A temperature control method based on fuzzy adaptive PID algorithm applied to electric heater

Abstract

The application provides a temperature control method based on a fuzzy adaptive PID algorithm applied to an electric heater, the electric heater comprising a fuzzy controller and a PID controller, the method comprising: sending an error value and an error change rate value to the fuzzy controller, and controlling the fuzzy controller to determine an error fuzzy value and an error change rate fuzzy value according to the error value and the error change rate value; controlling the fuzzy controller to determine a KP value and a KI value according to the error value, the error fuzzy value, the error rate fuzzy value and an optimization formula of a domain value of △KP; and controlling the PID controller to receive the error value, the KP value and the KI value, and to determine an output value according to the error value, the KP value and the KI value, wherein the values of A and B can be dynamically adjusted according to the change of the error value, A+B=1 is satisfied, the PID parameters can be obtained without searching a fuzzy control table, and the system has good performances such as strong stability and fast response.

Description

Technical Field

[0001] This application relates to the field of industrial control, and in particular to a temperature control method based on a fuzzy adaptive PID algorithm for use in electric heaters. Background Technology

[0002] Proportional-integral-derivative control (PID control) is widely used in industrial process control due to its simple algorithm, good robustness, and high reliability. About 90% of control loops have a PID structure. However, because real-world objects often have nonlinear, time-varying, uncertain, and strong disturbance characteristics, it is difficult to achieve ideal control results using conventional PID controllers. For example, in the temperature control system of electric heating, due to the complexity of parameter tuning methods, conventional PID controllers often have poorly tuned parameters and poor performance. These factors limit the application of PID control in complex systems and systems with high performance requirements.

[0003] Currently, existing technologies combine fuzzy control with conventional PID control. By utilizing the idea of ​​fuzzy reasoning, the parameters KP and KI of the PID controller are self-tuned online based on different errors and error change rates. This not only maintains the advantages of PID control systems, such as simple principle and good robustness, but also has greater flexibility and adaptability, resulting in better overall control accuracy.

[0004] However, existing fuzzy control methods usually require fuzzifying the error and the rate of change of error, then calculating the fuzzy control table, looking up the fuzzy control table to obtain the PID parameters, and then defuzzifying the PID parameters to obtain the actual control quantity, which makes the control process quite complex.

[0005] Therefore, in view of the shortcomings of the existing technology, how to obtain the PID parameters without looking up the fuzzy control table, and ensure the system has good performance such as strong stability and fast response, has become an urgent technical problem to be solved. Summary of the Invention

[0006] The main objective of this application is to at least solve one of the technical problems existing in the prior art, and to propose a temperature control method, operation control device, electric heater and computer storage medium based on fuzzy adaptive PID algorithm for electric heaters. This method can obtain PID parameters without looking up a fuzzy control table, and can ensure good performance such as strong system stability and fast response.

[0007] To achieve the above objectives, in a first aspect, embodiments of this application propose a temperature control method based on a fuzzy adaptive PID algorithm for use in electric heaters, wherein the electric heater includes a fuzzy controller and a PID controller, and the method includes:

[0008] The error value and the error rate of change value are sent to the fuzzy controller, and the fuzzy controller is controlled to determine the error fuzzy value and the error rate of change fuzzy value based on the error value and the error rate of change value;

[0009] The fuzzy controller determines the KP value and KI value based on the error value, the error fuzzy value, the error rate fuzzy value, and the optimization formula of the universe of discourse value of ΔKP.

[0010] The PID controller receives the error value, the KP value, and the KI value, and determines the output value based on the error value, the KP value, and the KI value.

[0011] The optimization formula is as follows: the universe value of △KP = -(A*error fuzzy value + B*error change rate fuzzy value), the values ​​of A and B can be dynamically adjusted according to the change of error value, and satisfy A+B=1, and △KP is the correction value of KP.

[0012] According to an embodiment of the present invention, a temperature control method based on a fuzzy adaptive PID algorithm for electric heaters has at least the following beneficial effects: By sending error values ​​and error rate of change values ​​to a fuzzy controller, the fuzzy controller can perform fuzzification processing on the error values ​​and error rate of change values ​​respectively, enabling the system to better handle uncertainties and nonlinear problems. Furthermore, the system can dynamically adjust the values ​​of A and B based on changes in the error values. By combining the values ​​of A, B, the processed fuzzy error values, the fuzzy error rate of change values, and the optimization formula derived from prior experiments, the system can continuously obtain the universe of discourse values ​​of ΔKP under different operating conditions through the optimization formula. Processing the universe of discourse values ​​of ΔKP yields the values ​​of KP and KI, which in turn controls the PID controller to receive the error values, KP values, and KI values, allowing the PID controller to adaptively adjust the output value. This ensures that the electric heater maintains good control performance under different operating conditions. Compared to obtaining PID parameters by querying a fuzzy control table, this method improves the temperature control efficiency and accuracy of the electric heater, ensuring stable and accurate temperature control and improving the overall performance of the system.

[0013] In some embodiments, the method further includes acquiring temperature data at first intervals, and obtaining the error value and the error change rate value by preprocessing the temperature data.

[0014] In some embodiments, controlling the fuzzy controller to determine the error fuzzy value and the error change rate fuzzy value based on the error value and the error change rate value includes:

[0015] The error fuzzy value is determined by comparing the error value with the values ​​in the first preset set.

[0016] The error change rate value is determined by comparing it with the values ​​in the second preset set.

[0017] The error fuzzy value and the error rate fuzzy value correspond to preset input fuzzy quantization values, which are divided into seven levels: NB, NM, NS, ZO, PS, PM, and PB.

[0018] In some embodiments, the process of controlling the fuzzy controller to determine the KP value and KI value based on the error value, the error fuzzy value, the error rate fuzzy value, and the optimization formula of the universe of discourse value of ΔKP includes:

[0019] The values ​​of A and B are determined based on the error value and the preset interval set;

[0020] The universe of discourse value of △KP is determined based on the error fuzzy value, the error rate of change fuzzy value, the values ​​of A and B, and the optimization formula;

[0021] The fuzzy value of △KP is determined based on the value of the universe of discourse of △KP;

[0022] The fuzzy value of △KI is determined based on the fuzzy value of △KP;

[0023] Defuzzing is performed based on the fuzzy values ​​of △KP and △KI to obtain △KP and △KI;

[0024] The values ​​of KP and KI are determined based on the initial preset values ​​of △KP, △KI, KP, and KI.

[0025] Where △KI is the correction value of KI, KP = △KP + the initial preset value of KP, and KI = △KI + the initial preset value of KI.

[0026] In some embodiments, determining the values ​​of A and B based on the error value and a preset interval set includes:

[0027] When the error value falls into the first preset interval of the preset interval set, the value of A is determined to be the first parameter value and the value of B is determined to be the second parameter value.

[0028] When the error value falls into the second preset interval of the preset interval set, the value of A is determined to be the third parameter value and the value of B is determined to be the fourth parameter value.

[0029] When the error value does not fall within the preset interval set, the value of A is determined to be the fifth parameter value and the value of B is determined to be the sixth parameter value.

[0030] Wherein, the first parameter value is greater than the fifth parameter value, and the fifth parameter value is greater than the third parameter value.

[0031] In some embodiments, determining the fuzzy value of △KI based on the fuzzy value of △KP includes:

[0032] The fuzzy controller inverts the fuzzy value of △KP to obtain the fuzzy value of △KI.

[0033] In some embodiments, a temperature controller is further included, which receives the output value from the PID controller and outputs a control signal based on the output value.

[0034] In a second aspect, embodiments of the present invention provide an operation control device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the temperature control method described in the first aspect embodiment.

[0035] Thirdly, embodiments of the present invention provide an electric heater, including a fuzzy controller, a PID controller, a temperature controller, and the operation control device described in the second aspect embodiment, wherein the operation control device is electrically connected to the fuzzy controller, the PID controller, and the temperature controller, respectively.

[0036] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to implement the temperature control method described in the first aspect embodiment. Attached Figure Description

[0037] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments;

[0039] Figure 1 This is a flowchart of a temperature control method based on a fuzzy adaptive PID algorithm provided by the present invention;

[0040] Figure 2 This is a flowchart of another temperature control method based on fuzzy adaptive PID algorithm provided by the present invention;

[0041] Figure 3 This is a flowchart of another temperature control method based on fuzzy adaptive PID algorithm provided by the present invention;

[0042] Figure 4 This is a flowchart of another temperature control method based on fuzzy adaptive PID algorithm provided by the present invention;

[0043] Figure 5 This is a schematic diagram of the control logic of a temperature control method based on a fuzzy adaptive PID algorithm provided by the present invention;

[0044] Figure 6 This is a flowchart of another temperature control method based on fuzzy adaptive PID algorithm provided by the present invention;

[0045] Figure 7 This is a schematic diagram of the operation control device provided by the present invention. Detailed Implementation

[0046] This section will describe in detail specific embodiments of the present invention. Preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and overall technical solution of the present invention, but they should not be construed as limiting the scope of protection of the present invention.

[0047] In the description of the embodiments of the present invention, "several" means one or more, "multiple" means two or more, "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. "At least one" refers to one or more, and "at least one of the following" and similar expressions refer to any combination of these items, including any combination of single or multiple items. If "first," "second," etc., are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the order of the indicated technical features.

[0048] It should be noted that the terms "setting," "installing," and "connecting" in the embodiments of this invention should be interpreted broadly. Those skilled in the art can reasonably determine the specific meaning of the above terms in the embodiments of this invention in conjunction with the specific content of the technical solution. For example, the term "connection" can be a mechanical connection, an electrical connection, or a connection that allows for mutual communication; it can be a direct connection or an indirect connection through an intermediate medium.

[0049] It should be noted that the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] Proportional-integral-derivative control (PID control) is widely used in industrial process control due to its simple algorithm, good robustness, and high reliability. About 90% of control loops have a PID structure. However, because real-world objects often have nonlinear, time-varying, uncertain, and strong disturbance characteristics, it is difficult to achieve ideal control results using conventional PID controllers. For example, in the temperature control system of electric heating, due to the complexity of parameter tuning methods, conventional PID controllers often have poorly tuned parameters and poor performance. These factors limit the application of PID control in complex systems and systems with high performance requirements.

[0051] Currently, existing technologies combine fuzzy control with conventional PID control. By utilizing the idea of ​​fuzzy reasoning, the parameters KP and KI of the PID controller are self-tuned online based on different errors and error change rates. This not only maintains the advantages of PID control systems, such as simple principle and good robustness, but also has greater flexibility and adaptability, resulting in better overall control accuracy.

[0052] However, existing fuzzy control methods usually require fuzzifying the error and the rate of change of error, then calculating the fuzzy control table, looking up the fuzzy control table to obtain the PID parameters, and then defuzzifying the PID parameters to obtain the actual control quantity, which makes the control process quite complex.

[0053] Therefore, in view of the shortcomings of the existing technology, how to obtain the PID parameters without looking up the fuzzy control table, and ensure the system has good performance such as strong stability and fast response, has become an urgent technical problem to be solved.

[0054] Based on this, embodiments of the present invention provide a temperature control method based on a fuzzy adaptive PID algorithm for use in electric heaters.

[0055] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0056] Firstly, referring to Figure 1 This invention application proposes a temperature control method based on a fuzzy adaptive PID algorithm for use in electric heaters. The electric heater includes a fuzzy controller and a PID controller. The method includes steps S100-S300:

[0057] Step S100: Send the error value and the error change rate value to the fuzzy controller, and control the fuzzy controller to determine the error fuzzy value and the error change rate fuzzy value based on the error value and the error change rate value;

[0058] Step S200: The fuzzy controller determines the KP value and KI value based on the optimization formula of the error value, the error fuzzy value, the error rate fuzzy value, and the universe of discourse value of ΔKP;

[0059] Step S300: Control the PID controller to receive the error value, KP value and KI value, and determine the output value based on the error value, KP value and KI value;

[0060] The optimization formula is: the universe value of △KP = -(A*error fuzzy value + B*error change rate fuzzy value), the values ​​of A and B can be dynamically adjusted according to the change of error value, and satisfy A+B=1, and △KP is the correction value of KP.

[0061] According to an embodiment of the present invention, a temperature control method based on a fuzzy adaptive PID algorithm for electric heaters is provided. By sending error values ​​and error rate of change values ​​to a fuzzy controller, the fuzzy controller can perform fuzzification processing on the error values ​​and error rate of change values ​​respectively. This enables the system to better handle uncertainties and nonlinear problems, and then dynamically adjust the values ​​of A and B according to the changes in the error value. By combining the values ​​of A, B, the processed fuzzy error value, the fuzzy error rate of change value, and the optimization formula obtained from pre-experiments, the system can continuously obtain the universe of discourse value of ΔKP under different operating conditions through the optimization formula. The processing of the universe of discourse value of ΔKP yields the values ​​of KP and KI, and then controls the PID controller to receive the error value, KP value, and KI value, so that the PID controller adaptively adjusts the output value. This allows the electric heater to maintain good control performance under different operating conditions. Compared with the method of obtaining PID parameters by querying a fuzzy control table, this method improves the temperature control efficiency and accuracy of the electric heater, ensures that the electric heater can stably and accurately control the temperature, and improves the overall performance of the system.

[0062] It should be noted that the PID controller receives the error value, KP value, and KI value, and determines the output value based on these values. The output value is calculated as: Output value = KP * Error value + KI * Integral value of the error + KD * Rate of change of the error. KP is the proportional coefficient, KI is the integral coefficient, and KD is the derivative coefficient. In this application, there is no need to process the parameter KD. The fuzzy controller uses a two-input, two-output configuration, with the error value and the rate of change of the error as inputs, and the two correction values ​​ΔKP and ΔKI of the PID controller as outputs. Furthermore, the value of ΔKP is accumulated at the preset initial value of KP in the PID controller to obtain the KP value, and the value of ΔKI is accumulated at the preset initial value of KI in the PID controller to obtain the KI value. The obtained KP value, KI value, and error value are sent to the PID controller to obtain the output value, enabling the heater to dynamically control the temperature based on changes in the output value.

[0063] In some embodiments, the method further includes acquiring temperature data at first intervals before step S100, and obtaining error values ​​and error change rate values ​​by preprocessing the temperature data.

[0064] Preferably, the first duration is 40 seconds. In this application, the first duration can be appropriately adjusted within the range of 20-60 seconds.

[0065] Understandably, by acquiring temperature data at regular intervals, the system can monitor the temperature changes of the heater in real time. This allows the system to adjust the PID parameters more frequently based on the changing error value and the rate of change of the error. This enables the system to better adapt to rapid temperature changes and improve the dynamic performance of the control.

[0066] Reference Figure 2 In some embodiments, step S100: controlling the fuzzy controller to determine the error fuzzy value and the error change rate fuzzy value based on the error value and the error change rate value, includes steps S110-S120:

[0067] Step S110: Determine the error fuzzy value by comparing the error value with the values ​​in the first preset set;

[0068] Step S120: Determine the fuzzy value of the error change rate by comparing the error change rate value with the values ​​in the second preset set;

[0069] Among them, the error fuzzy value and the error rate fuzzy value correspond to the preset input fuzzy quantization value, which is divided into seven levels: NB, NM, NS, ZO, PS, PM, and PB.

[0070] It should be noted that the language value corresponding to NB is negative large, NM is negative medium, NS is negative small, ZO is zero, PS is positive small, PM is positive medium, and PB is positive large. The value of NB can be -6, NM can be -4, NS can be -2, ZO can be 0, PS can be 2, PM can be 4, and PB can be 6. The preset input fuzzy quantization value can be updated according to the specific needs of the system. In this application, the preset input fuzzy quantization value is not subject to too many restrictions.

[0071] Preferably, the values ​​in the first preset set can be {-30, -10, -2, 2, 10, 30}. The values ​​in the first preset set can be updated according to the specific needs of the system. In this application, the values ​​in the first preset set are not subject to excessive limitations.

[0072] Preferably, if the error value is less than -30 in the first preset set, the error fuzzy value is determined to be -6 corresponding to NB; if the error value is greater than -30 but less than -10 in the first preset set, the error fuzzy value is determined to be -4 corresponding to NM; if the error value is greater than -10 but less than -2 in the first preset set, the error fuzzy value is determined to be -2 corresponding to NS; if the error value is greater than -2 but less than 2 in the first preset set, the error fuzzy value is determined to be 0 corresponding to ZO; if the error value is greater than 2 but less than 10 in the first preset set, the error fuzzy value is determined to be 2 corresponding to PS; if the error value is greater than 10 but less than 30 in the first preset set, the error fuzzy value is determined to be 4 corresponding to PM; and if the error value is greater than 30 in the first preset set, the error fuzzy value is determined to be 6 corresponding to PB.

[0073] Preferably, the values ​​in the second preset set can be {-11, -6, -1, 1, 6, 11}. The values ​​in the second preset set can be updated according to the specific needs of the system. In this application, the values ​​in the second preset set are not subject to too many restrictions.

[0074] Preferably, if the error change rate value is less than the value -11 in the second preset set, the fuzzy value of the error change rate is determined to be the value -6 corresponding to NB; if the error change rate value is greater than -11 but less than -6 in the second preset set, the fuzzy value of the error change rate is determined to be the value -4 corresponding to NM; if the error change rate value is greater than -6 but less than -1 in the second preset set, the fuzzy value of the error change rate is determined to be the value -2 corresponding to NS; if the error change rate value is greater than -1 but less than 1 in the second preset set, the fuzzy value of the error change rate is determined to be the value 0 corresponding to ZO; if the error change rate value is greater than 1 but less than 6 in the second preset set, the fuzzy value of the error change rate is determined to be the value 2 corresponding to PS; if the error change rate value is greater than 6 but less than 11 in the second preset set, the fuzzy value of the error change rate is determined to be the value 4 corresponding to PM; and if the error change rate value is greater than 11 in the second preset set, the fuzzy value of the error change rate is determined to be the value 6 corresponding to PB.

[0075] Understandably, by comparing the preset input fuzzy quantization value with the error value and the error rate of change value, the error fuzzy value and the error rate of change fuzzy value can be quickly determined. The system does not need to perform complex mathematical calculations or modeling. By using different preset input fuzzy quantization values, the system can flexibly adapt to different application scenarios and operating conditions. For example, under different temperature ranges or different control requirements, these preset values ​​can be adjusted to obtain the best control effect.

[0076] Reference Figure 3In some embodiments, step S200: controlling the fuzzy controller to determine the KP value and KI value according to the optimization formula of the error value, the error fuzzy value, the error rate fuzzy value, and the universe of discourse value of ΔKP, including steps S210-S260:

[0077] Step S210: Determine the values ​​of A and B based on the error value and the preset interval set;

[0078] Step S220: Determine the universe of discourse value of △KP based on the error fuzzy value, the error change rate fuzzy value, the values ​​of A and B, and the optimization formula;

[0079] Step S230: Determine the fuzzy value of △KP based on the universe of discourse value of △KP;

[0080] Step S240: Determine the fuzzy value of △KI based on the fuzzy value of △KP;

[0081] Step S250: Defuzzify △KP and △KI based on the fuzzy values ​​of △KP and △KI;

[0082] Step S260: Determine the values ​​of KP and KI based on △KP, △KI, the initial preset values ​​of KP and KI;

[0083] Where △KI is the correction value of KI, KP = △KP + the initial preset value of KP, and KI = △KI + the initial preset value of KI.

[0084] It should be noted that the error value and the rate of change of error have different effects on the controller at different control stages. In the initial stage, the system error value is large, and the error is the primary concern; therefore, the weight of the error needs to be increased, i.e., the value of A needs to be increased. In the intermediate stage, the system error decreases, and the system rises rapidly; therefore, the system overshoot needs to be reduced, i.e., the value of A needs to be decreased, and the value of B needs to be increased to highlight the role of the rate of change of error. In the final stage, when the system response is close to the desired value, since both the error value and the rate of change of error are relatively small, A and B can be taken to be approximately the same value.

[0085] Understandably, by determining the values ​​of A and B based on the error value and the preset interval set, the system can dynamically adjust the values ​​of A and B according to the current error situation. This allows the system to better adapt to different temperature control requirements and changes in operating conditions, thereby improving the system's adaptability and robustness.

[0086] It should be noted that the fuzzy value of △KP can be determined by a method similar to that used to determine the values ​​of A and B, i.e., by comparing the value of the universe of discourse of △KP with the preset interval set.

[0087] It should be noted that the fuzzy value of △KP can also correspond to the preset input fuzzy quantization value, which is divided into seven levels: NB, NM, NS, ZO, PS, PM, and PB.

[0088] It is understandable that by using optimization formula reasoning to determine the domain value of △KP, and then determining the fuzzy values ​​of △KP and △KI based on this value, and then defuzzifying the fuzzy values ​​of △KP and △KI, the system can optimize its control performance without having to use a fuzzy rule table. This helps to simplify the process of the fuzzy controller outputting parameters KP and KI, which can improve the efficiency of temperature control more quickly, and ensure the accuracy and stability of temperature control, ensuring that the system can achieve the best control effect under various conditions.

[0089] Reference Figure 4 In some embodiments, step S210: determining the values ​​of A and B based on the error value and a preset interval set includes step S211, step S212, or step S213:

[0090] Step S211: When the error value falls into the first preset interval of the preset interval set, determine that the value of A is the first parameter value and the value of B is the second parameter value;

[0091] Step S212: When the error value falls into the second preset interval of the preset interval set, determine that the value of A is the third parameter value and the value of B is the fourth parameter value;

[0092] Step S213: When the error value does not fall into the preset interval set, determine that the value of A is the fifth parameter value and the value of B is the sixth parameter value;

[0093] Among them, the value of the first parameter is greater than the value of the fifth parameter, and the value of the fifth parameter is greater than the value of the third parameter.

[0094] Preferably, the first preset interval can be [30, +∞) or (-∞, -30]. By using different first preset intervals, the system can flexibly adapt to different application scenarios and working conditions.

[0095] It should be noted that since the error value and the error rate of change have different effects on the controller at different control stages, in the initial stage, when the error value falls into the first preset range, the system error value is large and the error is the main problem. Therefore, it is necessary to increase the weight of the error, that is, to increase the value of A.

[0096] Preferably, when the error value falls into the first preset interval of the preset interval set, the value of A can be 0.6 and the value of B can be 0.4, wherein the value of A can be appropriately adjusted within the range of 0.1-0.8.

[0097] Preferably, the second preset interval can be [-10, 30) or (-30, -10]. By using different second preset intervals, the system can flexibly adapt to different application scenarios and working conditions.

[0098] It should be noted that in the mid-term stage, the system error decreases, that is, the error falls into the second preset range, and the system rises very quickly. Therefore, it is necessary to reduce the system overshoot, that is, to lower the value of A, and to increase the value of B in order to highlight the role of the error change rate.

[0099] Preferably, when the error value falls into the second preset interval of the preset interval set, the value of A can be 0.3 and the value of B can be 0.7, wherein the value of A can be appropriately adjusted within the range of 0.1-0.8.

[0100] It should be noted that in the final stage, when the system response is close to the expected value, since the error value and the error rate of change are relatively small at this time, that is, the error value does not fall into the preset interval set, both A and B can be 0.5.

[0101] In some embodiments, step S240: determining the fuzzy value of △KI based on the fuzzy value of △KP includes:

[0102] The fuzzy controller inverts the fuzzy value of △KP to obtain the fuzzy value of △KI.

[0103] Understandably, in some cases, directly adjusting the value of KP may lead to system instability or oscillation. Obtaining the fuzzy value of △KI by inverting the fuzzy value of △KP is relatively simple, and the system can automatically adjust the value of KI to avoid instability or oscillation, thereby improving the robustness of the system.

[0104] It is understandable that when the fuzzy value of △KP corresponds to level NB, the fuzzy value of △KI corresponds to level PB.

[0105] In some embodiments, refer to Figure 5 It also includes a temperature controller, which receives the output value from the PID controller and outputs a control signal based on the output value.

[0106] It should be noted that, in Figure 5 In this context, E represents the error value, and EC represents the error rate of change.

[0107] Understandably, the temperature controller can generate a control signal based on the output value of the PID controller to drive the heating element in the heater, thereby achieving a closed-loop temperature control and ensuring that the temperature is precisely controlled near the set value.

[0108] To more clearly illustrate the temperature control method based on fuzzy adaptive PID algorithm for electric heaters, the following specific embodiments will be used for further description.

[0109] Reference Figure 6 This includes steps S310-S390:

[0110] Step S310: Determine the error fuzzy value by comparing the error value with the values ​​in the first preset set;

[0111] Step S320: Determine the fuzzy value of the error change rate by comparing the error change rate value with the values ​​in the second preset set;

[0112] Step S331: When the error value falls into the first preset interval of the preset interval set, determine that the value of A is the first parameter value and the value of B is the second parameter value;

[0113] Alternatively, step S332: when the error value falls into the second preset interval of the preset interval set, determine that the value of A is the third parameter value and the value of B is the fourth parameter value;

[0114] Alternatively, step S333: when the error value does not fall into the preset interval set, determine that the value of A is the fifth parameter value and the value of B is the sixth parameter value;

[0115] Step S340: Determine the universe of discourse value of △KP based on the error fuzzy value, the error change rate fuzzy value, the values ​​of A and B, and the optimization formula;

[0116] Step S350: Determine the fuzzy value of △KP based on the universe of discourse value of △KP;

[0117] Step S360: Determine the fuzzy value of △KI based on the fuzzy value of △KP;

[0118] Step S370: Perform defuzzification processing based on the fuzzy values ​​of △KP and △KI to obtain △KP and △KI;

[0119] Step S380: Determine the values ​​of KP and KI based on △KP, △KI, the initial preset values ​​of KP and KI;

[0120] Step S390: Control the PID controller to receive the error value, KP value and KI value, and determine the output value based on the error value, KP value and KI value;

[0121] Steps S310-S380 involve the completion of the fuzzy controller processing, and step S390 involves the completion of the PID controller processing.

[0122] Secondly, referring to Figure 7This invention provides an operation control device 400, including a memory 420 and a processor 410. The memory 420 stores a computer program, and the processor 410 executes the computer program to implement the temperature control method of the first aspect embodiment.

[0123] Thirdly, embodiments of the present invention provide an electric heater, including a fuzzy controller, a PID controller, a temperature controller, and an operation control device according to the second aspect embodiment, wherein the operation control device is electrically connected to the fuzzy controller, the PID controller, and the temperature controller respectively.

[0124] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to implement the temperature control method of the first aspect embodiment.

[0125] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which may include computer storage media or non-transitory media and communication media or transient media. As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc DVD or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0126] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A temperature control method based on fuzzy adaptive PID algorithm applied to electric heaters, characterized in that, The electric heater includes: a fuzzy controller and a PID controller; the method includes: The error value and the error rate of change value are sent to the fuzzy controller, and the fuzzy controller is controlled to determine the error fuzzy value and the error rate of change fuzzy value based on the error value and the error rate of change value; The fuzzy controller determines the KP value and KI value based on the error value, the error fuzzy value, the error change rate fuzzy value, and the optimization formula of the universe of discourse value of ΔKP. The PID controller receives the error value, the KP value, and the KI value, and determines the output value based on the error value, the KP value, and the KI value. The optimization formula is: the universe of discourse value of △KP = -(A Error fuzzy value +B The error change rate (fuzzy value) can be dynamically adjusted according to the change of error value, and satisfies A+B=1, where △KP is the correction value of KP. The process of controlling the fuzzy controller to determine the KP and KI values ​​based on the optimization formula of the error value, the error fuzzy value, the error change rate fuzzy value, and the universe of discourse value of ΔKP includes: The values ​​of A and B are determined based on the error value and the preset interval set; The universe of discourse value of △KP is determined based on the error fuzzy value, the error rate of change fuzzy value, the values ​​of A and B, and the optimization formula; The fuzzy value of △KP is determined based on the value of the universe of discourse of △KP; The fuzzy value of △KI is determined based on the fuzzy value of △KP; Defuzzing is performed based on the fuzzy values ​​of △KP and △KI to obtain △KP and △KI; The values ​​of KP and KI are determined based on the initial preset values ​​of △KP, △KI, KP, and KI. Where △KI is the correction value of KI, KP = △KP + the initial preset value of KP, and KI = △KI + the initial preset value of KI.

2. The temperature control method according to claim 1, characterized in that, The method further includes acquiring temperature data at first intervals, and obtaining the error value and the error change rate value by preprocessing the temperature data.

3. The temperature control method according to claim 1, characterized in that, The process of controlling the fuzzy controller to determine the error fuzzy value and the error change rate fuzzy value based on the error value and the error change rate value includes: The error fuzzy value is determined by comparing the error value with the values ​​in the first preset set. The error change rate value is determined by comparing it with the values ​​in the second preset set. The error fuzzy value and the error change rate fuzzy value correspond to preset input fuzzy quantization values, which are divided into seven levels: NB, NM, NS, ZO, PS, PM, and PB.

4. The temperature control method according to claim 1, characterized in that, The step of determining the values ​​of A and B based on the error value and the preset interval set includes: When the error value falls into the first preset interval of the preset interval set, the value of A is determined to be the first parameter value and the value of B is determined to be the second parameter value. When the error value falls into the second preset interval of the preset interval set, the value of A is determined to be the third parameter value and the value of B is determined to be the fourth parameter value. When the error value does not fall within the preset interval set, the value of A is determined to be the fifth parameter value and the value of B is determined to be the sixth parameter value. Wherein, the first parameter value is greater than the fifth parameter value, and the fifth parameter value is greater than the third parameter value.

5. The temperature control method according to claim 1, characterized in that, The step of determining the fuzzy value of △KI based on the fuzzy value of △KP includes: The fuzzy controller inverts the fuzzy value of △KP to obtain the fuzzy value of △KI.

6. The temperature control method according to claim 1, characterized in that, It also includes a temperature controller, which receives the output value from the PID controller and outputs a control signal based on the output value.

7. An operation control device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the temperature control method according to any one of claims 1 to 6.

8. An electric heater, characterized in that, It includes a fuzzy controller, a PID controller, a temperature controller, and the operation control device as described in claim 7, wherein the operation control device is electrically connected to the fuzzy controller, the PID controller, and the temperature controller, respectively.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, characterized in that the computer program is executed by a processor to implement the temperature control method according to any one of claims 1 to 6.

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