CN114815922A - GPC and GPIO-based electric heating furnace temperature anti-interference control method - Google Patents
GPC and GPIO-based electric heating furnace temperature anti-interference control method Download PDFInfo
- Publication number
- CN114815922A CN114815922A CN202210315849.2A CN202210315849A CN114815922A CN 114815922 A CN114815922 A CN 114815922A CN 202210315849 A CN202210315849 A CN 202210315849A CN 114815922 A CN114815922 A CN 114815922A
- Authority
- CN
- China
- Prior art keywords
- electric heating
- heating furnace
- control system
- gpio
- temperature control
- 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.)
- Granted
Links
- 238000005485 electric heating Methods 0.000 title claims abstract description 74
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000012546 transfer Methods 0.000 claims abstract description 59
- 238000005094 computer simulation Methods 0.000 claims abstract description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 4
- 230000014509 gene expression Effects 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000020169 heat generation Effects 0.000 claims description 4
- 238000013459 approach Methods 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 239000002131 composite material Substances 0.000 description 8
- 238000010586 diagram Methods 0.000 description 4
- 230000005855 radiation Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000010363 phase shift Effects 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
- 229920003023 plastic Polymers 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000005060 rubber Substances 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000004753 textile Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
Abstract
The invention relates to a control technology of an electric heating furnace temperature control system, in particular to an electric heating furnace temperature anti-interference control method based on GPC and GPIO, which comprises the following steps: collecting and identifying operation data of the electric heating furnace temperature control system, wherein the operation data comprises a set temperature and a water supply temperature; carrying out system modeling on the electric heating furnace temperature control system through the acquired data to obtain a transfer function of the electric heating furnace temperature control system, and obtaining a state space model of the system according to the transfer function; designing a Generalized Proportional Integral Observer (GPIO) based on a transfer function of an electric heating furnace temperature control system; a prediction model based on GPIO vertical generalized prediction control; a Generalized Predictive Controller (GPC) is established to adjust the set temperature. The control method has the advantages of high precision, high adjusting speed, small overshoot, good anti-interference capability and robustness, and can effectively overcome lumped disturbance in the temperature control system of the electric heating furnace, so that the temperature is stabilized near a set value, and the fluctuation range is small.
Description
Technical Field
The invention relates to a control technology of an electric heating furnace temperature control system, in particular to a composite anti-interference control method of the electric heating furnace temperature control system.
Background
Along with the deterioration of ecological environment, the attention degree of China on environmental protection and pollution emission is continuously improved, and the traditional coal-fired and oil-fired furnace with larger pollution is gradually replaced by a clean and safe electric heating furnace. An electric heating furnace is used as a typical temperature control system, can convert electric energy into heat energy, and is widely applied to the fields of chemicals, textiles, plastics, rubber, food processing and the like. However, the temperature control system of the electric heating furnace with the characteristics of time-varying property, large inertia, large time lag and the like has difficulty in meeting the control requirements of the traditional PID control method.
Disclosure of Invention
The technical problem of the invention is mainly solved by the following technical scheme:
an electric heating furnace temperature anti-interference control method based on GPC and GPIO is characterized by comprising the following steps:
collecting and identifying operation data of the electric heating furnace temperature control system, wherein the operation data comprises a set temperature and a water supply temperature;
carrying out system modeling on the electric heating furnace temperature control system through the acquired data to obtain a transfer function of the electric heating furnace temperature control system, and obtaining a state space model of the system according to the transfer function;
designing a Generalized Proportional Integral Observer (GPIO) based on a transfer function of an electric heating furnace temperature control system;
a prediction model based on GPIO vertical generalized prediction control;
a Generalized Predictive Controller (GPC) is established to adjust the set temperature.
In the above mentioned anti-interference control method for temperature of electric heating furnace based on GPC and GPIO, the heat transfer of electric heating furnace comprises
Executing a component heat generation stage: is a first-order inertia element, and its transfer function is expressed as
Wherein k and ψ are constants;
the heat transfer stage of the electric heating furnace: for energy transfer of electric heating furnaces, electric heatingThe furnace energy transfer is expressed as
wherein ,Q1 For performing element heating, Q 2 Is heat loss; from the formula of specific heat capacity Q ═ MC Δ T
M is the mass of the electric heating wire, C is the specific heat capacity in the furnace, and T is the temperature in the furnace;
from the convective heat transfer rate equation Φ ═ α a (T-T) w ) Will Q 2 Is approximately Q 2 ≈αA(T-T w ) (ii) a Wherein alpha and A are convection heat transfer coefficient of the electric heating furnace and contact surface area of the furnace wall and the external environment, and T w Is the external ambient temperature;
combining electrothermal furnace energy transfer expressions and Q 2 Expression, get
Subjecting it to Ralsberg transformation, i.e. Q 1 (s)-αAT(s)＝MCsT(s)；
From the above formula, the transfer function of the heat transfer stage of the electric heating furnace is obtained as
Combining the transfer functions of the two stages, the transfer function of the temperature control system of the electric heating furnace is expressed as
The temperature control system model of the electric heating furnace is a second-order time lag system
obtaining a state space equation of the electric heating furnace temperature control system according to the transfer function G(s):
where ξ (t) is the lumped perturbation of the system; u (t-tau) is the input delay link of the system.
In the above anti-interference control method for the temperature of the electric heating furnace based on GPC and GPIO, the state space equation of the temperature control system of the electric heating furnace is systematically expanded:
wherein 
And satisfy
For the system state space equation after the upper extension, 4-order GPIO is constructed as follows:
wherein ,l1 、l 2 、l 3 、l 4 Is the adjustment parameter of the observer;
subtracting the above two equations to obtain:
wherein ,
the above equation can be regarded as a high-dimensional input/output system, and can be written as:
the eigen equation for matrix a is then:
p(λ)＝λ 4 +k 4 λ 3 +k 3 λ 2 +k 2 λ+k 1
selecting a suitable k 1 、k 2 、k 3 、k 4 The characteristic root of the matrix A is configured on the left half plane far away from the virtual axis, and the upper system can be obtained
The input and the output are bounded and stable; thus, when
The error e asymptotically approaches zero over time.
In the anti-interference control method for the temperature of the electric heating furnace based on GPC and GPIO, a controlled autoregressive integrated moving average (CARIMA) is used as a prediction model, and a controlled system is discretized to obtain
The CARIMA model was obtained as:
in the formula ,
u (k) and xi (k) respectively represent white noise sequences with zero average values of output, input and observed after GPIO; the transfer function of the electric heating furnace temperature control system is A (z) -1 )、B(z -1) and C(z-1 )：
The following objective function was used:
wherein n is the maximum prediction length, m is the control length, and λ (j) is the weighting coefficient;
w (k + j) is an expected output sequence value, wherein the output sequence value is observed by the GPIO; to predict the leading j-step output, a look-less-graph (dioadapt) equation is introduced to predict the leading j-step output:
in the formula ,
combining the charpy equation with the CARIMA model equation
Rewriting the above formula into vector form, i.e.
y＝Gu+Fy(k)+HΔu(k-1)+E
in the formula ,
let wT be [ w (k +1), … w (k + j) ], the objective function is expressed as
J＝(y-w) T (y-w)+λu T u；
Substituting the expression of y into the objective function J to calculate the minimum value of J, namely when
Then, get
The optimum control law of GPC is:
in the formula ,pT Is (G) T G+λI) -1 G T The first row of (2).
Has the advantages that: compared with the prior art, the invention has the following beneficial effects: 1. the proposed composite anti-interference control method estimates the lumped interference of the system through GPIO and feeds the estimated lumped interference back to a forward channel for interference compensation. A prediction model obtained after system lumped disturbance is eliminated, a generalized prediction controller is designed, GPC (phase-shift graphics) is established through the model, rolling optimization and feedback correction are carried out, and the response speed and the control precision of the system are improved. 2. Simulation results show that the provided composite control method has high precision, high adjusting speed, small overshoot, good anti-interference capability and robustness, and can effectively overcome lumped disturbance in the temperature control system of the electric heating furnace, so that the temperature is stabilized near a set value, and the fluctuation range is small.
Drawings
FIG. 1 is a flow chart of the method steps of the present invention.
FIG. 2 is a heat transfer diagram of the temperature control system of the electric heating furnace according to the present invention.
Fig. 3 is a structural diagram of a temperature control system of the electric heating furnace.
FIG. 4 is a graph comparing the effect of the present invention method with that of the conventional PID method.
Detailed Description
The invention will be further described in detail with reference to the following drawings and specific examples, which are not intended to limit the invention, but are for clear understanding.
The attached drawings are shown in figure 1, and the composite control method of the temperature control system of the electric heating furnace comprises the following steps:
(1) collecting and identifying operation data of the electric heating furnace temperature control system, wherein the operation data comprises a set temperature and a water supply temperature;
(2) carrying out system modeling on the electric heating furnace temperature control system through the acquired data to obtain a transfer function of the electric heating furnace temperature control system, and obtaining a state space model of the system according to the transfer function;
mathematical modeling is carried out based on a heat transfer model of an electric heating furnace temperature control system, and the attached drawing is shown in figure 2:
wherein I is the input of the execution element, Q 1 For performing element heating, Q 2 Heat is lost. Based on the heat transfer relationship of the electric heating furnace temperature control system, the heat transfer of the electric heating furnace is divided into two stages, namely an execution element heat generation stage and an electric heating furnace heat transfer stage. Wherein G is 1 For performing element heat-generating phases, G 2 Is the heat transfer stage of the electric heating furnace.
The heat generation stage of the execution element can be regarded as a first-order inertia link, so
The heat transfer stage of the electric heating furnace is the energy transfer of the electric heating furnace, and the energy transfer of the electric heating furnace can be expressed as
From the formula of specific heat capacity Q ═ MC Δ T
M is the mass of the electric heating wire, C is the specific heat capacity in the furnace, and T is the temperature in the furnace. The heat loss is mainly realized by two heat transfer modes of heat radiation and heat convection, but the heat radiation energy of the temperature control system of the electric heating furnace is very small and can be ignored. Therefore, the convection heat transfer rate equation phi is defined as alpha A (T-T) w ) Will Q 2 Is approximately Q 2 ≈αA(T-T w ). Wherein alpha and A are convection heat transfer coefficient of the electric heating furnace and contact surface area of the furnace wall and the external environment, and T w Is the outside ambient temperature.
Combining electrothermal furnace energy transfer expressions and Q 2 Expression, can obtain
Subjecting it to Ralsberg transformation, i.e. Q 1 (s)-αAT(s)＝MCsT(s)。
From the above formula, the transfer function of the heat transfer stage of the electric heating furnace is obtained as
Combining the transfer functions of the two stages, the transfer function of the temperature control system of the electric heating furnace is expressed as
The temperature control system model of the electric heating furnace is a second-order time lag system
Obtaining a state space equation of the electric heating furnace temperature control system according to the transfer function G(s):
the attached drawing is shown in fig. 3, which is a composite control block diagram of the electric heating furnace temperature control system of the invention, and the composite control block diagram comprises the following steps:
(3) designing a Generalized Proportional Integral Observer (GPIO) based on a transfer function of an electric heating furnace temperature control system;
carrying out system expansion on the state space equation of the electric heating furnace temperature control system:
wherein 
And satisfy
For the system state space equation after the upper extension, a 4-order GPI observer is constructed as follows:
wherein ,l1 、l 2 、l 3 、l 4 Is the adjustment parameter of the observer.
Subtracting the above two equations can obtain:
the above equation can be regarded as a high-dimensional input/output system, and can be written as:
the eigen equation for matrix a is then:
p(λ)＝λ 4 +k 4 λ 3 +k 3 λ 2 +k 2 λ+k 1
selecting a suitable k 1 、k 2 、k 3 、k 4 The characteristic root of the matrix A is configured on the left half plane far away from the virtual axis, and the upper system can be obtained
It is the input-output bounded stability. Thus, when
The error e asymptotically approaches zero over time.
(4) Establishing a prediction model of generalized predictive control based on GPIO;
(5) a Generalized Predictive Controller (GPC) is established to adjust the set temperature.
The controlled system is discretized by adopting a controlled autoregressive integral moving average model (CARIMA) as a prediction model
The CARIMA model can be obtained as follows:
in the formula ,
u (k) and ξ (k) represent the output observed by the GPIO, the system actual input, and the white noise sequence with the mean value zero, respectively; the transfer function of the temperature control system of the electric heating furnace is A (z) -1 )、B(z -1) and C(z-1 )：
The following objective function was used:
where n is the maximum prediction length, m is the control length, and λ (j) is the weighting factor.
To output the sequence value, ω (k + j) is the expected output sequence value. According to the prediction theory, in order to predict the output of the leading j steps, a loss of pattern (Dioaphanine) equation is introduced to predict the output of the leading j steps:
in the formula ,
combining the charpy equation with the CARIMA model equation
Rewriting the above formula into vector form, i.e.
y＝Gu+Fy(k)+HΔu(k-1)+E
in the formula ,
let w T ＝[w(k+1)，…w(k+j)]Then the objective function can be expressed as
J＝(y-w) T (y-w)+λu T u。
Substituting the expression of y into the objective function J to calculate the minimum value of J, namely when
Then, get
u＝(G T G+λI) -1 G T [w-Fy(k)-HΔu(k-1)]
The optimal control law of CPC is
in the formula ,pT Is (G) T G+λI) -1 G T The first row of (2).
The attached figure is shown in fig. 4, which shows that the composite control method provided by the invention has the advantages of high tracking speed, small overshoot and strong capacity of resisting disturbance amount by comparing the composite control method with the traditional PID control method.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (4)
1. An electric heating furnace temperature anti-interference control method based on GPC and GPIO is characterized by comprising the following steps:
collecting and identifying operation data of the electric heating furnace temperature control system, wherein the operation data comprises a set temperature and a water supply temperature;
carrying out system modeling on the electric heating furnace temperature control system through the acquired data to obtain a transfer function of the electric heating furnace temperature control system, and obtaining a state space model of the system according to the transfer function;
designing a Generalized Proportional Integral Observer (GPIO) based on a transfer function of an electric heating furnace temperature control system;
a prediction model based on GPIO vertical generalized prediction control;
a Generalized Predictive Controller (GPC) is established to adjust the set temperature.
2. The GPC and GPIO-based temperature anti-interference control method of claim 1, wherein electrothermal furnace heat transfer comprises
Executing a component heat generation stage: is a first-order inertia element, and its transfer function is expressed as
Wherein k and ψ are constants;
the heat transfer stage of the electric heating furnace: for electrothermal furnace energy transfer, the electrothermal furnace energy transfer is expressed as
wherein ,Q1 For performing element heating, Q 2 Is heat loss; from the formula of specific heat capacity Q ═ MC Δ T
M is the mass of the electric heating wire, C is the specific heat capacity in the furnace, and T is the temperature in the furnace;
from the convective heat transfer rate equation Φ ═ α a (T-T) w ) Will Q 2 Is approximately Q 2 ≈αA(T-T w ) (ii) a Wherein alpha and A are convection heat transfer coefficient of the electric heating furnace and contact surface area of the furnace wall and the external environment, and T w Is the external ambient temperature;
combining electrothermal furnace energy transfer expressions and Q 2 Expression, get
Subjecting it to Ralsberg transformation, i.e. Q 1 (s)-αAT(s)＝MCsT(s)；
From the above formula, the transfer function of the heat transfer stage of the electric heating furnace is obtained as
Combining the transfer functions of the two stages, the transfer function of the temperature control system of the electric heating furnace is expressed as
The temperature control system model of the electric heating furnace is a second-order time lag system
obtaining a state space equation of the electric heating furnace temperature control system according to the transfer function G(s):
where ξ (t) is the lumped perturbation of the system; u (t-tau) is the input delay link of the system.
3. The method for the anti-interference control of the temperature of the electric heating furnace based on GPC and GPIO as claimed in claim 1, wherein the state space equation of the electric heating furnace temperature control system is systematically expanded:
wherein 
And satisfy
For the system state space equation after the upper extension, 4-order GPIO is constructed as follows:
wherein ,l1 、l 2 、l 3 、l 4 Is the adjustment parameter of the observer;
subtracting the above two equations to obtain:
wherein ,
the above equation can be regarded as a high-dimensional input/output system, and can be written as:
the eigen equation for matrix a is then:
p(λ)＝λ 4 +k 4 λ 3 +k 3 λ 2 +k 2 λ+k 1
selecting a suitable k 1 、k 2 、k 3 、k 4 The characteristic root of the matrix A is configured on the left half plane far away from the virtual axis, and the upper system can be obtained
The input and the output are bounded and stable; thus, when
The error e asymptotically approaches zero over time.
4. The method as claimed in claim 1, wherein a controlled autoregressive integrated moving average (CARIMA) model is used as a prediction model to discretize a controlled system into a discrete value
The CARIMA model was obtained as:
in the formula ,
u (k) and xi (k) respectively represent white noise sequences with zero average values of output, input and observed after GPIO; the transfer function of the temperature control system of the electric heating furnace is A (z) -1 )、B(z -1) and C(z-1 )：
The following objective function was used:
wherein n is the maximum prediction length, m is the control length, and λ (j) is the weighting coefficient;
w (k + j) is an expected output sequence value, wherein the output sequence value is observed by the GPIO; to predict the leading j-step output, a look-less-graph (dioadapt) equation is introduced to predict the leading j-step output:
in the formula ,
combining the equation of the lost-hairplay map with the equation of the CARIMA model
Rewriting the above formula into vector form, i.e.
y＝Gu+Fy(k)+HΔu(k-1)+E
in the formula ,
let w T ＝[w(k+1)，…w(k+j)]Then the objective function is expressed as
J＝(y-w) T (y-w)+λu T u；
Substituting the expression of yInto the objective function J, the minimum value of J is calculated, i.e. when
Then, get
The optimum control law of GPC is:
in the formula ,pT Is (G) T G+λI) -1 G T The first row of (2).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210315849.2A CN114815922B (en) | 2022-03-28 | 2022-03-28 | GPC and GPIO-based electric heating furnace temperature anti-interference control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210315849.2A CN114815922B (en) | 2022-03-28 | 2022-03-28 | GPC and GPIO-based electric heating furnace temperature anti-interference control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114815922A true CN114815922A (en) | 2022-07-29 |
| CN114815922B CN114815922B (en) | 2023-10-20 |
Family
ID=82531156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210315849.2A Active CN114815922B (en) | 2022-03-28 | 2022-03-28 | GPC and GPIO-based electric heating furnace temperature anti-interference control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114815922B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4518351A (en) * | 1982-03-22 | 1985-05-21 | Mellen Sr Robert H | Method of providing a dynamic temperature gradient |
| JP2003195905A (en) * | 2001-12-28 | 2003-07-11 | Omron Corp | Control device and temperature adjusting unit |
| JP2012093818A (en) * | 2010-10-25 | 2012-05-17 | Netsusan Heat Kk | Heat treatment control unit and heat treatment control method |
| CN107769642A (en) * | 2017-11-01 | 2018-03-06 | 东南大学 | A kind of driving speed governing integral type constrained forecast control method of direct current generator |
| CN109634319A (en) * | 2018-11-29 | 2019-04-16 | 南通大学 | Electric furnace intelligent temperature control system design method based on PID control |
| CN112947123A (en) * | 2021-03-29 | 2021-06-11 | 南京工业大学 | Exoskeleton robot tracking control method and system for inhibiting multi-source interference |
| CN113050717A (en) * | 2021-03-25 | 2021-06-29 | 南通大学 | Control method of temperature control system based on generalized predictive control |
| CN113467243A (en) * | 2021-07-07 | 2021-10-01 | 湖北工业大学 | Hot pressing furnace temperature composite control method based on improved delay observer |
| WO2022001242A1 (en) * | 2020-06-29 | 2022-01-06 | 华中科技大学 | Designing method for fopd-geso controller |
-
2022
- 2022-03-28 CN CN202210315849.2A patent/CN114815922B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4518351A (en) * | 1982-03-22 | 1985-05-21 | Mellen Sr Robert H | Method of providing a dynamic temperature gradient |
| JP2003195905A (en) * | 2001-12-28 | 2003-07-11 | Omron Corp | Control device and temperature adjusting unit |
| JP2012093818A (en) * | 2010-10-25 | 2012-05-17 | Netsusan Heat Kk | Heat treatment control unit and heat treatment control method |
| CN107769642A (en) * | 2017-11-01 | 2018-03-06 | 东南大学 | A kind of driving speed governing integral type constrained forecast control method of direct current generator |
| CN109634319A (en) * | 2018-11-29 | 2019-04-16 | 南通大学 | Electric furnace intelligent temperature control system design method based on PID control |
| WO2022001242A1 (en) * | 2020-06-29 | 2022-01-06 | 华中科技大学 | Designing method for fopd-geso controller |
| CN113050717A (en) * | 2021-03-25 | 2021-06-29 | 南通大学 | Control method of temperature control system based on generalized predictive control |
| CN112947123A (en) * | 2021-03-29 | 2021-06-11 | 南京工业大学 | Exoskeleton robot tracking control method and system for inhibiting multi-source interference |
| CN113467243A (en) * | 2021-07-07 | 2021-10-01 | 湖北工业大学 | Hot pressing furnace temperature composite control method based on improved delay observer |
Non-Patent Citations (1)
| Title |
|---|
| 黄文聪: "一种新的智能控制器——五点控制器研究", 软件导刊, vol. 7, no. 6, pages 129 - 131 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114815922B (en) | 2023-10-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111336683B (en) | MPC-PID cascade control method for air source heat pump temperature control system | |
| CN110257577B (en) | Burning process control method and system for ball type hot blast stove | |
| CN110181510B (en) | Mechanical arm trajectory tracking control method based on time delay estimation and fuzzy logic | |
| CN108167802B (en) | Multi-model intelligent optimizing and predicting control method for boiler load under low load | |
| CN103134046B (en) | Superheated steam temperature two-stage coordination, prediction and control method of thermal power generating unit | |
| CN113552797A (en) | Heating furnace temperature control method and system based on improved particle swarm optimization | |
| CN112180737A (en) | Control system control method based on active disturbance rejection control and similar Smith estimation | |
| CN111123708B (en) | Coking furnace hearth pressure control method based on distributed dynamic matrix control optimization | |
| CN115933369A (en) | Substrate temperature control method of evaporation coating equipment based on optimized PID algorithm | |
| CN114839880A (en) | Self-adaptive control method based on flexible joint mechanical arm | |
| CN108762086B (en) | Secondary reheat steam temperature control device and control system based on model predictive control | |
| CN114815922A (en) | GPC and GPIO-based electric heating furnace temperature anti-interference control method | |
| WO2019205216A1 (en) | Rbf neural network predictive control-based control system and control method for double-input double-output ball mill | |
| CN109539359A (en) | The phase transformation electric heat-storage heating system and method for divided working status PID+ adaptive feedforward compensation | |
| CN108803342B (en) | Unit unit load quick response prediction control method | |
| CN106033189A (en) | Flight robot pose nerve network prediction controller | |
| Andriyashin et al. | Comparison of PID and MPC control for a boiler room | |
| CN113625556B (en) | Self-adaptive control method of complex industrial system of circulating fluidized bed | |
| Bitschnau et al. | Modeling and control of an industrial continuous furnace | |
| CN113282043A (en) | Multivariable state space model-based ultra-supercritical unit coordination control method | |
| CN114721253A (en) | Heating furnace temperature fractional order PID control system and method based on artificial bee colony algorithm | |
| CN110688758B (en) | Forging resistance furnace production optimization method based on SPEA2 algorithm | |
| Bıyıkoǧlu et al. | Temperature prediction in a coal fired boiler with a fixed bed by fuzzy logic based on numerical solution | |
| CN113534661A (en) | Resistance furnace temperature control method based on Kalman filtering and non-minimum state space | |
| CN111273563B (en) | Prediction control method based on AGC (automatic gain control) comprehensive index of heat supply unit |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |