ...Equation 6 A binary M-sequence signal is used as this identification signal v _k . Let the amplitude of the M sequence signal at this time be a _M.

The identification signal generating section 4 generates and stops the M-sequence signal according to commands from a central control section that controls each function. Note that when the identification signal is stopped, v _k =0.

Each of the above-mentioned deviation signal e _k , identification signal v _k , and feedback signal y _k has characteristics determined by the structure of the PID control calculation section 3 (D(Z ^-1 ) shown in the above-mentioned formula 4, C(Z ^-1 ), C(Z ^-1 )), and the output signals are input to filters 6, 7, and 8 having R
When _k , _Vk , and _Yk , it is expressed as the following equation.

R _k = D (Z ^-1 )・e _k ...7th formula V _k = C (Z ^-1 )・v _k ...8th formula Y _k = C (Z ^-1 )・y _k ...9th Equation Each output signal of the filter 6 and the filter 7, R _k and V _k , are added and inputted to the MA (moving average) identification model section 9. This MA identification model section 9 is an identification model on the molecule side shown by the fifth equation of the ARMA model of the controlled object 1, and its MA identification model signal B(Z) is expressed by the following equation.

B(Z)=b ₁ Z ^-1 +b ₂ Z ^-2 +...+bnZ ^-n ...Formula 10 The output signal Y _k of the filter 8 is the
The signal is input to an AR identification model section 10 which is an identification model for the AR (autoregressive) section on the denominator side shown by Equation 5 of the ARMA model.

This AR identification model section 10 is an AR expressed by the following equation.
The identified model signal A(Z ^-1 ) is output.

A (Z ^-1 ) = 1 + aZ ^-1 + a ₂ Z ^-2 +...+amZ ^-m
…Equation 11 Next, these identification model signals A(Z ^-1 ), ^B
(Z ^-1 ) is added or subtracted to obtain the identified residual signal ε _k expressed by the following equation.

ε _k =-A(Z ^-1 )Y _k +^B(Z ^-1 ) ・(R _k +V _k ) ...Formula 12 This identification residual signal ε _k is input to the identification calculation section 11, and The values of the parameters A(Z ^-1 ) and ^B(Z ^-1 ) are corrected so that the sum of the squares of the signal ε _k is minimized, and an identification calculation is performed.

Now, if the unknown parameter spectrum to be identified is θ, it is expressed by equation 13, θ ^T = (a ₁ , a ₂ ,..., am, b ₁ , b ₂ ,..., bm)
...Formula 13, where T represents transposition.

Letting _k be the vector of variables generated by each filter 6, 7, 8, ^T _k = (-Y _k-1 , -Y _k-2 , ..., -Y _kn , R _{k -1} +V _k-1 ,..., R _ko +V _ko )
...This will be the 14th ceremony. A Kalman filter algorithm is used to sequentially identify the unknown parameter vector θ. When this algorithm is used, calculations such as the well-known following equation can be performed from the above-mentioned equations 13 and 14.

θ _k = θ _k-1 + P _{k-1 k} (σ ² + ^T _k P _{k-1 k-1 k} ) ^-1 (Y _k − ^T _k・θ _k-1 ) ...Equation 15 P _k = P _k-1 −P _{k-1 k} (σ ² + ^T _k P _{k-1 k} ) ^{-1 T} _k P _k-1 ...Equation 16 However, σ ² is the variance of observation noise, and is generally not specified. , 10 ^-4 to 10 ^-6 considering the usage environment
A moderate value is fine. Also, P _k is (n+m)・(n+
m) is the covariance matrix of

Note that the initial values at the start of identification are θ ₀ = 0 and P ₀ = I, and if the previous identified value has not changed, the time until identification can be shortened by using θ ₀ as the previous value. However, as described above, the identification calculation section 11 is configured to perform the identification calculation of the value of the unknown parameter according to the 15th and 16th equations.

Identification results a ₁ , b ₁ obtained by this identification calculation unit 11
(however, i = 1 to m, j = 1 to n) is determined from this result so that the response during closed loop control is optimal.
Optimal control constant calculation unit 12 that calculates PID control constants
Enter. The optimum control constant calculated by the optimum control constant calculation section 12 is obtained by performing a series of calculations as shown in the following equations 17 to 25.

Here, when the identified pulse transfer function is expressed as G(Z ^-1 ), it becomes as shown in Equation 17.

The magnitude g ₁ of the pulse at this time is determined by the calculation shown in Equation 18.

The time when the maximum pulse is generated from among these max τ
From the pulse size gmax and the time constant T of the controlled object
and the waste time L are calculated as shown in the following equations 19 and 20.

Also, the plant gain K is calculated from the identification result as shown in Equation 21.

T=τ/gmax・K...Equation 19 Using these time constant T, dead time L, and plant gain K, the transfer function G(S) of the controlled object can be approximated by the following formula: G(S)=Ke ^−LS /TS+1 . . . Therefore, when calculating by adopting predetermined adjustment values from these, the proportional gain Kc,
Integral time constant Ti and differential time constant Td are Kc = 0.7T/KL (1/1 + 0.8τ/L)...23rd
Formula Ti=T...24th formula Td=0.5L (1+5τ/L) (however, τ<L)
. . . Equation 25 is obtained, and the optimum control constant u _k ' used in the PID control calculation unit 3 can be obtained. These series of calculations are performed in the control constant calculation section 12, and based on these set values,
The PID control calculation section 3 performs closed loop control on the controlled object.

In the above-described embodiment, closed-loop control was shown, but when control is performed with the control gain K of the control constant _d1 set to zero, open-loop control is achieved. However, even with such open-loop control, the identification signal v _k
Since this is superimposed on the operation signal u _k , it is possible to identify the controlled object even in such an open-loop operation without changing the circuit configuration.

In the above example, the ARMA (autoregressive moving average) model was set for the identification calculation.
Compared to the case where an AR (autoregressive) model or MA (moving average) model is set, the number of parameters to be identified is smaller, so the storage area for identified parameters can be configured to be smaller. Furthermore, this is to reduce the time required for calculation of the Kalman filter section.

As detailed above, the process control device according to the present invention controls a controlled object with a signal obtained by adding an identification signal using a known binary M-sequence signal to an operation signal,
Furthermore, the coefficients of the ARMA (autoregressive moving average) model of the control target are identified using a Kalman filter using the least squares method from the time series data of the filter output determined by the structure of the PID control calculation unit from the target value signal and feedback signal. , after identifying the pulse transfer function regardless of the control sample period, S
Since the control constants of the controller are determined from the transfer function of the S domain, the control constants can be easily calculated using simple calculation means by increasing the control sample period. It is possible to determine the control constants of the controller without using a large-scale computer capable of high-speed calculation, and the determined control constants allow the control constants of the controller to be optimally adjusted and the process to be stably controlled.

In other words, the control constants used in a control calculation device such as a PID controller are determined by the adjustment side that has already been used, the dynamic characteristics of the controlled object are identified using these temporarily set control constants, and the control constants are optimized after identification. Therefore, while the control is continued during closed-loop control, appropriate adjustment of the control constants is automatically performed. In other words, since there is almost no need for a trial run period for the process, actual operation can begin immediately, and as a result, costs during the trial run adjustment period for the process can be significantly reduced.

Furthermore, since the process control device of the present invention uses an M-sequence signal whose autocorrelation function is extremely close to white noise as its identification signal, it has all kinds of frequency components, so conventional transient response methods, frequency response methods, Compared to this type of device using the limit cycle method, the impulse response can be identified in a shorter time, about the time of the main time constant of the controlled object, and this identification signal uses an M-sequence signal that is limited to two values. Therefore, the output (control amount) of the controlled object is less likely to be disturbed. In addition, the Kalman filter used in the identification calculation section performs time series processing using the statistical least squares method, so even if the feedback signal fed back from the controlled object is disturbed by noise,
It works to reduce the effect of disturbance in the identification results caused by noise, and the identification results are corrected each time data is input sequentially, so it is possible to use large amounts of data when performing processing such as the correlation method of conventional equipment. No storage area is required, so the amount of hardware can be kept small.

In the embodiment of the present invention described above, an example was shown in which the dynamic characteristics of the controlled object are identified during closed-loop control, but if the control gain of the control constant is set to zero, the controlled object will be in open-loop mode. Become. Even in the open loop at this time, since the identification signal is added to the operation signal, the controlled object can be identified.

Furthermore, the process control device according to the present invention includes a target value signal component in the time series data used when identifying the controlled object, and even if the target value changes during identification, the change in the controlled object in response to the change in the target value is Since the process includes the following, it does not affect the obtained identification results in any way.

Each block constituting the apparatus of the present invention can be constructed as a digital system, and can be easily converted into a digital process controller using a microcomputer. Further, if the dynamic characteristics of the process are stable, there is no need to constantly perform the identification operation, and it is sufficient to stop the identification signal when the identification is thought to be completed and perform normal control.

[Brief explanation of the drawing]

The figure is a block diagram showing a process control device that is an embodiment of the present invention. DESCRIPTION OF SYMBOLS 1...Controlled object (process), 2...Target value signal generation device, 3...PID control calculation device, 4...Identification signal generation section, 5...Central control section, 6, 7, 8...
...Filter, 9...MA identification model section, 10...
RA identification model section, 11...Identification calculation section, 12...
...Control constant calculation section.

Claims (1)

[Claims] 1. A target value signal generating device that sets a target value of a controlled variable of a process to be controlled, a feedback signal that feeds back the controlled variable of the process, and a deviation between the target value signal from the target value signal generating device. a calculation unit that obtains a deviation signal from the calculation unit; a control calculation device that performs a control calculation based on the deviation signal obtained by the calculation unit in accordance with a control constant every sample control period to generate an operation signal to be input to the process; an identification signal generation section that generates an identification signal consisting of an M-sequence signal for identifying the dynamic characteristics of an AR; an addition section that adds the identification signal to the operation signal;
an AR identification model section that outputs an identification model signal;
an MA identification model section that inputs a sum signal of the identification signal and the deviation signal and outputs an MA identification model signal; and inputs an identification residual signal obtained by adding and subtracting the AR identification model signal and the MA identification model signal. an identification calculation unit that identifies the coefficients of the ARMA model of the process using a Kalman filter based on the least squares method;
and a control constant calculation unit configured to obtain a transfer function in the S region from the ARMA model and then calculate a control constant from this transfer function to adjust the control constant of the control calculation device. Process control equipment.