CN112782970A - Temperature self-setting method and system for GaN substrate growth heating furnace - Google Patents
Temperature self-setting method and system for GaN substrate growth heating furnace Download PDFInfo
- Publication number
- CN112782970A CN112782970A CN202011567985.8A CN202011567985A CN112782970A CN 112782970 A CN112782970 A CN 112782970A CN 202011567985 A CN202011567985 A CN 202011567985A CN 112782970 A CN112782970 A CN 112782970A
- Authority
- CN
- China
- Prior art keywords
- neural network
- heating furnace
- temperature
- self
- gan substrate
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000010438 heat treatment Methods 0.000 title claims abstract description 86
- 238000000034 method Methods 0.000 title claims abstract description 44
- JMASRVWKEDWRBT-UHFFFAOYSA-N Gallium nitride Chemical compound [Ga]#N JMASRVWKEDWRBT-UHFFFAOYSA-N 0.000 title claims abstract description 30
- 238000013528 artificial neural network Methods 0.000 claims abstract description 87
- 230000006870 function Effects 0.000 claims abstract description 37
- 239000011159 matrix material Substances 0.000 claims abstract description 23
- 238000012546 transfer Methods 0.000 claims abstract description 19
- IXKSXJFAGXLQOQ-XISFHERQSA-N WHWLQLKPGQPMY Chemical compound C([C@@H](C(=O)N[C@@H](CC=1C2=CC=CC=C2NC=1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N1CCC[C@H]1C(=O)NCC(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC(O)=O)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC=1C=CC(O)=CC=1)C(O)=O)NC(=O)[C@@H](N)CC=1C2=CC=CC=C2NC=1)C1=CNC=N1 IXKSXJFAGXLQOQ-XISFHERQSA-N 0.000 claims abstract description 8
- 230000008569 process Effects 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 6
- 238000002248 hydride vapour-phase epitaxy Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 10
- 239000000463 material Substances 0.000 description 6
- 238000004088 simulation Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 4
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 3
- 239000004065 semiconductor Substances 0.000 description 3
- 229910052710 silicon Inorganic materials 0.000 description 3
- 239000010703 silicon Substances 0.000 description 3
- 230000004913 activation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 239000010453 quartz Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
- Control Of Temperature (AREA)
Abstract
The invention provides a GaN substrate growth heating furnace temperature self-tuning method and a system, which are used for constructing a neural network and setting a transfer function, a performance index, a learning rate, an alpha factor and a weight initial matrix of the neural network; when the heating furnace actually runs, determining an input layer node of the neural network according to the actual temperature returned by the heating furnace system on site; when the temperature of the heating furnace exceeds the deviation allowable range, the neural network is utilized to adjust the control parameters of the PID controller, and the self-adaptive setting of the temperature of the heating furnace is realized. The invention realizes the self-adaptive setting of the temperature of the heating furnace; the temperature of the heating furnace is controlled in real time by adopting an online adjustment mode and utilizing a BP neural network; the control effect of the PID controller is improved by adopting the neural network, and the accuracy and the anti-interference performance of the temperature control of the system are improved.
Description
Technical Field
The invention belongs to the field of automation of GaN substrate growth equipment, and particularly relates to a GaN substrate growth heating furnace temperature self-setting method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the development of semiconductor industry technology, products in the field of semiconductor industry material growth are continuously updated, the requirement of a growth process on temperature field control is higher and higher, and a heating furnace is widely applied in the field of semiconductor material growth, particularly in the field of Hydride Vapor Phase Epitaxy (HVPE) material growth. The key technology is to ensure constant temperature field and high-precision control of the heating furnace, so the research of the high-precision constant temperature field heating mode becomes the key point.
In a vertical HVPE growth device, the existing heating furnace has the defects of poor temperature uniformity (large temperature gradient), low heating precision control, unreasonable temperature control mode and the like, and the nonlinear control problems of large lag, strong coupling, unknown interference, uncertainty and the like exist in the temperature control process. The traditional PID control has poor adaptability, so that the growth quality of the GaN substrate material is not high, and the control effect is not ideal.
Disclosure of Invention
In order to solve the problems, the invention provides a GaN substrate growth heating furnace temperature self-setting method and a system, which can carry out self-adaptive operation in the growth process of the GaN substrate and automatically adjust the parameters of a PID (proportion integration differentiation) controller of the heating furnace temperature so as to adjust the temperature of the heating furnace and provide a stable temperature environment for the growth of GaN substrate materials.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a GaN substrate growth heating furnace temperature self-tuning method, which comprises the following steps:
constructing a neural network, and setting a transfer function, a performance index, a learning rate, an alpha factor and a weight initial matrix of the neural network;
when the heating furnace actually runs, determining an input layer node of the neural network according to the actual temperature returned by the heating furnace system on site;
when the temperature of the heating furnace exceeds the deviation allowable range, the neural network is utilized to adjust the control parameters of the PID controller, and the self-adaptive setting of the temperature of the heating furnace is realized.
As an alternative embodiment, a neural network structure of 3-5-3 is constructed, input and output of the neural network are determined, and nodes of the input layer of the neural network are system input, system output and system deviation; and the nodes of the output layer of the neural network are control parameters of the PID controller.
As an alternative embodiment, the transfer function of the neural network is a sigmoid function, the transfer function of the hidden layer of the neural network selects a positive and negative symmetric sigmoid function, and the transfer function of the output layer of the neural network selects a non-negative sigmoid function.
As an alternative embodiment, the difference between the temperature set value and the actual output value of the on-site heating furnace is selected as the performance index.
As an alternative embodiment, the initial weight matrix of the neural network takes random values between [ -1,1 ].
As an alternative embodiment, the specific process of adjusting the control parameters of the PID controller by using the neural network comprises the following steps: and obtaining an input matrix by adopting an incremental PID control algorithm, wherein the input is system input, system output and system deviation, and the input matrix of the hidden layer can be obtained from the weight matrix from the input layer to the hidden layer.
As an alternative embodiment, the specific process of adjusting the control parameters of the PID controller by using the neural network comprises the following steps: according to the adjustment principle of a back propagation algorithm, firstly, the weight value from the hidden layer to the output layer is adjusted, then, the weight value from the input layer to the hidden layer is adjusted, the weight value after the current adjustment of the output layer is stored, the weight value after the current adjustment of the hidden layer is stored, the deviation is updated, and the next BP neural network adjustment is carried out.
A second aspect of the present invention provides a temperature self-tuning system for a GaN substrate growth heating furnace, comprising:
the neural network construction module is configured to construct a neural network and set a transfer function, a performance index, a learning rate, an alpha factor and a weight initial matrix of the neural network;
an acquisition module configured to acquire a temperature of a GaN substrate growth heating furnace;
the parameter correlation module is configured to determine an input layer node of the neural network according to the actual temperature returned by the acquisition module in the field when the heating furnace actually runs;
the self-adaptive setting module is configured to adjust the control parameters of the PID controller by utilizing the neural network when the temperature of the heating furnace exceeds the deviation allowable range;
and a PID controller configured to adjust the temperature of the GaN substrate growth heating furnace by the actuator based on the control parameter.
A third aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in a GaN substrate growth furnace temperature self-tuning method as described above.
A fourth aspect of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in a GaN substrate growth furnace temperature self-tuning method as described above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
the invention combines the neural network and the PID controller, thereby realizing the self-adaptive setting of the temperature of the heating furnace; the temperature of the heating furnace is controlled in real time by adopting an online adjustment mode and utilizing a BP neural network; the control effect of the PID controller is improved by adopting the neural network, and the accuracy and the anti-interference performance of the temperature control of the system are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic diagram of a neural network architecture according to an embodiment of the present invention;
FIG. 2 is a PID control simulation model of a BP neural network according to an embodiment of the present invention;
FIG. 3 shows the simulation results of the conventional PID control and BP neural network PID control according to the embodiment of the invention;
FIG. 4 is a schematic diagram of a control system according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
A heating furnace temperature self-tuning method based on BP neural network PID comprises the following steps:
(1) determining a network structure and a network layer number of a neural network;
(2) determining inputs and outputs of a neural network;
(3) determining a transfer function, a performance index, a learning rate, an alpha factor and a weight initial matrix of the neural network;
(4) when the heating furnace actually runs, the heating furnace system returns to the actual temperature on site, and the input layer node of the neural network is determined;
(5) when the temperature of the heating furnace exceeds the deviation allowable range, the neural network adjusts the K of the PID controllerp、Ki、KdAnd the three parameters are used for realizing the self-adaptive setting of the temperature of the heating furnace.
As shown in FIG. 1, firstly, determining the network structure and the network layer number of the neural network, wherein the neural network structure of 3-5-3 is adopted in the invention;
secondly, determining input and output of the neural network, wherein nodes of an input layer of the neural network are system input, system output and system deviation; the nodes of the output layer of the neural network are Kp, Ki and Kd of the PID controller;
then, the transfer function of the neural network is a sigmoid function, wherein the transfer function of the hidden layer of the neural network selects a positive and negative symmetric sigmoid function:
selecting a nonnegative sigmoid function by the transfer function of the neural network output layer:
setting the learning rate to be 0.2 and the alpha factor to be 0.05;
selecting the difference value between the temperature set value and the actual output value of the on-site heating furnace as a performance index:
the initial weight matrix of the neural network can be selected as a smaller random value between [ -1,1], such as:
the initial weight matrix from the input layer to the hidden layer may be selected as:
the initial weight matrix from the hidden layer to the output layer can be selected as:
a heating furnace temperature self-setting method based on BP neural network PID comprises the following specific implementation principles:
the heating furnace body adopts a vertical integral non-opening-closing structure, and the control part is centralized in a control cabinet and consists of a power control system and a temperature control system. A plurality of heating modules and built-in temperature sensors are arranged in each temperature area of the heating furnace and are respectively connected with a cascade automatic temperature control circuit formed by electric elements such as a silicon controlled power regulator, a transformer and the like, the heating power of each heating module is reasonably adjusted through the automatic temperature control circuit, and the temperature of each temperature area is independently and precisely controlled within a preset temperature range by combining the type and the heating mode of the heating module arranged in the heating temperature area so as to meet the control requirement of HVPE equipment.
Designing upper computer system control software, communicating with a programmable controller, setting the temperature of each temperature zone according to the process requirements, arranging temperature sensors in each temperature zone, carrying out A/D conversion on the collected temperature signals, then entering the programmable controller, calculating the control quantity by the programmable controller through comparing the actual temperature value with the set value during heating and combining a neural network control algorithm, then sending output signals to a silicon controlled power regulator through D/A conversion, and controlling the heating power of the resistance furnace by changing the duty ratio of the silicon controlled power regulator to realize heating control.
In the embodiment, the HVPE equipment mainly comprises a heating furnace body and a temperature control part to form a whole machine system, wherein the heating furnace body can comprise a quartz tube with the outer diameter of 210mm and comprises 5 groups of heating units, the total effective height from top to bottom is 900mm, and each two adjacent areas are provided with heat insulation baffles to reduce the mutual influence of temperature areas and eliminate the chimney effect.
Establishing an HVPE system heating furnace model: theoretical analysis and experimental verification prove that the HVPE system heating furnace has self-balancing capability and non-oscillation characteristics under the influence of step input, and a transfer function of the HVPE system heating furnace can be approximated by a proportion link, a first-order inertia link and a delay link, and can be approximated as follows:
where K is the static gain, τ is the delay time, T is the inertial time constant, G(s) -transfer function, s is the Laplace transform factor of the state variable.
Designing a BP neural network PID controller: the HVPE equipment heating furnace BP neural network PID controller adopts a 3-5-3 neural network structure, the preset temperature values of all temperature areas are defined as the input of the BP neural network PID controller, a plurality of heating modules and temperature sensors are arranged in all temperature areas in the heating furnace, the furnace temperature is controlled through related circuits, the actual furnace temperature is returned through a thermocouple and is compared with the preset values, and the preset temperature, the actual furnace temperature and the temperature difference form the input of the BP neural network controller; the input matrix passes through the input layer to the hidden layer and then passes through the hidden layer to the output layer, and the output parameter is K of the PID controllerp、Ki、KdSo as to realize the self-adjustment of the parameters of the heating furnace controller.
The output of the BP neural network PID controller adopts an incremental PID control algorithm, and the output expression of the PID controller is as follows:
Δu(k)＝Kp[e(k)-e(k-1)]+Kie(k)+Kd[e(k)-2e(k-1)+e(k-2)]
u(k)＝u(k-1)+Δu(k)
in order to verify the correctness of the design of the BP neural network PID controller, the function is transferred to the heating furnace system in the Simulink environmentCarrying out simulation analysis on the numbers; fitting actual engineering data through a Matlab fitting tool box to approximately obtain transfer function parameters: k is 14, T is 380 and T is 75, namely the transmission parameter of the HVPE equipment furnace temperature control model is
And (3) simulating the heating furnace temperature control system in Simulink, and building a system simulation model.
In a simulation model S function, the function of a BP neural network PID controller is realized, and the input matrix of the system is as follows: xi ═ rin (k), yout (k), error (k) ];
the weight matrix of the input matrix from the input layer to the hidden layer can obtain the input matrix I of the hidden layer, and the activation function is
The output of the output layer is a parameter K of a PID controllerp、Ki、KdThe activation function is
The output increment delta u of the heating furnace temperature control system can be obtained through an incremental control algorithm, and then the system output is obtained through a correction formula.
The neural network model is a BP neural network, so that the adjustment principle of a back propagation algorithm can be known, the weight adjustment from the hidden layer to the output layer is firstly carried out, then the weight adjustment from the input layer to the hidden layer is carried out, then the weight after the current adjustment of the output layer is stored, the weight after the current adjustment of the hidden layer is stored, the deviation is updated, the next BP neural network adjustment can be carried out, the BP neural network is continuously adjusted in the way, and a better temperature control effect can be obtained.
According to the HVPE equipment process requirements, a heating furnace temperature control system is researched and analyzed, a BP neural network is combined with conventional PID control, and the temperature control system of the HVPE system heating furnace is controlled and designed through a programmable controller. The experimental simulation and the actual field experimental effect show that compared with the traditional PID control, the system adopting BP neural network PID control has smaller overshoot, shorter regulation time and better robustness, obviously improves the temperature control precision and better meets the requirement of HVPE process material growth.
Example two:
a temperature self-tuning system of a GaN substrate growth heating furnace comprises:
the neural network construction module is configured to construct a neural network and set a transfer function, a performance index, a learning rate, an alpha factor and a weight initial matrix of the neural network;
an acquisition module configured to acquire a temperature of a GaN substrate growth heating furnace;
the parameter correlation module is configured to determine an input layer node of the neural network according to the actual temperature returned by the acquisition module in the field when the heating furnace actually runs;
the self-adaptive setting module is configured to adjust the control parameters of the PID controller by utilizing the neural network when the temperature of the heating furnace exceeds the deviation allowable range;
and a PID controller configured to adjust the temperature of the GaN substrate growth heating furnace by the actuator based on the control parameter.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A GaN substrate growth heating furnace temperature self-setting method is characterized in that: the method comprises the following steps:
constructing a neural network, and setting a transfer function, a performance index, a learning rate, an alpha factor and a weight initial matrix of the neural network;
when the heating furnace actually runs, determining an input layer node of the neural network according to the actual temperature returned by the heating furnace system on site;
when the temperature of the heating furnace exceeds the deviation allowable range, the neural network is utilized to adjust the control parameters of the PID controller, and the self-adaptive setting of the temperature of the heating furnace is realized.
2. The method of claim 1, wherein the temperature of the GaN substrate growth heating furnace is self-tuning, and the method comprises: constructing a 3-5-3 neural network structure, and determining input and output of the neural network, wherein nodes of an input layer of the neural network are system input, system output and system deviation; and the nodes of the output layer of the neural network are control parameters of the PID controller.
3. The method of claim 1, wherein the temperature of the GaN substrate growth heating furnace is self-tuning, and the method comprises: the transfer function of the neural network is a sigmoid function, the transfer function of the hidden layer of the neural network selects positive and negative symmetric sigmoid functions, and the transfer function of the output layer of the neural network selects non-negative sigmoid functions.
4. The method of claim 1, wherein the temperature of the GaN substrate growth heating furnace is self-tuning, and the method comprises: and selecting the difference value between the temperature set value and the actual output value of the on-site heating furnace as a performance index.
5. The method of claim 1, wherein the temperature of the GaN substrate growth heating furnace is self-tuning, and the method comprises: the initial weight matrix of the neural network selects random values between [ -1,1 ].
6. The method of claim 1, wherein the temperature of the GaN substrate growth heating furnace is self-tuning, and the method comprises: the specific process of adjusting the control parameters of the PID controller by using the neural network comprises the following steps: and obtaining an input matrix by adopting an incremental PID control algorithm, wherein the input is system input, system output and system deviation, and the input matrix of the hidden layer can be obtained from the weight matrix from the input layer to the hidden layer.
7. The method of claim 1, wherein the temperature of the GaN substrate growth heating furnace is self-tuning, and the method comprises: the specific process of adjusting the control parameters of the PID controller by using the neural network comprises the following steps: according to the adjustment principle of a back propagation algorithm, firstly, the weight value from the hidden layer to the output layer is adjusted, then, the weight value from the input layer to the hidden layer is adjusted, the weight value after the current adjustment of the output layer is stored, the weight value after the current adjustment of the hidden layer is stored, the deviation is updated, and the next BP neural network adjustment is carried out.
8. A GaN substrate growth heating furnace temperature self-tuning system is characterized in that: the method comprises the following steps:
the neural network construction module is configured to construct a neural network and set a transfer function, a performance index, a learning rate, an alpha factor and a weight initial matrix of the neural network;
an acquisition module configured to acquire a temperature of a GaN substrate growth heating furnace;
the parameter correlation module is configured to determine an input layer node of the neural network according to the actual temperature returned by the acquisition module in the field when the heating furnace actually runs;
the self-adaptive setting module is configured to adjust the control parameters of the PID controller by utilizing the neural network when the temperature of the heating furnace exceeds the deviation allowable range;
and a PID controller configured to adjust the temperature of the GaN substrate growth heating furnace by the actuator based on the control parameter.
9. A computer-readable storage medium characterized by: a computer program stored thereon, which when executed by a processor, implements the steps in a GaN substrate growth furnace temperature self-tuning method of any of claims 1-7.
10. A computer device, characterized by: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of a GaN substrate growth furnace temperature self-tuning method as claimed in any one of claims 1-7 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011567985.8A CN112782970A (en) | 2020-12-25 | 2020-12-25 | Temperature self-setting method and system for GaN substrate growth heating furnace |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011567985.8A CN112782970A (en) | 2020-12-25 | 2020-12-25 | Temperature self-setting method and system for GaN substrate growth heating furnace |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112782970A true CN112782970A (en) | 2021-05-11 |
Family
ID=75752653
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011567985.8A Pending CN112782970A (en) | 2020-12-25 | 2020-12-25 | Temperature self-setting method and system for GaN substrate growth heating furnace |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112782970A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113587120A (en) * | 2021-07-29 | 2021-11-02 | 光大环保技术研究院（深圳）有限公司 | Control method of plasma ash melting furnace |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5683605A (en) * | 1994-12-27 | 1997-11-04 | Sharp Kabushiki Kaisha | Heater controlling unit using a fuzzy neural network |
| CN106292785A (en) * | 2015-05-18 | 2017-01-04 | 广东兴发铝业有限公司 | Aluminum-bar heating furnace ignition temperature automaton based on neutral net |
| CN110684547A (en) * | 2019-10-22 | 2020-01-14 | 中国计量大学 | Optimized control method for biomass pyrolysis carbonization kiln |
-
2020
- 2020-12-25 CN CN202011567985.8A patent/CN112782970A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5683605A (en) * | 1994-12-27 | 1997-11-04 | Sharp Kabushiki Kaisha | Heater controlling unit using a fuzzy neural network |
| CN106292785A (en) * | 2015-05-18 | 2017-01-04 | 广东兴发铝业有限公司 | Aluminum-bar heating furnace ignition temperature automaton based on neutral net |
| CN110684547A (en) * | 2019-10-22 | 2020-01-14 | 中国计量大学 | Optimized control method for biomass pyrolysis carbonization kiln |
Non-Patent Citations (3)
| Title |
|---|
| 梁军等: "基于神经网络PLS方法的软测量建模研究", 《浙江大学学报(工学版)》 * |
| 王岩等: "基于神经网络的PID温度控制***", 《微计算机信息》 * |
| 秦立庆: "基于神经网络的PID控制技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113587120A (en) * | 2021-07-29 | 2021-11-02 | 光大环保技术研究院（深圳）有限公司 | Control method of plasma ash melting furnace |
| CN113587120B (en) * | 2021-07-29 | 2023-08-29 | 光大环保技术研究院（深圳）有限公司 | Control method of plasma ash melting furnace |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs | |
| Sahu et al. | A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system | |
| Tan | Unified tuning of PID load frequency controller for power systems via IMC | |
| Wu et al. | Design of dynamic matrix control based PID for residual oil outlet temperature in a coke furnace | |
| CN112884136B (en) | Bounded clustering projection synchronous regulation control method and system for coupled neural network | |
| CA2252428A1 (en) | Nonlinear-approximator-based automatic tuner | |
| CN109459930B (en) | Cooperative control method based on PD structure and neighbor lag control signal | |
| Zhang et al. | Parameter optimization of PID controllers based on genetic algorithm | |
| Luan et al. | Equivalent transfer function based multi-loop PI control for high dimensional multivariable systems | |
| Rajarathinam et al. | PID controller tuning for a multivariable glass furnace process by genetic algorithm | |
| CN110879620A (en) | Liquid level control method and system for vertical steam generator of nuclear power station | |
| CN112782970A (en) | Temperature self-setting method and system for GaN substrate growth heating furnace | |
| CN117093033A (en) | Resistance heating furnace temperature control system for optimizing PID parameters based on particle swarm optimization | |
| CN110488888A (en) | A kind of resistance-heated furnace temprature control method based on adaptive iteration study | |
| CN105720574A (en) | SPSA-based data drive control method for single region load frequency of power system | |
| Yousuf et al. | PSO based single and two interconnected area predictive automatic generation control | |
| De Almeida et al. | Application of genetic programming for fine tuning PID controller parameters designed through Ziegler-Nichols technique | |
| JP2002049401A (en) | Controller and control method | |
| CN106410829A (en) | Prediction control-based coordinated control system and method of energy storage network | |
| CN106647247B (en) | A kind of control algolithm suitable for servo controller | |
| Keliang et al. | PID controller parameters tuning of main steam temperature based on chaotic particle swarm optimization | |
| CN113641101B (en) | Multi-channel control parameter optimizing algorithm based on numerical simulation | |
| Park et al. | Decentralized output-feedback controller for uncertain large-scale nonlinear systems using higher-order switching differentiator | |
| Jin et al. | IMC-PID controller based on diagonal equivalent model for multivariable systems with multiple time delays | |
| Vu et al. | Independent design of multi-loop PI/PID controllers for multi-delay processes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210511 |