CN115712243A - Correction method, device, equipment and medium - Google Patents
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Abstract
The application discloses a correction method, a correction device, correction equipment and a correction medium, and relates to the technical field of automation. The method comprises the following steps: determining a controlled object and establishing a discrete state space model of the controlled object; determining output vectors of the controlled objects in a plurality of preset times according to the model; updating a plurality of state vectors corresponding to a controlled object through an extended Kalman filter, and compensating a part of each state vector; compensating the plurality of output vectors by a compensation term; at the moment, the original inaccurate output vector is compensated and corrected into an accurate output vector; updating the discrete state space model; based on the updated model, the decision vector is determined by using the MPC algorithm, and at the moment, the decision vector is also accurate, so that the control is convenient to be carried out according to the decision vector. At the moment, the control effect of the result determined by the MPC algorithm is accurate, and the prediction and control errors caused by model inaccuracy and state update inaccuracy are corrected.
Description
Technical Field
The present application relates to the field of automation technologies, and in particular, to a calibration method, apparatus, device, and medium.
Background
With the continuous enlargement of the scale and the increase of the complexity of the industrial production process, the requirements of enterprises on the product quality of various devices, the control of controlled variables, energy conservation, consumption reduction, yield increase and efficiency increase are higher and higher, the conventional proportional-integral-derivative controllers (proportional, integral, differential and PID) are far from being adapted to the requirements of future industrial production, and the advanced control becomes one of the preferred technologies of the enterprises. Among them, the Model Predictive Control (MPC) is the most representative advanced Control algorithm because it can directly deal with the problems of multivariable, constraint, etc. and the standardized implementation process is added, and it is successfully applied in the industrial process Control field and has great economic benefit. In an actual industrial field, noise interference exists in input signals and output signals, and meanwhile, a space model for measuring noise cannot obtain an accurate model state value, and a Kalman filter is generally adopted to estimate a state vector. However, this process is slow and takes some time to remove the error. If the deviation is large at first, the error can not be eliminated in a short time by a Kalman filter, at the moment, the state value of the model is inaccurate, the control effect of the MPC result calculated on the basis of the model is poor, and even a reverse control result appears, which is very dangerous in actual production.
In view of the above existing problems, it is an endeavor of those skilled in the art to find ways to correct prediction and control errors caused by model inaccuracy and state update inaccuracy.
Disclosure of Invention
An object of the present application is to provide a correction method, apparatus, device and medium for correcting prediction and control errors due to model inaccuracy and untimely state update.
In order to solve the above technical problem, the present application provides a calibration method, including:
determining a controlled object and establishing a discrete state space model of the controlled object;
determining output vectors of the controlled objects in a plurality of preset times according to the discrete state space model;
updating a plurality of state vectors corresponding to a controlled object through an extended Kalman filter, and compensating part of state vectors in each state vector;
compensating the output vectors through the compensation items, and updating the discrete state space model;
and determining a decision vector by using an MPC algorithm based on the updated discrete state space model so as to control according to the decision vector.
Preferably, the determining the output vector of the controlled object within a plurality of preset times according to the discrete state space model comprises:
determining a prediction matrix according to the discrete state space model;
a plurality of output vectors is determined from the prediction matrix.
Preferably, the establishing of the discrete state space model of the controlled object comprises:
acquiring a plurality of state vectors, a plurality of control input vectors and a plurality of measurable disturbance vectors of a controlled object;
and establishing a discrete state space model according to the state vector, the control input vector and the measurable disturbance vector.
Preferably, updating the state vector by the extended kalman filter comprises:
expanding the updated state vector to obtain an expanded state vector and an expanded disturbance vector;
the state vector is updated based on the extended state vector.
Preferably, after compensating the plurality of output vectors by the compensation term, before updating the discrete state space model, the method further includes:
judging whether the compensated output vector is consistent with the actual output vector;
if yes, entering a step of updating the discrete state space model;
if not, returning to the step of updating a plurality of state vectors corresponding to the controlled object through the extended Kalman filter and compensating part of state vectors in each state vector.
Preferably, the determining the decision vector using the MPC algorithm comprises:
and determining a decision vector according to a constraint condition, wherein the constraint condition is an objective function of the MPC algorithm.
Preferably, the objective function includes a first objective function representing set value tracking, a second objective function representing controlled variable tracking, and a third objective function representing minimum increment of the controlled variable.
In order to solve the above technical problem, the present application further provides a calibration apparatus, including:
the first determining module is used for determining a controlled object and establishing a discrete state space model of the controlled object;
the second determination module is used for determining output vectors of the controlled objects in a plurality of preset times according to the discrete state space model;
the first compensation module is used for updating a plurality of state vectors corresponding to the controlled object through the extended Kalman filter and compensating part of state vectors in each state vector;
the second compensation module is used for compensating the output vectors through the compensation items and updating the discrete state space model;
and the third determining module is used for determining a decision vector by using an MPC algorithm based on the updated discrete state space model so as to facilitate control according to the decision vector.
In addition, the device also comprises the following modules:
preferably, the determining the output vector of the controlled object within a plurality of preset times according to the discrete state space model comprises:
the fourth determination module is used for determining a prediction matrix according to the discrete state space model;
a fifth determining module to determine a plurality of output vectors from the prediction matrix.
Preferably, the establishing of the discrete state space model of the controlled object comprises:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a plurality of state vectors, a plurality of control input vectors and a plurality of measurable disturbance vectors of a controlled object;
and the establishing module is used for establishing a discrete state space model according to the state vector, the control input vector and the measurable disturbance vector.
Preferably, updating the state vector by the extended kalman filter comprises:
the expansion module is used for expanding the updated state vector and obtaining an expanded state vector and an expanded disturbance vector;
and the updating module is used for updating the state vector according to the expanded state vector.
Preferably, after compensating the plurality of output vectors by the compensation term, before updating the discrete state space model, the method further includes:
the judging module is used for judging whether the compensated output vector is consistent with the actual output vector;
if yes, entering a step of updating the discrete state space model;
if not, returning to the step of updating a plurality of state vectors corresponding to the controlled object through the extended Kalman filter and compensating part of state vectors in each state vector.
Preferably, the determining the decision vector using the MPC algorithm comprises:
a sixth determining module, configured to determine a decision vector according to a constraint condition, where the constraint condition is a target function of the MPC algorithm; the target function comprises a first target function for representing set value tracking, a second target function for representing controlled variable tracking and a third target function for representing minimum increment of the controlled variable.
In order to solve the above technical problem, the present application further provides a calibration apparatus, including:
a memory for storing a computer program;
a processor for pointing to a computer program implementing the steps of the correction method.
In order to solve the above technical problem, the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the above all correction methods are implemented.
The application provides a correction method, which comprises the following steps: determining a controlled object, and establishing a discrete state space model of the controlled object; determining output vectors of the controlled objects in a plurality of preset times according to the discrete state space model; updating a plurality of state vectors corresponding to a controlled object through an extended Kalman filter, and compensating part of state vectors in each state vector; compensating the plurality of output vectors by a compensation term; at the moment, the original inaccurate output vector is compensated and corrected into an accurate output vector; updating the discrete state space model; and determining a decision vector by using an MPC algorithm based on the updated discrete state space model, wherein the decision vector is also accurate, so that the control is convenient to be performed according to the decision vector. At this time, the control effect of the result determined by the MPC algorithm is accurate, and therefore, the prediction and control errors caused by model inaccuracy and state update inaccuracy are corrected.
The application also provides a correcting device, equipment and a medium, and the effects are the same as the above.
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In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a calibration method according to an embodiment of the present disclosure;
fig. 2 is a structural diagram of a calibration apparatus according to an embodiment of the present application;
fig. 3 is a structural diagram of a calibration apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the present application.
The core of the application is to provide a correction method, device, equipment and medium, which can correct prediction and control errors caused by inaccurate models and untimely state updating.
In order that those skilled in the art will better understand the disclosure, the following detailed description is given with reference to the accompanying drawings.
Fig. 1 is a flowchart of a calibration method according to an embodiment of the present disclosure. As shown in fig. 1, the calibration method includes:
s10: and determining a controlled object and establishing a discrete state space model of the controlled object.
The method specifically comprises the following steps:
acquiring a plurality of state vectors, a plurality of control input vectors and a plurality of measurable disturbance vectors of a controlled object;
and establishing a discrete state space model according to the state vector, the control input vector and the measurable disturbance vector.
The discrete state space model is represented by the following formula:
x(k+1)＝Ax(k)+B u u(k)+B v v(k)
y(k)＝Cx(k)+D u u(k)+D u v(k)
wherein k represents that the current time is k time; x represents a state vector of the controlled object, and the vector is an n-dimensional vector; u represents a control input vector of the controlled object, and the vector is n u A dimension vector; v represents an input measurable disturbance vector of a controlled object, and the vector is n v A dimension vector; y represents the output vector of the controlled object, and the vector is n y Dimension vector, A ∈ R n×n ,
S11: and determining output vectors of the controlled objects in a plurality of preset times according to the discrete state space model.
The method specifically comprises the following steps:
determining a prediction matrix according to the discrete state space model;
a plurality of output vectors is determined from the prediction matrix.
Let the current number of steps be 0 (where the current number of steps is understood to mean the current time instant is 0), where i =1,2,3 \8230andp, the predicted value for p future time instants can be determined according to the following formula:
in this embodiment, the prediction matrix is:
and wherein S x 、S u1 、S u 、H v Is shown below:
s12: and updating a plurality of state vectors corresponding to the controlled object through an extended Kalman filter, and compensating part of state vectors in each state vector.
The method specifically comprises the following steps:
expanding the updated state vector to obtain an expanded state vector and an expanded disturbance vector;
the state vector is updated based on the extended state vector.
The expanded state vector can be represented by the following formula:
wherein, the expanded state vector can be represented as:
and extends disturbance variable
The expanded state space matrix is:
wherein the content of the first and second substances,
is of size n y The unit matrix of (2).
The calculation process of the extended kalman filter is as follows:
wherein the content of the first and second substances,
to say thatIt is clear that, where x (k-1) represents the state vector of the controller at time k-1,
representing the state vector at time k estimated from the state vector of the controller at time k-1,
representing the estimated output value at time K, y (K) representing the actual output vector at time K, K k State compensation gain matrix, P, representing time k k And representing a state covariance matrix at the moment k, Q representing a process noise covariance matrix of the prediction model, and R representing a measurement noise covariance matrix of the prediction model.
S13: introducing output feedback correction, compensating a plurality of output vectors through a compensation term, and updating the discrete state space model, wherein the formula of the compensation term is as follows:
then, after compensation by the compensation term, the predicted value obtained by the updated discrete state space model can be expressed as the following formula:
s14: determining a decision vector by using an MPC algorithm based on the updated discrete state space model;
so as to control according to the decision vector.
The method specifically comprises the following steps:
and determining a decision vector according to a constraint condition, wherein the constraint condition is an objective function of the MPC algorithm. The target function comprises a first target function for representing set value tracking, a second target function for representing controlled variable tracking and a third target function for representing minimum increment of the controlled variable.
It should be noted that, at this time, the solution of the control variables of the MPC algorithm is converted into an optimal solution problem of a Quadratic Programming (QP) problem with constraint conditions, which is described as follows:
min J(z k )＝J y (z k )+J u (z k )+J Δu (z k )
st.
y j,min (i)≤y j (k+i|k)≤y j,max (i),i＝1:p,j＝1:n y
u j,min (i)≤u j (k+i-1|k)≤u j,max (i),i＝1:p,j＝1:n u
Δu j,min (i)≤Δu j (k+i-1|k)≤Δu j,max (i),i＝1:p,j＝1:n u
wherein, J (z) k ) Expressed as the objective function of the MPC algorithm, z k A decision vector, expressed as a quadratic programming problem, can be expressed as follows:
where k denotes a current time (current control time), p denotes a predicted time (may also be referred to as a predicted time domain), and J y (z k ) A first objective function characterizing the setpoint tracking, J u (z k ) Second objective function for characterizing the control quantity tracking, J Δu (z k ) To characterize the minimum third objective function of the increment of the control quantity, y j (k + i | k) is expressed as a predicted value of the ith step, u, calculated in the kth control cycle j (k + i | k) is a control amount of the ith step calculated in the kth control cycle, Δ u j (k + i | k) is expressed as a control amount increment of the ith step, y, calculated in the kth control cycle j,min (i) Expressed as the upper limit of the amplitude of the controlled quantity at step i, y j,max (i) Is expressed as the lower limit of the amplitude of the controlled quantity in the ith step, u j,min (i) Expressed as the upper limit of the amplitude of the controlled variable at step i, u j,max (i) Expressed as the lower limit of the amplitude of the controlled variable at step i, Δ u j,min (i) Expressed as control quantity increment at step iUpper limit of amplitude, Δ u j,max (i) Indicated as the lower limit of the amplitude of the control quantity increment at step i.
It is also to be noted that the first objective function J, wherein the setpoint value is tracked y (z k ) It can be expressed as follows:
wherein e is j (k + i | k) indicates the kth control cycle, and the predicted deviation of the jth output of the ith step is defined as:
e j (k+i|k)＝y j (k+i|k)-r j (k+i|k)
y j (k + i | k) represents the predicted value at the ith step in the kth control cycle;
r j (k + i | k) represents the set value at the ith step in the kth control cycle;
further, a second objective function J for control quantity tracking u (z k ) It can be expressed as follows:
wherein u is j,ta (k + i | k) represents a target value of the jth controlled variable of the ith prediction step in the kth control cycle;
the weight of the j control quantity of the controlled object under the ith prediction step length is represented;
finally, a third objective function J for the minimum of the control quantity increments Δu (z k ) It can be expressed as follows:
wherein, the first and the second end of the pipe are connected with each other,
and the weight of the jth control quantity increment of the controlled object under the ith prediction step length is shown.
The application provides a correction method, which comprises the following steps: determining a controlled object, and establishing a discrete state space model of the controlled object; determining output vectors of the controlled objects in a plurality of preset times according to the discrete state space model; updating a plurality of state vectors corresponding to a controlled object through an extended Kalman filter, and compensating part of state vectors in each state vector; compensating the plurality of output vectors by a compensation term; at the moment, the original inaccurate output vector is compensated and corrected into an accurate output vector; updating the discrete state space model; and determining a decision vector by using an MPC algorithm based on the updated discrete state space model, wherein the decision vector is also accurate at the moment, so that the control is convenient to be carried out according to the decision vector. At this time, the control effect of the result determined by the MPC algorithm is accurate, and therefore, the prediction and control errors caused by model inaccuracy and state update inaccuracy are corrected.
In the above embodiments, the correction method is described in detail, and the present application also provides embodiments corresponding to the correction device. It should be noted that the present application describes the embodiments of the apparatus portion from two perspectives, one from the perspective of the function module and the other from the perspective of the hardware.
Fig. 2 is a structural diagram of a calibration apparatus according to an embodiment of the present application. As shown in fig. 2, the present application further provides a correction device, including:
the first determining module 20 is used for determining the controlled object and establishing a discrete state space model of the controlled object;
a second determining module 21, configured to determine output vectors of the controlled objects within multiple preset times according to the discrete state space model;
the first compensation module 22 is configured to update a plurality of state vectors corresponding to the controlled object through an extended kalman filter, and compensate for a part of the state vectors in each state vector;
the second compensation module 23 is configured to compensate the multiple output vectors by using the compensation terms, and update the discrete state space model;
and a third determining module 24, configured to determine a decision vector by using an MPC algorithm based on the updated discrete state space model, so as to perform control according to the decision vector.
In addition, the device also comprises the following modules:
preferably, the determining the output vector of the controlled object within a plurality of preset times according to the discrete state space model comprises:
the fourth determination module is used for determining a prediction matrix according to the discrete state space model;
a fifth determining module to determine a plurality of output vectors from the prediction matrix.
Preferably, the establishing of the discrete state space model of the controlled object comprises:
the first acquisition module is used for acquiring a plurality of state vectors, a plurality of control input vectors and a plurality of measurable disturbance vectors of a controlled object;
and the establishing module is used for establishing a discrete state space model according to the state vector, the control input vector and the measurable disturbance vector.
Preferably, updating the state vector by the extended kalman filter comprises:
the expansion module is used for expanding the updated state vector and obtaining an expanded state vector and an expanded disturbance vector;
and the updating module is used for updating the state vector according to the expanded state vector.
Preferably, after compensating the plurality of output vectors by the compensation term, before updating the discrete state space model, the method further includes:
the judging module is used for judging whether the compensated output vector is consistent with the actual output vector;
if yes, entering a step of updating the discrete state space model;
if not, returning to the step of updating a plurality of state vectors corresponding to the controlled object through the extended Kalman filter and compensating part of state vectors in each state vector.
Preferably, the determining the decision vector using the MPC algorithm comprises:
a sixth determining module, configured to determine a decision vector according to a constraint condition, where the constraint condition is a target function of the MPC algorithm; the target function comprises a first target function for representing set value tracking, a second target function for representing controlled variable tracking and a third target function for representing minimum increment of the controlled variable.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here. The device part also has the corresponding beneficial effects of the method: determining a controlled object and establishing a discrete state space model of the controlled object; determining output vectors of the controlled objects in a plurality of preset times according to the discrete state space model; updating a plurality of state vectors corresponding to a controlled object through an extended Kalman filter, and compensating part of state vectors in each state vector; compensating the plurality of output vectors by a compensation term; at the moment, the original inaccurate output vector is compensated and corrected into an accurate output vector; updating the discrete state space model; and determining a decision vector by using an MPC algorithm based on the updated discrete state space model, wherein the decision vector is also accurate, so that the control is convenient to be performed according to the decision vector. At this time, the control effect of the result determined by the MPC algorithm is accurate, and therefore, the prediction and control errors caused by model inaccuracy and state update inaccuracy are corrected.
Fig. 3 is a structural diagram of a calibration apparatus according to an embodiment of the present application, and as shown in fig. 3, a calibration apparatus includes:
a memory 30 for storing a computer program;
a processor 31 for implementing the steps of the correction method as mentioned in the above embodiments when executing the computer program.
The calibration device provided by the embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, or a desktop computer.
The processor 31 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 31 may be implemented in at least one hardware form of Digital Signal Processing (DSP), field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 31 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in a wake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 31 may be integrated with a Graphics Processing Unit (GPU) which is responsible for rendering and drawing the content required to be displayed by the display screen. In some embodiments, processor 31 may further include an Artificial Intelligence (AI) processor for processing computational operations related to machine learning.
In some embodiments, the calibration device may further include a display screen, an input/output interface, a communication interface, a power source, and a communication bus.
Those skilled in the art will appreciate that the configuration shown in fig. 3 does not constitute a limitation of the correction device and may include more or fewer components than those shown.
The correction device provided by the embodiment of the application comprises a memory 30 and a processor 31, and the processor 31 can realize the correction method when executing the program stored in the memory 30.
Finally, the application also provides a corresponding embodiment of the computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps as set forth in the above-mentioned method embodiments.
It is understood that, if the method in the above embodiments is implemented in the form of software functional units and sold or used as a stand-alone product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the methods described in the embodiments of the present application, or all or part of the technical solutions. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (Read-Only Memory), a ROM, a Random Access Memory (RAM), a magnetic disk, or an optical disk.
A calibration method, apparatus, device and medium provided by the present application are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method of calibration, comprising:
determining a controlled object, and establishing a discrete state space model of the controlled object;
determining output vectors of the controlled object in a plurality of preset times according to the discrete state space model;
updating a plurality of state vectors corresponding to the controlled object through an extended Kalman filter, and compensating part of the state vectors in each state vector;
compensating the output vectors by a compensation term and updating the discrete state space model;
and determining a decision vector by using an MPC algorithm based on the updated discrete state space model so as to control according to the decision vector.
2. The calibration method according to claim 1, wherein the determining the output vector of the controlled object in a plurality of preset times according to the discrete state space model comprises:
determining a prediction matrix according to the discrete state space model;
determining a plurality of said output vectors from said prediction matrix.
3. The calibration method according to claim 1, wherein the establishing of the discrete state space model of the controlled object comprises:
obtaining a plurality of state vectors, a plurality of control input vectors and a plurality of measurable disturbance vectors of the controlled object;
and establishing the discrete state space model according to the state vector, the control input vector and the measurable disturbance vector.
4. The correction method according to claim 3, wherein said updating the state vector by the extended Kalman filter comprises:
expanding the updated state vector to obtain an expanded state vector and an expanded disturbance vector;
updating the state vector according to the extended state vector.
5. The correction method according to claim 1, further comprising, after said compensating the plurality of output vectors by a compensation term, prior to said updating the discrete state space model:
judging whether the compensated output vector is consistent with the actual output vector;
if yes, entering the step of updating the discrete state space model;
if not, returning to the step of updating the plurality of state vectors corresponding to the controlled object through the extended Kalman filter and compensating part of the state vectors in each state vector.
6. The correction method of claim 1, wherein said determining a decision vector using an MPC algorithm comprises:
determining the decision vector according to a constraint condition, wherein the constraint condition is an objective function of the MPC algorithm.
7. The correction method according to claim 6, characterized in that the objective functions include a first objective function characterizing a set point tracking, a second objective function characterizing a controlled quantity tracking, and a third objective function characterizing a minimum of an increment of the controlled quantity.
8. A correction device, comprising:
the first determination module is used for determining a controlled object and establishing a discrete state space model of the controlled object;
the second determination module is used for determining output vectors of the controlled object in a plurality of preset times according to the discrete state space model;
the first compensation module is used for updating a plurality of state vectors corresponding to the controlled object through an extended Kalman filter and compensating part of the state vectors in each state vector;
the second compensation module is used for compensating the output vectors through a compensation item and updating the discrete state space model;
and the third determination module is used for determining a decision vector by using an MPC algorithm based on the updated discrete state space model so as to facilitate control according to the decision vector.
9. A correction device, characterized by comprising:
a memory for storing a computer program;
a processor for implementing the steps of the correction method as claimed in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the correction method according to one of claims 1 to 7.
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| CN103117657A (en) * | 2013-01-31 | 2013-05-22 | 浙江大学 | Control method of full-bridge DC-DC system based on on-chip model predictive control |
| CN110908351A (en) * | 2019-11-25 | 2020-03-24 | 东南大学 | Support vector machine-fused SCR denitration system disturbance suppression prediction control method |
| CN111413938A (en) * | 2020-04-16 | 2020-07-14 | 南京英璞瑞自动化科技有限公司 | SCR denitration system disturbance suppression prediction control method based on converted ammonia injection amount |
| CN112015663A (en) * | 2020-09-15 | 2020-12-01 | 平安银行股份有限公司 | Test data recording method, device, equipment and medium |
| CN113377075A (en) * | 2021-07-01 | 2021-09-10 | 中国科学院过程工程研究所 | Method and device for optimizing rare earth extraction process in real time and computer readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103117657A (en) * | 2013-01-31 | 2013-05-22 | 浙江大学 | Control method of full-bridge DC-DC system based on on-chip model predictive control |
| CN110908351A (en) * | 2019-11-25 | 2020-03-24 | 东南大学 | Support vector machine-fused SCR denitration system disturbance suppression prediction control method |
| CN111413938A (en) * | 2020-04-16 | 2020-07-14 | 南京英璞瑞自动化科技有限公司 | SCR denitration system disturbance suppression prediction control method based on converted ammonia injection amount |
| CN112015663A (en) * | 2020-09-15 | 2020-12-01 | 平安银行股份有限公司 | Test data recording method, device, equipment and medium |
| CN113377075A (en) * | 2021-07-01 | 2021-09-10 | 中国科学院过程工程研究所 | Method and device for optimizing rare earth extraction process in real time and computer readable storage medium |
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