CN110501900A - A method of train fresh air system temperature is adjusted based on fuzzy controller - Google Patents
A method of train fresh air system temperature is adjusted based on fuzzy controller Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000008859 change Effects 0.000 claims abstract description 14
- 238000004378 air conditioning Methods 0.000 claims abstract description 13
- 230000000694 effects Effects 0.000 claims abstract description 11
- 230000005540 biological transmission Effects 0.000 claims abstract description 10
- 230000004044 response Effects 0.000 claims abstract description 10
- 238000010586 diagram Methods 0.000 claims abstract description 9
- 238000005457 optimization Methods 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims abstract description 4
- 238000004088 simulation Methods 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000013461 design Methods 0.000 claims description 5
- 230000009514 concussion Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 230000005484 gravity Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000004134 energy conservation Methods 0.000 claims description 2
- 238000013507 mapping Methods 0.000 claims description 2
- 239000011159 matrix material Substances 0.000 claims description 2
- 230000006641 stabilisation Effects 0.000 claims description 2
- 238000011105 stabilization Methods 0.000 claims description 2
- 238000002474 experimental method Methods 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 230000033228 biological regulation Effects 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 238000012546 transfer Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 239000012530 fluid Substances 0.000 description 3
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 2
- 229910052802 copper Inorganic materials 0.000 description 2
- 239000010949 copper Substances 0.000 description 2
- 230000008676 import Effects 0.000 description 2
- 229910052742 iron Inorganic materials 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
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- 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.
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Abstract
It is a kind of based on fuzzy controller adjust train fresh air system temperature method belong to technical field of automatic control.The present invention constructs the frame diagram of train fresh air system first, and the thermodynamic behaviour of the wind-force coil pipe by analysis air-conditioning, mathematical modeling, which is carried out, according to law of conservation of energy obtains transmission function, input quantity using temperature error and its change rate as fuzzy controller, three parameters of the output quantity real-time optimization PID controller of fuzzy controller, utilize fuzzy logic toolbox, establish corresponding fuzzy inference rule, fuzzy control model and PID are combined, fuzzy controller is formed, model is emulated.Beneficial effect is: Fuzzy PID accelerates the response speed of system, degree of regulation improves, steady-state performance is more preferable, significantly enhance control effect, the shortcomings that effectively improving conventional PID controllers reaches predetermined temperature by controlling air-conditioning intake intelligently to adjust the temperature of fresh air.
Description
Technical field
It is a kind of based on fuzzy controller adjust train fresh air system temperature method belong to automated control technology neck
Domain in particular belongs to fuzzy control technology field.
Background technique
High-speed rail has become one of the main means of transport of people's trip, and with the development of economy and living standards of the people
Raising, requirement of the people to indoor air quality is also improving, to the comfortable of the air quality inside crowded train
Degree requires also upgrading.Therefore, air-conditioning system becomes the environmental control equipment of train indispensability.And air-conditioned train compartment is mostly closed
Space, occupant's density is larger, and indoor air quality is poor, and generally requiring by introducing outdoor fresh air improves in train
Environment.To reduce equipment to the occupancy in space, fresh air supply is combined with air-conditioning system usually and is considered together.Train air-conditioning
System introduces the outer fresh air of a certain proportion of vehicle, and after fresh air system filters, swaps with in-vehicle air, by vehicle
Within the allowable range, Lai Tigao people are in train for the controls such as air themperature, relative humidity, air velocity and cleannes in compartment
Comfort level.Mostly use pid algorithm, PID controller by its structure is simple, stability is good, reliable operation, it is easy to adjust the advantages that
One of major technique as Industry Control.When the structure and parameter of controlled device cannot grasp completely, cannot accurately count
It is the most convenient using PID control technology when learning model.Have under very big time variation and nonlinear situation in control object, one
The pid parameter that group has been adjusted is far from satisfying the requirement of system.A kind of adjusting for overcoming the above drawback of innovation is needed to arrange
The method of vehicle fresh air system temperature reaches predetermined temperature intelligently to adjust the temperature of fresh air, to meet high-speed rail, general iron, subway, city
Passenger in rail train to train air quality increasingly higher demands, adapt to the national economic development there is an urgent need to.
Summary of the invention
The object of the invention is to a kind of adjusting train fresh air system temperature for overcoming prior art drawback of innovation
Method reaches predetermined temperature intelligently to adjust the temperature of fresh air, to meet high-speed rail, general iron, subway, the passenger couple in municipal rail train
Train air quality increasingly higher demands, adapt to the national economic development there is an urgent need to.
The technical scheme is that the present invention constructs the frame diagram of train fresh air system first, and pass through analysis air-conditioning
Wind-force coil pipe thermodynamic behaviour, according to law of conservation of energy carry out mathematical modeling obtain transmission function, using temperature error
And its input quantity of the change rate as fuzzy controller, three ginsengs of the output quantity real-time optimization PID controller of fuzzy controller
Number, using fuzzy logic toolbox, establishes corresponding fuzzy inference rule, and fuzzy control model and PID are combined, and is formed fuzzy
PID controller emulates model.
The beneficial effects of the present invention are: fuzzy PID control method accelerates the response speed of system, degree of regulation improves,
Steady-state performance is more preferable, it will be apparent that the shortcomings that improving control effect, effectively improving conventional PID controllers, by controlling air-conditioning
Intake reaches predetermined temperature intelligently to adjust the temperature of fresh air.
Detailed description of the invention
Fig. 1 is the train fresh air system model schematic of the embodiment of the present invention.
Fig. 2 is fan coil heat exchange model schematic.
Fig. 3 is the single order S type step response curve schematic diagram of model.
Fig. 4 is the simulation contact surface of the PID- simulink of train fresh air model.
Fig. 5 is the PID analogous diagram of train fresh air model.
Fig. 6 is the frame diagram of fuzzy controller.
Fig. 7 is the proportionality coefficient Δ K of PID controllerPCurved surface export figure.
Fig. 8 is the integral coefficient Δ K of PID controlleriCurved surface export figure.
Fig. 9 is the differential coefficient Δ K of PID controllerdCurved surface export figure.
Figure 10 is the simulink flow chart of fuzzy controller emulation.
Figure 11 is that the temperature of the fuzzy controller of train fresh air model adjusts analogous diagram.
Specific embodiment
In conjunction with the attached drawing of the invention details and the course of work that the present invention will be described in detail.Obviously, described embodiment is only
It is only one embodiment of the present of invention, instead of all the embodiments.Based on the embodiments of the present invention, those skilled in the art
Other embodiments obtained without creative efforts, shall fall within the protection scope of the present invention.It is of the invention public
The algorithm that train fresh air system temperature is adjusted based on fuzzy controller, so-called fuzzy controller, i.e., using fuzzy are opened
Logical algorithm simultaneously carries out real-time optimization to the ratio of PID control, integral, differential coefficient according to certain fuzzy rule, to reach
Ideal control effect；Fuzzy-adaptation PID control includes parameter fuzzy, fuzzy rule inference, parameter ambiguity solution, PID control
Several important components such as device；With the development of computer, and in conjunction with the knowledge of expert and the experience of operator and largely
Experimental data, according to the actual conditions of model, three parameters of adjust automatically PID；Computer according to set input and
Feedback signal calculates deviation and the current change of error of actual temperature and theoretical temperatures, and carries out mould according to fuzzy rule
Reasoning is pasted, ambiguity solution finally is carried out to fuzzy parameter, exports ratio, the integral, differential coefficient of PID controller.
The present invention constructs the frame diagram of train fresh air system first, and special by the thermodynamics of the wind-force coil pipe of analysis air-conditioning
Property, mathematical modeling is carried out according to law of conservation of energy and obtains transmission function, using temperature error and its change rate as Fuzzy Control
The input quantity of device processed, three parameters of the output quantity real-time optimization PID controller of fuzzy controller, utilizes fuzzy logic tool
Case establishes corresponding fuzzy inference rule, and fuzzy control model and PID are combined, and forms fuzzy controller, to model into
Row emulation.
Control method of the present invention the following steps are included:
Step A establishes train fresh air system model and coil pipe of air-conditioner blower heat exchange models, determines model by mathematical modeling
Transmission function；It is the copper tube of 0.016m as heat transfer tube that the present invention, which uses diameter, by law of conservation of energy it is found that not considering
Heat losses, hot water release heat and air absorb heat, the heat exchange amount of fan coil should be it is equal, obtain system
Instantaneous state energy conservation equation:
)；(1)
It (will in Fig. 2Brief note,)
Transmission function can be obtained by Laplace transform
(2)
By each setting parameter:I.e. fan coil imports and exports the recirculated water temperature difference, generally to cool down
Processing medium, that is, air conditioner in cold fluid it is cold after temperature be greater than 50, the control of the recirculated water temperature difference is 8 ~ 10, and to be cooled down
Temperature is less than 50 after cold fluid is cold in processing medium, that is, air conditioner, the control of the recirculated water temperature difference is 4 ~ 6, the present invention is with spring and summer
For section, then take；
Heat transfer coefficientFormula:(3)
、；It is controlled by the intake of air-conditioning 500 ~ 600, and
By taking the fresh air volume that a section first block compartment needs as an example, the intake of fan coil needs to control 500 ~ 624, thus
Synthesis obtains, indoor fan coil pipe runs under dry cooling condition, the temperature of the recirculated water height
In indoor dew-point temperature, outdoor air 18, make indoor drying-air temperature reach 25 DEG C by heat transfer system, relative humidity
50%, water capacity 9.8,15 DEG C of indoor dew-point temperature then has indoor and outdoor air poor, the side temperature difference of cold fluid
Mean difference, diameter is used to calculate for 0.016 meter of copper tube as heat transfer tube；By parameters value generation
Enter formula (2)
It can obtain transmission function:；(4)
Step B carrys out three parameters of auto-- tuning controller using fuzzy PID control method three obtained output quantity, into
The frame diagram of row real-time optimization, fuzzy controller is as shown in Figure 6；
Step C, designs simulation contact surface under the Simulink environment of MATLAB, and fuzzy controller is read in
' fuzzypid25.fis ' file imports the amount of outputting and inputting, determines fuzzy control rule, just completes fuzzy emulation mould
The design of type；Parameter setting therein has,, it is respectively 1,1,0 that initial value, which is set as PID controller default value,；Amount
Change the factor and conciliate fuzzy factor:,；
Step D, from image as can be seen that traditional PID effect concussion period is long, parameter tuning is complicated, and has added fuzzy control
Fuzzy controller obtained good simulation result rapidly, system has only used 1.4s just to reach stabilization；Mould of the invention
Type is a first order inertial loop, so simulated effect is especially good, the speed of response is greatly improved, and concussion is not present, with
The domain of our predetermined parameters has very big relationship, also real in the process of the voluntarily setting parameter of fuzzy controller
The process of intelligent control is showed；This fuzzy control PID controller still retains PID regulator in control loop, uses simultaneously
Self-adjusting structure of the Fuzzy inference method as conventional PID controller, has actually carried out non-linear place to PID controller
Reason realizes the Nonlinear Mapping relationship between system performance variation and control amount；In this sense, fuzzy self-adaption
PID controller is a kind of nonlinear pid controller.
Further, the step B specifically includes the following steps:
Step B-1, system is by the deviation of 25 degrees Celsius of set temperature and actual temperatureAnd temperature
Poor change rateAs input to the controller, with,,As the output of controller, adaptively adjust
PID controller, to achieve the purpose that control；
The adjusting of pid parameter must take into account effect and mutual interconnecting relation in 3 parameters of different moments；It is fuzzy
PID tune is on the basis of pid algorithm, by calculating current system errorAnd error change, using fuzzy rule into
Row fuzzy reasoning, inquiry fuzzy matrix table carry out parameter adjustment；
Parameters revision formula:
；(5)
,It is the initial design values of parameter, uses PID controller system default value in emulation link herein, by
Three outputs of fuzzy control,,Carry out the value of adaptive three control parameters of adjustment, replaces and manually enter
Adjusting parameter achievees the effect that intelligent control；
In view of the temperature of the spring and summer indoor and outdoor of train, the temperature error of this paper is setPractical domain, mould
Paste domain, quantizing factor;Difference variation ratePractical domain exist, obscure domain, quantizing factor；
According to the case where our model it is found that deviation and change rate are median size, in order to which the overshoot for responding system subtracts
It is small, and guarantee certain response speed,It should take smaller；In this case, Δ KdValue it is very big to systematic influence, answer
It takes smaller；Meanwhile to avoid output response from oscillating around in setting value, considers the anti-interference ability of system, principle should be taken
It is: when deviation variation rate is smaller, Δ KdTake the larger value；When deviation variation rate is larger, Δ KdSmaller value is taken, usuallyFor in
Etc. sizes；AndValue it is also appropriate；
Determine the output quantity of fuzzy controller,,Practical domain and fuzzy domain, then can repeatedly into
Row emulates to adjust the parameter of conventional PID controllers, it is possible to find obtains,,Practical domain is respectively；
And the method for general pid parameter adjusting is obtained frequently with Ziegler-Nichols method, and linear or parameter is become
Changing lesser object has good response performance, and generally for closed-loop system, Ziegler-Nichols method is available:
(6)
Wherein,It is the gain of controller and system response shake when system critical stable state under only proportional control
Swing the period；By a large amount of simulation calculations, the substantially fuzzy domain range of available three parameters is:
,,；
Calculating and appropriate enlarged area, then,,；
Then ambiguity solution operator is,,；
Step B-2 enters fuzzy editing machine in MATLAB, two input quantities is respectively set、, three output quantities P, I, D
Adjust three parameters of PID controller,,, and input corresponding fuzzy domain；At this time five input and
The membership function of output quantity all selects 7 trigonometric functions, is defined as NB, NM, NS, zero, PS, PM, PB, respectively correspond it is negative big,
In negative, bear it is small, zero, it is just small, center, honest；
Step B-3 establishes fuzzy rule inference, and specific rules are shown in Table 1 ~ table 3:
Table 1:Fuzzy rule
Table 2:Fuzzy rule
Table 3:Fuzzy rule
Then(,,) curved surface output figure as shown in attached drawing 7, attached drawing 8, attached drawing 9；
Step B-4, the method for ambiguity solution
The method of ambiguity solution is using gravity model appoach ambiguity solution method；Because the variation of gravity model appoach is smoother, will not generate mutation,
Jump is relatively suitble to fresh air system；The calculating formula of ambiguity solution is as follows:
(7)
The variable that ambiguity solution comes out, by the effect of frequency converter, adjusts the stream of system by being converted into the controllable signal of frequency converter
Magnitude, the intake of Controlling model finally efficiently control the new air temperature of system under the action of fuzzy controller, reach as early as possible
To our 25 degrees Celsius of predetermined value.
The step C specifically includes the following steps:
Step C-1 determines that the input quantity of controller is temperature error, the change rate of temperature errorBy temperature errorIt is micro-
Get, the output quantity of controller is the intake of air-conditioning wind-force coil pipe, PID controller is obtained by the following formula；
；(8)
Step C-2, model provides temperature error and temperature error by taking the temperature of the fresh air system of spring and summer as an example according to the present invention
Change rate practical domain, and combine a large amount of classical PID controllers emulation data and Ziegler-Nichols method, calculate
Three output quantities out,,Practical domain and fuzzy domain；
Step C-3 establishes corresponding fuzzy rule and makes inferences according to model of the present invention；
Step C-4, according to different errorsAnd error rate, PID is controlled with the output of the fuzzy controller model of foundation
Three parameters of device processed carry out real-time optimization, so that intelligence adjusts PID controller, it can be by errorRatio, integral, differential three
Item is in parameter,,Gain control under obtain corresponding output quantity.
The beneficial effects of the present invention are: Fuzzy PID accelerates the response speed of system, degree of regulation improves,
Steady-state performance is more preferable, it will be apparent that the shortcomings that improving control effect, effectively improving conventional PID controllers, by controlling air-conditioning
Intake reaches predetermined temperature intelligently to adjust the temperature of fresh air.
Claims (4)
1. a kind of method for adjusting train fresh air system temperature based on fuzzy controller, which is characterized in that present invention structure first
The frame diagram of train fresh air system, and the thermodynamic behaviour of the wind-force coil pipe by analysis air-conditioning are built, according to law of conservation of energy
It carries out mathematical modeling and obtains transmission function, the input quantity using temperature error and its change rate as fuzzy controller, Fuzzy Control
Three parameters of the output quantity real-time optimization PID controller of device processed establish corresponding fuzzy reasoning using fuzzy logic toolbox
Rule combines fuzzy control model and PID, forms fuzzy controller, emulates to model.
2. a kind of method for adjusting train fresh air system temperature based on fuzzy controller according to claim 1, special
Sign is, comprising the following steps:
Step A establishes train fresh air system model and coil pipe of air-conditioner blower heat exchange models, determines model by mathematical modeling
Transmission function；Obtain the instantaneous state energy conservation equation of system:
)；
It (will in Fig. 2Brief note,)
Transmission function can be obtained by Laplace transform
It is arranged by parameters, transmission function can be obtained:；
Step B carrys out three parameters of auto-- tuning controller using fuzzy PID control method three obtained output quantity, into
Row real-time optimization, the fuzzy PID control method illustraton of model of foundation；
Step C designs simulation contact surface under the Simulink environment of MATLAB, and then emulation experiment determines that fuzzy control is advised
Then, the design of fuzzy simulation model is just completed；
Step D, from image as can be seen that traditional PID effect concussion period is long, the complexity of parameter tuning, and added Fuzzy Control
The fuzzy controller of system has obtained rapidly good simulation result, and system has only used 1.4s just to reach stabilization；Of the invention
Model is a first order inertial loop, so simulated effect is especially good, the speed of response is greatly improved, and concussion is not present,
There is very big relationship with the domain of our predetermined parameters, in the process of the voluntarily setting parameter of fuzzy controller,
Realize the process of intelligent control；This fuzzy control PID controller still retains PID regulator in control loop, uses simultaneously
Self-adjusting structure of the Fuzzy inference method as conventional PID controller, has actually carried out non-linear place to PID controller
Reason realizes the Nonlinear Mapping relationship between system performance variation and control amount；In this sense, fuzzy self-adaption
PID controller is a kind of nonlinear pid controller.
3. a kind of method that train fresh air system temperature is adjusted based on fuzzy controller according to claim 1 or 2,
It is characterized in that, the step B specifically includes the following steps:
Step B-1, system is by the deviation of 25 degrees Celsius of set temperature and actual temperatureWith difference variation rateAs controller
Input, with,,As the output of controller, PID controller adaptively is adjusted, to reach the mesh of control
's；Fuzzy selftuning PID is on the basis of pid algorithm, by calculating current system errorAnd error change, utilize mould
Paste rule carries out fuzzy reasoning, and inquiry fuzzy matrix table carries out parameter adjustment；
In view of the temperature of the spring and summer indoor and outdoor of train, temperature error of the invention is setPractical domain,
Fuzzy domain, quantizing factor;Difference variation ratePractical domain exist, obscure domain, quantizing factor；
Determine the output quantity of fuzzy controller,,Practical domain and fuzzy domain, then can repeatedly carry out
It emulates to adjust the parameter of conventional PID controllers, it is possible to find obtain,,Practical domain is respectively；
By Ziegler-Nichols method and a large amount of simulation calculations, the substantially fuzzy domain range of three parameters can be obtained
It is:
,,；
Calculating and appropriate enlarged area, then,,；
Then ambiguity solution operator is,,；
Step B-2 enters fuzzy editing machine in MATLAB, two input quantities is respectively set、, three output quantities P, I, D
Adjust three parameters of PID controller,,, and input corresponding fuzzy domain；This corresponding membership function
Be defined as NB, NM, NS, zero, PS, PM, PB, respectively correspond in negative big, negative, bear it is small, zero, it is just small, center, honest；
Step B-3 establishes fuzzy rule inference, and specific rules are shown in Table 1 ~ table 3:
Table 1:Fuzzy rule
Table 2:Fuzzy rule
Table 3:Fuzzy rule
Then(,,) curved surface output figure as shown in attached drawing 7, attached drawing 8, attached drawing 9；
Step B-4, the method for ambiguity solution
The method of ambiguity solution is using gravity model appoach ambiguity solution method.
4. a kind of method that train fresh air system temperature is adjusted based on fuzzy controller according to claim 1 or 2,
It is characterized in that, the step C specifically includes the following steps:
Step C-1 determines that the input quantity of controller is temperature error, the change rate of temperature errorBy temperature errorDifferential
It obtains, the output quantity of controller is the intake of air-conditioning wind-force coil pipe；
Step C-2 by taking the temperature of the fresh air system of spring and summer as an example, provides temperature error and temperature error according to this paper model
The practical domain of change rate, and the emulation data and Ziegler-Nichols method of a large amount of classical PID controllers are combined, it calculates
Three output quantities,,Practical domain and fuzzy domain；
Step C-3 establishes corresponding fuzzy rule and makes inferences according to model of the present invention；
Step C-4, according to different errorsAnd error rate, PID is controlled with the output of the fuzzy controller model of foundation
Three parameters of device processed carry out real-time optimization, so that intelligence adjusts PID controller, it can be by errorRatio, integral, differential three
Item is in parameter,,Gain control under obtain corresponding output quantity.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112904709A (en) * | 2021-01-18 | 2021-06-04 | 青岛海信电子设备股份有限公司 | Air conditioner control method and air conditioner |
| CN118011781A (en) * | 2024-04-08 | 2024-05-10 | 深圳比特微电子科技有限公司 | Temperature control method and device of dryer, blockchain server and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112904709A (en) * | 2021-01-18 | 2021-06-04 | 青岛海信电子设备股份有限公司 | Air conditioner control method and air conditioner |
| CN118011781A (en) * | 2024-04-08 | 2024-05-10 | 深圳比特微电子科技有限公司 | Temperature control method and device of dryer, blockchain server and storage medium |
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