CN114779864A - Classroom temperature and humidity control method for optimizing PID (proportion integration differentiation) parameters based on wolf algorithm - Google Patents

Classroom temperature and humidity control method for optimizing PID (proportion integration differentiation) parameters based on wolf algorithm Download PDF

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Publication number
CN114779864A
CN114779864A CN202210694100.3A CN202210694100A CN114779864A CN 114779864 A CN114779864 A CN 114779864A CN 202210694100 A CN202210694100 A CN 202210694100A CN 114779864 A CN114779864 A CN 114779864A
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temperature
wolf
pid
classroom
humidity
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巩浩
王鹏飞
王帅
李海星
徐钰頔
史云飞
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Second Construction Co Ltd of China Construction Eighth Engineering Division Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
    • G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means

Abstract

The invention belongs to the field of temperature and humidity control, and particularly discloses a classroom temperature and humidity control method for optimizing PID (proportion integration differentiation) parameters based on a wolf algorithm, which comprises the following specific steps of: the temperature and humidity acquisition module is used for detecting the temperature and humidity in a classroom and sending temperature and humidity information of the terminal nodes to the ZigBee coordinator through the ZigBee network in a ZigBee networking mode; the ZigBee coordinator is communicated with the WiFi module through a serial port and is sent to the upper computer and the STM32 controller through a TCP/IP protocol; the temperature controller is controlled through PID regulation to drive the heating module and the cooling module to work; iterative updating by utilizing the gray wolf algorithm
Figure 756386DEST_PATH_IMAGE001
、
Figure 122776DEST_PATH_IMAGE002
And outputting the final PID parameter value to control the temperature. The invention optimizes PID parameters through the wolf algorithm, controls the temperature controller and is effectiveAnd classroom-oriented environment sensing, management and equipment control are realized.

Description

Classroom temperature and humidity control method for optimizing PID (proportion integration differentiation) parameters based on wolf algorithm
Technical Field
The invention relates to a classroom temperature and humidity control method, in particular to a classroom temperature and humidity control method for optimizing PID (proportion integration differentiation) parameters based on a wolf algorithm.
Background
Due to the continuous update of technological innovation, people enjoy the convenience brought by intellectualization more and more, and particularly, the appearance of intelligent home, so that the intelligent requirements of the people on the classroom are gradually changed from teaching intellectualization to comprehensive intellectualization of the classroom environment. Because the temperature is controlled by a manual switch through a manager in the traditional classroom, and the situation that the personnel leave the equipment and are not closed is easy to occur, the service life of the equipment is shortened, and energy is wasted. Meanwhile, classroom personnel are centralized, and the temperature and humidity of a classroom cannot be effectively fed back and controlled in time, so that a complete data storage, uploading and accurate control scheme becomes extremely important.
The IEEE802.15.4 standard protocol for the ZigBee technology realizes two-way wireless network communication with the advantages of low energy consumption, low complexity, low cost and capability of mutual communication in a short distance, and is mainly used for data transmission among some electronic equipment with short distance, low energy consumption and low transmission rate. As a wireless network communication technology, the ZigBee technology has the characteristics of low power consumption, self-organizing capability, high reliability and the like.
The PID controller is a typical proportional, integral and differential controller, and the PID control algorithm is simple, good in robustness and high in reliability, is suitable for various different conditions, and is easy to implement. The performance of the PID mainly depends on the parameter setting of the controller, different controlled objects and control parameters have different influences on the system, and the parameter setting of the PID mainly depends on a background empirical value. Meanwhile, in the aspect of applying intelligent control, the problem of parameter setting of the traditional PID is that the randomness is strong, temperature and humidity information feedback and real-time control cannot be obtained in time, the randomness of PID parameter setting timing is high, the requirements for high precision, rapidness and low power consumption of temperature and humidity are difficult to achieve, and a good adjusting effect is difficult to achieve for complex conditions.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a classroom temperature and humidity control method for optimizing PID parameters based on a wolf algorithm, which can automatically adjust the PID parameters according to detection data and reduce the randomness, the inefficiency and the instability during manual intervention.
In order to realize the purpose, the invention adopts the following technical scheme:
a classroom temperature and humidity control method based on a gray wolf algorithm optimization PID parameter comprises the following specific steps:
the temperature and humidity acquisition module is used for detecting the temperature and humidity in a classroom and sending temperature and humidity information of the terminal nodes to the ZigBee coordinator through the ZigBee network in a ZigBee networking mode;
the ZigBee coordinator is connected with the WiFi module and is sent to the upper computer and the STM32 controller through a TCP/IP protocol;
according to the preset classroom temperature regulation, the temperature controller is controlled through PID regulation to drive the heating module and the cooling module to work, and the mode of PID control PWM waves is monotonously controlled;
the temperature controller is regulated and controlled through the PID to drive the heating module and the cooling module to work specifically through three parameters of the PID:
Figure 635253DEST_PATH_IMAGE001
、
Figure 585891DEST_PATH_IMAGE002
the temperature of the liquid crystal display is adjusted and controlled by a temperature controller,
Figure 140501DEST_PATH_IMAGE001
is a proportionality coefficient;
Figure 961826DEST_PATH_IMAGE003
is an integral coefficient;
Figure 904374DEST_PATH_IMAGE004
is a differential time coefficient;
utilizing a gray wolfThe algorithm is updated iteratively
Figure 811150DEST_PATH_IMAGE001
、
Figure 700609DEST_PATH_IMAGE002
And (4) judging the iteration times by taking the fitness value as a judgment updating optimization mode, and finally outputting a PID parameter value to control the temperature controller when the target times is reached.
Further, the specific method for adjusting and controlling the temperature controller by the PID comprises the following steps:
according to given values
Figure 376441DEST_PATH_IMAGE005
And the actual output value
Figure 755470DEST_PATH_IMAGE006
Forming a control error:
Figure 382498DEST_PATH_IMAGE007
the deviation proportion (P), integral (I) and differential (D) are combined by certain linearity to form control quantity
Figure 75647DEST_PATH_IMAGE008
The controlled object is controlled, and the control formula is as follows:
Figure 668303DEST_PATH_IMAGE009
wherein the content of the first and second substances,
wherein:
Figure 155916DEST_PATH_IMAGE001
is a proportionality coefficient;
Figure 568443DEST_PATH_IMAGE003
is an integral coefficient;
Figure 65283DEST_PATH_IMAGE004
is a differential time coefficient.
Further, the grey wolf algorithm is used for iterative updating and updating
Figure 450128DEST_PATH_IMAGE001
、
Figure 170959DEST_PATH_IMAGE010
The specific steps of the parameters are as follows:
s1: setting the initialization conditions of the gray wolf location, i.e.
Figure 274045DEST_PATH_IMAGE001
、
Figure 308997DEST_PATH_IMAGE002
Initial values of parameters;
s2: calculating the fitness value according to the initialization condition of the grey wolf position and ITAE, calculating the fitness values of optimal, suboptimal and third-best solution and endowing the fitness values
Figure 610665DEST_PATH_IMAGE011
Wolf, the remaining values being given
Figure 705660DEST_PATH_IMAGE012
A wolf;
Figure 827200DEST_PATH_IMAGE013
wherein t represents time;
s3: updating the gray wolf position according to a formula;
Figure 197001DEST_PATH_IMAGE014
in the formula
Figure 353176DEST_PATH_IMAGE015
、
Figure 586449DEST_PATH_IMAGE016
、
Figure 664126DEST_PATH_IMAGE017
Respectively represent
Figure 775302DEST_PATH_IMAGE011
The distance between the wolf and the prey,
Figure 254825DEST_PATH_IMAGE018
respectively represent
Figure 488360DEST_PATH_IMAGE011
The current position of the wolf is determined,
Figure 787754DEST_PATH_IMAGE019
、
Figure 764938DEST_PATH_IMAGE020
is a random vector and is a vector of a random number,
Figure 364546DEST_PATH_IMAGE021
the position of the current wolf individual;
s4: re-screening all the gray wolf positions and re-aligning according to the fitness value
Figure 706666DEST_PATH_IMAGE011
Assigning a wolf;
s5: and judging whether the iteration times meet the preset times or not, and jumping to Step 3 if the iteration times do not meet the preset times.
S6: output of
Figure 290094DEST_PATH_IMAGE022
The location vector of the wolf as an optimum parameter, i.e.
Figure 772766DEST_PATH_IMAGE001
、
Figure 961302DEST_PATH_IMAGE002
And (4) parameter values.
Furthermore, the STM32 controller is also connected with an LCD display screen and used for displaying classroom temperature information.
Furthermore, the ZigBee coordinator sends the data to an upper computer after the data are processed and the data are connected with a WiFi module in a serial mode, and meanwhile the data are connected with an STM32 controller in a serial mode.
Furthermore, the temperature raising module is a PTC heater, and the temperature lowering module is a semiconductor refrigerating sheet and a fan.
The invention has the beneficial effects that: the invention takes a classroom as a research object, utilizes the sensor to collect data, processes the collected data through the ZigBee coordinator, and transmits the data to the controller and the upper computer. The temperature and humidity data are fully utilized, the comfort level in a classroom is guaranteed, the design is suitable for the classroom, the family, the office and the like, and the model application singleness is solved.
The design combines the technology of Internet of things, adopts the ZigBee networking technology and WiFi wireless transmission, optimizes PID parameters through the Grey wolf algorithm, controls the temperature controller, and effectively realizes classroom-oriented environmental perception, management and equipment control.
Drawings
FIG. 1 is a block diagram of a classroom temperature and humidity control system according to the present invention;
FIG. 2 is a grey wolf scale chart;
FIG. 3 is a gray wolf location update diagram;
FIG. 4 is a flow chart of the method of the present invention;
reference numerals are as follows: 1. an upper computer; an STM32 controller; 3. a temperature controller; 4, LCD display screen; 5, a WIFI module; 6, a Zigbee coordinator; 7. temperature and humidity acquisition module.
Detailed Description
The invention will be further described with reference to the accompanying drawings and the detailed description below:
as shown in fig. 1, classroom temperature control system includes a plurality of humiture collection modules 7, humiture collection modules 7 are connected with zigBee coordinator 6, zigBee coordinator 6 is connected with WIFI module 5 through the serial ports, WIFI module 5 passes through the serial ports respectively with host computer 1 and STM32 controller 2, STM32 controller 2 is connected with temperature controller 3 and LCD display screen 4, temperature controller 3 control intensification module and cooling module action, the module of rising temperature is the PTC heater, the cooling module is semiconductor refrigeration piece and fan, humiture collection modules is temperature and humidity sensor.
A classroom temperature and humidity control method based on a Grey wolf algorithm optimization PID parameter is applied to a classroom temperature control system, and comprises the following specific steps:
the temperature and humidity acquisition module 7 is used for detecting the temperature and humidity in a classroom and sending temperature and humidity information of the terminal nodes to the ZigBee coordinator 6 through the ZigBee network in a ZigBee networking mode;
the ZigBee coordinator 6 is connected with the WiFi module 5 and is sent to the upper computer 1 and the STM32 controller 2 through a TCP/IP protocol;
according to the preset classroom temperature regulation, the temperature controller 3 is controlled through PID regulation to drive the heating module and the cooling module to work, and the mode of PID control PWM waves is monotonously controlled;
the temperature controller 3 is regulated and controlled by the PID to drive the heating module and the cooling module to work specifically through three parameters of the PID:
Figure 739902DEST_PATH_IMAGE001
、
Figure 810626DEST_PATH_IMAGE002
the temperature controller 3 is adjusted and controlled to be,
Figure 67295DEST_PATH_IMAGE001
is a proportionality coefficient;
Figure 375916DEST_PATH_IMAGE003
is an integral coefficient;
Figure 122156DEST_PATH_IMAGE004
is a differential time coefficient;
iterative update is carried out by utilizing the grey wolf algorithm
Figure 883438DEST_PATH_IMAGE001
、
Figure 943798DEST_PATH_IMAGE002
And (4) judging the iteration times by taking the fitness value as a judgment updating optimization mode, and finally outputting a PID parameter value to control the temperature controller 3 when the target times are reached.
The specific method for PID regulation and control of the temperature controller comprises the following steps:
according to given values
Figure 169243DEST_PATH_IMAGE005
And the actual output value
Figure 289646DEST_PATH_IMAGE006
Forming a control error:
Figure 771180DEST_PATH_IMAGE007
the deviation proportion (P), the integral (I) and the derivative (D) are combined by certain linearity to form a control quantity
Figure 431969DEST_PATH_IMAGE008
The controlled object is controlled, and the control formula is as follows:
Figure 449603DEST_PATH_IMAGE009
wherein, the first and the second end of the pipe are connected with each other,
wherein:
Figure 740908DEST_PATH_IMAGE001
is a proportionality coefficient;
Figure 273520DEST_PATH_IMAGE003
is an integral coefficient;
Figure 675683DEST_PATH_IMAGE004
is a differential time coefficient.
As shown in FIG. 4, the grey wolf algorithm is used for iterative update
Figure 813403DEST_PATH_IMAGE001
、
Figure 72346DEST_PATH_IMAGE010
The specific steps of the parameters are as follows:
s1: setting the initialization conditions for the grey wolf location, i.e.
Figure 29938DEST_PATH_IMAGE001
、
Figure 501370DEST_PATH_IMAGE002
Initial values of parameters;
s2: calculating the fitness value according to the grey wolf position initialization condition through ITAE, and assigning the fitness value of the optimal, the suboptimal and the third optimal solution
Figure 290335DEST_PATH_IMAGE011
Wolf, the remaining values being given
Figure 657862DEST_PATH_IMAGE012
The wolf is a Chinese wolf with the functions of,
Figure 165067DEST_PATH_IMAGE013
wherein t represents time;
s3: updating the grey wolf position according to a formula;
Figure 673146DEST_PATH_IMAGE014
in the formula
Figure 254300DEST_PATH_IMAGE015
、
Figure 120625DEST_PATH_IMAGE016
、
Figure 52809DEST_PATH_IMAGE017
Each represents
Figure 600465DEST_PATH_IMAGE011
The distance between the wolf and the prey,
Figure 98442DEST_PATH_IMAGE018
respectively represent
Figure 73352DEST_PATH_IMAGE011
The current position of the wolf is set,
Figure 492832DEST_PATH_IMAGE019
、
Figure 640916DEST_PATH_IMAGE020
in the form of a random vector, the vector is,
Figure 462242DEST_PATH_IMAGE021
is the current location of the wolf individual; FIG. 3 shows a gray wolf location update diagram;
s4: re-screening all the gray wolf positions and re-aligning according to the fitness value
Figure 608052DEST_PATH_IMAGE011
Assigning a wolf;
s5: and judging whether the iteration times meet the preset times or not, and jumping to Step 3 if the iteration times do not meet the preset times.
S6: output of
Figure 311566DEST_PATH_IMAGE022
The location vector of the wolf as an optimum parameter, i.e.
Figure 965139DEST_PATH_IMAGE001
、
Figure 375392DEST_PATH_IMAGE002
The parameter values.
As shown in FIG. 2, the Grey wolf rating system is
Figure 754421DEST_PATH_IMAGE023
(ii) a Using the first three levels
Figure 148493DEST_PATH_IMAGE011
Updating the location of the wolf
Figure 576063DEST_PATH_IMAGE012
The calculation method of the wolf comprises the following steps:
Figure 371981DEST_PATH_IMAGE024
Figure 859594DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 537700DEST_PATH_IMAGE026
is composed of
Figure 768961DEST_PATH_IMAGE022
The individual location of the grey wolf is,
Figure 419385DEST_PATH_IMAGE027
is composed of
Figure 140217DEST_PATH_IMAGE028
Individual location of the wolf;
Figure 499695DEST_PATH_IMAGE029
is composed of
Figure 534647DEST_PATH_IMAGE030
Individual location of the wolf;
Figure 101895DEST_PATH_IMAGE031
to others
Figure 931311DEST_PATH_IMAGE012
Individual positions of wolves, a and C are coefficient vectors, where:
Figure 521692DEST_PATH_IMAGE032
Figure 157073DEST_PATH_IMAGE033
in the formula
Figure 516510DEST_PATH_IMAGE034
Linearly decreasing between 2 and 0 as a convergence factor,
Figure 782406DEST_PATH_IMAGE035
and
Figure 656821DEST_PATH_IMAGE036
is [0,1 ]]A random number in between.
The grey wolf algorithm is a novel group intelligent optimization algorithm which is provided by simulating the social grade system and hunting behaviors of grey wolf groups in the nature. To maintain population development, there are 4 hierarchical systems in the wolf population, as shown in FIG. 2, where
Figure 767997DEST_PATH_IMAGE022
In order to be the highest level of the hierarchy,
Figure 247520DEST_PATH_IMAGE028
subject to
Figure 481055DEST_PATH_IMAGE022
，
Figure 278984DEST_PATH_IMAGE030
Subject to
Figure 459430DEST_PATH_IMAGE028
Other gray wolves
Figure 590197DEST_PATH_IMAGE012
Is the lowest level. In the gray wolf algorithm,
Figure 932317DEST_PATH_IMAGE012
with possibility of updating, other high-level wolf guidance
Figure 984586DEST_PATH_IMAGE012
To update its location. In the grey wolf hunting process, the main three stages are divided: surround, hunt, and attack. In the algorithm space, initial positions are randomly assigned. Let the dimension of the population be N, the
Figure 765460DEST_PATH_IMAGE037
The position of the wolf is
Figure 688417DEST_PATH_IMAGE038
，
Figure 467017DEST_PATH_IMAGE039
. The grey wolf unified location update formula is as follows:
Figure 803321DEST_PATH_IMAGE040
Figure 59990DEST_PATH_IMAGE041
in the formula:
Figure 368611DEST_PATH_IMAGE042
the distance that exists between the gray wolf and the game,
Figure 114850DEST_PATH_IMAGE043
is the position of the prey, and is,
Figure 374668DEST_PATH_IMAGE044
and
Figure 435028DEST_PATH_IMAGE031
is a gray wolf
Figure 394894DEST_PATH_IMAGE037
Generation and combination
Figure 515297DEST_PATH_IMAGE045
The position of the generation, t, is the current number of iterations.
The social ranking of the wolf algorithm can be divided into wolfs
Figure 560613DEST_PATH_IMAGE022
Wolf of the lower genus
Figure 424664DEST_PATH_IMAGE028
Common wolf
Figure 442298DEST_PATH_IMAGE030
And the bottom layer wolf
Figure 530340DEST_PATH_IMAGE012
Four layers of which
Figure 636DEST_PATH_IMAGE022
Is responsible for leading the wolf group,
Figure 668377DEST_PATH_IMAGE028
assistance of
Figure 602835DEST_PATH_IMAGE022
The decision is made as to whether to take a decision,
Figure 65041DEST_PATH_IMAGE030
listening slave
Figure 521168DEST_PATH_IMAGE022
And
Figure 727021DEST_PATH_IMAGE028
can also direct the underlying individual
Figure 515985DEST_PATH_IMAGE012
. In the gray wolf optimization algorithm
Figure 149092DEST_PATH_IMAGE022
，
Figure 656297DEST_PATH_IMAGE028
And
Figure 400262DEST_PATH_IMAGE030
respectively representing a historical optimal solution, a suboptimal solution and a third optimal solution,
Figure 246995DEST_PATH_IMAGE012
the remaining individuals are indicated. In the process of the evolution of the algorithm,
Figure 847741DEST_PATH_IMAGE022
，
Figure 45504DEST_PATH_IMAGE028
and
Figure 327581DEST_PATH_IMAGE030
the system is responsible for positioning the position of a prey and guiding other individuals to finish actions such as approaching, surrounding, attacking and the like, and finally achieves the aim of preying on the prey.
Various other modifications and changes may occur to those skilled in the art based on the foregoing teachings and concepts, and all such modifications and changes are intended to be included within the scope of the appended claims.

Claims (6)

1. A classroom temperature and humidity control method based on a Grey wolf algorithm optimization PID parameter is characterized by comprising the following specific steps:
the temperature and humidity acquisition module is used for detecting the temperature and humidity in a classroom and sending temperature and humidity information of the terminal nodes to the ZigBee coordinator through the ZigBee network in a ZigBee networking mode;
the ZigBee coordinator is communicated with the WiFi module through a serial port and is sent to the upper computer and the STM32 controller through a TCP/IP protocol;
according to the preset regulation of classroom temperature, a PID regulation and control temperature controller drives a heating module and a cooling module to work, and the mode of PID control of PWM waves is monotonously controlled;
the temperature controller is regulated and controlled through the PID to drive the heating module and the cooling module to work specifically through three parameters of the PID:K p 、K i 、K D the temperature of the liquid crystal display is adjusted and controlled by a temperature controller,K p is a proportionality coefficient;K i is an integral coefficient;K D is a differential time coefficient;
iterative updating by utilizing the gray wolf algorithmK p 、K i 、K D And parameters, namely, judging the iteration times by taking the fitness value as a judgment updating optimization mode, and finally outputting PID parameter values to control the temperature controller when the target times are reached.
2. The classroom temperature and humidity control method based on the sirius algorithm for optimizing the PID parameters as claimed in claim 1, wherein the specific method for PID regulation and control of the temperature controller is as follows:
forming a control error according to the given value r (t) and the actual output value y (t)
Figure 345428DEST_PATH_IMAGE001
，
The deviation proportion (P), the integral (I) and the differential (D) are combined by certain linearity to form a control quantity u (t) to control the controlled object, and the control formula is
Figure 859586DEST_PATH_IMAGE002
，
Wherein, the first and the second end of the pipe are connected with each other,K p is a proportionality coefficient;K i is an integral coefficient;K D is a differential time coefficient.
3. The classroom temperature and humidity control method based on graywolf algorithm optimization PID parameters as claimed in claim 2, wherein the iterative update using graywolf algorithm is performed to updateK p 、K i 、K D The specific steps of the parameters are as follows:
s1, setting the initialization conditions of the gray wolf position, namelyK p 、K i 、K D Initial values of parameters;
s2, calculating fitness values through ITAE according to grey wolf position initialization conditions, calculating the fitness values of optimal, suboptimal and third-best solutions and giving alpha, beta and delta wolfs, and giving w wolfs to the rest values;
Figure 520375DEST_PATH_IMAGE003
wherein t represents time;
s3, updating the position of the wolf according to a formula;
Figure 538009DEST_PATH_IMAGE004
in the formula
Figure 593428DEST_PATH_IMAGE005
、
Figure 63723DEST_PATH_IMAGE006
、
Figure 262623DEST_PATH_IMAGE007
Respectively represent the distances between alpha, beta, delta wolf and the prey,X α 、X β 、X δ respectively represent the current positions of alpha, beta and delta wolf,
Figure 400344DEST_PATH_IMAGE008
、
Figure 862549DEST_PATH_IMAGE009
、
Figure 616878DEST_PATH_IMAGE010
is a random vector, and X is the position of the current wolf individual;
s4, re-screening all the gray wolf positions, and re-assigning values to alpha, beta and delta wolfs according to the fitness value;
s5, judging whether the iteration times meet the preset times or not, and jumping to Step 3 if the iteration times do not meet the preset times;
s6, outputting the position vector of the alpha wolf as an optimal parameter, namelyK p 、K i 、K D The parameter values.
4. The classroom temperature and humidity control method based on the graying algorithm optimization PID parameter as claimed in claim 1, wherein the STM32 controller is further connected with LCD display screen to display classroom temperature information.
5. The classroom temperature and humidity control method based on the grayish wolf algorithm optimization PID parameter as claimed in claim 1, characterized in that the ZigBee coordinator sends the processed data to the upper computer after being connected with the WiFi module in serial, and simultaneously, the serial is connected with the STM32 controller.
6. The classroom temperature and humidity control method based on sirius algorithm optimization PID parameters as claimed in claim 1, wherein the temperature raising module is a PTC heater and the temperature lowering module is a semiconductor cooling plate and a fan.
CN202210694100.3A 2022-06-20 2022-06-20 Classroom temperature and humidity control method for optimizing PID (proportion integration differentiation) parameters based on wolf algorithm Pending CN114779864A (en)

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Application publication date: 20220722