CN111983918A - Improved fuzzy Smith-PID-based electric heating furnace temperature control method - Google Patents
Improved fuzzy Smith-PID-based electric heating furnace temperature control method Download PDFInfo
- Publication number
- CN111983918A CN111983918A CN202010904009.0A CN202010904009A CN111983918A CN 111983918 A CN111983918 A CN 111983918A CN 202010904009 A CN202010904009 A CN 202010904009A CN 111983918 A CN111983918 A CN 111983918A
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- pid
- electric heating
- heating furnace
- controller
- 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
- 238000005485 electric heating Methods 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000008859 change Effects 0.000 claims abstract description 16
- 238000001914 filtration Methods 0.000 claims abstract description 12
- 238000013139 quantization Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 7
- 230000004044 response Effects 0.000 claims description 7
- 238000011217 control strategy Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 description 4
- 239000002131 composite material Substances 0.000 description 2
- 238000005034 decoration Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012360 testing method 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.
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
-
- 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
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/0275—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using fuzzy logic only
-
- 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
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention discloses an electric heating furnace temperature control method based on improved fuzzy Smith-PID. Firstly, acquiring the actual temperature of an electric heating furnace through a temperature sensor, and comparing the actual temperature with a given value to obtain a temperature deviation amount and a change rate of the deviation amount; fuzzy reasoning is carried out to obtain the integral quantity of the three control parameters, so that the control signal of the electric heating furnace is calculated through a PID controller; an improved Smith predictor is added in the system for prediction compensation, and a nine-point control method is used for adjusting a filtering time constant of a feedback channel of the Smith predictor. The invention adopts the parameter self-tuning fuzzy PID to replace the traditional PID, reduces the overshoot of the system and improves the robustness of the system. And on the basis of the traditional Smith predictor, a nine-point control method is introduced into a time lag system, the filtering time constant of a feedback channel is adjusted on line in real time, and the influence caused by mismatch of a prediction model is relieved while a lag link is compensated.
Description
Technical Field
The invention relates to an electric heating furnace temperature control method based on improved fuzzy Smith-PID, and belongs to the field of electric heating furnace temperature control.
Background
As a clean, safe and high-efficiency production device, the electric heating furnace is widely applied to the industrial field and the square surfaces of people's life. However, the electric heating furnace has a large hysteresis as a controlled object, and is characterized in that a large overshoot is likely to occur during a temperature rise. Large overshoot is not allowed in temperature control. Therefore, the control performance of the electric heating furnace object is improved, the stability of the control system is ensured, the requirements of the production process are ensured, and the safety production of equipment is ensured.
For the temperature control system of the electric heating furnace, PID control is the most classical control method. Due to the simple implementation, the electric heating furnace is still widely used in small and medium electric heating furnaces with low control requirements. However, in some electric furnace control systems with higher control performance requirements, conventional PID control has not been able to satisfy all industrial processes. Especially in the process object with the characteristics of large time lag, strong coupling, nonlinearity and the like, the conventional PID control cannot obtain an ideal control effect usually, and is limited by the difficulty of parameter setting, and the adaptability of the conventional PID controller is poor.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the background technology, the improved fuzzy Smith-PID-based electric heating furnace temperature control method is provided, so that the PID parameters are automatically adjusted on line, and the influence of lag time and model mismatching on the system stability is eliminated.
The technical scheme is as follows: an electric heating furnace temperature control method based on improved fuzzy Smith-PID comprises the following steps:
step 1: comparing the actual temperature value with a given value to obtain a temperature deviation e and a change rate ec of the deviation as the input of a fuzzy controller;
step 2: deducing the integral quantity delta K of three parameters of the PID controller through a fuzzy controllerp、△Ki、△Kd；
And step 3: integrating the three parameters into a quantity delta Kp、△Ki、△KdSending the obtained signal into a PID controller to realize PID parameter self-tuning, and then calculating a control signal of the electric heating furnace by using the PID controller;
and 4, step 4: a first-order inertia link is added in a feedback channel of the Smith predictor, and a nine-point control method is adopted to carry out real-time online adjustment on a filtering time constant of the feedback channel.
Further, the specific design rule of the fuzzy controller in step 2 comprises the following steps:
step A1: defining fuzzy domain of fuzzy controller, using temperature deviation e and change rate ec of deviation as input of fuzzy controller, three parameters of PID controller integrating quantity delta Kp、△Ki、△KdAs an output, the fuzzy domain definition of the input variables and the output variables of the controller is as shown in equation (1):
step A2: setting fuzzy language, dividing variables into 7 grades on the domain of discourse: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium) and PB (positive large), wherein the membership functions all select triangular membership functions, and seven fuzzy subsets are obtained by dividing the fuzzy domain respectively, namely the seven fuzzy subsets correspond to seven linguistic values;
step A3: setting a temperature deviation quantization factor K according to the system response curve characteristiceQuantization factor K of temperature deviation change rateec；
Step A4: combining the characteristics of a temperature control system of the electric heating furnace, and formulating K by adopting a Mamdani reasoning methodp、Ki、KdThe fuzzy tuning rule is shown in table 1:
TABLE 1
Step A5: the output obtained by reasoning according to the fuzzy setting rule is fuzzy quantity, and the maximum membership degree is adoptedThe method carries out defuzzification treatment to obtain three parameter integral quantity delta Kp、△Ki、△KdThe precise amount of (a).
Further, the PID parameter self-tuning in step 3 specifically includes: the integral quantity delta K of the three parameters is deduced according to a fuzzy controllerp、△Ki、△KdUsing the equation (2) for three parameters K of the PID controllerp、Ki、KdAnd (3) carrying out real-time online adjustment, and then calculating by using a formula (3) to obtain a control signal u of the electric heating furnace:
in the formula, Kp0、Ki0、Kd0The three initial parameters of the PID controller are u (t) a control signal of the electric heating furnace at the time t, and e (t) a temperature deviation amount at the time t.
Further, the nine-point control method in the step 4 specifically comprises: setting upper and lower limits e of temperature deviation0And-e0Upper and lower limits of temperature deviation change rate ec0And-ec0Dividing the temperature deviation into three states according to the numerical value, combining the upper and lower limits of the temperature deviation and the upper and lower limits of the degree deviation change rate to form nine states, each state representing a working condition, controlling the controller to give corresponding control according to the working condition information, and using K to control the controlleri±(i is 0,1,2,3) shows that different working conditions exert different control actions, and a specific control strategy is shown in table 2:
TABLE 2
According to a nine-point control strategy, by testing the filtering of the feedback channel when different parameters of the controlled object are maladjustedWave time constant tfInfluence effect of the value on the system, on the filtering time constant tfAnd carrying out on-line setting.
Has the advantages that: aiming at the temperature control system of the electric heating furnace, the invention adopts the parameter self-tuning fuzzy PID and Smith estimation compensation composite control, reduces the overshoot of the system and improves the robustness of the system; and the method is improved on the basis of the traditional Smith predictor, a first-order inertia link is added in a feedback channel, and a nine-point control method is introduced into a time-lag system to perform real-time online adjustment on a filtering time constant of the feedback channel, so that the influence caused by mismatch of a prediction model is relieved while the influence of lag time is compensated.
Drawings
FIG. 1 is a schematic diagram of the temperature control method of an electric heating furnace based on improved fuzzy Smith-PID of the invention;
FIG. 2 is a flow chart of the method for controlling the temperature of the electric heating furnace based on the improved fuzzy Smith-PID of the invention;
FIG. 3 is a diagram illustrating seven fuzzy subsets of the fuzzy controller corresponding to seven linguistic values in the method of the present invention;
FIG. 4 is a step response curve of the temperature control method of the electric heating furnace based on the improved fuzzy Smith-PID provided by the invention.
Detailed Description
The invention is further explained below with reference to the drawings.
A system based on the improved fuzzy Smith-PID electric heating furnace temperature control method is shown in figure 1 and comprises a temperature deviation change rate quantization factor 1, a temperature deviation quantization factor 2, a fuzzy controller 3, a PID controller 4, an electric heating furnace 5, an improved Smith predictor 6, a nine-point controller 7 and a temperature sensor 8.
As shown in fig. 2, the method comprises the following steps:
step 1: determining the input quantity of the fuzzy controller:
the actual temperature of the electric heating furnace is acquired through a temperature sensor, and the actual temperature value is compared with a given value to obtain a temperature deviation e and a change rate ec of the deviation as the input of a fuzzy controller.
Step 2: designing a fuzzy controller comprising:
step A1: defining fuzzy domain of fuzzy controller, using temperature deviation e and change rate ec of deviation as input of fuzzy controller, three parameters of PID controller integrating quantity delta Kp、△Ki、△KdAs an output. The fuzzy domain definition of the input variables and the output variables of the controller is shown as formula (1):
then, fuzzy language is set, and variables are classified into 7 levels on the domain of discourse: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), PB (positive large). The membership functions are all triangular membership functions, and seven fuzzy subsets, namely seven corresponding language values, are obtained by dividing the fuzzy domain, as shown in fig. 3.
Step A2: setting a temperature deviation quantization factor K according to the system response curve characteristic and combining an empirical valueeQuantization factor K of temperature deviation change rateec；
Step A3: combining the characteristics of a temperature control system of the electric heating furnace, and formulating K by adopting a Mamdani reasoning methodp、Ki、KdThe fuzzy tuning rule is shown in table 1:
TABLE 1
Step A5: the output obtained by reasoning according to the fuzzy rule is fuzzy quantity and can not be directly output, and the maximum membership method is adopted to carry out defuzzification processing to obtain the integral quantity delta K of the three parametersp、△Ki、△KdThe precise amount of (a).
And step 3: and (3) calculating a control signal of the electric heating furnace by using a PID controller:
based on fuzzy controllerThree parameters of integral quantity delta Kp、△Ki、△KdUsing the equation (2) for three parameters K of the PID controllerp、Ki、KdAnd (3) carrying out real-time online adjustment, and then calculating by using a formula (3) to obtain a control signal u of the electric heating furnace:
in the formula, Kp0，Ki0，Kd0The three initial parameters of the PID controller are u (t) a control signal of the electric heating furnace at the time t, and e (t) a temperature deviation amount at the time t.
And 4, step 4: on the basis of a conventional Smith predictor, a first-order inertia link is added in a feedback channel, and a nine-point control method is adopted to perform real-time online adjustment on a filtering time constant of the feedback channel. The method specifically comprises the following steps: setting upper and lower limits e of temperature deviation0And-e0Upper and lower limits of temperature deviation change rate ec0And-ec0And divided into three states according to their numerical values. Therefore, after being combined, the three states have nine states, each state represents an operating condition, and the controller is made to give corresponding control according to the operating condition information. By Ki±(i is 0,1,2,3) shows that different working conditions exert different control actions. With K3-For example, when the motion state of the system is at e>e0And ec>ec0In the meantime, it means that the temperature in the electric heating furnace is higher than the set temperature value and the deviation is gradually increasing, and the controller needs to apply a strong subtraction (K) to the system in order to rapidly return the system to the set temperature value3-) The control function of (1). The specific control strategy is summarized in table 2:
TABLE 2
According to the nine-point control strategy, the filtering time constant t of the feedback channel is tested when different parameters of the controlled object are maladjustedfInfluence effect of the value on the system, on the filtering time constant tfAnd carrying out on-line setting to obtain a control signal of the electric heating furnace.
The simulation result of the electric heating furnace temperature control method based on the improved fuzzy Smith-PID is shown in FIG. 4. Selecting G(s) ═ e in simulation-30sAnd (140s +1) is a control object, a unit step response is taken as an input signal, and the time lag constant of the controlled object is reduced by 50% in the conventional fuzzy Smith-PID control and the improved fuzzy Smith-PID control of the present invention. As can be seen from the simulation result, the composite control system with the Smith pre-estimation compensator has faster response than the conventional fuzzy PID control system, and eliminates the influence of the lag time on the system; when the model parameters of the controlled object are mismatched, the traditional fuzzy Smith-PID control generates overshoot, but the improved fuzzy Smith-PID control provided by the invention still has better control performance, the system response is fast, and overshoot is avoided.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (4)
1. The method for controlling the temperature of the electric heating furnace based on the improved fuzzy Smith-PID is characterized by comprising the following steps of:
step 1: comparing the actual temperature value with a given value to obtain a temperature deviation e and a change rate ec of the deviation as the input of a fuzzy controller;
step 2: deducing the integral quantity delta K of three parameters of the PID controller through a fuzzy controllerp、△Ki、△Kd；
And step 3: integrating the three parameters into a quantity delta Kp、△Ki、△KdSending into PID controller to realize PID parameter self-tuningCalculating a control signal of the electric heating furnace by using a PID controller;
and 4, step 4: a first-order inertia link is added in a feedback channel of the Smith predictor, and a nine-point control method is adopted to carry out real-time online adjustment on a filtering time constant of the feedback channel.
2. The method for controlling the temperature of the electric heating furnace based on the improved fuzzy Smith-PID as claimed in claim 1, wherein the specific design rule of the fuzzy controller in the step 2 comprises the following steps:
step A1: defining fuzzy domain of fuzzy controller, using temperature deviation e and change rate ec of deviation as input of fuzzy controller, three parameters of PID controller integrating quantity delta Kp、△Ki、△KdAs an output, the fuzzy domain definition of the input variables and the output variables of the controller is as shown in equation (1):
step A2: setting fuzzy language, dividing variables into 7 grades on the domain of discourse: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium) and PB (positive large), wherein the membership functions all select triangular membership functions, and seven fuzzy subsets are obtained by dividing the fuzzy domain respectively, namely the seven fuzzy subsets correspond to seven linguistic values;
step A3: setting a temperature deviation quantization factor K according to the system response curve characteristiceQuantization factor K of temperature deviation change rateec；
Step A4: combining the characteristics of a temperature control system of the electric heating furnace, and formulating K by adopting a Mamdani reasoning methodp、Ki、KdThe fuzzy tuning rule is shown in table 1:
TABLE 1
Step A5: the output obtained by reasoning according to the fuzzy setting rule is fuzzy quantity, and the maximum membership method is adopted to carry out defuzzification processing to obtain the integral quantity delta K of the three parametersp、△Ki、△KdThe precise amount of (a).
3. The method for controlling the temperature of the electric heating furnace based on the improved fuzzy Smith-PID as claimed in claim 1, wherein the PID parameter self-tuning in the step 3 is specifically as follows: the integral quantity delta K of the three parameters is deduced according to a fuzzy controllerp、△Ki、△KdUsing the equation (2) for three parameters K of the PID controllerp、Ki、KdAnd (3) carrying out real-time online adjustment, and then calculating by using a formula (3) to obtain a control signal u of the electric heating furnace:
in the formula, Kp0、Ki0、Kd0The three initial parameters of the PID controller are u (t) a control signal of the electric heating furnace at the time t, and e (t) a temperature deviation amount at the time t.
4. The method for controlling the temperature of the electric heating furnace based on the improved fuzzy Smith-PID as claimed in claim 1, wherein the nine-point control method in the step 4 is specifically as follows: setting upper and lower limits e of temperature deviation0And-e0Upper and lower limits of temperature deviation change rate ec0And-ec0Dividing the temperature deviation into three states according to the numerical value, combining the upper and lower limits of the temperature deviation and the upper and lower limits of the degree deviation change rate to form nine states, each state representing a working condition, controlling the controller to give corresponding control according to the working condition information, and using K to control the controlleri±(i-0, 1,2,3) representsDifferent control actions are exerted under different working conditions, and a specific control strategy is shown in table 2:
TABLE 2
According to the nine-point control strategy, the filtering time constant t of the feedback channel is tested when different parameters of the controlled object are maladjustedfInfluence effect of the value on the system, on the filtering time constant tfAnd carrying out on-line setting.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010904009.0A CN111983918A (en) | 2020-09-01 | 2020-09-01 | Improved fuzzy Smith-PID-based electric heating furnace temperature control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010904009.0A CN111983918A (en) | 2020-09-01 | 2020-09-01 | Improved fuzzy Smith-PID-based electric heating furnace temperature control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111983918A true CN111983918A (en) | 2020-11-24 |
Family
ID=73447230
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010904009.0A Pending CN111983918A (en) | 2020-09-01 | 2020-09-01 | Improved fuzzy Smith-PID-based electric heating furnace temperature control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111983918A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113741187A (en) * | 2021-08-25 | 2021-12-03 | 武汉理工大学 | Control system and method of fuzzy self-adaptive PID controller |
| CN113867438A (en) * | 2021-09-27 | 2021-12-31 | 湖南省计量检测研究院 | Method and system for measuring and controlling temperature of electric heating furnace of lubricating oil evaporation loss tester |
| CN114089795A (en) * | 2021-11-22 | 2022-02-25 | 江苏科技大学 | Fuzzy neural network temperature control system and method based on event triggering |
| CN114288502A (en) * | 2021-12-31 | 2022-04-08 | 江苏鱼跃医疗设备股份有限公司 | Temperature and humidity control method of respiratory therapy device and respiratory therapy device |
| CN114510092A (en) * | 2022-02-17 | 2022-05-17 | 太原理工大学 | Transition packet internal temperature control system and method based on fuzzy PID (proportion integration differentiation) of prediction variable universe |
| CN116068880A (en) * | 2023-01-28 | 2023-05-05 | 西安远通耐特汽车安全技术有限公司 | Modified nylon production process feed cylinder temperature regulation and control system based on fuzzy PID |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107894716A (en) * | 2017-11-28 | 2018-04-10 | 昆山艾派科技有限公司 | Temprature control method |
| CN110824908A (en) * | 2019-11-30 | 2020-02-21 | 华南理工大学 | Self-adjusting fuzzy Smith-PID temperature control system and method |
-
2020
- 2020-09-01 CN CN202010904009.0A patent/CN111983918A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107894716A (en) * | 2017-11-28 | 2018-04-10 | 昆山艾派科技有限公司 | Temprature control method |
| CN110824908A (en) * | 2019-11-30 | 2020-02-21 | 华南理工大学 | Self-adjusting fuzzy Smith-PID temperature control system and method |
Non-Patent Citations (2)
| Title |
|---|
| 张连会: "基于Smith预估器的模糊PID温度控制算法研究", 《中国硕士论文全文数据库》 * |
| 赵双萍等: "基于Smith-模糊电加热烤箱温度控制系统的开发", 《测控技术》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113741187A (en) * | 2021-08-25 | 2021-12-03 | 武汉理工大学 | Control system and method of fuzzy self-adaptive PID controller |
| CN113867438A (en) * | 2021-09-27 | 2021-12-31 | 湖南省计量检测研究院 | Method and system for measuring and controlling temperature of electric heating furnace of lubricating oil evaporation loss tester |
| CN114089795A (en) * | 2021-11-22 | 2022-02-25 | 江苏科技大学 | Fuzzy neural network temperature control system and method based on event triggering |
| CN114089795B (en) * | 2021-11-22 | 2022-08-16 | 江苏科技大学 | Fuzzy neural network temperature control system and method based on event triggering |
| CN114288502A (en) * | 2021-12-31 | 2022-04-08 | 江苏鱼跃医疗设备股份有限公司 | Temperature and humidity control method of respiratory therapy device and respiratory therapy device |
| CN114510092A (en) * | 2022-02-17 | 2022-05-17 | 太原理工大学 | Transition packet internal temperature control system and method based on fuzzy PID (proportion integration differentiation) of prediction variable universe |
| CN114510092B (en) * | 2022-02-17 | 2023-02-10 | 太原理工大学 | Transition packet internal temperature control system and method based on fuzzy PID (proportion integration differentiation) of prediction variable universe |
| CN116068880A (en) * | 2023-01-28 | 2023-05-05 | 西安远通耐特汽车安全技术有限公司 | Modified nylon production process feed cylinder temperature regulation and control system based on fuzzy PID |
| CN116068880B (en) * | 2023-01-28 | 2023-06-16 | 西安远通耐特汽车安全技术有限公司 | Modified nylon production process feed cylinder temperature regulation and control system based on fuzzy PID |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111983918A (en) | Improved fuzzy Smith-PID-based electric heating furnace temperature control method | |
| CN110101106B (en) | Moisture control method and system for dampening and humidifying process based on fuzzy feedforward feedback algorithm | |
| KR100511670B1 (en) | Control Device, Temperature Controller, and Heat Treatment Device | |
| US5159660A (en) | Universal process control using artificial neural networks | |
| CN101761917B (en) | Boiler overheating steam temperature fuzzy control method | |
| JPH09501525A (en) | Method and apparatus for fuzzy logic control with automatic adjustment | |
| CN110824908A (en) | Self-adjusting fuzzy Smith-PID temperature control system and method | |
| Xie et al. | Fuzzy adaptive internal model control | |
| JPH11513148A (en) | How to incorporate independent feedforward control into a multivariable prediction controller | |
| Kumar et al. | Real-time performance evaluation of a fuzzy PI+ fuzzy PD controller for liquid-level process | |
| CN109557810B (en) | Heating furnace temperature control method based on novel two-degree-of-freedom internal model PID | |
| CN105807615A (en) | Fuzzy feedforward-feedback controller | |
| CN103576711B (en) | Based on the chemical reactor temperature-controlled process that quantitative one-parameter PID controls | |
| JP2875023B2 (en) | Regulator for especially non-linear time-varying processes | |
| CN116700393A (en) | Reaction kettle temperature control method based on fuzzy control | |
| CN110471285B (en) | Trend event driven fuzzy control method for zinc smelting roasting process | |
| CN112965548B (en) | Automatic control method for temperature of reaction kettle, and upper and lower limit controllers and system for opening degree of valve | |
| CN113325696B (en) | Single neuron PID and model prediction combined hybrid control method applied to crosslinked cable production equipment | |
| CN112180733B (en) | Fuzzy logic-based building energy consumption system prediction control parameter setting method | |
| CN111650829B (en) | Embedded PID module parameter adjusting method, system and device based on artificial intelligence | |
| JPH05313705A (en) | Method and device for process control | |
| CN114063445A (en) | Design method of fuzzy controller | |
| CN112172129A (en) | Operation method of 3D printer nozzle temperature self-adaptive Fuzzy-PID control system | |
| JPS62135902A (en) | Optimum control method for process | |
| CN111077771A (en) | Self-tuning fuzzy PID control method |
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 |
Application publication date: 20201124 |
|
| RJ01 | Rejection of invention patent application after publication |