CN117130410A - Temperature controller and control method of intelligent closestool - Google Patents

Temperature controller and control method of intelligent closestool Download PDF

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Publication number
CN117130410A
CN117130410A CN202310713967.3A CN202310713967A CN117130410A CN 117130410 A CN117130410 A CN 117130410A CN 202310713967 A CN202310713967 A CN 202310713967A CN 117130410 A CN117130410 A CN 117130410A
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temperature
pid
neuron
control process
controller
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吴杰
何一坚
胡靖超
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Zhejiang University ZJU
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Zhejiang University ZJU
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D23/00—Control of temperature
    • G05D23/19—Control of temperature characterised by the use of electric means
    • G05D23/30—Automatic controllers with an auxiliary heating device affecting the sensing element, e.g. for anticipating change of temperature
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The application discloses a temperature controller and a control method of an intelligent closestool, wherein the temperature controller comprises a temperature control system for controlling the temperature of the intelligent closestool, the temperature control system comprises a control module, a temperature module and a temperature acquisition module, the control module comprises an information transmission module, an information input terminal and a controller, the temperature acquisition module and the information transmission module are respectively connected with the controller, and the temperature control system is characterized in that a back propagation algorithm of a neural network is adopted and K is set G 、w p '(n)(k)、w i ' (n) (k) and w d ' n, (k) realizes the function of self-learning and temperature control of the neural network, is beneficial to ensuring the convergence of the whole self-learning process of the neural network, and realizes a good temperature control effect through a refrigerating/heating element temperature control system which can be well fitted through the self-learning capability of the neural network, and has extremely strong self-adaptive capability and better stability.

Description

Temperature controller and control method of intelligent closestool
Technical Field
The application belongs to the field of temperature control of intelligent toilets, and relates to a temperature controller and a control method of an intelligent toilet.
Background
Temperature control techniques are particularly critical for industries where temperature is a requirement, such as smelting, smart home, etc. Aiming at scenes with different temperature control precision and different environmental conditions, a PID algorithm is often adopted to control the temperature. The PID algorithm has good fitting degree for a linear system, but has poor control effect for a nonlinear system, and is difficult to realize control with higher precision.
The intelligent toilet model adopts a refrigerating/heating element (semiconductor chip) to realize refrigeration or heating, and the temperature control system of the refrigerating/heating element of the intelligent toilet model becomes a typical nonlinear system due to the nonlinear electrical property and the discrete spatial distribution of the refrigerating/heating element, so that the traditional PID algorithm is difficult to realize a good temperature control effect.
Disclosure of Invention
The application provides a temperature control method of an intelligent closestool for overcoming the defects in the prior art.
In order to achieve the above purpose, the present application adopts the following technical scheme: the utility model provides a temperature controller of intelligent closestool, includes based on temperature control system controls intelligent closestool's temperature, and temperature control system includes control module, temperature acquisition module, and control module includes information transmission module, information input terminal and controller, and temperature acquisition module, information transmission module are connected with the controller respectively, include the following step:
step 1: inputting a set target temperature TS;
the information transmission module acquires target temperature data of the information input terminal and transmits set target temperature TS to the controller;
step 2: obtaining the actual temperature T R (n)(k)；
Step 3: determining basic parameters;
step 4: the controller obtains the target temperature T S Actual temperature T R (n) (k) obtaining the duty ratio of a certain k paths of PWM waves in a certain n control processes;
step 5: the controller outputs PWM waves to the temperature module to enable the temperature of each measuring point to reach the target temperature;
step 6: after each control flow execution is completed, the information transmission module reads data of the information input terminal once, so as to obtain the setting of a user on the target temperature, if updated target temperature data exists, the information transmission module transmits the updated target temperature data to the controller, the step 1 is executed, and if not, the next step is executed.
Further, in the primary control process in step 2, a temperature acquisition module acquires the temperature of the measuring point where the temperature module is located to form an actual temperature data T which is transmitted to the controller R (1) (1) the kth temperature acquisition module acquires the temperature of the region where the kth temperature module is positioned to form k paths of actual temperature data T which are transmitted to the controller R (1) (k) in the nth control process, a temperature acquisition module acquires the temperature of the area where the temperature module is located to form actual temperature data T which is transmitted to the controller in one path R (n) (1), the kth temperature acquisition module acquires the temperature of the region where the kth temperature module is located to form k paths of actual temperature data T which are transmitted to the controller R (n)(k)。
Further, the value of k is the same as the collection times of the temperature collection modules and the number of the temperature modules.
Further, the step 3 includes the following steps:
step 3.1: determining all weight parameters of PID;
w p (n+1)(k)＝w p (n)(k)+v p u(n)(k)e(n)(k)x1(n)(k)；
w i (n+1)(k)＝w i (n)(k)+v i u(n)(k)e(n)(k)x 2 (n)(k)；
w i (n+1)(k)＝w i (n)(k)+v i u(n)(k)e(n)(k)x 2 (n)(k)；
wherein n is the number of times of control process that the actual temperature reaches the target temperature, k is the measuring point mark of the intelligent closestool,
w p (n) (k) is the PID proportional term weight parameter of the kth neuron in the nth control process, w p (n+1) (k) is the weight parameter of the PID proportion term of the kth neuron in the (n+1) th control process，v p For the learning rate of the proportion term of the PID of the neuron, when x1 (n) (k) is the input 1 of the neuron, the proportion term in the PID;
w i (n) (k) is the PID integral term weight parameter of the kth neuron in the nth control process, w i (n+1) (k) is the PID integral term weight parameter, v of the kth neuron in the (n+1) th control process i Learning rate, x, for neuron PID integral term 2 (n) (k) is the integral term in the PID when neuron input 2;
w d (n) (k) the weight parameter, w, of the PID derivative term of the kth neuron in a certain n-time control process d (n+1) (k) the weight parameter, v, of the PID derivative term of the kth neuron in a certain n+1 control process d Learning rate, x, for neuron PID derivative 3 (n) (k) is the derivative term in PID when neuron input 3;
in the formula, e (n) (k) is the actual temperature T of a certain k paths in a certain n-time control process R (n) (k) and the set target temperature T S The error between the current temperature and the set target temperature in the k-th path in the previous n-1 control process is e (n-1) (k), the error between the current temperature and the set target temperature in the k-th path in the previous n-2 control process is e (n-2) (k), and the duty ratio of the PWM wave in the k-th path in the n-th control process is u (n) (k);
step 3.2: determining a duty cycle increment;
the duty cycle increment Deltau (n) (k) is the output of each n paths of the nth control process:
△u(n)(k)＝KG(w p '(n)(k)x1(n)(k)+wi'(n)(k)x2(n)(k)+wd'(n)(k)x3(n)(k))；
KG in the formula is the total learning rate of the neurons;
step 3.3: determining a duty cycle of the PWM wave;
the duty cycle is set to u (n) (k),
u(n)(k)＝△u(n)(k)+△u(n-1)(k)+...+△u(1)(1)+u a ；
the formula ua is an initial value set by the duty ratio of the PWM wave;
further, x in the step 3.1 1 (n)(k)＝e(n)(k)-e(n-1)(k)。
Further, x in the step 3.1 2 (n)(k)＝e(n)(k)+e(n-1)(k)+e(n-2)(k)。
Further, in the step 3.1
x 3 (n)(k)＝e(n)(k)-2×e(n-1)(k)+e(n-2)(k)。
Further, vp, vi and vd are set values.
Further, KG is a set value.
Further, the information transmission module is set to be a WIFI module and used for data transmission between the controller and the information input terminal, the information input terminal comprises keys or/and a remote controller or/and a mobile phone APP, and the information transmission module acquires temperature data of the information input terminal.
The utility model provides a temperature control system of intelligent closestool, includes intelligent closestool, control module, temperature acquisition module, and control module includes information transmission module, information input terminal and controller, and temperature acquisition module, information transmission module are connected with the controller respectively.
Further, the information transmission module is set to be a WIFI module and used for data transmission between the controller and the information input terminal, the information input terminal comprises keys or/and a remote controller or/and a mobile phone APP, and the information transmission module acquires temperature data of the information input terminal.
Further, the temperature module is arranged as a semiconductor wafer, and the temperature module is arranged in the intelligent closestool.
Further, the temperature module sets up a plurality of, and a plurality of temperature module distributes at intelligent closestool's packing ring.
Further, the temperature acquisition module and the temperature module are at least arranged in one-to-one correspondence.
Further, a temperature acquisition module acquires the temperature of a measuring point where the temperature module is located to form actual temperature data which is transmitted to the control module in one path.
A temperature controller for an intelligent toilet, the temperature controller comprising:
a target temperature setting unit that sets a target temperature TS;
a temperature acquisition unit forAcquiring the actual temperature T of the intelligent toilet bowl measuring point R (n)(k)；
A parameter setting unit for determining each weight parameter of PID, duty cycle increment and duty cycle of PWM wave;
an operation part for obtaining the target temperature T S Actual temperature T R (n) (k) obtaining the duty ratio of a certain k paths of PWM waves in a certain n control processes;
an output part for outputting PWM waves to the temperature module to enable the temperature of each measuring point to reach the target temperature;
and a data confirmation unit for receiving the updated target temperature data.
Further, determining various weight parameters of the PID;
w p (n+1)(k)＝w p (n)(k)+v p u(n)(k)e(n)(k)x1(n)(k)；
w i (n+1)(k)＝w i (n)(k)+v i u(n)(k)e(n)(k)x 2 (n)(k)；
w i (n+1)(k)＝w i (n)(k)+v i u(n)(k)e(n)(k)x 2 (n)(k)；
wherein n is the number of times of control process that the actual temperature reaches the target temperature, k is the measuring point mark of the intelligent closestool, and w p (n) (k) is the PID proportional term weight parameter of the kth neuron in the nth control process, w p (n+1) (k) is the weight parameter of the PID proportion term of the kth path neuron in the n+1th control process, vp is the learning rate of the PID proportion term of the neuron, and x 1 (n) (k) is the proportional term in PID when neuron input 1;
w i (n) (k) is the PID integral term weight parameter of the kth neuron in the nth control process, w i (n+1) (k) is the PID integral term weight parameter, v of the kth neuron in the (n+1) th control process i Learning rate, x, for neuron PID integral term 2 (n) (k) is the integral term in the PID when neuron input 2;
w d (n) (k) the weight parameter, w, of the PID derivative term of the kth neuron in a certain n-time control process d (n+1) (k) the weight parameter, v, of the PID derivative term of the kth neuron in a certain n+1 control process d Is neuron PLearning rate of ID differentiation term, x 3 (n) (k) is the derivative term in PID when neuron input 3;
in the formula, e (n) (k) is the actual temperature T of a certain k paths in a certain n-time control process R (n) (k) and the set target temperature T S The error between the current temperature and the set target temperature in the k-th path in the previous n-1 control process is e (n-1) (k), the error between the current temperature and the set target temperature in the k-th path in the previous n-2 control process is e (n-2) (k), and the duty ratio of the PWM wave in the k-th path in the n-th control process is u (n) (k).
Further, determining a duty cycle increment;
the duty cycle increment Deltau (n) (k) is the output of each n paths of the nth control process:
△u(n)(k)＝KG(w p '(n)(k)x1(n)(k)+wi'(n)(k)x2(n)(k)+wd'(n)(k)x3(n)(k))；
KG in the formula is the total learning rate of neurons.
Further, determining the duty cycle of the PWM wave;
the duty cycle is set to u (n) (k),
u(n)(k)＝△u(n)(k)+△u(n-1)(k)+...+△u(1)(1)+u a ；
in the formula, ua is an initial value set by the duty ratio of the PWM wave;
further, x 1 (n)(k)＝e(n)(k)-e(n-1)(k)。
Further, x 2 (n)(k)＝e(n)(k)+e(n-1)(k)+e(n-2)(k)。
Further, x 3 (n)(k)＝e(n)(k)-2×e(n-1)(k)+e(n-2)(k)。
Further, vp, vi and vd are set values.
Further, KG is a set value.
In summary, the application has the following advantages:
the application sets KG and w through the back propagation algorithm of the neural network p '(n)(k)、w i ' (n) (k) and w d ' n (k) realizes the function of self-learning and temperature control of the neural network, and is beneficial to protectionThe convergence of the whole neural network self-learning process is proved, the refrigerating/heating element temperature control system which can be well fitted through the neural network self-learning capability realizes a good temperature control effect, and the self-adaptive temperature control system has extremely strong self-adaptive capability and better stability.
Drawings
FIG. 1 is a flow chart of the present application.
Detailed Description
Other advantages and effects of the present application will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application with reference to specific examples. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present application by way of illustration, and only the components related to the present application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
All directional indications (such as up, down, left, right, front, rear, lateral, longitudinal … …) in embodiments of the present application are merely used to explain the relative positional relationship, movement, etc. between the components in a particular gesture, and if the particular gesture changes, the directional indication changes accordingly.
For reasons of installation errors, the parallel relationship referred to in the embodiments of the present application may be an approximately parallel relationship, and the perpendicular relationship may be an approximately perpendicular relationship.
Embodiment one:
as shown in FIG. 1, the temperature control system of the intelligent closestool comprises the intelligent closestool, a control module, a temperature module and a temperature acquisition module, wherein the control module comprises an information transmission module, an information input terminal and a controller, and the temperature acquisition module and the information transmission module are respectively connected with the controller.
In this embodiment, the information transmission module is configured as a WIFI module and is used for data transmission between the controller and the information input terminal, the information input terminal includes a key or/and a remote controller or/and a mobile phone APP, and the information transmission module obtains temperature data of the information input terminal.
In this embodiment, the temperature module is set to be a semiconductor wafer, and the temperature module is set to be in the intelligent closestool, preferably set to be in the gasket of intelligent closestool, heats or refrigerates the gasket, realizes the accurate control of temperature.
In this embodiment, temperature module sets up a plurality of, a plurality of temperature module evenly distributed is at intelligent closestool's packing ring, guarantee that the packing ring heats or refrigerates evenly, temperature acquisition module sets up in the packing ring and is close to temperature module, preferably, temperature acquisition module and temperature module set up in at least one-to-one correspondence, a temperature acquisition module gathers the regional of a temperature module place, the temperature of a measurement point forms the actual temperature data who carries to control module all the way, a plurality of measurement points form multichannel data, in this embodiment, carry out the reference mark with a plurality of measurement points, form 1..k way data, k is intelligent closestool's measurement point's reference mark.
The application also provides a temperature controller of the intelligent closestool, which is used for controlling the temperature of the intelligent closestool based on a temperature control system, wherein the temperature control system comprises a control module, a temperature module and a temperature acquisition module, the control module comprises an information transmission module, an information input terminal and a controller, the temperature acquisition module and the information transmission module are respectively connected with the controller, and the temperature controller specifically comprises the following steps:
step 1: inputting a set target temperature TS;
the information transmission module acquires target temperature data of the information input terminal and transmits a set target temperature TS to the controller.
Step 2: obtaining the actual temperature T R (n)(k)；
In the primary control process, one temperature acquisition module acquires the position of one temperature moduleThe temperature of a zone measuring point, i.e. the temperature of one measuring point, forms the actual temperature data T which is transmitted to the controller R (1) (1) the temperature acquisition module acquires the temperature of the measuring point of the region where the kth temperature module is positioned to form k paths of actual temperature data T which are transmitted to the controller R (1)(k)；
In the nth control process, a temperature acquisition module acquires the temperature of the area where the temperature module is located to form actual temperature data T which is transmitted to the controller R (n) (1), the kth temperature acquisition module acquires the temperature of the region where the kth temperature module is located to form k paths of actual temperature data T which are transmitted to the controller R (n)(k)；
In this embodiment, k is the reference number of the measurement point of the intelligent toilet.
In this embodiment, n is the number of times of the control process that the actual temperature reaches the target temperature, and the actual temperature of each measurement point reaches the set target temperature through the n times of the control process.
Step 3: determining basic parameters;
step 3.1: determining all weight parameters of PID;
w p (n+1)(k)＝w p (n)(k)+v p u(n)(k)e(n)(k)x1(n)(k)； (1)
w i (n+1)(k)＝w i (n)(k)+v i u(n)(k)e(n)(k)x 2 (n)(k)； (2)
w d (n+1)(k)＝w d (n)(k)+v d u(n)(k)e(n)(k)x 3 (n)(k)； (3)
w in formula (1) p Weighting parameters, w, of proportion item of neuron PID p (n) (k) is the PID proportional term weight parameter of the kth neuron in the nth control process, w p (n+1) (k) is the weight parameter of the PID proportion term of the kth path neuron in the n+1th control process, vp is the learning rate of the PID proportion term of the neuron, and x 1 (n) (k) is the proportional term, x in PID when neuron inputs 1 1 (n)(k)＝e(n)(k)-e(n-1)(k)；
W in formula (2) i Integral term weight parameter for neuron PID, w i (n) (k) is the PID integral term weight parameter of the kth neuron in the nth control process, w i (n+1) (k) is the weight parameter of the PID integral term of the kth neuron in the n+1th control process, vi is the learning rate of the PID integral term of the neuron, and x 2 (n) (k) is the integral term in PID, x when 2 is the neuron input 2 (n)(k)＝e(n)(k)+e(n-1)(k)+e(n-2)(k)；
W in formula (3) d Weight parameter, w, is the PID derivative term of neuron d (n) (k) the weight parameter, w, of the PID derivative term of the kth neuron in a certain n-time control process d (n+1) (k) the weight parameter of the PID derivative term of the kth neuron in a certain n+1 control process, vd is the learning rate of the PID derivative term of the neuron, and x 3 (n) (k) is the derivative term, x in PID when neuron input 3 3 (n)(k)＝e(n)(k)-2×e(n-1)(k)+e(n-2)(k)；
E (n) (k) in the formulas (1), (2) and (3) is a certain k-path actual temperature T in a certain n-time control process R (n) (k) and the set target temperature T S Error between e (n) (k) =t R (n)(k)-T S E (n-1) (k) is the error between the actual temperature of the kth path and the set target temperature in the previous n-1 control process, and e (n-2) (k) is the error between the actual temperature of the kth path and the set target temperature in the previous n-2 control process;
u (n) (k) in the formulas (1), (2) and (3) is the duty ratio of a certain k paths of PWM waves in a certain n-time control process; vp, vi and vd in equations (1), (2) and (3) are set values.
Step 3.2: determining a duty cycle increment;
duty cycle increment Δu (n) (k), Δu (n) (k) is the output per n-th path of the nth control process: deltau (n) (k) =KG (w) p ' n (k) x1 (n) (k) +wi ' (n) (k) x2 (n) (k) +wd ' (n) (k) x3 (n) (k)); KG in the formula is the total learning rate of the neurons, and KG is a set value.
w p ' n (k) is the proportion term weight duty ratio of the PID of the neuron,
w i ' n (k) is the PID integral term weight ratio of the neuron,
w d ' n (k) is the neuron PID derivative term weight ratio,
step 3.3: determining a duty cycle of the PWM wave;
through the back propagation algorithm of the neural network, u (n) (k) = Δu (n) (k) +Δu (n-1) (k) + a ；
The formula ua is an initial value of the PWM wave duty cycle setting.
The duty cycle of the PWM wave is the sum of the initial value and the duty cycle increment.
Step 4: the controller obtains the target temperature T S Actual temperature T R And (n) (k) is obtained, wherein u (n) (k) is the duty ratio of a PWM wave of a certain k paths in a certain n times of control process.
The controller controls the output PWM wave, and under the same conditions, such as the same materials, the direction of the PWM wave controls the temperature module to refrigerate or heat, the duty ratio of the PWM wave is respectively in direct proportion to the refrigerating or heating power of the temperature module, and the proportion is set according to the actual situation.
In this embodiment, the actual temperatures of the k-way measurement points in the primary control process are obtained and processed in combination with the target temperatures, and the actual temperatures of the k-way measurement points in the n-time control process are obtained and processed in combination with the target temperatures, so as to adjust the weight parameters of the k-way PID in the n-time control process and increase w p 'n' (k), wi '(n) (k), wd' (n) (k) are beneficial to ensuring the convergence of the whole neural network self-learning process, so that the controller generates multiple PWM waves with a certain duty ratio for controlling the refrigerating/heating module.
Step 5: the controller outputs PWM waves to the temperature module to enable the temperature of each measuring point to reach the target temperature.
Step 6: after each control flow is executed, the information transmission module reads information once to obtain the setting of the target temperature by the user, if updated target temperature data exist, the target temperature set previously is changed, the information transmission module transmits the updated target temperature data to the controller, the step 1 is executed, and if not, the next step is executed.
In this embodiment, KG and w are set by a back propagation algorithm of a neural network p 'n' (k), wi '(n) (k) and wd' (n) (k) realize the function of performing self-learning and temperature control on the neural network, are beneficial to ensuring the convergence of the whole self-learning process of the neural network, and can be well fitted through the self-learning capability of the neural network, thereby realizing good temperature control effect, extremely strong self-adaptive capability and better stability.
The application also provides a temperature controller of the intelligent closestool, which comprises:
a target temperature setting unit that sets a target temperature TS;
a temperature acquisition part for acquiring the actual temperature T of the intelligent toilet measurement point R (n)(k)；
A parameter setting unit for determining each weight parameter of PID, duty cycle increment and duty cycle of PWM wave;
an operation part for obtaining the target temperature T S Actual temperature T R (n) (k) obtaining the duty ratio of a certain k paths of PWM waves in a certain n control processes;
an output part for outputting PWM waves to the temperature module to enable the temperature of each measuring point to reach the target temperature;
and a data confirmation unit for receiving the updated target temperature data.
It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

Claims (10)

1. A temperature controller of intelligent closestool, its characterized in that: comprising
A target temperature setting unit that sets a target temperature TS;
a temperature acquisition part for acquiring the actual temperature T of the intelligent toilet measurement point R (n)(k)；
A parameter setting unit for determining each weight parameter of PID, duty cycle increment and duty cycle of PWM wave;
an operation part for obtaining the target temperature T S Actual temperature T R (n) (k) obtaining the duty ratio of a certain k paths of PWM waves in a certain n control processes;
an output part for outputting PWM waves to the temperature module to enable the temperature of each measuring point to reach the target temperature;
and a data confirmation unit for receiving the updated target temperature data.
2. The intelligent toilet temperature controller of claim 1, wherein:
determining all weight parameters of PID;
w p (n+1)(k)＝w p (n)(k)+v p u(n)(k)e(n)(k)x1(n)(k)；
w i (n+1)(k)＝w i (n)(k)+v i u(n)(k)e(n)(k)x 2 (n)(k)；
w d (n+1)(k)＝w d (n)(k)+v d u(n)(k)e(n)(k)x 3 (n)(k)；
wherein n is the number of times of control process that the actual temperature reaches the target temperature, k is the measuring point mark of the intelligent closestool, and w p (n) (k) is the PID proportional term weight parameter of the kth neuron in the nth control process, w p (n+1) (k) is the weight parameter of the PID proportion term of the kth path neuron in the n+1th control process, vp is the learning rate of the PID proportion term of the neuron, and x 1 (n) (k) is the proportional term in PID when neuron input 1;
w i (n) (k) is the PID integral term weight parameter of the kth neuron in the nth control process, w i (n+1) (k) is the PID integral term weight parameter, v of the kth neuron in the (n+1) th control process i Learning rate, x, for neuron PID integral term 2 (n) (k) is the integral term in the PID when neuron input 2;
w d (n) (k) the weight parameter, w, of the PID derivative term of the kth neuron in a certain n-time control process d (n+1) (k) the weight parameter, v, of the PID derivative term of the kth neuron in a certain n+1 control process d Learning rate, x, for neuron PID derivative 3 (n) (k) is the derivative term in PID when neuron input 3;
in the formula, e (n) (k) is the actual temperature T of a certain k paths in a certain n-time control process R (n) (k) and the set target temperature T S The error between the current temperature and the set target temperature in the k-th path in the previous n-1 control process is e (n-1) (k), the error between the current temperature and the set target temperature in the k-th path in the previous n-2 control process is e (n-2) (k), and the duty ratio of the PWM wave in the k-th path in the n-th control process is u (n) (k).
3. A temperature controller for a smart toilet according to claim 2, wherein: determining a duty cycle increment:
the duty ratio increment Deltau (n) (k) is the output quantity of each n paths in the nth control process, and the calculation formula of Deltau (n) (k) is as follows:
△u(n)(k)＝KG(w p '(n)(k)x1(n)(k)+wi'(n)(k)x2(n)(k)+wd'(n)(k)x3(n)(k))；
where KG is the total learning rate of the neurons;
w p ' n (k) is the proportion term weight duty ratio of the PID of the neuron,
w i ' n (k) is the PID integral term weight ratio of the neuron,
w d '(n) (k) is the neuron PID derivative term weight ratio,
4. a temperature controller for a smart toilet according to claim 3, wherein: determining the duty cycle of the PWM wave:
the duty cycle is set to u (n) (k):
u(n)(k)＝△u(n)(k)+△u(n-1)(k)+...+△u(1)(1)+u a ；
where ua is the initial value of the PWM wave duty cycle setting.
5. The intelligent toilet temperature controller of claim 1, wherein: when the neuron inputs 1, the proportional term in the PID is shown as follows:
x 1 (n)(k)＝e(n)(k)-e(n-1)(k)。
6. a temperature controller for a smart toilet according to claim 2, wherein: when the neuron inputs 2, the integral term in the PID is shown as follows:
x 2 (n)(k)＝e(n)(k)+e(n-1)(k)+e(n-2)(k)。
7. a temperature controller for a smart toilet according to claim 2, wherein: at neuron input 3, the derivative term in PID is shown as follows:
x 3 (n)(k)＝e(n)(k)-2×e(n-1)(k)+e(n-2)(k)。
8. a temperature controller for a smart toilet according to claim 2, wherein: the vp, vi and vd are set values.
9. A temperature controller for a smart toilet according to claim 3, wherein: and KG is a set value.
10. A temperature control method of an intelligent closestool is characterized in that: based on temperature control system controls intelligent closestool's temperature, temperature control system includes control module, temperature acquisition module, control module includes information transmission module, information input terminal and controller, temperature acquisition module, information transmission module are connected with the controller respectively, information transmission module sets up to the WIFI module for the data transmission of controller and information input terminal, information input terminal includes button or/and remote controller or/and cell-phone APP, information transmission module acquires information input terminal's temperature data, includes the following steps:
step 1: inputting a set target temperature TS;
the information transmission module acquires target temperature data of the information input terminal and transmits set target temperature TS to the controller;
step 2: obtaining the actual temperature T R (n)(k)；
In the primary control process in the step 2, a temperature acquisition module acquires the temperature of a measuring point where the temperature module is located to form actual temperature data T which is transmitted to the controller in one path R (1) (1) the kth temperature acquisition module acquires the temperature of the region where the kth temperature module is positioned to form k paths of actual temperature data T which are transmitted to the controller R (1) (k) in the nth control process, a temperature acquisition module acquires the temperature of the area where the temperature module is located to form actual temperature data T which is transmitted to the controller in one path R (n) (1), the kth temperature acquisition module acquires the temperature of the region where the kth temperature module is located to form k paths of actual temperature data T which are transmitted to the controller R (n)(k)；
Step 3: determining basic parameters;
the step 3 comprises the following steps:
step 3.1: determining all weight parameters of PID;
w p (n+1)(k)＝w p (n)(k)+v p u(n)(k)e(n)(k)x1(n)(k)；
w i (n+1)(k)＝w i (n)(k)+v i u(n)(k)e(n)(k)x 2 (n)(k)；
w d (n+1)(k)＝w d (n)(k)+v d u(n)(k)e(n)(k)x 3 (n)(k)；
wherein n is the number of times of control process that the actual temperature reaches the target temperature, k is the measuring point mark of the intelligent closestool, and w p (n) (k) is the PID proportional term weight parameter of the kth neuron in the nth control process, w p (n+1) (k) is the weight parameter of the PID proportion term of the kth path neuron in the n+1th control process, vp is the learning rate of the PID proportion term of the neuron, and x 1 (n) (k) is the proportional term in PID when neuron input 1;
w i (n) (k) is the PID integral term weight parameter of the kth neuron in the nth control process, w i (n+1) (k) is the PID integral term weight parameter, v of the kth neuron in the (n+1) th control process i Learning rate, x, for neuron PID integral term 2 (n) (k) is the integral term in the PID when neuron input 2;
w d (n) (k) the weight parameter, w, of the PID derivative term of the kth neuron in a certain n-time control process d (n+1) (k) the weight parameter, v, of the PID derivative term of the kth neuron in a certain n+1 control process d Learning rate, x, for neuron PID derivative 3 (n) (k) is the derivative term in PID when neuron input 3;
in the formula, e (n) (k) is the actual temperature T of a certain k paths in a certain n-time control process R (n) (k) and the set target temperature T S The error between the current temperature and the set target temperature in the k-th path in the previous n-1 control process is e (n-1) (k), the error between the current temperature and the set target temperature in the k-th path in the previous n-2 control process is e (n-2) (k), and the duty ratio of the PWM wave in the k-th path in the n-th control process is u (n) (k);
step 3.2: determining a duty cycle increment;
the duty ratio increment Deltau (n) (k) is the output quantity of each n paths in the nth control process, and the calculation formula of Deltau (n) (k) is as follows:
△u(n)(k)＝KG(w p ' n (k) x1 (n) (k) +wi ' (n) (k) x2 (n) (k) +wd ' (n) (k) x3 (n) (k)) wherein KG is the total learning rate of the neuron.
w p ' n (k) is the proportion term weight duty ratio of the PID of the neuron,
w i ' n (k) is the PID integral term weight ratio of the neuron,
w d ' n (k) is the neuron PID derivative term weight ratio,
step 3.3: determining a duty cycle of the PWM wave;
the duty cycle u (n) (k) is calculated as:
u(n)(k)＝△u(n)(k)+△u(n-1)(k)+...+△u(1)(1)+u a ；
wherein ua is an initial value set by the duty cycle of the PWM wave;
step 4: the controller obtains the target temperature T S Actual temperature T R (n) (k) obtaining the duty ratio of a certain k paths of PWM waves in a certain n control processes;
step 5: the controller outputs PWM waves to the temperature module to enable the temperature of each measuring point to reach the target temperature;
step 6: after each control flow execution is completed, the information transmission module reads data of an information input terminal for one time, and is used for acquiring the setting of a user on target temperature, if updated target temperature data exist, the information transmission module transmits the updated target temperature data to the controller, the step 1 is executed, and if not, the next step is executed;
the duty cycle of the PWM wave is proportional to the cooling or heating power of the temperature module, respectively.
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