JPH06342303A - Imc controller - Google Patents

Abstract

PURPOSE:To provide the IMC controller which need not identify an internal model again when a sensor is changed. CONSTITUTION:When the sensor 6 is already known, an internal model identification assisting device 10 calculates parameters of a process model from characteristics of the sensor 6 and parameters of the internal model obtained by identifying the internal model by using the sensor 6. When a process to be controlled is already known, parameters of a sensor model are calculated from characteristics of the process and the parameters of the internal model. Then the parameters of the process model and sensor model are outputted to a process model storage part 7a and a sensor model storage part 8a and then the internal model is separated into the process model and sensor model.

Description

Detailed Description of the Invention

[0001]

The present invention relates to an IMC (Internal Model)
Control) controller, and particularly to an IMC controller that does not need to perform internal model identification again when the sensor is changed.

[0002]

2. Description of the Related Art Conventionally used PID controllers have a difference between a target value (e.g., if the controller is an indoor air conditioner, it corresponds to an indoor temperature set value) and a feedback amount by an operation unit for performing PID control. Calculates the operation amount (temperature of hot air or cold air from the indoor air conditioner) output from this controller, outputs this operation amount to the process to be controlled (indoor environment), and outputs the control result (control amount (indoor) This is a feedback control system that returns (temperature) as a feedback amount. However, the PID controller has a problem that it is difficult to cope with the dead time of the process to be controlled.

Therefore, there has been proposed a controller using a control algorithm of an IMC structure for controlling by incorporating a process model in which a control target process is expressed by a mathematical expression. FIG. 5 is a block diagram of a control system using this IMC controller. Reference numeral 13 denotes a first subtraction processing unit that subtracts a feedback amount, which will be described later, from a target value, 12 denotes a filter unit for preventing a change in the output of the first subtraction processing unit 13 from being rapidly transmitted, and 14 denotes a filter unit 12 An operation unit that calculates an operation amount based on the output of the process model, a process model 17 that approximates the control target process by a mathematical expression, and outputs a reference control amount corresponding to the control amount of the control target process,
Reference numeral 19 is a second subtraction processing unit that subtracts the reference control amount from the process model 17 from the control amount and outputs a feedback amount, and 20 is a control target process.

Further, F, Gc, Gm, and Gp are transfer functions of the filter unit 12, the operation unit 14, the process model 17, and the controlled process 20, respectively, r is a target value, u is an operation amount, and d is, for example, for an indoor environment. A disturbance corresponding to an outdoor environment, y is a control amount, ym is a reference control amount, and e is a feedback amount.

Next, the operation of such an IMC controller will be described. First, the first subtraction processing unit 13 subtracts the feedback amount e from the target value r, and the result is output to the filter unit 12. Then, the operation amount u is calculated by the operation unit 14 from the output of the filter unit 12, and is output to the control target process 20 and the process model 17 of the controller. Then, the second subtraction processing unit 19 subtracts the reference control amount ym from the process model 17 that approximates the control target process 20 from the control amount y of the control target process 20, and the result is the feedback amount e. A feedback control system that is fed back to the first subtraction processing unit 13 is configured as.

Ideally, the process model 17 of such an IMC controller is mathematically expressed so as to be exactly the same as the control target process 20, and the operation unit 1 is also used.
4 is the inverse characteristic of the transfer function of the process model 17 (1 / G
m) is ideal, but it is impossible to reciprocate the elements of the dead time in the process model 17, so
Normally, the dead time factor is ignored.

Therefore, the control amount y can be obtained by the following equation from the target value r and the disturbance d with such a configuration. y = F * Gp * Gc * r / {1 + F * Gc * (Gp-Gm)} + (1-F * Gm * Gc) * d / {1 + F * Gc * (Gp-Gm)} ... (1 Here, the transfer function Gm of the process model 17 is equal to the transfer function Gp of the control target process 20, and the transfer function Gc of the operation unit 14 is the reciprocal of the transfer function of the process model 17 (1
Assuming an ideal state equal to / Gm = 1 / Gp), equation (1) is as follows. y = F × r + (1-F) × d (2)

Further, under ideal conditions in which the target value r does not change suddenly, the filter unit 12 is unnecessary and F = 1 can be set, so that the control amount y becomes equal to the target value r (y =
r), it is possible to realize the control without any influence of the disturbance d. Focusing on the disturbance d in the control system of FIG. 5, even if the controlled object process 20 and the process model 17 have a large dead time, both exhibit the same characteristic with respect to the manipulated variable u, and therefore the second subtraction is performed. It can be seen that the feedback amount e that is the output of the processing unit 19 is only the disturbance d, and the disturbance d can be suppressed.

Several model identification techniques for determining the process model 17 of such an IMC controller have been proposed. However, in a sensor such as a room temperature measuring instrument that measures a control amount y used for actual control, room temperature change is caused. However, the time constant may be large so that the output follows the time constant of about 10 minutes. Therefore, in actual model identification, a sensor model that is a mathematical expression of this sensor is added, and internal model identification is performed as an internal model including the process model 17 and the sensor model.

Therefore, when a sensor having a different characteristic is to be replaced for maintenance of the sensor, the internal model identification must be performed again. As a means to avoid this internal model identification again, it is possible to recalculate the internal model identification result in consideration of the sensor characteristics, but in this case, specialized knowledge such as transfer function calculation is required. . Further, if the time constant of the sensor is large, it means that the followability of the control amount measured by the sensor with respect to the actual control amount y is poor.

[0011]

Since the conventional IMC controller is constructed as described above, the internal model must be identified again when the sensor is replaced, and the internal model is recalculated from the characteristics of the sensor. The method had a problem that specialized knowledge was required. Also, if the time constant of the sensor is large, the measured control amount will not sufficiently follow the change in the actual value, so it must be interpreted based on empirical knowledge when monitoring such information. ,
There is a problem that it becomes difficult to add an information processing system that monitors the IMC controller using this information. In order to solve the above problems, it is an object of the present invention to provide an IMC controller that does not need to perform internal model identification again when changing a sensor. Moreover, it aims at providing the IMC controller which can monitor the actual controlled variable which does not include the characteristic of a sensor.

[0012]

The present invention stores a process model parameter for specifying a process model, and updates the parameter when a new process model parameter is input. , The result of performing internal model identification by using the sensor model storage unit that stores the parameter of the sensor model for specifying the sensor model and updates to this parameter when the parameter of the new sensor model is input The internal model is separated into a process model and a sensor model based on the obtained internal model, the characteristics of the sensor, and the characteristics of the process to be controlled, and the parameters of the process model and the sensor model are stored in the process model storage unit and the sensor model storage unit, respectively. And an internal model identification assisting device for outputting.

Further, it has an information processing apparatus for processing the reference control amount output from the process model as a control amount estimated value.

[0014]

According to the present invention, when the characteristics of the sensor are known, the parameters of the process model are calculated based on the characteristics of the sensor and the parameters of the internal model, and the parameters of the process model and the sensor model based on the characteristics of the sensor are calculated. The parameters are output to the process model storage unit and the sensor model storage unit, respectively. When the characteristics of the controlled process are known, the parameters of the sensor model are calculated based on the characteristics of the controlled process and the parameters of the internal model, and the parameters of the process model and the sensor model based on the characteristics of the controlled process. Are output to the process model storage unit and the sensor model storage unit, respectively. Further, information processing such as display is performed by the information processing device on the reference control amount output from the process model.

[0015]

1 is a block diagram of an IMC controller showing an embodiment of the present invention, and FIG. 2 is a block diagram of a control system using this IMC controller. In FIG. 1, 1
Is a target value input unit for inputting a target value r set by an operator (not shown) to this controller, and 2 is a target value filter unit for outputting the target value r from the target value input unit 1 with a characteristic that the transfer function has a first-order lag. Reference numeral 3 denotes a first subtraction processing unit that subtracts the feedback amount e from the output of the target value filter unit 4, and 4 denotes an operation amount from the output of the first subtraction processing unit 3 based on a parameter from a process model storage unit described later. It is a manipulated variable calculation unit that calculates u.

Further, 5 is a signal output unit for outputting the manipulated variable u output from the manipulated variable calculating unit 4 to a control target process (not shown in FIG. 1), 6 is a sensor for measuring the control amount y of the control target process, and 7a. Is a process model storage unit that stores the parameters of the process model of the IMC controller, 7b is a process model output operation unit that performs an operation as a process model based on the parameters of the process model, and outputs a reference control amount ym, and 8a is a sensor 6 Is a sensor model storage unit that stores the parameters of the sensor model, and 8b calculates a reference control amount including the sensor model from the reference control amount ym output from the process model output calculation unit 7b based on the parameters of the sensor model. This is a sensor model output calculation unit.

Further, 9 is a second subtraction processing unit for subtracting the reference control amount output from the sensor model output operation unit 8b from the control amount output from the sensor 6 and outputting the feedback amount e, and 10 is the sensor 6 Y2, an internal model identification assisting device for separating the internal model obtained as a result of the internal model identification using
Is the control amount output from the sensor 6, and ym2 is the reference control amount output from the sensor model output calculation unit 8b.

In FIG. 2, reference numeral 4a indicates the inside of the manipulated variable calculation unit 4, and the transfer function of the output of the first subtraction processing unit 3 is 1
Target value / disturbance filter unit that outputs with the characteristics of the next delay, 4b
Is also inside the target value / disturbance filter unit 4a.
Is an operation unit for calculating an operation amount u from the output of the process model, 7 is a process model including a process model storage unit 7a and a process model output calculation unit 7b, and 8 is a sensor model including a sensor model storage unit 8a and a sensor model output calculation unit 8b. is there.

Further, F1, F2, Gs, and Gsm are the target value filter unit 2, the target value / disturbance filter unit 4a, and the target value / disturbance filter unit 4a, respectively.
It is a transfer function of the sensor 6 and the sensor model 8. 2 shows the IMC controller of FIG.
And the disturbance d, the control system is rewritten except for the internal model identification assisting device 10. Further, as is clear from FIGS. 1 and 2, the IMC controller handles the internal model separately into the process model 7 and the sensor model 8.

Next, the operation of such an IMC controller will be described. The target value r is set by the operator or the like of this IMC controller, and is input to the target value filter unit 2 via the target value input unit 1. The target value filter unit 2 outputs the target value r with the characteristic of the transfer function F1 as shown in the following equation, the time constant of which is T1. F1 = 1 / (1 + T1 × s) (3) Next, the first subtraction processing unit 3 uses the target value filter unit 2
The feedback amount e output from the second subtraction processing unit 9 is subtracted from the output of.

Then, the target value / disturbance filter unit 4a in the manipulated variable calculation unit 4 outputs the output of the first subtraction processing unit 3 with the characteristic of the transfer function F2 such that the time constant is T2. . F2 = 1 / (1 + T2 × s) (4)

Similarly, the internal operation unit 4b calculates the operation amount u from the output of the target value / disturbance filter unit 4a, and its transfer function Gc is determined by the internal model identification assisting device 10 which will be described later. The gain Km and the time constant T of the process model 7 stored in the storage unit 7a
The following equation is given by m, which is the reciprocal of the transfer function Gm of the process model 7 excluding the elements of the dead time Lm as in the example of FIG. Gc = (1 + Tm × s) / Km (5)

Therefore, the transfer function of the operation amount computing section 4 as a whole is given by the following equation. F2 × Gc = (1 + Tm × s) / {Km × (1 + T2 × s)} (6) In this way, the manipulated variable u is calculated from the output of the first subtraction processing unit 3 and the signal output unit It is output to the control target process 20 via 5 and is also output to the process model output calculation unit 7b. Then, the sensor 6 measures the control amount y, which is the result of the operation amount u output from the IMC controller, with the characteristic of the transfer function Gs, and outputs it as the control amount y2.

Next, the process model 7 is determined by the internal model identification assisting device 10 and stored in the process model storage unit 7a by the parameters including the gain Km, the dead time Lm, and the time constant Tm. 20 is a mathematical expression that has elements of a first-order lag and a dead time, and the process model output operation unit 7b calculates the reference control amount ym from the operation amount u output from the operation amount operation unit 4. The transfer function Gm is given by the following equation. Gm = Km × exp (−Lm × s) / (1 + Tm × s) (7)

The sensor model 8 is a mathematical expression of the sensor 6 having a first-order lag characteristic by a sensor model time constant T3 which is a sensor model parameter, and the sensor model storage unit 8a includes an internal model identification assisting device. This sensor model time constant T3 input from 10 is stored. Then, the sensor model output calculation unit 8b
The reference control amount ym2 considering the sensor 6 is calculated from the reference control amount y output from the process model output calculation unit 7b, and its transfer function Gsm is given by the following equation using the sensor model time constant T3. Gsm = 1 / (1 + T3 × s) (8)

Next, the second subtraction processing unit 9 calculates the sensor model output calculation unit 8 from the control amount y2 output from the sensor 6.
The reference control amount ym2 from b is subtracted and the feedback amount e is output. Then, this feedback amount e is input to the first subtraction processing unit 3 as described above. This completes the feedback control system consisting of this IMC controller.

Here, in order to perform actual control, it is necessary to identify the internal model, but this IMC using the sensor 6 is used.
After the internal model identification is done in the controller,
The internal model identification assisting device 10 determines the parameters of the process model 7 and the sensor model 8 from the parameters of the internal model as follows.

FIG. 3 is a block diagram showing an embodiment of the internal model identification assisting device 10. Reference numeral 10a is an internal model parameter input unit to which the parameters of the internal model obtained as a result of performing internal model identification using the sensor 6 are input, and 10b is a process model parameter input unit to which the parameters of the known controlled process 20 are input. 10c
Is a sensor model parameter input unit to which a known time constant of the sensor 6 is input, and 10d is a parameter of the process model 7 or the sensor model 8 based on the parameter of the internal model and the characteristic of the sensor 6 or the characteristic of the controlled process 20. The model separation calculation unit 10e that calculates the parameters of the calculated process model 7 or sensor model 8 is respectively a process model storage unit 7a and a sensor model storage unit 8a.
It is a parameter output unit for outputting to.

Next, the operation of such an internal model identification assisting device 10 will be described. Internal model parameter input section 1
0a is a gain K which is a parameter of the internal model obtained as a result of performing internal model identification using the sensor 6 whose time constant is known in the IMC controller of the present embodiment.
0, dead time L0, and time constant T0 are input from the outside (not shown). That is, this internal model includes the process model 7 and the sensor model 8, and its transfer function G0
Is given by G0 = K0 × exp (−L0 × s) / (1 + T0 × s) (9)

Next, the sensor model parameter input unit 10
A known time constant T4 of the sensor 6 is input to c from the outside (not shown). The transfer function Gs2 of the sensor 6 based on the time constant T4 is given by the following equation. Gs2 = 1 / (1 + T4 × s) (10)

The model separation calculation unit 10d receives the internal model gain K0, the dead time L0, and the time constant T0 from the internal model parameter input unit 10a, and the sensor model parameter input unit 10c inputs the time constant T4 of the sensor 6. Then, the process model parameter calculation mode for calculating the parameters of the unknown process model 7 is determined.

In the process model parameter calculation mode, the internal model gain K0, dead time L0,
Based on the time constant T0 and the time constant T4 of the sensor 6, the gain Km, the dead time Lm, and the time constant Tm, which are the parameters of the process model 7, are calculated by the following equations. Km = K0 ··· (11) Tm = (T0 2 -T4 2) 1/2 ··· (12) Lm = L0 + T0-Tm-T4 ··· (13)

This means that the parameters of the process model 7 can be inversely calculated from the parameters of the internal model using the sensor 6, and these are output and stored in the process model storage unit 7a via the parameter output unit 10e. .
Further, the time constant T4 of the sensor 6 is determined by the model separation calculation unit 10
The sensor model time constant T3 is directly output from d and is stored in the sensor model storage unit 8a via the parameter output unit 10e. Therefore, the internal model can be identified in the form in which the process model 7 and the sensor model 8 are separated, and the actual control is performed by the above operation.

Next, when replacing the sensor 6, the time constant T4 of the sensor 6 is input to the sensor model parameter input section 10c. When only the time constant T4 of the sensor 6 is input, the model separation calculation unit 10d receives the time constant T4 of the sensor 6.
Is output as it is as a sensor model time constant T3. Then, the sensor model time constant T3 is output to the sensor model storage unit 8a via the parameter output unit 10e.

Therefore, the sensor model 8 is changed by updating the sensor model time constant T3 stored in the sensor model storage unit 8a, and the internal model identification is substantially completed. In this way, once the internal model is identified by using the sensor 6, this internal model is separated into the process model 7 and the sensor model 8. Therefore, even if the sensor 6 is replaced, only the sensor model 8 is changed and the internal model is again determined. It is possible to save the trouble of performing identification.

Contrary to the above case, when the characteristic of the controlled process 20 is known and the characteristic of the sensor 6 is unknown, the gain Kp of this controlled process 20, the dead time Lp,
And the time constant Tp are the process model parameter input unit 10
Enter in b. The transfer function Gp2 is given by the following equation. Gp2 = Kp × exp (−Lp × s) / (1 + Tp × s) (14)

Next, the model separation calculation unit 10d receives the internal model gain K0, the dead time L0, and the time constant T0 from the internal model parameter input unit 10a in the same manner as described above, and controls from the process model parameter input unit 10b. When the gain Kp of the target process 20, the dead time Lp, and the time constant Tp are input, it is determined to be the sensor model parameter calculation mode.

In the sensor model parameter calculation mode, the parameters of the sensor model 8 are based on the gain K0 of the internal model, the dead time L0, the time constant T0, the gain Kp of the controlled process 20, the dead time Lp, and the time constant Tp. The sensor model time constant T3 is calculated according to the following equation. T3 = (T0 ² −Tp ² ) ^1/2 (15)

This means that the sensor model time constant T3 can be calculated back from the parameters of the internal model using the sensor 6, and this sensor model time constant T3 is output to the sensor model storage unit 8a via the parameter output unit 10e. Are stored. In addition, the gain K of the controlled process 20
p, the dead time Lp, and the time constant Tp are the gain Km of the process model 7, the dead time Lm, and the time constant T, respectively.
m is output as it is, is output to the process model storage unit 7a via the parameter output unit 10e, and is stored. Therefore, the process model 7 and the sensor model 8 are separated as described above.

Next, when the characteristics of the controlled object process 20 change, the same as above, the gain Kp, dead time Lp, and time constant Tp of this controlled object process 20 are input to the process model parameter input unit 10b. To do. The model separation calculation unit 10d receives the input only from the process model parameter input unit 10b, the gain Kp, the dead time Lp, and the time constant Tp of the control target process 20.
Are the process model 7 gain Km and dead time L, respectively.
m and the time constant Tm are output as they are.

Therefore, since the process model 7 is changed, even if there is a change in the control-target process 20, if the parameters of this control-target process 20 are known, it is possible to save the trouble of identifying the internal model again.

FIG. 4 is a block diagram of an IMC controller showing another embodiment of the present invention. Reference numeral 11 denotes an information processing device that uses the reference control amount ym output from the process model output calculation unit 7b as a control amount estimated value to perform information processing and display of the control amount estimated value. The operation of the IMC controller of this embodiment is basically the same as that of the example of FIG. 1, but the information processing device 11 has a computer, a display, an interface, etc., and is a reference output from the process model output operation unit 7b. Information processing and display of the control amount ym are performed, and connection to an information processing system that monitors this IMC controller is performed.

The process model 7 is a model of the control target process 20 that does not include the characteristics of the sensor 6, and the reference control amount ym sufficiently follows the actual control amount y. Therefore, the reference control amount ym is controlled. The use and reliability as a monitor are improved by treating it as a quantity estimation value.

[0044]

According to the present invention, since the internal model obtained as a result of the internal model identification using the sensor is separated into the process model and the sensor model, it is necessary to perform the internal model identification again when the sensor is replaced. Therefore, the maintenance efficiency of the sensor can be improved.

Further, since the reference control amount output from the process model is treated as a control amount estimation value which can sufficiently follow the change of the actual control amount, the use as a monitor and the reliability are improved, and the controller It becomes possible to add an information processing system for monitoring and the like.

[Brief description of drawings]

FIG. 1 is a block diagram of an IMC controller showing an embodiment of the present invention.

FIG. 2 is a block diagram of a control system using the IMC controller of FIG.

FIG. 3 is a block diagram showing an embodiment of the internal model identification assisting device of FIG.

FIG. 4 is a block diagram of an IMC controller showing another embodiment of the present invention.

FIG. 5 is a block diagram of a control system using a conventional IMC controller.

[Explanation of symbols]

 6 sensor 7 process model 7a process model storage unit 7b process model output calculation unit 8 sensor model 8a sensor model storage unit 8b sensor model output calculation unit 10 internal model identification auxiliary device 10a internal model parameter input unit 10b process model parameter input unit 10c sensor Model parameter input unit 10d Model separation calculation unit 11 Information processing device

Claims (2)

[Claims] 1. A process model in which an operation amount to be output to a control target process is calculated from a control target value, and a process model in which the control target process is expressed by a mathematical formula and a sensor for measuring the control amount of the control target process, which is a control result, are expressed in a mathematical formula. In the IMC controller that performs control by calculating the reference control amount corresponding to the control amount with the internal model including the sensor model and feeding back the difference between the control amount output from the sensor and the reference control amount, It stores the parameters of the process model for specifying, and stores the parameters of the sensor model for specifying the sensor model, and the process model storage unit that updates to this parameter when a new parameter of the process model is input, When a new sensor model parameter is input, update it to this parameter Sensor model storage unit, and based on the internal model obtained as a result of identifying the internal model using the sensor, the characteristics of the sensor, and the characteristics of the process to be controlled, the internal model is separated into a process model and a sensor model. An IMC controller, comprising: an internal model identification auxiliary device that outputs parameters of a model and a sensor model to the process model storage unit and the sensor model storage unit, respectively. 2. A reference output from a process model in an IMC controller for controlling a reference control amount corresponding to a control amount of a control target process, which is a control result, by a process model expressing a control target process by a mathematical expression. An IMC controller having an information processing device that processes a controlled variable as a controlled variable estimated value.