WO2020001523A1 - 温度控制方法和温度控制装置 - Google Patents

温度控制方法和温度控制装置 Download PDF

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Publication number
WO2020001523A1
WO2020001523A1 PCT/CN2019/093216 CN2019093216W WO2020001523A1 WO 2020001523 A1 WO2020001523 A1 WO 2020001523A1 CN 2019093216 W CN2019093216 W CN 2019093216W WO 2020001523 A1 WO2020001523 A1 WO 2020001523A1
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temperature
temperature difference
fuzzy
control
value
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PCT/CN2019/093216
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English (en)
French (fr)
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于越
衣祝松
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京东方科技集团股份有限公司
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Priority to US16/648,706 priority Critical patent/US11485195B2/en
Publication of WO2020001523A1 publication Critical patent/WO2020001523A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00735—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models
    • B60H1/00807—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models the input being a specific way of measuring or calculating an air or coolant temperature
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00735—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/0073—Control systems or circuits characterised by particular algorithms or computational models, e.g. fuzzy logic or dynamic models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00814—Control systems or circuits characterised by their output, for controlling particular components of the heating, cooling or ventilating installation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00814—Control systems or circuits characterised by their output, for controlling particular components of the heating, cooling or ventilating installation
    • B60H1/00821—Control systems or circuits characterised by their output, for controlling particular components of the heating, cooling or ventilating installation the components being ventilating, air admitting or air distributing devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00814—Control systems or circuits characterised by their output, for controlling particular components of the heating, cooling or ventilating installation
    • B60H1/00878—Control systems or circuits characterised by their output, for controlling particular components of the heating, cooling or ventilating installation the components being temperature regulating devices
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/0275—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using fuzzy logic only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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
    • G05D23/1917—Control of temperature characterised by the use of electric means using digital means
    • 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
    • G05D23/1927—Control of temperature characterised by the use of electric means using a plurality of sensors

Definitions

  • the present disclosure relates to the field of intelligent control technology, and in particular, the present disclosure relates to a temperature control method and a temperature control device.
  • a first aspect of the fundamental disclosure provides a temperature control method including:
  • the temperature of the target environment is controlled by a variable universe fuzzy PID control algorithm.
  • the controlling the temperature of the target environment by the variable universe fuzzy PID control algorithm includes: calculating a first temperature difference change rate by a differential method; and according to the first temperature difference and the first temperature difference change Rate, the adjustment variable is determined by the variable universe fuzzy control algorithm; the real-time control parameter is determined by the PID control algorithm according to the first temperature difference and the adjustment variable; and a control signal is generated according to the real-time control parameter to control the temperature of the target environment.
  • the determining an adjustment variable by a variable universe fuzzy control algorithm according to the first temperature difference and the first temperature difference change rate includes: determining according to the first temperature difference and the first temperature difference change rate Input universe scaling factor and output universe scaling factor; fuzzify the first temperature difference and the first temperature difference change rate to obtain a fuzzy input amount; perform fuzzy inference according to fuzzy control rules to convert the fuzzy input amount into a fuzzy output amount; The blur output is processed to obtain the adjusted variable.
  • the input universe scaling factor is determined by the following piecewise proportional function:
  • the output scaling factor is determined by the following formula:
  • the performing fuzzy inference according to fuzzy control rules to convert a fuzzy input amount into a fuzzy output amount includes performing fuzzy inference using a Mamdani algorithm.
  • the performing a clearing process on the fuzzy output to obtain the adjusted variable includes: performing a clearing process on the fuzzy output using a centroid method.
  • determining the real-time control parameter by a PID control algorithm according to the first temperature difference and the adjustment variable includes: performing parameter tuning by the PID control algorithm to obtain the initial control parameter according to the first temperature difference ; Determine the real-time control parameters by accumulating the initial control parameters and the adjustment variables.
  • the temperature control method further comprising: in response to the absolute value of the first temperature difference not exceeding the first temperature difference threshold, calculating any one of the plurality of temperature detection points according to the temperature data The temperature difference between two adjacent temperature detection points is used as the second temperature difference; determining whether the absolute value of the second temperature difference exceeds the second temperature difference threshold; and in response to the absolute value of the second temperature difference exceeding the second temperature difference threshold, adjusting the target environment's air flow.
  • the temperature control method further comprising: calculating an average temperature value of the plurality of temperature detection points according to the temperature data, and an average temperature value between the average temperature value and a target temperature value. Before the difference, the setting of the target temperature value is accepted.
  • a temperature control device including:
  • a data acquisition module configured to collect temperature data of multiple temperature detection points in a target environment
  • a data processing module configured to calculate an average temperature value of the plurality of temperature detection points and a difference between the average temperature value and a target temperature value according to the temperature data, which is taken as a first temperature difference;
  • a judging module configured to judge whether an absolute value of the first temperature difference exceeds a first temperature difference threshold
  • the first temperature control module is configured to control the temperature of the target environment through a variable universe fuzzy PID control algorithm in response to the absolute value of the first temperature difference exceeding the first temperature difference threshold.
  • the first temperature control module includes: a differentiator configured to calculate a first temperature difference change rate by a differential method according to the first temperature difference; and a fuzzy controller configured to be based on the first The temperature difference and the first temperature difference change rate are determined by a variable universe fuzzy control algorithm; the PID controller is configured to determine the real-time control parameters by the PID control algorithm according to the first temperature difference and the adjustment variable and thereby generate a control signal for use in Control the temperature of the target environment.
  • a second temperature control module configured to: in response to that the absolute value of the first temperature difference does not exceed the first temperature difference threshold, calculate according to the temperature data The temperature difference between any two adjacent temperature detection points in the plurality of temperature detection points is used as the second temperature difference; determining whether the absolute value of the second temperature difference exceeds the second temperature difference threshold; and responding to the absolute value of the second temperature difference exceeding The second temperature difference threshold adjusts the air flow of the target environment.
  • the temperature control device further comprising an execution mechanism configured to adjust a target ambient temperature
  • the first temperature control module is further configured to utilize the execution through a variable universe fuzzy PID control algorithm The agency controls the temperature of the target environment.
  • the actuator includes a heating system and a refrigeration system.
  • the temperature control device further comprising: a target temperature setting module configured to receive the setting of the target temperature value; and a display module configured to display the temperature data and the average temperature value At least one of them.
  • a computer-readable storage medium having stored thereon computer-readable instructions that, when executed, implement a temperature control method according to some embodiments of the present disclosure.
  • FIG. 1 schematically illustrates a flowchart of a temperature control method according to some embodiments of the present disclosure
  • FIG. 2 schematically illustrates a flowchart of a temperature control method according to some embodiments of the present disclosure
  • FIG. 3 schematically illustrates a principle of variable universe fuzzy PID control in a temperature control method according to some embodiments of the present disclosure
  • FIG. 5 illustrates a membership function function curve of an input-output fuzzy universe in a variable universe fuzzy PID control process in a temperature control method according to some embodiments of the present disclosure
  • FIG. 6 schematically illustrates a flowchart of a temperature control method according to some embodiments of the present disclosure.
  • FIG. 7 schematically illustrates a structural block diagram of a temperature control device according to some embodiments of the present disclosure.
  • the environmental temperature control method includes PID (Proportional-Integral-Derivative, proportional-integral-derivative) control, fuzzy control, and the like.
  • PID control has been applied earlier in environmental temperature control, but due to the characteristics of non-linear, hysteresis, time-varying, uncertainty, etc., the conventional PID's effect on environmental temperature is not ideal; fuzzy control does not need to be established accurately
  • the mathematical model is suitable for hysteresis and nonlinear systems, but it has problems of static difference and low control accuracy. Therefore, there are certain defects in using the above control methods to control the ambient temperature alone.
  • the present invention uses a variable universe fuzzy control method combined with a PID control method to control the temperature of the target environment (such as the interior space of a car), in order to overcome the disadvantages of the conventional fuzzy control method and PID when used alone, thereby improving the target.
  • the accuracy of temperature control in the environment enhances the user experience.
  • the variable universe fuzzy PID control according to the present disclosure is a hybrid control method that combines fuzzy control and PID control to complement each other's advantages, which is not only applicable to the control of non-linear systems and reduces the static difference;
  • the variable domain is equivalent to increasing the density of control rules and further improving the control accuracy without changing the fuzzy control rules.
  • FIG. 1 schematically illustrates a flowchart of a temperature control method according to some embodiments of the present disclosure. As shown in FIG. 1, the temperature control method includes the following steps S110-S140. Each step is explained in detail below.
  • S110 a data collection step: collecting temperature data of multiple temperature detection points in a target environment.
  • the target environment can be any environment or space, such as inside a car, inside a room.
  • a temperature in a target environment can be sensed by a temperature sensor.
  • a plurality of temperature detection points may be evenly distributed in the target environment, so as to obtain the overall temperature data in the entire target environment more comprehensively and accurately.
  • the temperature detection points can be set according to the importance of different locations in the target environment or the subjective needs of the user.
  • the temperature data may include the temperature value sensed by the temperature sensor, and may also include other temperature-related data, such as the humidity value in the environment.
  • Data processing step calculating an average temperature value of the plurality of temperature detection points and a difference between the average temperature value and a target temperature value according to the collected temperature data, which is taken as a first temperature difference.
  • step S120 the average temperature in the target environment is set as a control object, so when the average temperature deviates from the target temperature, control of the average temperature or a temperature difference between the average temperature and the target temperature may be started so that the average temperature approaches The absolute value of the target temperature or the temperature difference between the two, that is, the first temperature difference, is small or approaches zero.
  • the reason why the average temperature is selected as the control object is that the average temperature can reflect the temperature situation in the target environment as a whole, so it can more comprehensively represent the temperature levels in different locations, and avoid errors that are partial.
  • the control object is not limited to the average temperature.
  • the geometric center point in the target environment or the temperature at the most important position for the user may be selected as the control object.
  • the target temperature value may be preset, for example, it may be set by the user subjectively or automatically set according to objective factors.
  • the objective factors may include, for example, the geographic location where the target environment is located, the season, and climatic conditions.
  • step S130 Judging step: judging whether the absolute value of the first temperature difference exceeds the first temperature difference threshold, if yes, go to step S140; otherwise, return to step S110.
  • the first temperature difference threshold may be a preset positive number, which may be set according to specific conditions and requirements for temperature control.
  • Step S130 actually compares the absolute value of the first temperature difference with the first temperature difference threshold, and then performs temperature control according to the comparison result, that is, when the absolute value of the first temperature difference is greater than the first temperature difference threshold, the method proceeds to step S140-temperature measurement. Control; when the absolute value of the first temperature difference is less than or equal to the first temperature difference threshold, the temperature control of the target environment is not started, because this is that the average temperature value is sufficiently close to the target temperature value (or that the temperature difference between the two is sufficiently small, the average The temperature value does not significantly deviate from the target temperature value), and it is not necessary to perform temperature control.
  • the method can return to step S110-restart the collection of temperature data and start cyclic control to achieve real-time temperature control.
  • the target environment temperature may also be controlled uniformly, that is, the temperature of two adjacent temperature detection points with a large temperature difference is controlled so that The temperature distribution is more uniform.
  • temperature uniformity control please refer to FIG. 6 and its corresponding description.
  • Temperature control step In response to that the absolute value of the first temperature difference exceeds the first temperature difference threshold, the temperature of the target environment is controlled by a variable universe fuzzy PID control algorithm.
  • variable universe fuzzy PID control algorithm refers to a hybrid control algorithm that combines variable universe fuzzy control and PID control.
  • the variable universe refers to the fuzzy universe of input and / or fuzzy universe of fuzzy control.
  • the input and output fuzzy universe can be scaled by the input universe scaling factor and the output universe scaling factor, respectively.
  • the density of the control rules can be indirectly increased, thereby improving the control accuracy.
  • the target environment can more accurately reflect the temperature conditions of different locations in the current environment; and based on the temperature data of multiple temperature detection points, the determined average temperature value is selected as the controlled
  • the object can reflect the temperature situation in the target environment as a whole, so it can more comprehensively represent the temperature levels in different locations, avoid the error of generalization, achieve precise and uniform control of the ambient temperature, and enhance the user experience.
  • variable universe fuzzy PID control algorithm when using the variable universe fuzzy PID control algorithm to control the ambient temperature, because it combines the advantages of fuzzy control and PID control, it can not only improve the response speed and control accuracy, but also be applicable to non-linear, hysteretic.
  • the time-varying and uncertain system has a wider scope of application.
  • by expanding and contracting the universe it can indirectly increase the density of control rules without changing the control rules, thereby improving the control accuracy.
  • Fig. 2 shows a flowchart of step S140-controlling the temperature of the target environment by a variable universe fuzzy PID control algorithm-in the temperature control method shown in FIG. 1 according to some embodiments of the present disclosure.
  • the following describes a specific manner of performing ambient temperature control according to variable universe fuzzy PID control according to some embodiments of the present disclosure with reference to FIG. 2.
  • controlling the temperature of the target environment by the variable universe fuzzy PID control algorithm includes steps S241 to S244.
  • variable universe fuzzy PID control there are mainly two control methods, namely variable universe fuzzy control and PID control.
  • the input amount of these two control algorithms can be set to the first temperature difference and the first temperature difference change rate.
  • the first temperature difference change rate which is a characteristic of how fast the first temperature difference changes, by a differential method.
  • the input amount is the first temperature difference and the first temperature difference change rate
  • the output amount is an adjustment variable, which is used to adjust or modify the initial control parameter determination output by the PID control algorithm to obtain Control parameters in real time.
  • the correction method can be performed by accumulation.
  • the PID control algorithm determines the initial control parameters through parameter tuning, and then adjusts the initial control parameters by adjusting variables to obtain real-time control parameters.
  • step S223 may include: performing parameter tuning through a PID control algorithm to obtain an initial control parameter according to the first temperature difference; and determining the real-time control parameter by accumulating the initial control parameter and the adjustment variable.
  • the formula (1) below can be referred to.
  • variable universe fuzzy PID controller itself has a parameter self-tuning function and a good self-adaptation ability, it can continuously detect the first temperature difference and the first temperature difference change rate during operation and adjust the parameter adjustment amount according to the fuzzy control rules. On-line modification to meet different control parameters of different first temperature difference and first temperature difference change rate, so that the controlled object has good dynamic and static performance, which makes the system have strong self-adaptation ability, and can be outside When disturbance occurs, it will recover in time, so as not to cause excessive oscillation.
  • a control signal can be generated according to the real-time control parameters to adjust the controlled object, such as the average temperature of the target environment.
  • the specific adjustment method of the average temperature can control the temperature adjustment device to adjust the average temperature in real time according to the control parameters.
  • the control signal may be a voltage signal or a current signal for controlling the temperature adjustment device.
  • the temperature adjustment device may include: a hot air heating system and an air conditioning refrigeration system.
  • controlling the temperature adjustment device to adjust the temperature of the current environment according to the control signal may include controlling the hot air heating system to heat according to the control signal to increase the temperature of the current environment, or controlling the air conditioning refrigeration system to perform cooling according to the control signal to reduce The temperature of the current environment. Therefore, the difference between the average temperature value and the target temperature value of multiple temperature detection points in the current environment can be continuously reduced until the difference is within the first temperature difference threshold range.
  • FIG. 3 illustrates a schematic diagram of a temperature control method according to some embodiments of the present disclosure.
  • the variable universe fuzzy PID controller (or algorithm) is composed of a fuzzy controller (or algorithm) and a PID controller (or algorithm).
  • r is the target temperature value
  • y is the average temperature value of each temperature detection point in the target environment
  • e and ec are the first temperature differences (that is, the difference between the average temperature value and the target temperature value, yr or ry).
  • k e and k ec are quantization factors of the first temperature difference e and the first temperature difference change rate ec, respectively, and are used to match the input basic domain and input fuzzy universe
  • l p , l i , l d is the scale factor of ⁇ K p , ⁇ K i , ⁇ K d , respectively, used to match the basic domain of output and fuzzy domain of output
  • the input scaling factor and output scaling factor can be used to achieve the input by changing the quantization factor and the scaling factor equivalent, respectively.
  • output scaling of fuzzy universe can be used to achieve the input by changing the quantization factor and the scaling factor equivalent, respectively.
  • variable-indication fuzzy PID controller or module includes a fuzzy controller and a PID controller.
  • the input amount of the fuzzy controller is the first temperature difference e and the first temperature difference change rate ec
  • the output amount is an adjustment variable ⁇ K p , ⁇ K i , used to adjust the three control parameters K p , K i , K d in the PID controller.
  • the input of the PID controller is the first temperature difference e and the adjustment variables ⁇ K p , ⁇ K i , ⁇ K d , and the output is used to control the actuator (that is, the temperature adjustment device, such as the heating system and the cooling system) to be controlled
  • the actuator that is, the temperature adjustment device, such as the heating system and the cooling system
  • a control signal u to be adjusted by the object (the current ambient temperature or average temperature).
  • the control parameters K p , K i , K d represent proportional, integral, and differential coefficients, respectively.
  • the PID controller In the process of the PID controller generating the control signal u, the PID controller first generates an initial control signal according to the first temperature difference e, and then adjusts the control signal u in real time according to the three control parameters K p , K i , K d , so that it can be adjusted A more accurate control signal u is obtained; the adjustment of the three real-time control parameters K p , K i , K d can be performed by the following formula:
  • K p0 , K i0 , and K d0 are preset PID initial control parameters.
  • FIG. 4 shows a flowchart of a step S222 of variable universe fuzzy control in the temperature control method shown in FIG. 2 according to some embodiments of the present disclosure.
  • step S242 shown in FIG. 2-determining an adjustment variable through a fuzzy control algorithm according to the first temperature difference and the first temperature difference change rate include S4421-S4424.
  • the input scaling factor increases the input amount by increasing the quantization factor, which is equivalent to compressing the input fuzzy universe, and the closer the input amount is to zero, the input scaling The greater the factor, the greater the quantization factor.
  • the quantization factor is divided by the input scaling factor.
  • the input scaling factor may be a scaling factor based on a function or a scaling factor based on a fuzzy inference.
  • the input scaling factor may be a piecewise proportional function, and the piecewise threshold of the piecewise proportional function includes a temperature difference threshold and a temperature difference change rate threshold.
  • the scaling factor of the output universe can be correspondingly reduced according to the degree of increase of the quantization factor, which is equivalent to compressing the fuzzy universe of output.
  • the scale factor is multiplied by the output scaling factor.
  • the output scaling factor may be a scaling factor based on a function or a scaling factor based on a fuzzy inference.
  • S4422 Obfuscate the first temperature difference and the first temperature difference change rate to obtain a fuzzy input amount, and use the input universe scaling factor to scale the input fuzzy universe.
  • a fuzzy membership function can be designed, and the precise input values e and ec are converted into fuzzy input quantities to obtain the fuzzy input quantities E and EC.
  • the shape of the fuzzy membership function can be a triangular membership function and a Gaussian type. Membership functions, etc.
  • the basic universe of interest of the first temperature difference e and the first temperature difference change rate ec may be [-10,10], [-1,1] and the basic universe of output variables ⁇ K p , ⁇ K i , ⁇ K d may be Select [-1.5, 1.5], [-0.015, 0.015], [-3, 3], select the fuzzy universe of input and output as [-3, 3], and use 7 fuzzy subsets to input and
  • the output fuzzy universe is divided, and the membership function uses a combination of triangular and Gaussian.
  • the fuzzy language values corresponding to the fuzzy subset are ⁇ NB (negative large), NM (negative middle), NS (negative small), and ZE ( Zero), PS (positive small), PM (positive middle), PB (positive large) ⁇ .
  • first temperature difference e and first temperature difference change rate ec are converted into fuzzy universes, and their clear values are converted into fuzzy language values, so that fuzzy inference can be performed in subsequent steps to Realize the transition from fuzzy input to fuzzy output.
  • S4423 Perform fuzzy inference according to the fuzzy control rule to convert the fuzzy input amount into the fuzzy output amount, and use the output universe scaling factor to achieve the scaling of the output fuzzy universe;
  • fuzzy inference can be performed on parameter adjustments according to the fuzzy control rules corresponding to the input fuzzy universe and the output fuzzy universe.
  • the fuzzy control rules can be set according to actual application scenarios. For example, preliminary fuzzy control rules can be obtained based on existing empirical models or empirical data in the actual application scenarios, and the preliminary fuzzy control rules can be adaptively adapted according to actual needs. Adjust to get fuzzy control rules.
  • the general principle of fuzzy inference is as follows: the input fuzzy universe includes multiple input fuzzy subsets with numerical magnitude order, and the output fuzzy universe includes multiple output fuzzy subsets with numerical magnitude order; each input fuzzy subset corresponds to one The fuzzy linguistic value of the first temperature difference and the fuzzy linguistic value of the first temperature difference change rate, each output fuzzy universe includes a fuzzy linguistic value of a parameter adjustment amount; the fuzzy linguistic value of each first temperature difference and the first temperature difference change
  • the fuzzy language value of the rate has a certain fuzzy relationship with the fuzzy language value of a parameter adjustment amount. According to the fuzzy language value of the first temperature difference and the fuzzy language value of the first temperature difference change rate and the fuzzy relationship, the parameter adjustment can be determined. The amount of fuzzy language value. More specific setting methods and fuzzy control rules will be detailed in subsequent examples.
  • the fuzzy inference of the parameter adjustment amount according to the fuzzy control rules corresponding to the input fuzzy universe and the output fuzzy universe may include: first determining the first fuzzy difference and the input fuzzy subset corresponding to the first temperature difference change rate For example, the fuzzy language value corresponding to the first temperature difference and the fuzzy language value corresponding to the first temperature difference change rate are determined according to the corresponding input fuzzy subset; second, the fuzzy language value corresponding to the determined first temperature difference and the first temperature difference change are determined; The fuzzy language value corresponding to the rate determines the fuzzy language value corresponding to the parameter adjustment amount.
  • variable universe fuzzy control algorithm includes two parts: variable universe fuzzy control and PID control.
  • FIG. 4 actually shows a flowchart of the variable universe fuzzy control.
  • the input universe scaling factor and output universe scaling factor in step S4421 may be designed or determined by the following formulas (2)-(4).
  • a piecewise functional scaling factor can be designed based on the traditional proportional scaling factor.
  • the input fuzzy universe can be conditionally scaled. While ensuring that the control accuracy is not affected, the amount of calculation can be reduced, thereby improving the response speed and real-time control.
  • the scaling factor can be expressed as:
  • ⁇ (e) represents the input universe expansion factor of the first temperature difference e
  • ⁇ (ec) represents the input universe expansion factor of the first temperature difference change rate ec
  • ⁇ i represents the segmentation threshold, that is, the temperature difference threshold (that is, the first The threshold value of the absolute value of the temperature difference e) or the threshold value of the temperature difference change rate (that is, the threshold value of the absolute value of the first temperature difference change rate ec).
  • ⁇ 1 1 in the expression (2)
  • ⁇ 1 represents the temperature difference threshold
  • i 2 in the expression (3)
  • ⁇ 2 is the threshold representing the temperature difference change rate.
  • ⁇ 1 and ⁇ 2 are positive, ⁇ 1 may be a boundary of the first temperature difference threshold range of absolute values.
  • ⁇ i and ⁇ i represent the input sensitivity adjustment parameters. The larger the former value and the smaller the latter value, the more sensitive the controller is to changes in the input, which is more conducive to reducing the influence of the dead zone and improving the control accuracy.
  • the sizes of ⁇ 1 and ⁇ 2 determine the control accuracy and the amount of calculation.
  • the specific values of ⁇ 1 and ⁇ 2 can be set according to actual needs.
  • ⁇ 1 10 -5
  • variable universe fuzzy PID control the piecewise proportional function is used as the input scaling factor, so that when the input quantity is in a numerical range, the fuzzy universe of the input quantity is scaled by the input scaling factor, which guarantees the accuracy of the control.
  • unnecessary calculations caused by universe scaling are avoided on the entire universe, thereby reducing the computational burden of microcontrollers in the current environment; as the amount of calculation is greatly reduced, the response speed is greatly improved, which increases Real-time temperature control.
  • the scaling factor can be changed by output scaling factor ⁇ (e, ec) in a large range, so as to achieve And output scaling of fuzzy universe.
  • step S4423- fuzzy inference is performed according to the fuzzy control rule to convert the fuzzy input amount into the fuzzy output amount, that is, the specific implementation manner of adjusting the variables ⁇ K p , ⁇ K i , ⁇ K d , for example, a Mamdani algorithm may be adopted Perform fuzzy reasoning.
  • Mamdani algorithm is used for fuzzy inference.
  • the inference algorithm is simple and feasible.
  • each fuzzy language value corresponds to a set of output quantities ⁇ K p , ⁇ K The value of i and ⁇ K d .
  • the membership function based on the division method can be a triangular function or a Gaussian function.
  • the fuzzy language values corresponding to the 7 fuzzy subsets are NB (negative large), NM (negative middle), NS (negative small), and ZE. (Zero), PS (positive small), PM (center), PB (positive).
  • FIG. 5 illustrates a membership function curve of an input and output fuzzy universe in a temperature control method according to some embodiments of the present disclosure.
  • the membership function curve shown in FIG. 5 shows a fuzzy division of the input fuzzy universe and the output fuzzy universe, where ,
  • the horizontal axis represents the values of e and ec, and the vertical axis represents the degree of membership of e and ec to each fuzzy language value.
  • the output quantities ⁇ K p , ⁇ K i , ⁇ K d of the fuzzy controller can be controlled.
  • the above-mentioned fuzzy control rules are specifically: when the first temperature difference e is large, in order to speed up the response speed, the real-time control parameter K p of the PID controller is increased, and to avoid overshoot, the real-time control parameter K d is appropriately increased as Decrease the integral saturation and the real-time control parameter K i ;
  • the first temperature difference e is a medium value, in order to reduce the overshoot, reduce K p , in order to enhance the stability of the system, increase K i , in order to ensure the response speed, Make the value of K d moderate;
  • the first temperature difference e is small, in order to ensure the stability of the system, reduce K p , and to increase the steady-state accuracy of the system, increase K i appropriately;
  • the first temperature difference change rate ec is large, K d is decreased, and when ec is small, K d is decreased.
  • a fuzzy control rule table regarding fuzzy inputs to fuzzy outputs as shown in Table 1 can be obtained.
  • E and EC represent the first temperature difference e, the first The fuzzy language variable corresponding to the temperature difference change rate ec.
  • the fuzzy language values of the corresponding output quantities ⁇ K p , ⁇ K i , ⁇ K d may be determined according to changes in the input temperature first temperature difference e and the first temperature difference change rate ec.
  • the fuzzy language values of the fuzzy language variables E and EC are both NB
  • the values of the output quantities ⁇ K p , ⁇ K i , and ⁇ K d are the fuzzy language values PB, NB, and PS, respectively.
  • step S4424-clearing process after determining the fuzzy language values of the parameter adjustment amounts ⁇ K p , ⁇ K i , ⁇ K d , the fuzzy language value (ie, the fuzzy output amount) can be clarified by the gravity center method deal with.
  • the real-time control parameters K p , K i , K d can be adjusted to obtain the real-time control parameters K p , K i of the PID controller.
  • a control signal u can be output to realize the control of the actuator and enable the actuator to adjust the controlled object.
  • the center of gravity method is reasonable and intuitive, and can better reflect the information contained in the fuzzy set.
  • FIG. 6 illustrates a flowchart of a temperature control method according to some embodiments of the present disclosure.
  • the temperature control method is similar to the temperature control method shown in FIG. 1, that is, steps S610-S640 are basically the same as steps S110-S140 of FIG.
  • the method further includes the following steps:
  • S660 Determine whether the absolute value of the second temperature difference exceeds the second temperature difference threshold. If it exceeds, go to S670, otherwise go to S610;
  • the temperature control method shown in FIG. 6 actually increases the function of controlling the uniformity of the temperature distribution of each location in the target environment, that is, suppressing excessive temperature differences between adjacent detection points, so that the temperature difference between different locations in the target environment Reduced, uniform hot and cold, to prevent the unbalanced temperature distribution caused to the user's physical and mental effects, thereby enhancing the user experience
  • the temperature uniformity control in FIG. 6 is described below with a specific example. If there are three temperature detection points A, B, and C adjacent to each other in the target environment, use T A (t), T B (t), and T C (t) to represent the temperature data of the three temperature detection points at time t, respectively. , Determine the second temperature difference between A and B, between A and C, and between B and C at time t [T A (t) -T B (t)], [T B (t) -T C ( t)], [T C (t) -T A (t)].
  • the second temperature difference threshold is set to 1, that is, the second temperature difference range is [-1, 1] (unit: ° C), when any one of the second temperature differences is greater than 1 ° C or less than -1 ° C, That is
  • every second temperature difference belongs to [-1, 1], that is, at the same time,
  • the temperature control method shown in FIG. 6 has the following beneficial effects:
  • the embodiment of the present disclosure can determine between any two adjacent temperature detection points. And adjust the air flow of the current environment accordingly according to the magnitude of the second temperature difference, so that the temperature near at least two temperature detection points becomes uniform through the air flow, thereby controlling the entire temperature field of the current environment , Improve the uniformity of temperature distribution in the entire temperature field, and optionally, can improve the user experience.
  • FIG. 7 shows a structural block diagram of a temperature control device according to some embodiments of the present disclosure.
  • the temperature control device 700 may include:
  • a data acquisition module 710 configured to collect temperature data of a plurality of temperature detection points in a target environment
  • the data processing module 720 is configured to calculate an average temperature value of the plurality of temperature detection points and a difference between the average temperature value and a target temperature value according to the temperature data, which is used as a first temperature difference;
  • a determining module 730 configured to determine whether an absolute value of the first temperature difference exceeds a first temperature difference threshold
  • the first temperature control module 740 is configured to control the temperature of the target environment through a variable universe fuzzy PID control algorithm in response to the absolute value of the first temperature difference exceeding the first temperature difference threshold.
  • the temperature control device 700 further includes:
  • the second temperature control module 750 is configured to:
  • a temperature difference between any two adjacent temperature detection points in the plurality of temperature detection points is calculated as the second temperature difference according to the temperature data.
  • the air flow of the target environment is adjusted.
  • the temperature control device 700 may further include an actuator 760 configured to adjust the target ambient temperature, and the first temperature control module 740 is further configured to utilize the variable universe fuzzy PID control algorithm to utilize The actuator controls the temperature of the target environment.
  • the actuator may include a heating system and a refrigeration system.
  • the temperature control device 700 may further include a target temperature setting module 770 configured to receive a setting of the target temperature value, and a display module 780 configured to display the target temperature. At least one of temperature data and average temperature value.
  • the target temperature setting module 770 may be an input device for receiving a target temperature value input by a user; the display module 780 may include a liquid crystal display or a touch display.
  • the first temperature control module 740 includes:
  • a differentiator 741 configured to calculate a first temperature difference change rate by a differential method according to the first temperature difference
  • the fuzzy controller 742 is configured to determine an adjustment variable through a variable universe fuzzy control algorithm according to the first temperature difference and the first temperature difference change rate;
  • the PID controller 743 is configured to determine a real-time control parameter through a PID control algorithm according to the first temperature difference and the adjustment variable, and thereby generate a control signal for controlling the temperature of the target environment.
  • temperature control device 700 may be used to execute the temperature control method according to some embodiments of the present disclosure described above.
  • the implementation principles and corresponding beneficial effects are similar. I will not repeat them here.
  • An embodiment of the present disclosure further provides a computer-readable storage medium on which computer-readable instructions are stored.
  • the computer-readable instructions are executed to implement a temperature control method according to some embodiments of the present disclosure.
  • first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as “first” and “second” may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of “plurality” is at least two, for example, two, three, etc., unless it is specifically and specifically defined otherwise.
  • Any process or method description in a flowchart or otherwise described herein can be understood as representing a module, fragment, or portion of code that includes one or more executable instructions for implementing steps of a custom logic function or process, And the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed (including in a substantially simultaneous manner or in the reverse order according to the functions involved, which should be performed by It is understood by those skilled in the art to which the embodiments of the present disclosure belong.
  • a sequenced list of executable instructions that can be considered to implement a logical function can be embodied in any computer-readable medium,
  • the instruction execution system, device, or device such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from the instruction execution system, device, or device), or combine these instruction execution systems, devices, or devices Or equipment.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Computer-readable media may include, for example, the following: electrical connections (electronic devices) with one or more wirings, portable computer disk cartridges (magnetic devices), random access memories (Random Access Memory), Read-only memory (Read Only Memory), erasable and editable read-only memory (Erasable, Programmable, Read Only Memory) or flash memory, optical fiber devices, and compact disc read-only memory (Compact Disc Read Only Memory).
  • the computer-readable medium may even be paper or other suitable medium on which the program can be printed, because, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other suitable Processing to obtain the program electronically and then store it in computer memory.
  • portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
  • multiple steps or methods may be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if it is implemented in hardware, it may be implemented in any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a suitable combination Application-specific integrated circuits for logic gate circuits, Programmable Gate Arrays, Field Programmable Gate Arrays, etc.
  • each functional unit in each embodiment of the present disclosure may be integrated into one processing module, or each unit may exist separately physically, or two or more units may be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or software functional modules. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

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Abstract

公开了一种温度控制方法，包括：采集目标环境中多个温度检测点的温度数据；根据所述温度数据，计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差，其被作为第一温差；判断第一温差的绝对值是否超过第一温差阈值；响应于所述第一温差的绝对值超过第一温差阈值，通过变论域模糊PID控制算法控制目标环境的温度。

Description

温度控制方法和温度控制装置
相关申请
本申请要求2018年6月29日提交的申请号为201810716531.9的中国专利申请的优先权，该专利申请的所有内容通过引用合并于此。
技术领域
本公开涉及智能控制技术领域，具体而言，本公开涉及温度控制方法和温度控制装置。
背景技术
在当代社会中，汽车的使用越来越普遍，车内温度对于驾驶人员和乘客的驾驶和乘坐体验和安全都有一定影响，尤其是夏天车内温度过高对小孩和患有高血压的老人十分不利，因此需要对车内温度进行控制。
目前一般的车辆均配置有空调系统以控制车内温度，但通常只通过一个温度传感器对局部空间的温度进行检测和控制，控制精度较低，不能很好地满足乘客的乘坐体验。
发明内容
根本公开的第一方面，提供一种温度控制方法，包括：
采集目标环境中多个温度检测点的温度数据；
根据所述温度数据，计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差，其被作为第一温差，
判断第一温差的绝对值是否超过第一温差阈值；
响应于所述第一温差的绝对值超过第一温差阈值，通过变论域模糊PID控制算法控制目标环境的温度。
在根据本公开的温度控制方法的一些实施例中，所述通过变论域模糊PID控制算法控制目标环境的温度包括：通过微分法计算第一温差变化率；根据第一温差和第一温差变化率，通过变论域模糊控制算法确定调整变量；根据第一温差和调整变量，通过PID控制算法确定实时控制参数；根据所述实时控制参数生成控制信号以控制目标环境 的温度。
在根据本公开的温度控制方法的一些实施例中，所述根据第一温差和第一温差变化率通过变论域模糊控制算法确定调整变量包括：根据第一温差和第一温差变化率，确定输入论域伸缩因子和输出论域伸缩因子；对第一温差和第一温差变化率进行模糊化处理以得到模糊输入量；根据模糊控制规则进行模糊推理以将模糊输入量转换成模糊输出量；对模糊输出量进行清晰化处理以得到所述调整变量。
在根据本公开的温度控制方法的一些实施例中，所述输入论域伸缩因子通过下述分段比例函数确定：
Figure PCTCN2019093216-appb-000001
Figure PCTCN2019093216-appb-000002
其中：e和ec分别表示第一温差和第一温差变化率，其基本论域分别为X e＝[-x emax，x emax]和X ec＝[-x ecmax，x ecmax]，θ i为分段阈值，参数λ i、ε i为敏感度调节参数，i＝1，2。
在根据本公开的温度控制方法的一些实施例中，所述输出伸缩因子通过下式确定：
Figure PCTCN2019093216-appb-000003
其中：e和ec分别表示第一温差和第一温差变化率，其基本论域分别为X e＝[-x emax，x emax]和X ec＝[-x ecmax，x ecmax]。
在根据本公开的温度控制方法的一些实施例中，所述根据模糊控制规则进行模糊推理以将模糊输入量转换成模糊输出量包括采用Mamdani算法进行模糊推理。
在根据本公开的温度控制方法的一些实施例中，所述对模糊输出量进行清晰化处理以得到所述调整变量包括：采用重心法对模糊输出量进行清晰化处理。
在根据本公开的温度控制方法的一些实施例中，所述根据第一温差和调整变量通过PID控制算法确定实时控制参数包括：根据第一温 差，通过PID控制算法进行参数整定以获得初始控制参数；通过将初始控制参数与调整变量进行累加确定实时控制参数。
在根据本公开的温度控制方法的一些实施例中，进一步包括：响应于所述第一温差的绝对值未超过第一温差阈值，根据所述温度数据，计算所述多个温度检测点中任意两个相邻温度检测点的之间的温差，作为第二温差；判定第二温差的绝对值是否超过第二温差阈值；响应于第二温差的绝对值超过第二温差阈值，调节目标环境的空气流动。
在根据本公开的温度控制方法的一些实施例中，进一步包括：在所述根据所述温度数据计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差之前，接收目标温度值的设定。
根据本公开的第二方面，提供一种温度控制装置，包括：
数据采集模块，配置成采集目标环境中多个温度检测点的温度数据；
数据处理模块，配置成根据所述温度数据，计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差，其被作为第一温差；
判断模块，配置成判断第一温差的绝对值是否超过第一温差阈值；；以及
第一温度控制模块，配置成响应于所述第一温差的绝对值超过第一温差阈值，通过变论域模糊PID控制算法控制目标环境的温度。
在根据本发明的温度控制装置的一些实施例中，第一温度控制模块包括：微分器，配置成根据第一温差，通过微分法计算第一温差变化率；模糊控制器，配置成根据第一温差和第一温差变化率，通过变论域模糊控制算法确定调整变量；PID控制器，配置成根据第一温差和调整变量，通过PID控制算法确定实时控制参数并且由此生成控制信号以用于控制目标环境的温度。
在根据本发明的温度控制装置的一些实施例中，进一步包括：第二温度控制模块，配置成：响应于所述第一温差的绝对值未超过第一温差阈值，根据所述温度数据，计算所述多个温度检测点中任意两个相邻温度检测点的之间的温差，作为第二温差；判断第二温差的绝对值是否超过第二温差阈值；响应于第二温差的绝对值超过第二温差阈值，调节目标环境的空气流动。
在根据本发明的温度控制装置的一些实施例中，进一步包括配置成调节目标环境温度的执行机构，并且所述第一温度控制模块进一步配置成：通过变论域模糊PID控制算法利用所述执行机构控制目标环境的温度。
在根据本发明的温度控制装置的一些实施例中，所述执行机构包括加热系统和制冷系统。
在根据本发明的温度控制装置的一些实施例中，进一步包括：目标温度设定模块，配置成接收所述目标温度值的设定；以及显示模块，配置成显示所述温度数据和平均温度值中至少之一。
根据本公开的第三方面，提供一种计算机可读存储介质，其上存储有计算机可读指令，所述计算机可读指令在被执行时实现根据本公开一些实施例的温度控制方法。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对本申请实施例描述中所需要使用的附图作简单地介绍。
图1示意性示出根据本公开一些实施例的温度控制方法的流程图；
图2示意性示出根据本公开一些实施例的温度控制方法的流程图；
图3示意性示出根据本公开一些实施例的温度控制方法中变论域模糊PID控制原理图；
图4示意性示出根据本公开一些实施例的温度控制方法的流程图；
图5示出根据本公开一些实施例的温度控制方法中变论域模糊PID控制过程中输入输出模糊论域的隶属度函数曲线图；
图6示意性示出根据本公开一些实施例的温度控制方法的流程图；以及
图7示意性示出根据本公开一些实施例的温度控制装置的结构框图。
具体实施方式
下面详细描述本公开的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，仅用 于解释本公开，而不能解释为对本公开的限制。
本技术领域技术人员可以理解，除非特意声明，这里使用的单数形式“一”、“一个”和“该”也可包括复数形式。应该进一步理解的是，本公开的说明书中使用的措辞“包括”是指存在所述特征、整数、步骤、操作、元件和/或组件，但是并不排除存在或添加一个或多个其他特征、整数、步骤、操作、元件、组件和/或它们的组。应该理解，当我们称元件被“连接”或“耦接”到另一元件时，它可以直接连接或耦接到其他元件，或者也可以存在中间元件。此外，这里使用的“连接”或“耦接”可以包括无线连接或无线耦接。这里使用的措辞“和/或”包括一个或更多个相关联的列出项的全部或任一单元和全部组合。
为使本公开的目的、技术方案和优点更加清楚，下面将结合附图对本公开实施方式作可选地详细描述。下面以具体地实施例对本公开的技术方案以及本公开的技术方案如何解决上述技术问题进行详细说明。下面这几个具体的实施例可以相互结合，对于相同或相似的概念或过程可能在某些实施例中不再赘述。下面将结合附图，对本公开的实施例进行描述。
在相关技术中，环境温度控制方法包括PID(Proportional-Integral-Derivative，比例-积分-微分)控制、模糊控制等。PID控制在环境温度控制中的应用较早，但由于环境温度具有非线性、滞后性、时变性、不确定性等特点，常规PID对环境温度的控制效果并不理想；模糊控制不需建立精确的数学模型，适用于滞后性、非线性系统，但存在静差、控制精度低等问题。因而，单独采用以上控制方法控制环境温度都存在一定的缺陷。鉴于上述情况，本发明采用变论域模糊控制方法结合PID控制方法对目标环境(例如汽车内部空间)的温度进行控制，以期克服常规模糊控制方法和PID二者单独使用时的弊端，从而改善目标环境中温度控制的精度，增强用户体验。具体地，根据本公开的变论域模糊PID控制是一种混合控制方法，将模糊控制和PID控制相结合，进行优势互补，不仅适用于对非线性系统的控制且降低了静差；而论域可变相当于在不改变模糊控制规则的情况下，增加了控制规则密度，进一步提高了控制精度。
图1示意性示出根据本公开一些实施例的温度控制方法的流程图。如图1所示，该温度控制方法包括如下步骤S110-S140。下面详细说明 各个步骤。
S110，数据采集步骤：采集目标环境中多个温度检测点的温度数据。
在本文中，目标环境可以是任意环境或空间，例如汽车内部、房间内部。一般地，可以通过温度传感器来感测目标环境中的温度。在步骤S110中，多个温度检测点可以均匀地分布于目标环境中，以便更全面且更精确地获得整个目标环境中的总体温度数据。当然，温度检测点可以根据目标环境中不同位置的重要程度或用户的主观需要来设置。此外，温度数据可以包括温度传感器感测的温度值，也可以包括其他温度相关数据，例如环境中的湿度值等。
S120，数据处理步骤：根据所采集的温度数据，计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差，其被作为第一温差。
在温度采集之后及控制温度之前，需要对温度数据进行必要的处理以确定控制对象。在步骤S120中，目标环境中的平均温度被设定为控制对象，因此当平均温度偏离目标温度时，可以启动对平均温度或平均温度与目标温度之间的温差的控制，以使得平均温度接近目标温度或二者之间的温差，即第一温差，的绝对值较小或趋近于零。平均温度之所以被选定为控制对象，是因为平均温度可以从整体上反映目标环境中的温度情况，因而可以更全面地代表各个不同位置的温度水平，避免以偏概全的错误。当然，控制对象并不限于平均温度，例如也可以选定目标环境中的几何中心点或对用户而言最重要的位置处的温度为控制对象。目标温度值可以是预先设定的，例如可以是用户主观设定或者根据客观因素自动设定的。该客观因素例如可以包括目标环境的所在的地理位置、季节、气候条件等。
S130，判断步骤：判断第一温差的绝对值是否超过第一温差阈值，若是，转到步骤S140；否则回到步骤S110。
第一温差阈值可以是预先设定的正数，其可以根据具体情况和对温度控制的要求进行设定。步骤S130实际上是将第一温差的绝对值与第一温差阈值进行比较，随后根据比较结果进行温度控制，即当第一温差绝对值大于第一温差阈值时，方法转到步骤S140-进行温度控制；当第一温差的绝对值小于或等于第一温差阈值时，不启动对目标环境 的温度控制，因为这是平均温度值与目标温度值足够接近(或者说二者的温差足够小，平均温度值并未明显偏离目标温度值)，没有必要进行温度调控，这时方法可以回到步骤S110-重新开始采集温度数据，开始循环控制以实现实时的温度调控。可选地，当第一温差的绝对值小于或等于第一温差阈值时，也可以对目标环境温度进行均匀性控制，即对温差较大的两个相邻温度检测点的温度进行控制以使温度分布更均匀。关于温度均匀性控制，请参见图6及其相应描述。
S140，温度控制步骤：响应于第一温差的绝对值超过第一温差阈值，通过变论域模糊PID控制算法控制目标环境的温度。
当第一温差绝对值大于第一温差阈值时，表明目标环境中的平均温度明显偏离目标温度值，因而需要进行温度调节和控制。在本文中，变论域模糊PID控制算法是指将变论域模糊控制和PID控制结合起来的混合控制算法，其中变论域是指模糊控制中输入模糊论域和/或输出模糊论域可以变化，例如可以分别通过输入论域伸缩因子和输出论域伸缩因子实现输入和输出模糊论域的伸缩。在利用变论域模糊PID算法进行温度控制中，通过论域的伸缩，可以在控制规则不变的前提下，可以间接增加控制规则密度，从而提高控制精度。关于论域伸缩因子的选择和确定，请参见图4及其相应描述。
在图1所示的根据本公开一些实施例的温度控制方法中，至少存在下述有益效果：
首先，由于目标环境中设置多个温度检测点，能够更准确的反映当前环境不同位置的温度状况；而且以多个温度检测点的温度数据为数据基础，确定的平均温度值被选作被控对象可以从整体上反映目标环境中的温度情况，因而可以更全面地代表各个不同位置的温度水平，避免以偏概全的错误，实现环境温度的确切且均匀的控制，增强了用户体验。
其次，在利用变论域模糊PID控制算法对环境温度进行控制时，由于其结合了模糊控制和PID控制的优势，既能提高响应速度和控制精度，又可适用于具有非线性、滞后性、时变性、不确定性的系统，适用范围更广；同时通过论域的伸缩，可以在控制规则不变的前提下，可以间接增加控制规则密度，从而提高控制精度。
图2示出根据本公开一些实施例的图1所示的温度控制方法中步 骤S140-通过变论域模糊PID控制算法控制目标环境的温度-的流程图。下面结合图2描述根据本公开一些实施例的根据变论域模糊PID控制进行环境温度控制的具体方式。如图2所示，通过变论域模糊PID控制算法控制目标环境的温度包括步骤S241-S244。
S241，通过微分法计算第一温差变化率。
在变论域模糊PID控制过程中，主要包括两种控制方式，即变论域模糊控制以及PID控制，这两种控制算法的输入量可以设定为第一温差以及第一温差变化率，因此在实现控制之前需要通过微分法计算表征第一温差变化的快慢程度的第一温差变化率。
S242，根据第一温差和第一温差变化率，通过变论域模糊控制算法确定调整变量。
在变论域模糊控制算法中，输入量为第一温差和第一温差变化率，而其输出量为调整变量，其用于对由PID控制算法输出的初始控制参数确定进行调整或修正以得到实时控制参数。修正方法可以通过累加进行。
S243，根据第一温差和调整变量，通过PID控制算法确定实时控制参数。
在PID控制算法中，以第一温差和调整变量为输入量，以实时控制参数为输出量。具体过程为，首先PID控制算法通过参数整定确定初始控制参数，随后通过调整变量修正该初始控制参数以得到实时控制参数。
在一些实施例中，步骤S223可以包括：根据第一温差，通过PID控制算法进行参数整定以获得初始控制参数；通过将初始控制参数与调整变量进行累加确定实时控制参数。具体地，关于实时控制参数的确定，可以参照下文中的公式(1)。
由于变论域模糊PID控制器本身具有参数自整定功能，具有良好的自适应能力，能够在运行中通过不断检测第一温差和第一温差变化率，并根据模糊控制规则来对参数调整量进行在线修改，以满足不同的第一温差和第一温差变化率对控制参数的不同要求，而使被控对象有良好的动、静态性能，使得系统具有了较强的自适应能力，能够在外来扰动时及时恢复，不至于产生过大的振荡。
S244，根据实时控制参数生成控制信号以控制目标环境的温度。
在得到实时控制参数之后，可以根据实时控制参数生成控制信号来调节被控对象，例如目标环境的平均温度。平均温度的具体调节方式可以根据控制参数控制温度调节装置对平均温度进行实时调节。可选地，控制信号可以是用于控制温度调节装置的电压信号或电流信号。
可选地，温度调节装置可以包括：热风加热系统和空调制冷系统。例如，根据控制信号控制温度调节装置对当前环境的温度进行调节可以包括：根据控制信号控制热风加热系统进行加热，以升高当前环境的温度，或根据控制信号控制空调制冷系统进行制冷，以降低当前环境的温度。从而可使当前环境中多个温度检测点的平均温度值与目标温度值之间的差值不断缩小，直至该差值在第一温差阈值范围内。
图3示出根据本公开一些实施例的温度控制方法的原理示意图。如图3所示，变论域模糊PID控制器(或算法)由模糊控制器(或算法)和PID控制器(或算法)组成。在图3中，r为目标温度值，y为目标环境中各温度检测点的平均温度值；e和ec分别为第一温差(即平均温度值与目标温度值之间的差y-r或r-y)和第一温差变化率；k e与k ec分别为第一温差e与第一温差变化率ec的量化因子，用于匹配输入基本论域与输入模糊论域；而l p、l i、l d分别为ΔK p、ΔK i、ΔK d的比例因子，用于匹配输出基本论域和输出模糊论域；输入伸缩因子与输出伸缩因子可以用于分别通过改变量化因子和比例因子等效实现输入和输出模糊论域的伸缩。
如图3所示，变论模糊PID控制器或模块包括模糊控制器和PID控制器。模糊控制器的输入量为第一温差e和第一温差变化率ec，输出量为用于调整PID控制器中三个控制参数K p、K i、K d的调整变量ΔK p、ΔK i、ΔK d；PID控制器的输入量为第一温差e以及调整变量ΔK p、ΔK i、ΔK d，输出量为用于控制执行机构(即温度调节装置，例如加热系统和冷却系统)对被控对象(当前环境的温度或平均温度)进行调节的控制信号u。控制参数K p、K i、K d分别表示比例、积分和微分系数。
在PID控制器生成控制信号u的过程中，PID控制器首先根据第一温差e生成初始控制信号，然后根据三个控制参数K p、K i、K d对控制信号u进行实时调整，从而可得到更精确的控制信号u；三个实时控制参数K p、K i、K d的调整可以通过下式进行：
Figure PCTCN2019093216-appb-000004
其中，K p0、K i0、K d0为预先设置的PID初始控制参数。
图4示出图2所示的根据本公开一些实施例的温度控制方法中变论域模糊控制控制步骤S222的流程图。如图4所示，图2所示的步骤S242-根据第一温差和第一温差变化率通过模糊控制算法确定调整变量包括S4421-S4424。
S4421，根据第一温差和第一温差变化率，确定输入论域伸缩因子和输出论域伸缩因子。
当输入量(包括第一温差和第一温差变化率)接近零点时，输入伸缩因子通过增大量化因子来增大输入量，相当于压缩输入模糊论域，且输入量越接近零点，输入伸缩因子增大量化因子的程度越大。其中量化因子与输入伸缩因子是相除关系。
输入伸缩因子可以是基于函数型的伸缩因子或基于模糊推理型的伸缩因子。当选用基于函数型的伸缩因子时，输入伸缩因子可以是分段比例函数，该分段比例函数的分段阈值包括温差阈值和温差变化率阈值。采用分段比例函数作为伸缩因子，可以对输入模糊论域进行有条件的伸缩，在保证控制精度不被影响的同时可减小计算量，从而提高响应速度以及控制的实时性。
可选地，输出论域伸缩因子可以根据量化因子增加的程度相应减小比例因子，相当于压缩输出模糊论域。比例因子与输出伸缩因子是相乘关系。可选地，输出伸缩因子可以是基于函数型的伸缩因子或基于模糊推理型的伸缩因子。
S4422，对第一温差和第一温差变化率进行模糊化处理以得到模糊输入量，其中利用输入论域伸缩因子实现输入模糊论域的伸缩；
关于输入量的模糊化过程，可以设计模糊隶属函数，将输入精确值e和ec转换为模糊输入量，得到模糊输入量E和EC，模糊隶属函数的形状可以是三角型隶属度函数、高斯型隶属函数等。例如，假设第一温差e和第一温差变化率ec的基本论域可选为[-10，10]、[-1，1]，输出变量ΔK p、ΔK i、ΔK d的基本论域可选为[-1.5，1.5]、[-0.015，0.015]、[-3，3]， 各输入、输出的模糊论域均选为[-3，3]，采用7个模糊子集对输入和输出模糊论域进行划分，隶属度函数采用三角型和高斯型的结合，模糊子集对应的模糊语言值分别为{NB(负大)、NM(负中)、NS(负小)、ZE(零)、PS(正小)、PM(正中)、PB(正大)}。通过模糊化处理，输入变量第一温差e和第一温差变化率ec的基本论域被转换成模糊论域，且其清晰值被转换为模糊语言值，以便能够在后续步骤中进行模糊推理以实现模糊输入到模糊输出的转变。
S4423，根据模糊控制规则进行模糊推理以将模糊输入量转换成模糊输出量，其中利用输出论域伸缩因子实现输出模糊论域的伸缩；
一般地，根据与输入模糊论域和输出模糊论域相对应的模糊控制规则，可对参数调整量进行模糊推理。其中，模糊控制规则可根据实际应用场景进行设置，例如，可根据实际应用场景中已有的经验模型或经验数据得出初步模糊控制规则，并根据实际需求对该初步模糊控制规则进行适应性地调整，从而得出模糊控制规则。模糊推理的大致原理如下：输入模糊论域包括具有数值大小顺序的多个输入模糊子集，输出模糊论域包括具有数值大小顺序的多个输出模糊子集；每个输入模糊子集均对应一个第一温差的模糊语言值和一个第一温差变化率的模糊语言值，每个输出模糊论域均包括一个参数调整量的模糊语言值；每个第一温差的模糊语言值和第一温差变化率的模糊语言值，均与一个参数调整量的模糊语言值具有一定的模糊关系，根据第一温差的模糊语言值和第一温差变化率的模糊语言值以及该模糊关系，即可确定参数调整量的模糊语言值。更具体的设置方式以及模糊控制规则将在后续示例中详述。
可选地，根据与输入模糊论域和输出模糊论域相对应的模糊控制规则，对参数调整量进行模糊推理，可包括：首先确定第一温差、第一温差变化率对应的输入模糊子集，例如，根据对应的输入模糊子集确定第一温差对应的模糊语言值和第一温差变化率对应的模糊语言值；其次，根据确定出的第一温差对应的模糊语言值和第一温差变化率对应的模糊语言值，确定参数调整量对应的模糊语言值。
S4424，对模糊输出量进行清晰化处理以得到所述调整变量。
参照图3，根据本公开的变论域模糊PID控制算法包括变论域模糊控制和PID控制两部分，图4实际上示出是变论域模糊控制部分的流 程图。
在一些实施例中，关于步骤S4421中输入论域伸缩因子和输出论域伸缩因子可以通过下面的公式(2)-(4)进行设计或确定。
首先，关于输入论域伸缩因子，可以在传统的比例性伸缩因子的基础上，设计一种分段函数型伸缩因子。采用分段比例函数作为伸缩因子，可以对输入模糊论域进行有条件的伸缩，在保证控制精度不被影响的同时可减小计算量，从而提高响应速度以及控制的实时性。具体地，假设第一温差e和第一温差变化率ec的基本论域分别为：X e＝[-x emax，x emax]，X ec＝[-x ecmax，x ecmax]；则输入论域伸缩因子可表示为：
Figure PCTCN2019093216-appb-000005
Figure PCTCN2019093216-appb-000006
其中，α(e)表示第一温差e的输入论域伸缩因子，α(ec)表示第一温差变化率ec的输入论域伸缩因子；θ i表示分段阈值，即温差阈值(即第一温差e的绝对值的阈值)或温差变化率阈值(即第一温差变化率ec的绝对值的阈值)。具体地，若表达式(2)中i＝1，此时θ 1表示温差阈值，若表达式(3)中i＝2，此时θ 2为表示温差变化率阈值。θ 1和θ 2均为正值，θ 1可以是第一温差阈值范围的边界绝对值。λ i、ε i表示输入敏感度调节参数，前者值越大，后者值越小，控制器对输入的变化越敏感，越有利于减小死区的影响和提高控制精度。
由表达式(2)和(3)可知，仅当输入的第一温差e的绝对值小于温差阈值θ 1，且第一温差变化率小于温差变化率阈值θ 2时，才通过伸缩因子改变量化因子等效实现对输入模糊论域的伸缩，避免了在整个论域上都进行论域伸缩所带来的不必要的计算，从而降低车内微控制器的计算负担。
θ 1和θ 2的大小决定了控制精度和运算量，θ 1和θ 2的具体值可根据实际需求进行设置。当表达式(2)中i＝1，表达式(3)中i＝2时，对于参数λ 1、λ 2、ε 1、ε 2，前两个参数的值越大，后两个参数的值越小，模 糊控制器对输入的变化越敏感，越有利于减小死区的影响和提高控制精度。例如，可以选取λ 1＝λ 2＝0.7、ε 1＝10 -5、ε 2＝10 -6。
在进行变论域模糊PID控制的过程中，采用分段比例函数作为输入伸缩因子，使得当输入量在一个数值范围才通过输入伸缩因子对输入量的模糊论域进行伸缩，在保证控制精度的同时避免了在整个论域上都进行论域伸缩所带来的不必要的计算，从而降低当前环境中的微控制器的计算负担；由于计算量大大减小，响应速度大大提高，从而增加了温度控制的实时性。
若第一温差e和第一温差变化率ec的基本论域分别为：X e＝[-x emax，x emax]，X ec＝[-x ecmax，x ecmax]；则输出论域伸缩因子可表示为：
Figure PCTCN2019093216-appb-000007
由于输出论域伸缩因子直接影响到PID控制器参数自整定，为保证控制精度，不设置阈值，可在一个较大的范围内通过输出伸缩因子β(e，ec)改变比例因子，从而实现对和输出模糊论域的伸缩。
在一些实施例中，关于步骤S4423-根据模糊控制规则进行模糊推理以将模糊输入量转换成模糊输出量，即调整变量ΔK p、ΔK i、ΔK d，的具体实现方式，例如可以采用Mamdani算法进行模糊推理。采用Mamdani算法进行模糊推理，该推理算法计算简单、切实可行。
下面介绍Mamdani算法的原理。
采用7个模糊子集对伸缩后的输入模糊论域和输出模糊论域进行模糊划分，并为7个模糊子集设置模糊语言值，每个模糊语言值均对应一组输出量ΔK p、ΔK i、ΔK d的取值。其中，划分方式所依据的隶属度函数可以采用三角型函数或高斯函数，7个模糊子集对应的模糊语言值分别为NB(负大)、NM(负中)、NS(负小)、ZE(零)、PS(正小)、PM(正中)、PB(正大)。
图5示出了根据本公开一些实施例的温度控制方法中输入和输出模糊论域的隶属度函数曲线。以输入模糊论域和输出模糊论域均为[-3，3]为例，图5所示的隶属度函数曲线示出了输入模糊论域和输出模糊论域的一种模糊划分情况，其中，横轴表示e和ec的值，纵轴表示e和ec对各模糊语言值的隶属度，某一组e和ec的隶属度越接近1， 则表示该组e和ec越接近对应的模糊语言值，也即该组e和ec属于该模糊语言值对应的模糊子集的程度越高。
基于上述对输入模糊论域和输出模糊论域的模糊划分，以及预先设置的模糊控制规则，可对模糊控制器的输出量ΔK p、ΔK i、ΔK d进行控制。
上述模糊控制规则具体为：当第一温差e为较大值时，为使响应速度加快，增加PID控制器的实时控制参数K p，为避免超调，适当增大实时控制参数K d，为减小积分饱和，减小实时控制参数K i；当第一温差e为中等值时，为降低超调，减小K p，为增强系统的稳定性，增大K i，为保证响应速度，使K d的取值适中；当第一温差e为较小值时，为保证系统的稳定性，减小K p，为提高系统的稳态精度，适当增大K i；为避免振荡，在第一温差变化率ec较大时减小K d，在ec较小时减小K d。
可选地，将上述模糊控制规则与模糊划分结合，可得到如表1所示的关于模糊输入到模糊输出的模糊控制规则表，表1中，E、EC分别表示第一温差e、第一温差变化率ec对应的模糊语言变量。
表1 模糊控制规则表
Figure PCTCN2019093216-appb-000008
参照该模糊规则控制表，根据输入量第一温差e和第一温差变化率ec的变化，可确定对应的输出量ΔK p、ΔK i、ΔK d的模糊语言值。例如，如表1所示，当模糊语言变量E和EC的模糊语言值均为NB时，输出量ΔK p、ΔK i、ΔK d的取值分别为模糊语言值PB、NB、PS。
在一些实施例中，关于步骤S4424-清晰化处理，在确定参数调整量ΔK p、ΔK i、ΔK d的模糊语言值后，可以通过重心法对模糊语言值(即模糊输出量)进行清晰化处理。处理后的调整变量ΔK p、ΔK i、ΔK d通 过前述的表达式(1)对实时控制参数K p、K i、K d进行调整，可得到PID控制器的实时控制参数K p、K i、K d，根据该实时控制参数K p、K i、K d，可输出控制信号u，以实现对执行机构的控制，使执行机构对被控对象进行调节。重心法合理直观，能更好的体现模糊集合所包含的信息。
图6示出根据本公开一些实施例的温度控制方法的流程图。
如图6所示，该温度控制方法与图1所示的温度控制方法类似，即步骤S610-S640与图1的步骤S110-S140基本相同，这里不再赘述；另外图6所示的温度控制方法进一步包括如下步骤：
S650，响应于所述第一温差的绝对值未超过第一温差阈值，根据所述温度数据，计算所述多个温度检测点中任意两个相邻温度检测点的之间的温差，作为第二温差；
S660，判定第二温差的绝对值是否超过第二温差阈值，若超过，转到S670，否则转到S610；
S670，响应于第二温差的绝对值超过第二温差阈值，调节目标环境的空气流动。
与图1相比，图6所示的温度控制方法事实上增加了控制目标环境中各个位置温度分布均匀性的功能，即抑制相邻检测点的过大温差，使得目标环境中不同地点温差异减小，冷热均匀，以防温度分布不平衡造成对用户的身心的不良影响，从而增强用户体验
下面以一个具体示例对图6中的温度均匀性控制进行说明。若目标环境中存在彼此相邻的A、B、C三个温度检测点，用T A(t)、T B(t)、T C(t)分别表示t时刻三个温度检测点的温度数据，分别确定t时刻A与B之间、A与C之间以及B与C之间的第二温差[T A(t)-T B(t)]、[T B(t)-T C(t)]、[T C(t)-T A(t)]。
若将第二温差阈值设定为1，即第二温差范围为为[-1，1](单位：℃)，则当上述第二温差中的任意一个大于1℃或小于-1℃时，即|T A(t)-T B(t)|＞1℃，或|T B(t)-T C(t)|＞1℃，或|T C(t)-T A(t)|＞1℃时，则控制通风系统通风，以调节当前环境的空气流动，直至当前的t+d时刻每一个第二温差均属于[-1，1]，即同时满足|T A(t+d)-T B(t+d)|≤1℃、|T B(t+d)-T C(t+d)|≤1℃、|T C(t+d)-T A(t+d)|≤1℃。
图6所示的温度控制方法具有如下有益效果：本公开实施例可在平均温度值与目标温度值的第一温差在第一温差阈值范围内时，确定任意相邻两个温度检测点之间的第二温差，并根据第二温差的大小对应地调节当前环境的空气流动，通过空气的流动使至少两个温度检测点附近的温度趋于均匀，从而实现对当前环境的整个温度场的控制，提高整个温度场中温度分布的均匀性，可选地，可提高用户体验。
图7示出本公开一些实施例的温度控制装置的结构框图，如图6所示，温度控制装置700可以包括：
数据采集模块710，配置成采集目标环境中多个温度检测点的温度数据；
数据处理模块720，配置成根据所述温度数据，计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差，其被作为第一温差；
判断模块730，配置成判断第一温差的绝对值是否超过第一温差阈值；；以及
第一温度控制模块740，配置成响应于所述第一温差的绝对值超过第一温差阈值，通过变论域模糊PID控制算法控制目标环境的温度。
在一些实施例中，如图7所示，温度控制装置700进一步包括：
第二温度控制模块750，其配置成：
响应于所述第一温差的绝对值未超过第一温差阈值，根据所述温度数据，计算所述多个温度检测点中任意两个相邻温度检测点的之间的温差，作为第二温差；
判断第二温差的绝对值是否超过第二温差阈值，以及
响应于第二温差的绝对值超过第二温差阈值，调节目标环境的空气流动。
在一些实施例中，如图7所示，温度控制装置700可以进一步包括配置成调节目标环境温度的执行机构760，并且第一温度控制模块740进一步配置成：通过变论域模糊PID控制算法利用所述执行机构控制目标环境的温度。可选地，执行机构可以包括加热系统和制冷系统。
在一些实施例中，如图7所示，温度控制装置700可以进一步包括：目标温度设定模块770，其配置成接收所述目标温度值的设定，以及显示模块780，配置成显示所述温度数据和平均温度值中至少之一。 可选地，目标温度设定模块770可以是输入装置以用于接收用户输入的目标温度值；显示模块780可以包括液晶显示屏或触控显示屏
在一些实施例总，如图7所示，第一温度控制模块740包括：
微分器741，配置成根据第一温差，通过微分法计算第一温差变化率；
模糊控制器742，配置成根据第一温差和第一温差变化率，通过变论域模糊控制算法确定调整变量；以及
PID控制器743，配置成根据第一温差和调整变量，通过PID控制算法确定实时控制参数并且由此生成控制信号以用于控制目标环境的温度。
应当注意，图7所示的根据本公开一些实施例的温度控制装置700可用于执行上文所述的根据本公开一些实施例的温度控制方法，其实现原理和对应的有益效果相类似，此处不再赘述。
本公开实施例还提供了一种计算机可读存储介质，该计算机可读存储介质上存储有计算机可读指令，该计算机可读指令在被执行是实现根据本公开一些实施例的温度控制方法。
以上所述仅是本公开的部分实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本公开原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本公开的保护范围。
在本说明书的描述中，术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点被包含于本公开的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本公开的描述中，“多个”的含义是至少两个，例如两个、三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或多个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本公开的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序(包括根据所涉及的功能按基本同时的方式或按相反的顺序)来执行功能，这应被本公开的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，“计算机可读介质”可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例可以例如包括以下各项：具有一个或多个布线的电连接部(电子装置)、便携式计算机盘盒(磁装置)、随机存取存储器(Random Access Memory)、只读存储器(Read Only Memory)，可擦除可编辑只读存储器(Erasable Programmable Read Only Memory)或闪速存储器、光纤装置、以及便携式光盘只读存储器(Compact Disc Read Only Memory)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本公开的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。例如，如果用硬件来实现，则可用本领域公知的下列技术中的任一项或它们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路、具有合适的组合逻辑门电路的专用集成电路、可编程门阵列(Programmable Gate Array)、现场可编程门阵列(Field Programmable Gate Array)等。
本技术领域的普通技术人员可以理解上述实施例方法的全部或部 分步骤可以通过程序指令相关的硬件完成，所述程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括执行方法实施例的步骤之一或其组合。
此外，在本公开各个实施例中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。
应当注意，在权利要求书中，动词“包括/包含”及其变体的使用并没有排除存在权利要求中未陈述的元件或步骤。措词“一”或“一个”并没有排除多个。

Claims (17)

  1. 一种温度控制方法，包括：
    采集目标环境中多个温度检测点的温度数据；
    根据所述温度数据，计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差，其被作为第一温差；
    判断第一温差的绝对值是否超过第一温差阈值；
    响应于所述第一温差的绝对值超过第一温差阈值，通过变论域模糊PID控制算法控制目标环境的温度。
  2. 根据权利要求1所述的方法，其中所述通过变论域模糊PID控制算法控制目标环境的温度包括：
    通过微分法计算第一温差变化率；
    根据第一温差和第一温差变化率，通过变论域模糊控制算法确定调整变量；
    根据第一温差和调整变量，通过PID控制算法确定实时控制参数；
    根据所述实时控制参数生成控制信号以控制目标环境的温度。
  3. 根据权利要求2所述的方法，其中所述根据第一温差和第一温差变化率通过变论域模糊控制算法确定调整变量包括：
    根据第一温差和第一温差变化率，确定输入论域伸缩因子和输出论域伸缩因子；
    对第一温差和第一温差变化率进行模糊化处理以得到模糊输入量；
    根据模糊控制规则进行模糊推理以将模糊输入量转换成模糊输出量；以及
    对模糊输出量进行清晰化处理以得到所述调整变量。
  4. 根据权利要求3所述的方法，其中所述输入论域伸缩因子通过下述分段比例函数确定：
    Figure PCTCN2019093216-appb-100001
    Figure PCTCN2019093216-appb-100002
    其中：e和ec分别表示第一温差和第一温差变化率，其基本论域分别为X e＝[-x emax，x emax]和X ec＝[-x ecmax，x ecmax]，
    θ i为分段阈值，参数λ i、ε i为敏感度调节参数，i＝1，2。
  5. 根据权利要求3所述的方法，其中所述输出伸缩因子通过下式确定：
    Figure PCTCN2019093216-appb-100003
    其中：e和ec分别表示第一温差和第一温差变化率，其基本论域分别为X e＝[-x emax，x emax]和X ec＝[-x ecmax，x ecmax]。
  6. 根据权利要求3所述的方法，其中所述根据模糊控制规则进行模糊推理以将模糊输入量转换成模糊输出量包括采用Mamdani算法进行模糊推理。
  7. 根据权利要求3所述的方法，其中所述对模糊输出量进行清晰化处理以得到所述调整变量包括：采用重心法对模糊输出量进行清晰化处理。
  8. 根据权利要求2所述的方法，其中所述根据第一温差和调整变量通过PID控制算法确定实时控制参数包括：
    根据第一温差，通过PID控制算法进行参数整定以获得初始控制参数；
    通过将初始控制参数与调整变量进行累加确定实时控制参数。
  9. 根据权利要求1所述的方法，其中进一步包括：
    响应于所述第一温差的绝对值未超过第一温差阈值，根据所述温度数据，计算所述多个温度检测点中任意两个相邻温度检测点的之间的温差，作为第二温差；
    判定第二温差的绝对值是否超过第二温差阈值；
    响应于第二温差的绝对值超过第二温差阈值，调节目标环境的空气流动。
  10. 根据权利要求1所述的方法，进一步包括：在所述根据所述温度数据计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差之前，接收目标温度值的设定。
  11. 一种温度控制装置，包括：
    数据采集模块，配置成采集目标环境中多个温度检测点的温度数 据；
    数据处理模块，配置成根据所述温度数据，计算所述多个温度检测点的平均温度值以及所述平均温度值与目标温度值之间的差，其被作为第一温差；
    判断模块，配置成判断第一温差的绝对值是否超过第一温差阈值；以及
    第一温度控制模块，配置成响应于所述第一温差的绝对值超过第一温差阈值，通过变论域模糊PID控制算法控制目标环境的温度。
  12. 根据权利要求11所述的温度控制装置，其中第一温度控制模块包括：
    微分器，配置成根据第一温差，通过微分法计算第一温差变化率；
    模糊控制器，配置成根据第一温差和第一温差变化率，通过变论域模糊控制算法确定调整变量；以及
    PID控制器，配置成根据第一温差和调整变量，通过PID控制算法确定实时控制参数并且由此生成控制信号以用于控制目标环境的温度。
  13. 根据权利要求12所述的温度控制装置，进一步包括：
    第二温度控制模块，其配置成：
    响应于所述第一温差的绝对值未超过第一温差阈值，根据所述温度数据，计算所述多个温度检测点中任意两个相邻温度检测点的之间的温差，作为第二温差；
    判断第二温差的绝对值是否超过第二温差阈值；
    响应于第二温差的绝对值超过第二温差阈值，调节目标环境的空气流动。
  14. 根据权利要求11所述的温度控制装置，进一步包括配置成调节目标环境温度的执行机构，并且所述第一温度控制模块进一步配置成：通过变论域模糊PID控制算法利用所述执行机构控制目标环境的温度。
  15. 根据权利要求14所述的温度控制装置，其中所述执行机构包括加热系统和制冷系统。
  16. 根据权利要求11所述的温度控制装置，进一步包括：
    目标温度设定模块，配置成接收所述目标温度值的设定；以及
    显示模块，配置成显示所述温度数据和平均温度值中至少之一。
  17. 一种计算机可读存储介质，其上存储有计算机可读指令，所述计算机可读指令在被执行时实现权利要求1所述的方法。
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