CN107894716A - Temprature control method - Google Patents
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- CN107894716A CN107894716A CN201711216618.1A CN201711216618A CN107894716A CN 107894716 A CN107894716 A CN 107894716A CN 201711216618 A CN201711216618 A CN 201711216618A CN 107894716 A CN107894716 A CN 107894716A
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- 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
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Abstract
A kind of temprature control method, comprises the following steps：(1) control system model G (s) is described with the one order inertia delay component with interference and Parameter uncertainties, defines deviation and deviation variation rate that e, e＆ are respectively desired temperature and actual measured value；(2) model parameter is recognized using relay feedback method, the PID initial values of fuzzy is obtained based on Z N equations；(3) fuzzy rule based on optimization, pid parameter is adaptively adjusted on the basis of initial value according to deviation e and deviation variation rate e＆；And (4) determine the quantizing factor of fuzzy according to variable universe thought.The present invention realizes preferably temperature control performance.
Description
Technical field
The present invention relates to temperature controller field, especially a kind of temprature control method.
Background technology
Temperature is one of basic parameter in industrial processes, the measurement and control effect due to temperature usually with life
It is closely bound up to produce every technical-economic indexes such as efficiency, production safety, energy management.As temperature measurement controller is in industry
Environment uses on a large scale, to temperature controlled accuracy, stability, reliability requirements also more and more higher.Temperature control is calculated
Method receives the highest attention of numerous scholars as the core technology of temperature measurement controller, due to traditional single PID (ratios
Example (proportion), integration (integral), derivative (derivative)) algorithm can not meet demand for control, because
This various parameters self-setting PID arithmetic arises.Self-setting PID arithmetic based on identification of Model Parameters industrially obtains
Extensive use, but its parameter tuning effect depends critically upon system model and easily disturbed by environmental change, it is difficult to reach higher
Control accuracy.Based on the PID tune of fuzzy algorithmic approach in Practical, PID initial values have a great influence to its control performance,
Fuzzy rule, quantizing factor also have certain restriction to its parameter tuning effect.
The content of the invention
In order to overcome drawbacks described above, the present invention provides a kind of temprature control method, and the temprature control method realizes work
The high-precision temperature control of industry environment, there is more preferable control effect.
The present invention in order to solve its technical problem used by technical scheme be：A kind of temprature control method, including it is as follows
Step：(1) control system model G (s) is described with the one order inertia delay component with interference and Parameter uncertainties, defines e, e＆
The respectively deviation and deviation variation rate of desired temperature and actual measured value；(2) using relay feedback method to model parameter
Recognized, the PID initial values of fuzzy are obtained based on Z-N equations；(3) fuzzy rule based on optimization, according to deviation e and
Deviation variation rate e＆ is adaptively adjusted to pid parameter on the basis of initial value；And (4) determine according to variable universe thought
The quantizing factor of fuzzy.
As a further improvement on the present invention, in the step (1), deviation e and system model G (s) formula difference
For：
E=sv-pv (1)
As a further improvement on the present invention, in the step (2), determined using the relay feedback method based on Z-N formula
PID initial value, process are as follows：
In formula (3)-(4)：D is relay characteristics amplitude, and A is stable constant amplitude amplitude, T caused by systemuIt is threshold oscillation week
Phase, ymaxAnd t1It is peak value and the time of first crest respectively, yminAnd t2It it is peak value and the time of second crest, according to formula
(5) PID initial values K can be calculatedp0、Ki0、Kd0。
As a further improvement on the present invention, in the step (3), the online adaptive adjustment amount of pid parameter is by obscuring
Control obtains, and E and EC are the fuzzy language after e and e＆ inputs quantify respectively, normalizes thought of the fuzzy domain according to variable universe
Determine that membership function uses triangular function shown in its quantizing factor such as formula (6)：
As a further improvement on the present invention, described pid parameter is adjusted and Adaptive adjusting algorithm is：
U (t)=(Kp0+ΔKp)×e(t)+(Ki0+ΔKi)×∫e(t)+(Kd0+ΔKd)×e&(t)
(7)。
As a further improvement on the present invention, the membership function of described Indistinct Input output quantity all uses triangle
Function, fuzzy domain are normalized to [0,1], and fuzzy subset is：Z (zero), S (small), M (in), B (big), fuzzy rule is directly determined
The performance quality of fuzzy control is determined, to reduce fuzzy control to the sensitiveness of interference, general fuzzy rule has been closed
And and optimization, rule from 49 reduced to 16, not only increase control performance and also reduce system-computed spending.
As a further improvement on the present invention, | E | when larger, in order to accelerate the response speed of system, and prevent because starting
When moment become big, may caused by differential overflow, larger KP and less KD should be taken；To prevent from integrating saturation, avoid
There is larger overshoot in system response, should now remove integral action, take KI=0.
As a further improvement on the present invention, | E | during median size, in order that the overshoot of system response reduces and protected
Certain response speed is demonstrate,proved, less KI, KP and KD numerical values recited should be taken moderate.
As a further improvement on the present invention, | E | when smaller, in order that system has good steady-state behaviour, it should increase
KP and KI numerical value, at the same to avoid vibration of the output response near setting value, and consider the interference free performance of system, should
KD is suitably chosen, its principle is：When | EC | when value is smaller, KD takes larger；When | EC | when being worth larger, KD takes less number
Value, usual KD is median size.
As a further improvement on the present invention, user terminal includes three kinds of control modes：Manual mode, self-regulation pattern, intelligence
Can pattern.
The beneficial effects of the invention are as follows：At the beginning of temprature control method of the present invention determines the PID of fuzzy using Z-N equations
Value, the quantizing factor of fuzzy is determined according to variable universe thought, a kind of improved mould is proposed based on the fuzzy rule after optimization
Paste PID control methods are used for industrial environment high accuracy temperature control, so as to reach more preferable control effect, realization it is more excellent be
System performance.
Brief description of the drawings
Fig. 1 is the theory diagram of temprature control method of the present invention.
Fig. 2 be temprature control method of the present invention MATLAB (Matrix Laboratory, MathWorks companies of the U.S. go out
The business mathematics software of product) Control System Imitation model.
Fig. 3 is the contrast simulation result of temprature control method of the present invention and single PID and conventional fuzzy.
Fig. 4 is the user's control mode process for using figure of temprature control method of the present invention.
Embodiment
Fig. 1 to Fig. 4 is refer to, the present invention relates to a kind of temprature control method, is comprised the following steps：
(1) control system model G (s) can use the one order inertia delay component with interference and Parameter uncertainties to describe, definition
E, e＆ is respectively the deviation and deviation variation rate of desired temperature and actual measured value；
(2) model parameter is recognized using relay feedback method, at the beginning of obtaining the PID of fuzzy based on Z-N equations
Value；
(3) fuzzy rule based on optimization, according to deviation e and deviation variation rate e＆ to pid parameter on the basis of initial value
Adaptively adjusted；And
(4) quantizing factor of fuzzy is determined according to variable universe thought.
In the step (1), deviation e and system model G (s) formula are respectively：
E=sv-pv (1)
In the step (2), PID initial value is determined using the relay feedback method based on Z-N formula, process is as follows：
In formula (3)-(4)：D is relay characteristics amplitude, and A is stable constant amplitude amplitude, T caused by systemuIt is threshold oscillation week
Phase, ymaxAnd t1It is peak value and the time of first crest respectively, yminAnd t2It it is peak value and the time of second crest, according to formula
(5) PID initial values K can be calculatedp0、Ki0、Kd0。
In the step (3), the online adaptive adjustment amount of pid parameter is obtained by fuzzy control, E and EC be respectively e and
Fuzzy language after input quantization, normalize fuzzy domain and its quantizing factor such as formula (6) institute is determined according to the thought of variable universe
Show, membership function uses triangular function：
Described pid parameter is adjusted and Adaptive adjusting algorithm is：
U (t)=(Kp0+ΔKp)×e(t)+(Ki0+ΔKi)×∫e(t)+(Kd0+ΔKd)×e&(t) (7)。
The membership function of described Indistinct Input output quantity all uses triangular function, fuzzy domain be normalized to [0,
1], fuzzy subset is：Z (zero), S (small), M (in), B (big), fuzzy rule directly determine fuzzy control performance quality,
To reduce fuzzy control to the sensitiveness of interference, general fuzzy rule is merged and optimized, rule is reduced to from 49
16, not only increase control performance and also reduce system-computed spending.
| E | when larger, in order to accelerate the response speed of system, and preventing moment during because starting from becoming big, may cause
Differential overflow, larger KP and less KD should be taken；To prevent from integrating saturation, system response is avoided larger overshoot occur,
Integral action should now be removed, take KI=0.
| E | during median size, in order that the overshoot of system response reduces and ensures certain response speed, should take compared with
Small KI, KP and KD numerical values recited is moderate.
| E | when smaller, in order that system has good steady-state behaviour, KP and KI numerical value should be increased, while to avoid
Vibration of the output response near setting value, and consider the interference free performance of system, KD should be suitably chosen, its principle is：When
| EC | when value is smaller, KD takes larger；When | EC | when being worth larger, KD takes less numerical value, and usual KD is median size.
Present invention determine that the fuzzy relation between pid parameter and deviation and deviation variation rate, in the process of running temperature survey
Amount controller constantly detects E and EC, and adjustment amount is calculated further according to fuzzy rule inference, with satisfaction in different system deviation and partially
The difference of controller parameter is required during poor rate of change, so as to reach preferably temperature control performance.
In Fig. 1, switch is first switched to PID initial values to adjust, model parameter K can be picked out using relay feedback method
=2, τ=1, T=5, tri- parameter K of PID can be calculated according to Z-N formulap0=3, Ki0=1.5, Kd0=1.5, due to
Model error and external disturbance in systems in practice be present, parameter obtained above is generally unable to reach preferably control performance.
Initial value that can be using it as PID, self-adaptive sites are carried out to pid parameter with reference to fuzzy control.
In Fig. 1, switch is switched to improved fuzzy-adaptation PID control, temperature deviation e and deviation variation rate e＆ are as mould
The input quantity of fuzzy controllers, then be blurred by quantizing factor to obtain corresponding fuzzy quantity E and EC.Measure in actual applications
It is larger to the parameter tuning influential effect of fuzzy control to change the selection of the factor, user is reduced simultaneously to improve fuzzy control performance
Parameter testing workload, quantizing factor is determined using formula based on variable universe thought, allows quantizing factor with error and error
Rate of change and change i.e.：
Improve fuzzy MATLAB contrast simulations model as shown in Fig. 2 with single PID and pair of conventional fuzzy
Than simulation result as shown in figure 3, as can be seen that improved fuzzy has faster response speed, smaller steady from Fig. 3
State error and antijamming capability, Comprehensive Control performance is more excellent to meet that most of high-precision industrial temperature controls require.
User terminal includes three kinds of control modes：Manual mode, self-regulation pattern, intelligent mode.
In order to meet three kinds of control modes of various commercial Application scenes and Customer Requirement Design, it is respectively：Manual mould
Formula, self-regulation pattern, intelligent mode, user's control mode process for using are as shown in Figure 4.Wherein manual mode is single PID
Control, designed exclusively for the user for being familiar with classical PID control, user can be according to real-time response curve adjustment Kp0、Ki0、Kd0 is same
When other parameters set to 0；It is the relay feedback parameter self-tuning PID control based on Z-N formula to automatically adjust pattern, and this pattern is simple
Single, easy, effective, simple operation, it is not necessary to adjust any parameter, it is only necessary to self-regulation is re-executed after environmental change；Intelligence
Energy pattern is improved fuzzy-adaptation PID control, on the basis of PID initial values are adjusted, the ginseng using improved fuzzy algorithmic approach to PID
Number is adaptively adjusted, and user can adjust the ambiguity solution factor according to response curveRule adjusting is similar to
Single pid parameter regulation, preferably control performance and easy to use is can reach in such a mode.
Claims (10)
- A kind of 1. temprature control method, it is characterised in that：Comprise the following steps：(1) control system model G (s) is described with the one order inertia delay component with interference and Parameter uncertainties, definition e,Respectively For desired temperature and the deviation and deviation variation rate of actual measured value；(2) model parameter is recognized using relay feedback method, the PID initial values of fuzzy is obtained based on Z-N equations；(3) fuzzy rule based on optimization, according to deviation e and deviation variation ratePid parameter is carried out certainly on the basis of initial value Adapt to adjustment；And(4) quantizing factor of fuzzy is determined according to variable universe thought.
- 2. temprature control method according to claim 1, it is characterized in that：In the step (1), deviation e and system model G (s) formula is respectively：E=sv-pv (1)
- 3. temprature control method according to claim 1, it is characterized in that：In the step (2), using based on Z-N formula Relay feedback method determine PID initial value, process is as follows：In formula (3)-(4)：D is relay characteristics amplitude, and A is stable constant amplitude amplitude, T caused by systemuIt is critical period of the oscillation, ymax And t1It is peak value and the time of first crest respectively, yminAnd t2It is peak value and the time of second crest, can be counted according to formula (5) Calculate PID initial values Kp0、Ki0、Kd0。
- 4. temprature control method according to claim 1, it is characterized in that：In the step (3), pid parameter it is online from Adapt to adjustment amount to be obtained by fuzzy control, E and EC are e and the fuzzy language inputted after quantifying respectively, normalize fuzzy domain root Determine that membership function uses triangular function shown in its quantizing factor such as formula (6) according to the thought of variable universe：
- 5. temprature control method according to claim 1, it is characterized in that：Described pid parameter is adjusted and adaptively adjusted Algorithm is：
- 6. temprature control method according to claim 1, it is characterized in that：The degree of membership letter of described Indistinct Input output quantity Number all uses triangular function, and fuzzy domain is normalized to [0,1], and fuzzy subset is：Z (zero), S (small), M (in), B (big), Fuzzy rule directly determines the performance quality of fuzzy control, to reduce sensitiveness of the fuzzy control to interference, to general fuzzy Rule is merged and optimized, and rule has reduced to 16 from 49, not only increases control performance and also reduces system-computed Spending.
- 7. temprature control method according to claim 6, it is characterized in that：| E | when larger, in order to accelerate the response of system speed Degree, and prevent moment during because starting from becoming big, may caused by differential overflow, larger KP and less KD should be taken；It is anti- Saturation is only integrated, avoids system response from larger overshoot occur, should now remove integral action, take KI=0.
- 8. temprature control method according to claim 6, it is characterized in that：| E | during median size, in order that system response Overshoot reduces and ensures certain response speed, and less KI, KP and KD numerical values recited should be taken moderate.
- 9. temprature control method according to claim 6, it is characterized in that：| E | when smaller, in order that system has well Steady-state behaviour, KP and KI numerical value should be increased, while to avoid vibration of the output response near setting value, and consider system Interference free performance, KD should be suitably chosen, its principle is：When | EC | when value is smaller, KD takes larger；When | EC | when being worth larger, KD takes less numerical value, and usual KD is median size.
- 10. temprature control method according to claim 6, it is characterized in that：User terminal includes three kinds of control modes：Manual mould Formula, self-regulation pattern, intelligent mode.
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Cited By (11)
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| CN108790696A (en) * | 2018-06-29 | 2018-11-13 | 京东方科技集团股份有限公司 | Temprature control method, device, electronic equipment and storage medium |
| CN108828934A (en) * | 2018-09-26 | 2018-11-16 | 云南电网有限责任公司电力科学研究院 | A kind of fuzzy PID control method and device based on Model Distinguish |
| CN109656138A (en) * | 2018-12-19 | 2019-04-19 | 龙口盛福达食品有限公司 | A kind of brewed spirit process temperature intelligent control method based on fuzzy reasoning |
| CN109856978A (en) * | 2019-03-26 | 2019-06-07 | 广东电网有限责任公司 | A kind of method and device obtaining plant model |
| CN110686242A (en) * | 2019-08-28 | 2020-01-14 | 光大环保技术装备（常州）有限公司 | Method and system for controlling hearth temperature of plasma fly ash melting furnace |
| CN110716593A (en) * | 2019-10-31 | 2020-01-21 | 河北科技大学 | Method and device for predicting and controlling temperature of reaction kettle and terminal equipment |
| CN111766777A (en) * | 2020-07-30 | 2020-10-13 | 北京环境特性研究所 | PID controller and PID control method |
| CN111983918A (en) * | 2020-09-01 | 2020-11-24 | 南通大学 | Improved fuzzy Smith-PID-based electric heating furnace temperature control method |
| CN112680789A (en) * | 2019-12-13 | 2021-04-20 | 山东大学 | HVPE temperature control system and method based on fuzzy control |
| CN113110033A (en) * | 2021-04-27 | 2021-07-13 | 南通大学 | Heat collection control system based on fuzzy PID algorithm ASHP |
| CN113741174A (en) * | 2021-09-03 | 2021-12-03 | 中石化石油机械股份有限公司三机分公司 | Self-adaptive pressure control algorithm of reciprocating natural gas compressor |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108790696A (en) * | 2018-06-29 | 2018-11-13 | 京东方科技集团股份有限公司 | Temprature control method, device, electronic equipment and storage medium |
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| CN108828934A (en) * | 2018-09-26 | 2018-11-16 | 云南电网有限责任公司电力科学研究院 | A kind of fuzzy PID control method and device based on Model Distinguish |
| CN109656138A (en) * | 2018-12-19 | 2019-04-19 | 龙口盛福达食品有限公司 | A kind of brewed spirit process temperature intelligent control method based on fuzzy reasoning |
| CN109856978B (en) * | 2019-03-26 | 2022-02-15 | 广东电网有限责任公司 | Method and device for obtaining controlled object model |
| CN109856978A (en) * | 2019-03-26 | 2019-06-07 | 广东电网有限责任公司 | A kind of method and device obtaining plant model |
| CN110686242A (en) * | 2019-08-28 | 2020-01-14 | 光大环保技术装备（常州）有限公司 | Method and system for controlling hearth temperature of plasma fly ash melting furnace |
| CN110716593A (en) * | 2019-10-31 | 2020-01-21 | 河北科技大学 | Method and device for predicting and controlling temperature of reaction kettle and terminal equipment |
| CN112680789A (en) * | 2019-12-13 | 2021-04-20 | 山东大学 | HVPE temperature control system and method based on fuzzy control |
| CN111766777A (en) * | 2020-07-30 | 2020-10-13 | 北京环境特性研究所 | PID controller and PID control method |
| CN111766777B (en) * | 2020-07-30 | 2023-06-16 | 北京环境特性研究所 | PID controller and PID control method |
| CN111983918A (en) * | 2020-09-01 | 2020-11-24 | 南通大学 | Improved fuzzy Smith-PID-based electric heating furnace temperature control method |
| CN113110033A (en) * | 2021-04-27 | 2021-07-13 | 南通大学 | Heat collection control system based on fuzzy PID algorithm ASHP |
| CN113741174A (en) * | 2021-09-03 | 2021-12-03 | 中石化石油机械股份有限公司三机分公司 | Self-adaptive pressure control algorithm of reciprocating natural gas compressor |
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Application publication date: 20180410 |