A control method for fuzzy PID temperature control system

Abstract

The present invention discloses a control method for a fuzzy PID temperature control system, and relates to the technical field of temperature control systems. The method comprises the following steps: obtaining input temperature control parameter data of the fuzzy PID temperature control system; designing fuzzy control parameters for different temperature rise and fall processes based on the input temperature control parameter data; and setting corresponding fuzzy control rules to perform segmented fuzzy control on the fuzzy PID temperature control system based on the individually designed fuzzy control parameters. The method proposed by the present invention can effectively make the fuzzy control parameters applicable to multiple temperature ranges, and perform parameter compensation when the performance of the heat dissipation module decreases during rapid temperature rise and fall, so as to improve the temperature control effect of the fuzzy PID temperature control system applied on a rapid PCR instrument.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control systems, and in particular to a control method for a fuzzy PID temperature control system. Background Art

[0002] Polymerase Chain Reaction (PCR) is a technology that rapidly amplifies specific DNA fragments in vitro. The principle of PCR technology is based on the natural replication process of DNA. Through repeated cycles of three basic steps: denaturation, annealing, and extension, the exponential amplification of the target DNA fragment is achieved. Denaturation is generally at 95°C, annealing at 65°C, and extension at 72°C. With each cycle, the number of target DNA fragments doubles. A fast PCR instrument is required to complete 35-40 cycles of the entire PCR process within a few minutes, so the temperature control needs to be more precise and efficient, that is, to achieve rapid heating and cooling, and stable and precise temperature.

[0003] The fuzzy PID temperature control algorithm combines the precision of the PID control algorithm with the flexibility and adaptability of the fuzzy control algorithm to form a new intelligent control method. The PID control algorithm uses the proportional, integral, and differential stages to adjust the system output to the desired target. Fuzzy control, on the other hand, utilizes fuzzy logic and fuzzy set theory to effectively control complex systems that are difficult to accurately model.

[0004] Existing rapid PCR instruments, despite their short cycle times and rapid ramping, still suffer from the following drawbacks: Rapid PCR instruments commonly cycle at 95°C, 65°C, and 72°C. Conventional fuzzy-PID temperature control algorithms are ineffective for temperatures as low as 25°C. This is because the corresponding parameters cannot simultaneously accommodate rapid temperature changes from 25°C to 95°C, 95°C to 65°C, 65°C to 72°C, and 72°C to 95°C. In particular, the ramp-up and ramp-down processes differ significantly. Due to heat exchange between the heating block and ambient air, there is a tendency for the temperature to drop. Using the same parameters for ramp-down as for ramp-up can result in significant overshoot (~10°C). Furthermore, fuzzy control generates different control parameters based on varying errors, and the magnitude of these errors varies across temperature variations. For example, a 1°C error may be minimal for a temperature change from room temperature to 95°C but significant for a temperature change from 65°C to 72°C. Therefore, using the same set of fuzzy control parameters makes it difficult to ensure fast response, good stability, and minimal overshoot across all temperature variations. At the same time, the heat sink also shows performance degradation as the number of cycles increases, resulting in inaccurate annealing temperature.

[0005] In summary, the existing fuzzy PID temperature control system used in rapid PCR instruments has the following technical deficiencies: first, temperature overshoot, which may exceed 10°C; second, the same set of fuzzy control parameters cannot be applied to multiple temperature ranges; third, the performance degradation of the heat dissipation module during rapid temperature rise and fall cannot be compensated by parameters, resulting in poor temperature control effect of the fuzzy PID temperature control system. Summary of the Invention

[0006] In order to overcome the problems in the prior art of applying a fuzzy PID temperature control system to a rapid PCR instrument, in which the fuzzy control parameters cannot be applied to multiple temperature ranges, and the performance of the heat dissipation module degrades during rapid temperature rise and fall and cannot be compensated by the parameters, resulting in poor temperature control effect of the fuzzy PID temperature control system, the present invention provides a control method for a fuzzy PID temperature control system, which can effectively make the fuzzy control parameters applicable to multiple temperature ranges, and perform parameter compensation when the performance of the heat dissipation module degrades during rapid temperature rise and fall, so as to improve the temperature control effect of the fuzzy PID temperature control system applied to the rapid PCR instrument.

[0007] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0008] A control method for a fuzzy PID temperature control system, the method comprising the following steps:

[0009] Obtain the input temperature control parameter data of the fuzzy PID temperature control system;

[0010] According to the input temperature control parameter data, fuzzy control parameters are individually designed for different temperature rising and falling processes;

[0011] According to the individually designed fuzzy control parameters, the corresponding fuzzy control rules are set to perform segmented fuzzy control on the fuzzy PID temperature control system.

[0012] In the above technical solution, according to the set segmented fuzzy control rules, segmented fuzzy control can be performed on the fuzzy PID temperature control system with different set temperatures, so as to adjust the parameters of the fuzzy PID temperature control system according to the temperature change range of the fuzzy PID temperature control system, so that the fuzzy control parameters are applicable to multiple temperature intervals, thereby improving the temperature control effect of the fuzzy PID temperature control system applied on the rapid PCR instrument.

[0013] Furthermore, the process of obtaining the input temperature control parameter data of the fuzzy PID temperature control system includes:

[0014] Obtain the error |e| of the input temperature control parameter in the fuzzy PID temperature control system, and calculate the error change rate based on the error |e| The expression is:

[0015]

[0016] The error |e| and the error change rate Combine them to obtain the input temperature control parameter data of the fuzzy PID temperature control system;

[0017] Where e(t) represents the error at the current moment, e(t-Δt) represents the error at the previous moment, and Δt represents the parameter sampling time interval.

[0018] Furthermore, the process of designing fuzzy control parameters for different heating and cooling processes includes:

[0019] Performing fuzzy processing on the input temperature control parameter data, and setting a first fuzzy rule and a second fuzzy rule according to the fuzzy processing result;

[0020] According to the set first fuzzy rule or second fuzzy rule, the input temperature control parameter data is divided into several temperature control parameter subsets;

[0021] Based on the input temperature control parameter data in each temperature control parameter subset, the corresponding output rules are set to perform fuzzy reasoning on the fuzzy PID temperature control system and output the fuzzy reasoning results;

[0022] Defuzzification is performed based on the fuzzy inference results, and PID parameters are output;

[0023] Among them, PID parameters represent fuzzy control parameters, including the proportional term coefficient K p , integral term coefficient K i , differential term coefficient K d .

[0024] Furthermore, the process of outputting PID parameters includes:

[0025] The triangle function is used as the membership function, the centroid method is used for defuzzification, and PID calculation is performed. The expression of PID calculation is:

[0026]

[0027] Calculate the error |e| and error change rate of the input temperature control parameter data respectively In different temperature control parameter subsets, the proportional term coefficient K of the output PID parameter P , integral term coefficient K i , differential term coefficient K d The membership degree of is expressed as:

[0028] μ KPID (E,DE)=triangular function(E,DE);

[0029] Among them, u(k) represents the output of the fuzzy PID temperature control system, e(k) represents the current error, K p , K i , K d are the coefficients of proportional term, integral term and differential term respectively, T s represents the sampling time, T i Indicates the integration time, T d Indicates the differential time, triangularfunction(E,DE) indicates that for a given error E and error change rate DE, the membership function returns K p , K i , K d Membership value.

[0030] In the above technical solution, different fuzzy rules are set for fuzzy reasoning, which can make the obtained PID parameters effectively applicable to multiple temperature ranges; the centroid method is used for defuzzification calculation, which can effectively improve the efficiency of defuzzification, thereby improving the temperature control effect of the fuzzy PID temperature control system applied on the rapid PCR instrument.

[0031] Furthermore, the method further comprises the following steps:

[0032] According to the temperature rise and fall state or cyclic temperature change process of the fuzzy PID temperature control system, the adjustment mechanism is set. The process includes:

[0033] According to the heating and cooling state or the cyclic temperature change process of the fuzzy PID temperature control system, an integral adjustment mechanism is set when the temperature rising error crosses zero and an integral adjustment mechanism is set when the temperature falling error crosses zero;

[0034] According to the integral coefficient K in the output PID parameters i , determine whether to start the integral adjustment mechanism when the temperature rise error crosses zero or the integral adjustment mechanism when the temperature fall error crosses zero.

[0035] Furthermore, the process of determining whether to start the integral adjustment mechanism when the temperature rise error crosses zero or the integral adjustment mechanism when the temperature fall error crosses zero includes:

[0036] Set up the integral limiting mechanism and set the integral term threshold;

[0037] When the integral coefficient K i When the preset integral term threshold is reached, the integral limit mechanism is activated and the accumulation of the integral term coefficient K is stopped. i ;

[0038] When the current temperature of the fuzzy PID temperature control system reaches the set temperature for the first time during the heating process, the integral adjustment mechanism is started when the temperature error crosses zero, and the accumulated integral coefficient K iPerform reduction processing and recalculate PID parameters, and use the calculated PID parameters for the fuzzy PID temperature control system;

[0039] When the current temperature of the fuzzy PID temperature control system reaches the set temperature for the first time during the cooling process, the integral adjustment mechanism is started when the cooling error crosses zero, the integral error is corrected to k times the original integral error, and the PID parameters are recalculated. The calculated PID parameters are used for the fuzzy PID temperature control system.

[0040] In the above technical solution, the integral term threshold is set, which can effectively monitor the fuzzy PID temperature control system according to the PID parameters of the fuzzy PID temperature control system, and effectively monitor the fuzzy PID temperature control system in the cyclic temperature change process in multiple temperature intervals according to the set integral adjustment mechanism when the temperature rising error passes through zero and the integral adjustment mechanism when the temperature cooling error passes through zero, and then adjust the PID parameters of the fuzzy PID temperature control system to further optimize the parameters of the fuzzy PID temperature control system, thereby improving the temperature control effect of the fuzzy PID temperature control system applied on the fast PCR instrument.

[0041] Furthermore, the method further comprises the following steps:

[0042] According to the cyclic temperature change process of the fuzzy PID temperature control system, a temperature control gain compensation mechanism is set;

[0043] When the fuzzy PID temperature control system is in the cyclic temperature change process of the PCR reaction, the proportional term coefficient K of the PID parameter in the cyclic temperature change process is adjusted according to the temperature control gain compensation mechanism. p Perform dynamic adjustments to compensate for the gain of the fuzzy PID temperature control system.

[0044] Furthermore, the expression for gain compensation of the fuzzy PID temperature control system is:

[0045] G(n)=1+αn;

[0046] According to the temperature control gain compensation mechanism, the proportional term coefficient K of the PID parameter in the cycle temperature change process is p The expression for dynamic adjustment is:

[0047] K′ p =K p *G(n)=K p *(1+αn);

[0048] Among them, α represents the gain coefficient, n represents the number of cycles, K′ p Indicates the proportional term coefficient after gain compensation.

[0049] In the above technical solution, according to the cyclic temperature change process of the fuzzy PID temperature control system, a temperature control gain compensation mechanism is set up, which can perform parameter compensation when the performance of the heat dissipation module decreases during the rapid temperature rise and fall process, so as to improve the temperature control effect of the fuzzy PID temperature control system applied on the rapid PCR instrument.

[0050] A rapid PCR instrument based on a fuzzy PID temperature control system, the rapid PCR instrument is used to implement the function of a control method of a fuzzy PID temperature control system, the PCR instrument comprises: a main control module, a temperature controller module and a reaction module;

[0051] The main control module includes a host computer loaded with control method program instructions of the fuzzy PID temperature control system, which is used to send temperature control instructions and receive temperature feedback;

[0052] The temperature controller module includes a temperature controller connected to the host and the reaction module, and is used to execute the temperature control instructions sent by the host;

[0053] The reaction module includes a heat-conducting carrier, a heating and cooling plate, and a heat dissipation device, which are used to accurately control the temperature of the PCR instrument and feed the temperature back to the host.

[0054] A computer-readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps of a control method for a fuzzy PID temperature control system.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention proposes a control method for a fuzzy PID temperature control system. According to the set segmented fuzzy control rules, the fuzzy PID temperature control system with different set temperatures can be subjected to segmented fuzzy control, so that the parameters of the fuzzy PID temperature control system can be adjusted according to the temperature change range of the fuzzy PID temperature control system, so that the fuzzy control parameters are applicable to multiple temperature intervals; according to the temperature increase and decrease state or the cyclic temperature change process of the fuzzy PID temperature control system, the set adjustment mechanism can effectively adopt the form of integration to monitor the state of the fuzzy PID temperature control system according to the temperature increase and decrease state or the cyclic temperature change process, and timely adjust the parameters of the fuzzy PID temperature control system according to the preset adjustment mechanism, and combine the segmented fuzzy control rules to make the fuzzy control parameters applicable to multiple temperature intervals, so as to improve the temperature control effect of the fuzzy PID temperature control system applied on a rapid PCR instrument. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flowchart of a control method for a fuzzy PID temperature control system provided in an embodiment of the present application;

[0058] Figure 2Schematic diagram of the optimization principle of the fuzzy PID temperature control system provided in the embodiment of the present application;

[0059] Figure 3 A comparative test data diagram of the traditional fuzzy PID system and the traditional PID system provided in the embodiment of the present application;

[0060] Figure 4 This is a diagram showing the effect of the segmented fuzzy control rules provided in the embodiment of the present application on the temperature control of a rapid PCR instrument;

[0061] Figure 5 This is a comparison experiment diagram of the temperature curve with and without the integral saturation mechanism provided in the embodiment of the present application;

[0062] Figure 6 This is a comparison experiment diagram of the temperature curve with and without error zero-crossing integral adjustment provided in the embodiment of the present application;

[0063] Figure 7 This is a comparative test data chart of the optimized fuzzy PID system provided in the embodiment of the present application and the traditional fuzzy PID system. DETAILED DESCRIPTION

[0064] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the accompanying drawings. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present disclosure.

[0065] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0066] The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0067] Example 1:

[0068] This embodiment provides a control method for a fuzzy PID temperature control system. Figure 1 and Figure 2 , the method comprises the following steps:

[0069] Step S1: Obtain input temperature control parameter data of the fuzzy PID temperature control system;

[0070] Step S2: designing fuzzy control parameters for different temperature rise and fall processes according to the input temperature control parameter data;

[0071] Step S3: According to the individually designed fuzzy control parameters, corresponding fuzzy control rules are set to perform segmented fuzzy control on the fuzzy PID temperature control system.

[0072] As a preferred embodiment, in step S1, see Figure 2 ,The process of obtaining the input temperature control parameter data of the fuzzy PID temperature control system includes:

[0073] Obtain the error |e| of the input temperature control parameter in the fuzzy PID temperature control system, and calculate the error change rate based on the error |e| The expression is:

[0074]

[0075] The error |e| and the error change rate Combine them to obtain the input temperature control parameter data of the fuzzy PID temperature control system;

[0076] Among them, e(t) represents the error at the current moment, e(t-Δt) represents the error at the previous moment, Δt represents the parameter sampling time interval, and error |e| represents the set temperature T set Compared with the actual measured temperature T actual The difference between .

[0077] In step S2, see Figure 2 The process of designing fuzzy control parameters for different heating and cooling processes includes:

[0078] S21: fuzzifying the input temperature control parameter data, and setting a first fuzzy rule and a second fuzzy rule according to the fuzzy processing result;

[0079] S22: Dividing the input temperature control parameter data into a plurality of temperature control parameter subsets according to the set first fuzzy rule or the second fuzzy rule;

[0080] S23: Based on the input temperature control parameter data in each temperature control parameter subset, set corresponding output rules to perform fuzzy reasoning on the fuzzy PID temperature control system, and output the fuzzy reasoning result;

[0081] S24: Defuzzification is performed according to the fuzzy inference result, and PID parameters are output;

[0082] Among them, PID parameters represent fuzzy control parameters, including the proportional term coefficient K p , integral term coefficient K i , differential term coefficient K d .

[0083] Specifically, in step S21, for large-scale temperature regulation (>20°C), a first fuzzy rule is designed, and for small-scale temperature regulation (<20°C), a second fuzzy rule is designed. Different fuzzy rules use different proportional term coefficients K. p range, and switch parameters according to the temperature adjustment range (wherein, the first and second mentioned in the present invention do not represent quantity, but are only used for distinguishing explanations).

[0084] Specifically, in step S22, the input temperature control parameter data is divided into 7 temperature control parameter subsets, including {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, recorded as {NB, NM, NS, Z, PS, PB, PM}.

[0085] For each input design, the corresponding output rules are designed, and the value range of each subset is appropriately expanded based on the empirical values ​​of the traditional PID parameters; the rules are shown in Tables 1, 2 and 3 below.

[0086] Table 1: K P Fuzzy control rules

[0087]

[0088] Table 2: K I Fuzzy control rules

[0089]

[0090] Table 3: K D Fuzzy control rules

[0091]

[0092] Specifically, in step S23, MATLAB Fuzzy Logic Designer technology is used to perform fuzzy reasoning on the fuzzy PID temperature control system, and the fuzzy reasoning result is output.

[0093] Specifically, in step S24, the process of outputting PID parameters includes:

[0094] The triangle function is used as the membership function, the centroid method is used for defuzzification, and PID calculation is performed. The expression of PID calculation is:

[0095]

[0096] Calculate the error |e| and error change rate of the input temperature control parameter data respectively In different temperature control parameter subsets, the proportional term coefficient K of the output PID parameter P , integral term coefficient K i , differential term coefficient K dThe membership degree of is expressed as:

[0097] μ KPID (E,DE)=triangular function(E,DE);

[0098] Among them, u(k) represents the output of the fuzzy PID temperature control system, e(k) represents the current error, K p , K i , K d are the coefficients of proportional term, integral term and differential term respectively, T s represents the sampling time, T i Indicates the integration time, T d Indicates the differential time, triangularfunction(E,DE) indicates that for a given error E and error change rate DE, the membership function returns K p , K i , K d Membership value.

[0099] It can be understood that setting different fuzzy rules for fuzzy reasoning can make the obtained PID parameters effectively applicable to multiple temperature ranges; using the center of gravity method for defuzzification calculation can effectively improve the efficiency of defuzzification, so as to improve the temperature control effect of the fuzzy PID temperature control system applied on the fast PCR instrument.

[0100] As a preferred embodiment, the method further includes the following steps:

[0101] Step S4, setting a regulation mechanism according to the temperature rise and fall state or the cyclic temperature change process of the fuzzy PID temperature control system, the process includes:

[0102] According to the heating and cooling state or the cyclic temperature change process of the fuzzy PID temperature control system, an integral adjustment mechanism is set when the temperature rising error crosses zero and an integral adjustment mechanism is set when the temperature falling error crosses zero;

[0103] According to the integral coefficient K in the PID parameters i , determine whether to start the integral adjustment mechanism when the temperature rise error crosses zero or the integral adjustment mechanism when the temperature fall error crosses zero.

[0104] Specifically, the process of determining whether to start the integral adjustment mechanism when the temperature rise error crosses zero or the integral adjustment mechanism when the temperature fall error crosses zero includes:

[0105] Set up the integral limiting mechanism and set the integral term threshold;

[0106] When the integral coefficient K i When the preset integral term threshold is reached, the integral limit mechanism is activated and the accumulation of the integral term coefficient K is stopped. i ;

[0107] When the current temperature of the fuzzy PID temperature control system reaches the set temperature for the first time during the heating process, the integral adjustment mechanism is started when the temperature error crosses zero, and the accumulated integral coefficient K i Perform a reduction process and recalculate the PID parameters, and use the calculated PID parameters for the fuzzy PID temperature control system (wherein, the process of recalculating the PID parameters is to multiply ∑e(k) by 0.8 to participate in the PID calculation of formula (1).);

[0108] When the current temperature of the fuzzy PID temperature control system reaches the set temperature for the first time during the cooling process, the integral adjustment mechanism is started when the cooling error passes through zero, the integral error is corrected to k times the original integral error (k is a negative value), and the PID parameters are recalculated. The calculated PID parameters are used for the fuzzy PID temperature control system (wherein, the process of recalculating the PID parameters is to multiply ∑e(k) by -1 to participate in the PID calculation of formula (1)).

[0109] The preset threshold can be expressed as integer_limit, and the calculation expression is:

[0110] It can be understood that setting the integral term threshold can effectively monitor the fuzzy PID temperature control system according to the PID parameters of the fuzzy PID temperature control system, and effectively monitor the fuzzy PID temperature control system in the cyclic temperature change process in multiple temperature intervals according to the set integral adjustment mechanism when the temperature rising error passes through zero and the integral adjustment mechanism when the temperature cooling error passes through zero, and then adjust the PID parameters of the fuzzy PID temperature control system to further optimize the parameters of the fuzzy PID temperature control system, thereby improving the temperature control effect of the fuzzy PID temperature control system applied on the fast PCR instrument.

[0111] As a preferred embodiment, the method further includes the following steps:

[0112] Step S5: setting a temperature control gain compensation mechanism according to the cyclic temperature change process of the fuzzy PID temperature control system;

[0113] Step S6: When the fuzzy PID temperature control system is in the cyclic temperature change process of the PCR reaction, the proportional term coefficient K of the PID parameter in the cyclic temperature change process is adjusted according to the temperature control gain compensation mechanism. p Perform dynamic adjustments to compensate for the gain of the fuzzy PID temperature control system.

[0114] Specifically, in step S5, the expression for gain compensation of the fuzzy PID temperature control system is:

[0115] G(n)=1+αn;

[0116] According to the temperature control gain compensation mechanism, the proportional term coefficient K of the PID parameter in the cycle temperature change process is p The expression for dynamic adjustment is:

[0117] K′ p =K p *G(n)=K p *(1+αn);

[0118] Where α is the gain coefficient, which is set according to the speed at which the radiator performance decreases and the desired degree of compensation; n is the number of cycles, K′ p Indicates the proportional term coefficient after gain compensation.

[0119] For example, in a 96-well rapid PCR instrument, when the denaturation temperature is set to 95° C. and the annealing temperature is set to 65° C., and in the 96-well rapid PCR instrument, the α parameter is selected as 0.005.

[0120] The PID controller outputs temperature control information according to the calculation of the above-mentioned adjusted KP, KI, and KD according to formula (1) to achieve temperature control of the reaction module.

[0121] It can be understood that according to the cyclic temperature change process of the fuzzy PID temperature control system, the temperature control gain compensation mechanism set can perform parameter compensation when the performance of the heat dissipation module decreases during the rapid temperature rise and fall process, so as to improve the temperature control effect of the fuzzy PID temperature control system applied on the fast PCR instrument.

[0122] In the embodiment, according to the set segmented fuzzy control rules, segmented fuzzy control can be performed on fuzzy PID temperature control systems with different set temperatures, so as to adjust the parameters of the fuzzy PID temperature control system according to the temperature change range of the fuzzy PID temperature control system, so that the fuzzy control parameters are applicable to multiple temperature intervals; according to the temperature increase and decrease state or the cyclic temperature change process of the fuzzy PID temperature control system, the set adjustment mechanism can effectively adopt the form of integration to monitor the state of the fuzzy PID temperature control system according to the temperature increase and decrease state or the cyclic temperature change process, and timely adjust the parameters of the fuzzy PID temperature control system according to the preset adjustment mechanism, and combine the segmented fuzzy control rules to make the fuzzy control parameters applicable to multiple temperature intervals, so as to improve the temperature control effect of the fuzzy PID temperature control system applied on the rapid PCR instrument.

[0123] By using the method of the present application, an overshoot of less than 1.5°C can be achieved at a heating and cooling rate of 4.5°C / s, and the temperature can be quickly within an error band of ±0.5°C.

[0124] Example 2:

[0125] This example provides corresponding experimental verification data based on Example 1, which are as follows:

[0126] Verification 1: Comparison between ordinary fuzzy PID algorithm and traditional PID algorithm

[0127] join Figure 3 , using PID algorithm and fuzzy PID algorithm to achieve temperature control for comparison, Figure 3 In the figure, the control effects of PID and fuzzy PID are compared when the temperature rises from room temperature to 55°C. It can be seen that the fuzzy PID of the present application has a faster response and a smaller overshoot.

[0128] Verification 2: Segmented Blur and Compensation Parameters

[0129] See also Figure 4 ,For large-range temperature regulation (>20℃) and small-range temperature regulation (<20℃), two sets of fuzzy control rules are combined to set compensation parameters, respectively. Figure 4 In the three temperature stages of PCR commonly used temperatures of 95℃, 65℃, and 72℃, the overshoot of the PCR temperature cycle after adjustment was controlled within 1.5℃.

[0130] Verification 3: Comparison of temperature curves with and without integral saturation mechanism

[0131] See also Figure 5 The integral limiter mechanism typically prevents excessive error accumulation from exceeding a threshold, but here it is used to reduce overshoot. Therefore, setting an integral threshold, at which the integral term stops accumulating, effectively reduces overshoot. As shown in the figure, after implementing the integral limiter mechanism, the overshoot is reduced from 3.91°C to below 2.0°C.

[0132] Verification 4: Error Zero-Crossing Integral Adjustment

[0133] In order to solve the problem of long adjustment time, an integral adjustment mechanism is introduced when the error crosses zero: Figure 5 As can be seen from the figure, after the actual temperature reaches the set temperature, the actual temperature will continue to rise and remain slightly higher than the set temperature for a long period of time. This is because the integral error accumulated in the early stage of temperature rise takes some time to eliminate. Therefore, a strategy is designed to reduce the previously accumulated integral error by a certain proportion when the actual temperature reaches the set temperature for the first time, so as to quickly stabilize the temperature near the set temperature. Figure 6 It can be seen that after adding integral adjustment, the overshoot is reduced from 2.0℃ to 0.92℃, the time to reach the set temperature for the first time is 10.5s, and the error band of ±0.25℃ is achieved within 15s.

[0134] Verification 5: Comparison of optimized fuzzy PID

[0135] See also Figure 7By comprehensively comparing PID, fuzzy PID, and the fuzzy PID optimized by the control method in Example 1 of the present invention, it can be seen that the optimized fuzzy PID achieves the smallest overshoot, the fastest response, and basically no oscillation.

[0136] Example 3:

[0137] This embodiment provides a rapid PCR instrument based on a fuzzy PID temperature control system, which is used to implement the function of a control method of a fuzzy PID temperature control system. The PCR instrument includes: a main control module, a temperature controller module, and a reaction module;

[0138] The main control module includes a host computer loaded with control method program instructions of the fuzzy PID temperature control system, which is used to send temperature control instructions and receive temperature feedback;

[0139] The temperature controller module includes a temperature controller connected to the host and the reaction module, and is used to execute the temperature control instructions sent by the host;

[0140] The reaction module includes a heat-conducting carrier, a heating and cooling plate, and a heat dissipation device, which are used to accurately control the temperature of the PCR instrument and feed the temperature back to the host.

[0141] Example 4:

[0142] This embodiment provides a computer-readable storage medium storing a computer program. The computer program is executed by a processor to implement the steps of a control method for a fuzzy PID temperature control system.

[0143] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A control method for a fuzzy PID temperature control system, characterized in that: The method comprises the following steps: Obtain the input temperature control parameter data of the fuzzy PID temperature control system; According to the input temperature control parameter data, fuzzy control parameters are individually designed for different temperature rising and falling processes; According to the individually designed fuzzy control parameters, the corresponding fuzzy control rules are set to perform segmented fuzzy control on the fuzzy PID temperature control system; The process of designing fuzzy control parameters for different heating and cooling processes includes: The input temperature control parameter data is fuzzified, and the first fuzzy rule and the second fuzzy rule are set according to the fuzzy processing results; the first fuzzy rule is designed for range temperature regulation (>20°C), and the second fuzzy rule is designed for small range temperature regulation (<20°C); According to the set first fuzzy rule or second fuzzy rule, the input temperature control parameter data is divided into several temperature control parameter subsets; Based on the input temperature control parameter data in each temperature control parameter subset, the corresponding output rules are set to perform fuzzy reasoning on the fuzzy PID temperature control system and output the fuzzy reasoning results; Defuzzification is performed based on the fuzzy inference results, and PID parameters are output; Among them, PID parameters represent fuzzy control parameters, including the proportional term coefficient K p , integral term coefficient K i , differential term coefficient K d ; The method further comprises the following steps: According to the cyclic temperature change process of the fuzzy PID temperature control system, a temperature control gain compensation mechanism is set; When the fuzzy PID temperature control system is in the cyclic temperature change process of the PCR reaction, the proportional term coefficient K of the PID parameter in the cyclic temperature change process is adjusted according to the temperature control gain compensation mechanism. p Perform dynamic adjustments to compensate for the gain of the fuzzy PID temperature control system; The expression for gain compensation of fuzzy PID temperature control system is: G(n)=1+αn; According to the temperature control gain compensation mechanism, the proportional term coefficient K of the PID parameter in the cycle temperature change process is p The expression for dynamic adjustment is: K′ p =K p *G(n)=K p *(1+αn); Among them, α represents the gain coefficient, n represents the number of cycles, K′ p Indicates the proportional term coefficient after gain compensation.

2. The control method of the fuzzy PID temperature control system according to claim 1, characterized in that: The process of obtaining the input temperature control parameter data of the fuzzy PID temperature control system includes: Obtain the error |e| of the input temperature control parameter in the fuzzy PID temperature control system, and calculate the error change rate based on the error |e| The expression is: The error |e| and the error change rate Combine them to obtain the input temperature control parameter data of the fuzzy PID temperature control system; Where e(t) represents the error at the current moment, e(t-Δt) represents the error at the previous moment, and Δt represents the parameter sampling time interval.

3. The control method of the fuzzy PID temperature control system according to claim 2, characterized in that: The process of outputting PID parameters includes: The triangle function is used as the membership function, the centroid method is used for defuzzification, and PID calculation is performed. The expression of PID calculation is: Calculate the error |e| and error change rate of the input temperature control parameter data respectively In different temperature control parameter subsets, the proportional term coefficient K of the output PID parameter P , integral term coefficient K i , differential term coefficient K d The membership degree of is expressed as: μ KPID (E,DE)=triangular function(E,DE); Among them, u(k) represents the output of the fuzzy PID temperature control system, e(k) represents the current error, K p , K i , K d are the coefficients of proportional term, integral term and differential term respectively, T s represents the sampling time, T i Indicates the integration time, T d Indicates the differential time, triangularfunction(E,DE) indicates that for a given error E and error change rate DE, the membership function returns K p , K i , K d Membership value.

4. The control method of the fuzzy PID temperature control system according to claim 1, characterized in that: The method further comprises the following steps: According to the temperature rise and fall state or cyclic temperature change process of the fuzzy PID temperature control system, the adjustment mechanism is set. The process includes: According to the heating and cooling state or the cyclic temperature change process of the fuzzy PID temperature control system, an integral adjustment mechanism is set when the temperature rising error crosses zero and an integral adjustment mechanism is set when the temperature falling error crosses zero; According to the integral coefficient K in the output PID parameters i , determine whether to start the integral adjustment mechanism when the temperature rise error crosses zero or the integral adjustment mechanism when the temperature fall error crosses zero; Among them, PID parameters represent fuzzy control parameters, including the proportional term coefficient K p , integral term coefficient K i , differential term coefficient K d .

5. The control method of the fuzzy PID temperature control system according to claim 4, characterized in that: The process of determining whether to start the integral adjustment mechanism when the temperature rise error crosses zero or the integral adjustment mechanism when the temperature fall error crosses zero includes: Set up the integral limiting mechanism and set the integral term threshold; When the integral coefficient K i When the preset integral term threshold is reached, the integral limit mechanism is activated and the accumulation of the integral term coefficient K is stopped. i ; When the current temperature of the fuzzy PID temperature control system reaches the set temperature for the first time during the heating process, the integral adjustment mechanism is started when the temperature error crosses zero, and the accumulated integral coefficient K i Perform reduction processing and recalculate PID parameters, and use the calculated PID parameters for the fuzzy PID temperature control system; When the current temperature of the fuzzy PID temperature control system reaches the set temperature for the first time during the cooling process, the integral adjustment mechanism is started when the cooling error crosses zero, the integral error is corrected to k times the original integral error, and the PID parameters are recalculated. The calculated PID parameters are used for the fuzzy PID temperature control system.

6. A fast PCR instrument based on a fuzzy PID temperature control system, characterized in that: The rapid PCR instrument is used to implement the function of the control method of the fuzzy PID temperature control system according to any one of claims 1 to 5, and the PCR instrument includes: a main control module, a temperature controller module and a reaction module; The main control module includes a host computer loaded with control method program instructions of the fuzzy PID temperature control system, which is used to send temperature control instructions and receive temperature feedback; The temperature controller module includes a temperature controller connected to the host and the reaction module, and is used to execute the temperature control instructions sent by the host; The reaction module includes a heat-conducting carrier, a heating and cooling plate, and a heat dissipation device, which are used to accurately control the temperature of the PCR instrument and feed the temperature back to the host.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method according to any one of claims 1 to 5.

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