JP7276633B1 - Control device, control method, and program - Google Patents

Abstract

A technique capable of suppressing deterioration in control performance is provided. A control device according to one aspect of the present disclosure is a control device that outputs a manipulated variable for a controlled object and causes the controlled variable of the controlled object to follow a target value, the control device comprising: a main control unit that outputs the next manipulated variable ua for the controlled object by model predictive control based on the manipulated variable and the controlled variable; and a second control cycle Tf shorter than the first control cycle Tc. and a high-speed complementary control unit for outputting the next manipulated variable ub for the controlled object, wherein the high-speed complementary control unit performs the above-mentioned A provisional target value rf that interpolates the control quantity at time t0 and the target value at time t0+Tp is calculated based on the control quantity, the predetermined look-ahead length Tp, and the target value, and the provisional target value rf and The operation amount ub is calculated as the sum of the average of the instantaneous operation change amount calculated based on the deviation from the current control amount and the operation amount ua. [Selection drawing] Fig. 1

Description

The present disclosure relates to control devices, control methods, and programs.

Model predictive control is known as one of control methods for the purpose of causing the controlled variable of a controlled object to follow a target value. For example, a technology has been proposed that automatically adjusts control parameters necessary for model predictive control while realizing model predictive control on edge devices such as PLC (Programmable Logic Controller) and DCS (Distributed Control System) (Patent document 1).

Japanese Patent No. 7014330

However, the control cycle of model predictive control needs to be determined appropriately according to the time constant of the controlled object. This is because, although it is necessary to determine a control cycle that is long enough according to the time constant in order to sufficiently exhibit the effect of prediction, control performance may be degraded if the control cycle is too long.

The present disclosure has been made in view of the above points, and an object thereof is to provide a technology capable of suppressing deterioration in control performance.

A control device according to one aspect of the present disclosure is a control device that outputs a manipulated variable to a controlled object and causes the controlled variable of the controlled object to follow a target value, wherein the manipulated _variable and the controlled variable, a main control unit that outputs the next manipulated variable u _a for the controlled object by model predictive control, and a main control unit that outputs the next manipulated variable u a for the controlled object by model predictive control, and every second control cycle T _f shorter than the first control cycle T _c and a high-speed complementary control unit that outputs the next manipulated variable u _b for the controlled object, and the high-speed complementary control unit outputs the manipulated variable u _a from the main control unit at time t A provisional target value _rf for interpolating the _{control amount at time t0 and the target value at time t0+Tp based on the} control amount at ₀ , a predetermined look-ahead length Tp, and the target value The operation amount _ub is calculated as the sum of the average instantaneous operation change amount calculated based on the deviation between the provisional target value _rf and the current control amount and the operation amount _ua .

A technique is provided that can suppress deterioration in control performance.

It is a figure which shows an example of the whole structure of the control apparatus which concerns on 1st embodiment. It is a figure for demonstrating an example of operation|movement of a plant response function. 4 is a flowchart for explaining an example of calculation processing of a plant response function; 7 is a flowchart for explaining an example of model parameter estimation processing; It is a figure for demonstrating an example of operation|movement of a look-ahead response correction|amendment part. FIG. 10 is a diagram for explaining an example of the operation of an operation change amount calculator; 4 is a flowchart for explaining an example of control parameter calculation processing; FIG. 11 is a diagram for explaining an example of a calculation outline of look-ahead length; 9 is a flowchart for explaining an example of processing for calculating a look-ahead length; It is a figure for demonstrating an example of operation|movement of a high-speed complement control part. FIG. 10 is a flowchart for explaining an example of calculation processing of a complementary manipulated variable; FIG. It is a figure for demonstrating an example of a provisional target value. It is a figure for demonstrating an example of the response at the time of using a high-speed complement control part. It is a figure which shows an example of the hardware constitutions of the control apparatus which concerns on 1st embodiment. It is a figure which shows an example of the whole structure of the control apparatus which concerns on 2nd embodiment. It is a figure which shows the plant response in an Example. FIG. 10 is a diagram showing controlled variables, target values, and manipulated variables when high-speed complementary control is disabled in the embodiment; FIG. 10 is a diagram showing controlled variables, target values, and manipulated variables when high-speed complementary control is effective in the embodiment;

An embodiment of the present invention will be described in detail below with reference to the drawings. Below, the control device 10 capable of suppressing deterioration in control performance due to an excessively long control cycle of model predictive control will be described. It is assumed that the control device 10 described below is, for example, an edge device (PLC, DCS, etc.) with less computational resources than a PC (personal computer).

Here, in general, if a control period that is too short is set for a control target that has a long time constant, the prediction effect may not be sufficiently exhibited. Also, it tends to consume a lot of memory and the model cannot be correctly identified. On the other hand, if a control period that is too long is set, changes in the target value (target value for the control amount) that occur during the control timing and the effects of disturbances, etc. cannot be reflected in the control, resulting in a decrease in control performance. There is Therefore, although it is necessary to determine an appropriately long control cycle according to the time constant of the controlled object, there is a dilemma that the control performance may deteriorate due to the control cycle being too long.

Therefore, in the control device 10 described below, in addition to the model predictive control controller (main control unit 104 described later), the model predictive control control period T _c (hereinafter also referred to as "main control period T _c "). ) is provided with a controller (high-speed complementary control section 105, which will be described later) that controls the object to be controlled at high speed in a control cycle T _f (hereinafter also referred to as “high-speed control cycle T _f ”) shorter than ). As a result, even if the main control cycle _Tc of the model predictive control is long, the controlled object is controlled at high speed in each high-speed control cycle _Tf . It is possible to quickly follow the influence of disturbance, etc., and it is possible to suppress deterioration of control performance. In addition, even if the calculation resources of the control device 10 are scarce and the main control period _Tc has to be lengthened, it is possible to suppress the deterioration of the control performance (in other words, the calculation resources of the control device 10 Even in the case of poor control performance, it is possible to perform model predictive control with a long main control period _Tc while suppressing deterioration of control performance. Control deviation due to shoots, etc.) can also be expected to be improved.An example of the controlled variable is the temperature of the controlled object, and an example of the target value is the set temperature, etc. However, these are all examples, and the control The amount and the target value are not limited to the temperature and the set temperature, and it is possible to use an arbitrary controlled variable in the controlled object and a target target value of the controlled variable.

Below, a plant is assumed as a controlled object, and the case where the control apparatus 10 controls the controlled plant 20 is demonstrated. However, the object to be controlled is not limited to the plant, and any device, device, facility, or the like can be the object to be controlled.

[First embodiment]
A first embodiment will be described below.

<Overall Configuration Example of Control Device 10>
First, an example of the overall configuration of the control device 10 according to the first embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of the overall configuration of a control device 10 according to the first embodiment.

As shown in FIG. 1, the control device 10 according to the first embodiment includes a model parameter estimation unit 101, a measurement unit 102, a differentiator 103, a main control unit 104, a high-speed complementary control unit 105, and a 1 timer 106 , a second timer 107 and a switch 108 . These units are implemented by, for example, one or more programs installed in the control device 10 causing a processor such as a CPU (Central Processing Unit) or MPU (Micro-Processing Unit) to execute processing.

The first timer 106 operates as an operation trigger for the model parameter estimation section 101 and the main control section 104 every main control cycle _Tc . Note that the main control cycle _Tc is a cycle of controlling the controlled plant 20 by model predictive control, and its value is set in advance.

The second timer 107 operates as an operation trigger for the measurement unit 102 and the high-speed complementary control unit 105 every high-speed control period _Tf . Note that the high-speed control cycle T _f is a cycle represented by nT _f =T _c where n is a predetermined natural number, and is a cycle in which the plant 20 to be controlled is controlled by the high-speed complementary control unit 105. be. The value of the high-speed control period _Tf is also set in advance similarly to the main control period _Tc .

The measuring unit 102 measures (observes) the controlled variable y of the controlled plant 20 at each high-speed control cycle _Tf , and outputs the measured controlled variable y. The controlled variable y of the controlled plant 20 is determined according to the manipulated variable u and the disturbance v. An example of the disturbance v is a drop or rise in outside air temperature when the controlled variable y is temperature.

The measurement unit 102 also acquires (observes) the manipulated variable u output from the switch 108 and outputs the acquired manipulated variable u at each high-speed control period _Tf . An example of the manipulated variable u is the opening/closing amount of a valve that controls the flow rate of the heat medium when the controlled variable y is the temperature.

Hereinafter, when the time t at which the controlled variable y is measured is expressed as y(t). Similarly, when specifying the time t at which the manipulated variable u is acquired, it is expressed as u(t). Similarly, for other variables, when specifying the time t, "(t)" shall be written after the variable.

The differentiator 103 outputs the difference (deviation) between the target value r and the controlled variable y as the target deviation _e0 . The target deviation e ₀ (t) at time t is calculated by e ₀ (t)=r(t)−y(t). In addition, below, as an example, the target value shall be constant, that is, r(t)=constant. However, this is just an example, and the target value r(t) may vary depending on the time t.

The main control unit 104 controls the controlled plant 20 by model predictive control every main control cycle _Tc . That is, the main control unit 104 uses the plant response function {S _θ (t)} representing the plant response model of the controlled plant 20 at each main control cycle T _c to determine the manipulated variable u _a for the controlled plant 20. is calculated and output. In the following, as an example, a counter called a main controller update counter is stored in the memory, and each time the operation amount _ua is calculated and output, the main controller update counter is updated (for example, incremented by 1). ). Note that the plant response function {S _θ (t)} includes the model parameter θ to be estimated by the model parameter estimator 101 .

Here, the main controller 104 includes a control parameter calculator 111 , a look-ahead response corrector 112 , an operation variation calculator 113 , and an adder 114 .

The control parameter calculator 111 inputs the plant response function {S _θ (t)} and the adjustment coefficients α and β, calculates the control gain k _I and the look-ahead length T _p as control parameters, and calculates the amount of change in operation. It outputs to the unit 113 and the look-ahead response correction unit 112, respectively. However, the control parameter calculator 111 may not be included in the main controller 104 .

The lookahead response correction unit 112 calculates the plant response function {S _θ (t)}, the target deviation e ₀ (t), and the operation change _amount time series { du(t)} and the look-ahead length T _p , a corrected target deviation e ^* (t) is calculated by correcting the target deviation e ₀ (t).

The operation change amount calculator 113 calculates the operation change amount du(t) based on the corrected target deviation e ^* (t) and the control gain _kI . The manipulation change amount calculation unit 113 calculates and outputs the manipulation change amount du(t) in the order of du(t−3T _c ), du(t−2T _c ), du(t−T _c ), for example. Note that the operation change amount du is the amount by which the operation amount _ua changes for each main control cycle _Tc .

The adder 114 adds the operation amount u output from the measurement unit 102 and the operation change amount du acquired from the operation change amount calculation unit 113 to calculate a new operation amount _ua . That is, the adder 114 adds a new manipulated variable u _a (t)=u(t−T _c )+du(t) every time t satisfying t=n ₁ T _c for an arbitrary integer n ₁ . calculate. The adder 114 then outputs this new manipulated variable _ua to the switch 108 .

The model parameter estimator 101 inputs the controlled variable y and the manipulated variable u for each main control period _Tc , calculates the model parameter estimated value θ _est of the plant response function {S _θ (t)}, and outputs do. The model parameter θ set in the plant response function {S _θ (t)} is hereinafter also referred to as “model parameter set value θ”. Note that the initial value θ ₀ of the model parameter may be input when calculating the estimated value θ _est of the model parameter.

The high-speed complementary control unit 105 controls the controlled plant 20 in each high-speed control period _Tf . That is, the high-speed complementary control unit 105 calculates and outputs the manipulated variable u _b for the controlled plant 20 for each high-speed control cycle _Tf . However, for time t when t=n ₂ T _f and t=n ₃ T _c for certain integers n ₂ and n ₃ , u _b (t)=u _a (t).

The switch 108 inputs the manipulated variable u _b or both u _a and u _b and outputs the manipulated variable u to the controlled plant 20 . That is, the switch 108 inputs the manipulated variable _{u b} at each time t that satisfies t≠n ₄ T _c and t=n ₅ T _f , where n ₄ and n ₅ are arbitrary integers, and the manipulated variable u=u. _b , and inputs the manipulated variables u _a and u _b at each time t satisfying t=n ₄ T _c and t=n ₅ T _f , and outputs the manipulated variable u=u _a =u _b .

Note that the control device 10 according to the first embodiment includes, for example, a display unit that sequentially displays model parameters and control parameters (control gain, look-ahead length) calculated for each main control cycle _Tc on a display device such as a display. or a display control unit for sequentially displaying these model parameters and control parameters on a display device of a terminal such as a PC connected via a communication network. As a result, users and administrators of the control device 10 (hereinafter also referred to as “users, etc.”) can know the trend of model parameters and control parameters, and the identification state of the plant response model and the adjustment state of the control parameters. can be confirmed. In addition, it is also possible to find signs of sudden fluctuations in the plant response model.

<Operation of plant response function {S _θ (t)}>
Next, the operation of the plant response function {S _θ (t)} will be described with reference to FIG. FIG. 2 is a diagram for explaining an example of the operation of the plant response function {S _θ (t)}.

As shown in FIG. 2, the plant response function {S _θ (t)} is a unit step response S Output _θ (t). The unit step response is the response (that is, the output of the plant response model of the controlled plant 20) when the manipulated variable is the unit step input.

<Calculation of plant response function {S _θ (t)}>
Next, the process of calculating the unit step response S _θ (t) of the plant response function {S _θ (t)} will be described with reference to FIG. FIG. 3 is a flowchart for explaining an example of calculation processing of the plant response function {S _θ (t)}. In FIG. 3, it is assumed that the model parameter set value θ and the time t are input.

Here, in this embodiment, as a calculation model of the plant response function S _θ (t), autoregression of the past N points is used for the controlled variable y, and a moving average of the current value and the past M points is used for the manipulated variable u. A case where an ARMA (autoregressive moving average) model is adopted will be described. Note that N and M are set in advance by, for example, a user or the like. However, this is only an example, and other models such as the ARMAX model may be adopted.

At this time, the model parameter setting value θ is

とする。モデルパラメータ設定値θの各要素は、以下の数２に示すＡＲＭＡモデルの係数である。

and Each element of the model parameter setting value θ is a coefficient of the ARMA model shown in Equation 2 below.

ここで、ｋはインデックスである。ただし、これは一例であって、例えば、過去Ｌ点（Ｌは予め設定された１以上の整数）の外乱ｖを考慮したモデルが用いられてもよい。

where k is an index. However, this is only an example, and a model that considers the disturbance v of past L points (L is a preset integer of 1 or more) may be used.

Step S11: The main control unit 104 initializes the time τ and the state vector φ(0) with τ as the variable representing the time and φ(k) as the state vector at the index k. Also, the main control unit 104 initializes the index k to k=0.

where the state vector φ(k) is

と表される。

is represented.

For example, when considering the disturbance v of L points in the past, the state vector φ(k) is a state having L disturbances v(k) as elements in addition to y(k) and u(k). Just expand it to a vector.

The main control unit 104, for example, initializes τ=0,

と初期化する。すなわち、φ（０）はｕ（ｋ）に相当する要素のみ１、他の要素は０に初期化される。これは、単位ステップ応答を模擬する際の初期値として設定していることを意味する。

and initialize. That is, φ(0) is initialized to 1 only for the element corresponding to u(k) and to 0 for the other elements. This means that it is set as an initial value when simulating a unit step response.

Step S12: Next, the main control section 104 calculates the control amount predicted value y(k) based on the model parameter set value θ and the state vector φ(k). The main control unit 104 calculates the control amount prediction value y(k) by, for example, y(k)=φ(k) ^Tθ . Here, T represents transposition.

Step S13: Next, the main control unit 104 updates the state vector φ(k+1) at the next index (that is, k+1) based on the control amount predicted value y(k) and the state vector φ(k). . The main control unit 104, for example, updates the state vector φ(k+1) as follows.

ここで、ｙ（ｋ）には上記のステップＳ１２で計算した制御量予測値を設定し、ｕ（ｋ＋１）には１を設定する。また、ｙ（ｋ－１），・・・，ｙ（ｋ－Ｎ＋１）及びｕ（ｋ），・・・，ｕ（ｋ－Ｍ＋１）には、状態ベクトルφ（ｋ）と同じ値を設定する（すなわち、状態ベクトルφ（ｋ）に含まれるｙ（ｋ－１），・・・，ｙ（ｋ－Ｎ＋１）及びｕ（ｋ），・・・，ｕ（ｋ－Ｍ＋１）をそれぞれ設定する。）。

Here, y(k) is set to the control amount predicted value calculated in step S12, and u(k+1) is set to 1. y(k−1), . . . , y(k−N+1) and u(k), . (That is, y(k−1), . . . , y(k−N+1) and u(k), . ).

Step S14: Next, the main controller 104 updates the time τ to τ+ΔT and updates the index k to k+1. Here, ΔT represents the time width of one step of the ARMA model. The time step ΔT of the plant response function can be arbitrarily set according to the time constant of the plant 20 to be controlled, but is preferably set to be the same as the main control cycle _Tc , for example.

Step S15: Next, the main controller 104 determines whether or not τ≧t. Then, if it is not determined that τ≧t (NO in step S15), the main control unit 104 returns to step S12. As a result, steps S12 to S14 are repeatedly executed until τ≧t.

On the other hand, if it is determined that τ≧t (YES in step S15), the main control unit 104 terminates the process. This gives the final calculated y(k) as the unit step response S _θ (t) (that is, S _θ (t)=y(k) is the step response function {S _θ (t)} is calculated as the unit step response at time t of ).

<Estimation of model parameter θ>
Next, the process of estimating the model parameter θ of the plant response function {S _θ (t)} (that is, the process of calculating the model parameter estimated value θ _est ) will be described with reference to FIG. FIG. 4 is a flowchart for explaining an example of estimation processing of the model parameter θ. FIG. 4 illustrates a case where the model parameter estimated value θ _est at a certain index k is calculated assuming that the controlled variable y(t) and the manipulated variable u(t) at the current time t are input. Note that this index k is a value independent of the index k used to calculate the plant response function in FIG. 3, and represents an index during model parameter estimation processing.

Step S21: The model parameter estimation unit 101 determines whether or not to initialize the model parameter estimated value θ _est (k) and the covariance matrix P(k). Here, as a case where it is determined to be initialized, for example, when the model parameter estimated value θ _est is calculated for the first time (that is, when the model parameter estimated value θ _est is calculated for the first time), the user or the like issues an initialization instruction. and the like.

If it is determined to initialize the model parameter estimation value θ _est (k) and the covariance matrix P(k) (YES in step S21), the model parameter estimation unit 101 proceeds to step S22. On the other hand, if it is determined not to initialize the model parameter estimated value θ _est (k) and the covariance matrix P(k) (NO in step S21), the model parameter estimation unit 101 proceeds to step S23.

Step S22: If it is determined in step S21 above that the model parameter estimation value θ _est (k) and the covariance matrix P(k) are to be initialized, the model parameter estimation unit 101 sets θ _est (0)=θ ₀ and P(0)=I, the previous control amount value y ₋₁ ←y(t), and k←0. Here, as θ ₀ , a model parameter setting value θ that has already been set in the plant response function {S _θ (t)} may be used, or an initial value assumed in advance by a user or the like may be used. However, a fixed initial value such as a vector in which all elements are 0 may be used. Also, I may be a unit matrix or any predetermined matrix.

Step S23: The model parameter estimator 101 updates the state vector φ(k) based on the state vector φ(k−1), the manipulated variable u(t), and the controlled variable previous value y ₋₁ . That is, model parameter estimation section 101 updates state vector φ(k) as follows.

ここで、ｙ（ｋ－１）にはｙ_－１を設定し、ｕ（ｋ）にはｕ（ｔ）を設定する。また、ｙ（ｋ－２），・・・，ｙ（ｋ－Ｎ）及びｕ（ｋ－１），・・・，ｕ（ｋ－Ｍ）には、状態ベクトルφ（ｋ－１）と同じ値を設定する（すなわち、状態ベクトルφ（ｋ－１）に含まれるｙ（ｋ－２），・・・，ｙ（ｋ－Ｎ）及びｕ（ｋ－１），・・・，ｕ（ｋ－Ｍ）をそれぞれ設定する。）。更に、モデルパラメータ推定部１０１は、制御量前回値ｙ_－１←ｙ（ｔ）と更新する。

Here, y _-1 is set to y(k-1), and u(t) is set to u(k). y(k−2), . . . , y(kN) and u(k−1), . , y(kN) and u(k-1), . . . , u(k -M) respectively). Furthermore, the model parameter estimation unit 101 updates the previous control amount value y ₋₁ ←y(t).

For example, when considering the disturbance v of L points in the past, as described above, the state vector φ(k) is added to y(k) and u(k), and L disturbances v(k) as elements, and estimate the covariance matrix and model parameters for the disturbance.

Step S24: Next, the model parameter estimation unit 101 calculates the prediction error ε(k) based on the model parameter estimated value θ _est (k−1), the state vector φ(k), and the control amount y(t). calculate. Note that the model parameter estimated value θ _est (k−1) is the estimated value of the model parameter θ previously estimated (that is, at k−1).

The model parameter estimation unit 101, for example, calculates the prediction error ε(k) as follows.

y(k)=y(t)
ε(k) _{=y(k)−φ(k) Τθest} ⁽ k−1)

Step S25: Next, the model parameter estimation unit 101 updates the covariance matrix P(k). Model parameter estimation section 101 updates covariance matrix P(k), for example, as follows.

ここで、Ｐ（ｋ－１）は前回（つまり、ｋ－１のとき）得られた共分散行列である。また、λは０＜λ≦１を取る忘却係数であり、予め設定された値である。忘却係数λは過去データを忘却するための係数であり、０に近いほど急激に過去データの影響が減少し、１に近いほど過去データの影響が保持される。

where P(k-1) is the covariance matrix obtained last time (that is, at k-1). Also, λ is a forgetting coefficient that takes 0<λ≦1 and is a preset value. The forgetting coefficient λ is a coefficient for forgetting past data.

Step S26: Next, the model parameter estimation unit 101 determines whether or not to update the model parameter estimated value θ _est . Here, as a case where it is determined to update the model parameter estimated value θ _est , for example, when the model parameter estimated value θ _est has not been updated until a predetermined period has passed since the initialization in step S22. , a case where an update instruction is given by a user or the like.

If it is determined to update the model parameter estimated value θ _est (YES in step S26), the model parameter estimation unit 101 proceeds to step S27. On the other hand, if it is determined not to update the model parameter estimated value θ _est (NO in step S26), the model parameter estimation unit 101 ends without executing step S27.

Step S27: If it is determined in step S26 to update the model parameter estimation value θ _est , the model parameter estimation unit 101 updates the model parameter estimation value θ _est (k). The model parameter estimator 101 updates the model parameter estimated value θ _est (k), for example, as follows.

ステップＳ２８：そして、モデルパラメータ推定部１０１は、モデルパラメータ推定処理の実行時インデックスｋをｋ＋１に更新する。

Step S28: Then, the model parameter estimation unit 101 updates the execution time index k of the model parameter estimation process to k+1.

<Operation of look-ahead response correction unit 112>
Next, the operation of the look-ahead response corrector 112 will be described with reference to FIG. FIG. 5 is a diagram for explaining an example of the operation of the look-ahead response correction unit 112. As shown in FIG.

As shown in FIG. 5, the lookahead response correction unit 112 includes a plant response function {S _θ (t)}, a target deviation e ₀ (t), an operation change amount time series {du(t)}, and a lookahead length When T _p is input, a value predicted to change the control amount after T _p has passed from the current time t is calculated as the look-ahead response correction value y _n (t) due to the amount of change in the past operation. Note that the look-ahead length _Tp is calculated by the control parameter calculator 111 .

Then, the look-ahead response correction unit 112 outputs a corrected target deviation e ^* (t) obtained by correcting the target deviation e ₀ (t) with the look-ahead response correction value y _n (t). Here, the corrected target deviation e ^* (t) is calculated by e ^* (t)=r(t)-(y(t)+y _n (t))=e ₀ (t)-y _n (t) be. Note that the look-ahead response correction value y _n (t) can be calculated, for example, by the method described in International Publication No. 2016/092872, Japanese Patent Application Laid-Open No. 2020-21411, and the like.

<Operation of Operation Variation Calculation Unit 113>
Next, the operation of the operation change amount calculator 113 will be described with reference to FIG. FIG. 6 is a diagram for explaining an example of the operation of the operation change amount calculation unit 113. As shown in FIG.

As shown in FIG. 6, when the correction target deviation e ^* (t) and the control gain _kI are input, the operation change amount calculation unit 113 calculates the control gain _kI for the correction target deviation e ^* (t). is multiplied by to output the operation change amount du(t). That is, the operation change amount du(t) is calculated by du(t)=k _I ×e ^* (t).

However, if the result of multiplying the corrected target deviation e ^* (t) by the control gain _kI exceeds the upper limit du _max , the operation change amount calculator 113 sets du _max to the operation change amount du(t). . Similarly, when the result of multiplying the corrected target deviation e ^* (t) by the control gain _kI is below the lower limit du _min , the operation change amount calculation unit 113 treats du _min as the operation change amount du(t). do. This makes it possible to provide a limiter for the upper and lower limits. Note that du _max and du _min are set each time so that the manipulated variable ua obtained by adding the manipulated variable u(t) and the manipulated change amount _du by the adder 114 does not deviate from the predetermined upper and lower limits. good too.

<Calculation of control parameters>
Next, processing for calculating the control gain _kI and the look-ahead length _Tp as control parameters will be described with reference to FIG. FIG. 7 is a flowchart for explaining an example of control parameter calculation processing. In FIG. 7, it is assumed that the plant response function {S _θ (t)} and the adjustment coefficients α and β are input.

Step S31: The control parameter calculator 111 calculates the look-ahead length T _p based on the plant response function {S _θ (t)} and the adjustment coefficient β. Using the plant _response function _{ S _θ (t)}, _the control parameter calculator 111 calculates the adjustment coefficient β The time point at which the plant response function value becomes equal to the value obtained by multiplying by is the look-ahead length _Tp time point. That is, the control parameter calculator 111 calculates the look-ahead length _Tp as follows.

Find T _p , where S _θ (T _p ) = β x S _θ (T _max )
Here, β preferably satisfies 0<β≦1. When the response of the closed loop is slow, the speed of the response can be adjusted by setting the adjustment coefficient β to a smaller value.

Further, the control parameter calculator 111 calculates the gain _gp at the look-ahead length _Tp as follows.

g _p = S _θ (T _p )
Thus, the control gain _gp is calculated based on the look-ahead length _Tp . As a result, for example, even if the dead time and time constant are long, it is possible to respond more quickly.

Note that T _max may be a value that is sufficient for the plant response function to converge, or may be a value determined based on memory restrictions or the like. Alternatively, the maximum value of the period that can be predicted by the look-ahead response correction unit 112 may be set to _Tmax .

Step S32: Then, the control parameter calculator 111 calculates the control gain _kI based on the gain _gp and the adjustment coefficient α at the look-ahead length _Tp . The control parameter calculator 111 calculates the control gain _kI as follows.

すなわち、先読み長Ｔ_ｐ時点でのゲインｇ_ｐの逆数に対して調整係数αを乗じた値を制御ゲインｋ_Ｉとする。ここで、αは、０＜α≦１であることが好ましい。また、εはゲインが大きくなりすぎるのを回避するための微小な非負の値であり、ε≧０を満たす。

That is, the control gain _kI is obtained by multiplying the reciprocal of the gain _gp at the look-ahead length _Tp by the adjustment coefficient α. Here, α preferably satisfies 0<α≦1. Also, ε is a minute non-negative value for avoiding the gain from becoming too large, and satisfies ε≧0.

<Outline of calculation of look-ahead length _Tp >
Next, an overview of the calculation of the look-ahead length _Tp in step S31 will be described with reference to FIG. FIG. 8 is a diagram for explaining an example of a calculation outline of the look-ahead length _Tp .

As shown in FIG. 8, the value of the plant response function S _θ (t) gradually changes from time 0 to T _max , but at a certain time T _p S _θ (T _p )=β×S _θ ( T _max ) holds. Therefore, such a time _Tp is set as the look-ahead length. As a result, since the behavior of the plant at the time of the look-ahead length _Tp is linked to the final behavior of the plant, it is possible to suppress useless changes in the manipulated variable, thereby suppressing the occurrence of overshoot and undershoot. can. In addition, it is possible to determine a look-ahead length that avoids the initial response period during which dead time and reverse response occur.

It should be noted that PID (Proportional-Integral-Differential) control, which is known as a conventional technique, needs to increase the differential gain in accordance with the amount of dead time, and has the disadvantage of being susceptible to noise. For example, in the control law based on the Ziegler-Nichols step response method, k _p =1.2/(RL), T _I =2L, T _D =0.5L, and R=g _p /T _p are used, Since the differential time _TD increases in proportion to the dead time L and the differential strength increases, the control becomes vulnerable to noise and impulse-like disturbances. On the other hand, in this embodiment, since the differential signal of the observed value is not used, robust control against noise and dead time can be realized.

<Calculation details of look-ahead length _Tp >
Next, details of the calculation of the look-ahead length _Tp in step S31 will be described with reference to FIG. FIG. 9 is a flowchart for explaining an example of details of calculation of the look-ahead length _Tp .

Step S41: First, the control parameter calculator 111 sets _{T1 ←} 0 as a variable for searching for the look-ahead length _Tp .

Step S42: Next, the control parameter calculator 111 determines whether or not T ₁ ≧T _max .

If it is determined that T ₁ ≧T _max (YES in step S42), the control parameter calculator 111 proceeds to step S46. On the other hand, if it is not determined that T ₁ ≧T _max (NO in step S42), the control parameter calculator 111 proceeds to step S43.

Step S43: The control parameter calculator 111 determines whether S _θ (T ₁ )≧β×S _θ (T _max ) is satisfied.

If it is determined that S _θ (T ₁ )≧β×S _θ (T _max ) is satisfied (YES in step S43), the control parameter calculator 111 proceeds to step S45. On the other hand, if it is not determined that S _θ (T ₁ )≧β×S _θ (T _max ) is satisfied (NO in step S43), the control parameter calculator 111 proceeds to step S44.

Step S44: The control parameter calculator 111 updates T ₁ ←T ₁ +ΔT, and returns to step S42. Accordingly, step S44 is repeatedly executed until either T ₁ ≧T _max or S _θ (T ₁ )≧β×S _θ (T _max ) is satisfied.

Step S45: When it is determined that S _θ (T ₁ )≧β×S _θ (T _max ) is satisfied, the control parameter calculator 111 sets T _p ←T ₁ . That is, the control parameter calculator 111 sets T 1 that satisfies S _θ (T ₁ )≧β×S _θ (T _{max )} as the look-ahead length T _p .

Step S46: If it is not determined that T ₁ ≧T _max , the control parameter calculator 111 sets T _p ←γ×T _max . where 0<γ<1. That is, if T ₁ that satisfies S _θ (T ₁ )≧β×S _θ (T _max ) cannot be searched, the value obtained by multiplying T _max by γ is set as the look-ahead length T _p .

<Operation of high-speed complement control unit 105>
Next, the operation of the high-speed complement control section 105 will be described with reference to FIG. 10A and 10B are diagrams for explaining an example of the operation of the high-speed complement control unit 105. FIG.

As shown in FIG. 10, the operation amount u _a is output from the main control unit 104 in each main control cycle _Tc , while the high-speed complementary control unit 105 outputs the operation amount u in each high-speed control cycle _Tf . output _b . However, for time t when t=n ₂ T _f and t=n ₃ T _c for certain integers n ₂ and n ₃ , u _b (t)=u _a (t). This is because both the main control unit 104 and the high-speed complementary control unit 105 are calculated based on the control amount y(t) at the current time t. Hereinafter, the manipulated variable u _a output from the main control unit 104 is also referred to as the “main control manipulated variable u _a ”, and the manipulated variable u _b output from the high-speed complementary control unit 105 is referred to as the “complementary manipulated variable u Also called _"b ".

<Calculation of complementary operation amount u _b >
Next, the processing for calculating the complementary manipulated variable u _b will be described with reference to FIG. 11 . FIG. 11 is a flowchart for explaining an example of calculation processing of the complement operation amount u _b . In FIG. 11, a case will be described in which the complementary manipulated variable _ub at a certain index k is calculated assuming that the target value r and the controlled variable y(t) at the current time t are input. Note that this index k is a value independent of the index k used for calculating the plant response function in FIG. 3 and the index k used for estimating the model parameter θ in _FIG . represents the run-time index of the .

Step S51: The high-speed complement control section 105 determines whether or not the value of the main control section update counter has changed (updated). In other words, the high-speed complementary control section 105 determines whether or not the main control manipulated variable _ua is output from the main control section 104 based on the change in the value of the main control section update counter. It should be noted that determining whether or not the main control manipulated variable u _a has been output based on a change in the value of the main control unit update counter is an example, and the main control manipulated variable u _a can be determined without using the main control unit update counter. It may be determined whether or not is output. For example, the high-speed complementary control unit 105 determines whether or not the main control manipulated variable _ua has been output based on whether or not the main control cycle _Tc has elapsed since the previous main control manipulated variable _ua was output. may

If it is determined that the value of the main control unit update counter has changed (YES in step S51), the high-speed complementary control unit 105 proceeds to step S52. On the other hand, if it is determined that the value of the main control unit update counter has not changed (NO in step S51), the high-speed complementary control unit 105 proceeds to step S53.

Step S52: The high-speed complement control unit 105 initializes _y0 =y(t), _mdu (0)=0, and k=0. Here, m _du (k) is the average manipulation change at index k, as will be described later.

Step S53: The high-speed complement control unit 105 updates the index k to k←k+1. Since the index k has a role of a counter, it may be called a "complementary counter", for example.

Step S54: The high-speed supplement control section 105 calculates the provisional target value r _f (k). The provisional target value r _f (k) is a provisional target value used by the high-speed complementary control section 105 and is calculated below.

なお、暫定目標値ｒ_ｆ（ｋ）の例については後述する。

An example of the provisional target value r _f (k) will be described later.

Step S55: The high-speed complement control unit 105 calculates the average amount of change in operation m _du (k). The average manipulated change m _du (k) is calculated as follows.

ここで、ｄｕ_ｆ（ｋ）は、瞬時的な操作変化量を表す瞬時操作変化量である。このように、平均操作変化量ｍ_ｄｕ（ｋ）は、ｋ＝０以降の瞬時操作変化量ｄｕ_ｆ（ｋ）の平均となっている。

Here, du _f (k) is an instantaneous operation change amount representing an instantaneous operation change amount. Thus, the average amount of change in operation m _du (k) is the average of the amount of instantaneous change in operation du _f (k) after k=0.

Step S56: The high-speed complement control unit 105 calculates the complement operation amount u _b . The complementary manipulated variable u _b (k) at the index k is calculated below using the main control manipulated variable u _a and the average manipulated variable m _du (k).

ここで、ｕ_ｍｉｎ及びｕ_ｍａｘは操作量に対して課された上下限制約である。このｕ_ｍｉｎ及びｕ_ｍａｘは、例えば、主制御部１０４から出力される操作量ｕ_ａに対して課されている上下限制約をそのまま用いてもよい。

where u _min and u _max are upper and lower limits imposed on the manipulated variable. For u _min and u _max , for example, the upper and lower limits imposed on the manipulated variable u _a output from the main control unit 104 may be used as they are.

As described above, the complementary operation amount u _b (t)=u _b (k) at the current time t is obtained.

<Temporary target value r _f (k)>
Next, the temporary target value r _f (k) calculated in S54 will be described with reference to FIG. FIG. 12 is a diagram for explaining an example of the provisional target value r _f (k).

As shown in FIG. 12 , the provisional target value r _f (k) is calculated from the control amount y ₀ =y(t ₀ ) at time t=t ₀ immediately after the update counter of the main control unit is updated. It is calculated using the index k as the waypoint of the trajectory such that the target value r is reached at _Tp . That is, the provisional target value r _f (k) is calculated as a point of linear interpolation between the controlled variable y ₀ and the target value r at time t ₀ +T _p .

However, it is an example that the provisional target value r _f (k) is calculated as a point of linear interpolation between the control amount y ₀ and the target value r at time t ₀ +T _p , and is calculated as a point of nonlinear interpolation. good too.

<Response when using high-speed complement control unit 105>
Next, the response when using the high-speed complement control unit 105 will be described with reference to FIG. FIG. 13 is a diagram for explaining an example of a response when the high-speed complement control section 105 is used.

As shown in FIG. 13, when the main control unit 104 performs control such that the target deviation becomes 0 at the time point ahead of the look-ahead length _Tp , the response when using the high-speed complementary control unit 105 also gives the look-ahead length T Control along the trajectory is performed so that the target deviation becomes 0 at the point _p ahead. However, the response when using the high-speed complementary control unit 105 does not necessarily match the trajectory using only the main control unit 104, and for example, it is conceivable that the trajectory becomes a more suppressed overshoot.

<Hardware Configuration of Control Device 10>
Next, the hardware configuration of the control device 10 according to this embodiment will be described with reference to FIG. 14 . FIG. 14 is a diagram showing an example of the hardware configuration of the control device 10 according to the second embodiment.

As shown in FIG. 14, the control device 10 according to the second embodiment includes an input device 201, a display device 202, an external I/F 203, a communication I/F 204, a processor 205, and a memory device 206. have. Each of these pieces of hardware is communicably connected via a bus 207 .

The input device 201 is, for example, a touch panel or various buttons. The display device 202 is, for example, a display panel or the like. Note that the control device 10 may not have at least one of the input device 201 and the display device 202 .

The external I/F 203 is an interface with an external device such as the recording medium 203a. Examples of the recording medium 203a include an SD memory card (Secure Digital memory card) and a USB (Universal Serial Bus) memory card.

Communication I/F 204 is an interface for connecting control device 10 to a communication network. The processor 205 is, for example, various arithmetic units such as a CPU and an MPU. The memory device 206 is, for example, various storage devices such as SSD (Solid State Drive), RAM (Random Access Memory), ROM (Read Only Memory), and flash memory.

Note that the hardware configuration shown in FIG. 14 is an example, and the control device 10 may have another hardware configuration. For example, the control device 10 may have various hardware other than the illustrated hardware.

[Second embodiment]
A second embodiment will be described below. In addition, in the second embodiment, differences from the first embodiment will be explained, and explanations of components that may be the same as those in the first embodiment will be omitted.

<Overall Configuration Example of Control Device 10>
An overall configuration example of the control device 10 according to the second embodiment will be described with reference to FIG. 15 . FIG. 15 is a diagram showing an example of the overall configuration of the control device 10 according to the second embodiment.

As shown in FIG. 15, the control device 10 according to the second embodiment sets the pre-estimated model parameter θ to the plant response function {S _θ (t)}, so that the first embodiment The model parameter estimating unit 101 is removed from the overall configuration example described in .

[Example]
Next, examples will be described. In this embodiment, as an example, a case where a controlled plant 20 is controlled by the control device 10 according to the second embodiment will be described.

In this embodiment, the main control cycle T _c =5 [sec] and the high-speed control cycle T _f =0.5 [sec] are set. That is, it is assumed that the high-speed complementary control unit 105 operates at ten times the speed of the main control unit 104 . Further, the upper limit of the manipulated variable u _max =90, the lower limit u _min =0, and the adjustment coefficients α=1.0 and β=0.5.

FIG. 16 shows the step response of the controlled plant 20 in this embodiment. As shown in FIG. 16, in this embodiment, it takes about 150 [sec] until the step response converges. Therefore, for example, when the main control period is T _c =0.5 [sec], a prediction buffer of approximately 300 points is required for prediction until the step response converges. On the other hand, in the present embodiment, the main control cycle is set to T _c =5 [sec], so prediction until convergence of the step response requires only 30 prediction buffers. Therefore, by lengthening the main control cycle to T _c =5 [sec], the prediction buffer of the control device 10, which is an edge device or the like, can be saved to about 1/10, and the memory can be reduced and the calculation time can be shortened. can be done.

As a comparative example with the control device 10 of the present embodiment, FIG. 17 shows the control amount, target value, and operation amount when the high-speed complementary control section 105 of the control device 10 according to the second embodiment is disabled. In FIG. 17, the control amount (PV) and the target value (SP) are shown in the upper part, and the manipulated variable (MV) is shown in the lower part. The right figure is an enlarged view of a part of the left figure. As shown in FIG. 17, in the control using only the main control unit 104 with the high-speed complementary control disabled, the operation amount changes stepwise every main control cycle T _c =5 [sec].

On the other hand, the control amount, target value, and manipulated variable by the control device 10 in this embodiment (that is, the controlled amount, target value, and manipulated variable when the high-speed complementary control by the high-speed complementary control unit 105 is effective) are shown in FIG. show. In FIG. 18, the control amount (PV) and the target value (SP) are shown in the upper part, and the manipulated variable (MV) is shown in the lower part. The right figure is an enlarged view of a part of the left figure. As shown in FIG. 18, compared with FIG. 17, the followability of the control amount to the target value is improved, and the overshoot is reduced. Also, looking at the enlarged view of the operation amount, it can be confirmed that the operation amount changes in the high-speed control period _Tf by the high-speed complementary control section 105 in addition to the change in the main control period _Tc . Therefore, by enabling the high-speed complementary control by the high-speed complementary control section 105, the control performance can be improved more than when only the main control section 104 is used.

[summary]
As described above, the control device 10 according to the first and second embodiments has the high-speed complementary control unit 105 that controls the controlled plant 20 at a higher speed with a shorter control cycle _Tf than the main control unit 104. there is As a result, even if the control cycle _Tc of the main control unit 104 is long, the controlled plant 20 is controlled at high speed every control cycle _Tf . It is possible to quickly follow the influence of fast disturbance, etc., and it is possible to suppress the deterioration of the control performance. In addition, even when the control device 10 lacks computational resources and the main control cycle _Tc must be lengthened, it is possible to suppress the deterioration of the control performance. Furthermore, improvement of control deviation (for example, control deviation due to overshoot, etc.), which is insufficiently suppressed by model predictive control, can be expected.

Note that when the model parameter θ is estimated by the model parameter estimating unit 101 as in the first embodiment, if the high-speed complementary control unit 105 is effective, the identification of the closed loop including the high-speed complementary control is performed. The model parameter θ may be estimated by the model parameter estimating unit 101 only when the high-speed complementary control unit 105 is disabled.

In addition, the operation of the main control unit 104 is not limited to that described in the first embodiment, and any operation can be adopted as long as it adopts model predictive control as the control method. Since the control unit 105 controls the trajectory that reaches the target value r after the look-ahead length _Tp as the provisional target value _rf using the control gain _kI , it is preferable that the main control unit 104 also perform the same operation.

The present invention is not limited to the specifically disclosed embodiments described above, and various variations, modifications, combinations with known techniques, etc. are possible without departing from the scope of the claims. be.

REFERENCE SIGNS LIST 10 control device 20 controlled plant 101 model parameter estimator 102 measuring unit 103 differentiator 104 main controller 105 high-speed complementary controller 106 first timer 107 second timer 108 switch 111 control parameter calculator 112 look-ahead response corrector 113 operation variation calculator 114 adder 201 input device 202 display device 203 external I/F
203a recording medium 204 communication I/F
205 processor 206 memory device 207 bus

Claims (9)

A control device that outputs a manipulated variable for a controlled object and causes the controlled variable of the controlled object to follow a target value,
a main control unit that outputs the next manipulated variable ua for the controlled object by model predictive control based on the manipulated variable and the controlled _variable at each first control cycle _Tc ;
a high-speed complementary control unit that outputs the next manipulated variable u _b for the controlled object every second control cycle _Tf shorter than the first control cycle _Tc ,
The high-speed complement control unit is
Based on the control amount at time _t0 immediately after the operation amount _ua is output from the main control unit, a predetermined look-ahead length _Tp , and the target value, the control amount at time _t0 and the time calculating a provisional target value r _f that interpolates with the target value at t ₀ +T _p ;
A control device that calculates a sum of an average of instantaneous operation change amounts calculated based on a deviation between the provisional target value _rf and the current control amount and the operation amount _ua as the operation amount _ub . The high-speed complement control unit is
The control according to claim ₁ , wherein a provisional target value _rf is calculated by linearly interpolating the control amount at time t0 and the target value at time _t0 + _Tp every second control cycle _Tf . Device. The high-speed complement control unit is
3. The instantaneous operation change amount is calculated as a value obtained by multiplying a deviation between the provisional target value _rf and the controlled variable at time _t0 by a predetermined gain _kI . Control device. The high-speed complement control unit is
2. An average of said instantaneous operation change amounts for said counter k is calculated using a counter k which is incremented every said second control period _Tf with an initial value at said time _t0 being 0. The control device according to . The look-ahead length T _p and the gain k _I are
4. The control device according to claim 3, which is calculated based on a predictive model used for said model predictive control. 6. The control device according to claim 5, further comprising a model parameter estimator that estimates the parameters of the prediction model based on the controlled variable and the manipulated variable observed from the controlled object. The high-speed complement control unit is
2. The control device according to claim 1, wherein an upper and lower limit range applied to said manipulated variable _ua output from said main control unit is used to calculate said manipulated variable _ub that satisfies said upper and lower limit range. A control device that outputs a manipulated variable for a controlled object and causes the controlled variable of the controlled object to follow a target value,
a main control procedure for outputting the next manipulated variable ua for the controlled object by model predictive control based on the manipulated variable and the controlled _variable at each first control cycle _Tc ;
a high-speed complementary control procedure for outputting the next manipulated variable _ub for the controlled object every second control cycle _Tf shorter than the first control cycle _Tc ;
The fast complementary control procedure is
Based on the control amount at time _t0 immediately after the operation amount _ua is output in the main control procedure, a predetermined look-ahead length _Tp , and the target value, the control amount at time _t0 and the time calculating a provisional target value r _f that interpolates with the target value at t ₀ +T _p ;
A control method, wherein the operation amount _ub is calculated as the sum of an average of instantaneous operation change amounts calculated based on the deviation between the provisional target value _rf and the current control amount and the operation amount _ua . A control device that outputs a manipulated variable for a controlled object and causes the controlled variable of the controlled object to follow a target value,
a main control procedure for outputting the next manipulated variable ua for the controlled object by model predictive control based on the manipulated variable and the controlled _variable at each first control cycle _Tc ;
executing a high-speed complementary control procedure for outputting the next manipulated variable u _b for the controlled object every second control cycle _Tf shorter than the first control cycle _Tc ;
The fast complementary control procedure is
Based on the control amount at time _t0 immediately after the operation amount _ua is output in the main control procedure, a predetermined look-ahead length _Tp , and the target value, the control amount at time _t0 and the time calculating a provisional target value r _f that interpolates with the target value at t ₀ +T _p ;
A program for calculating the operation amount _ub as the sum of an average of instantaneous operation change amounts calculated based on the deviation between the temporary target value _rf and the current control amount and the operation amount _ua .

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