Control method of fuzzy PID temperature control system

Abstract

The invention discloses a control method of a fuzzy PID temperature control system, and relates to the technical field of temperature control systems. The method comprises the following steps: acquiring input temperature control parameter data of a fuzzy PID temperature control system; independently designing fuzzy control parameters for different heating and cooling processes according to the input temperature control parameter data; and according to independently designed fuzzy control parameters, setting a corresponding fuzzy control rule to carry out segmented fuzzy control on the fuzzy PID temperature control system. According to the method provided by the invention, the fuzzy control parameters can be effectively enabled to be suitable for multiple temperature intervals, and parameter compensation is carried out when the performance of the heat dissipation module is reduced in the rapid temperature increasing and decreasing process, so that the temperature control effect of the fuzzy PID temperature control system applied to the rapid PCR instrument is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control systems, and in particular to a control method of 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. The denaturation temperature is generally 95°C, the annealing temperature is 65°C, and the extension temperature can be selected at 72°C. With each cycle, the number of target DNA fragments doubles. The fast PCR instrument requires that the entire PCR process of 35-40 cycles be completed 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 accuracy 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 adjusts the system output through three links: proportional, integral, and differential to make it close to the expected target; while fuzzy control uses fuzzy logic and fuzzy set theory to effectively control complex systems that are difficult to accurately model.

[0004] In the use of existing rapid PCR instruments, due to the short cycle time and fast temperature rise and fall speed, the following deficiencies still exist: the rapid PCR instrument usually cycles at 95°C, 65°C, and 72°C. If the room temperature is 25°C, the conventional fuzzy PID temperature control algorithm is not effective because the corresponding parameters cannot be simultaneously applied to the rapid changes from 25°C to 95°C, from 95°C to 65°C, from 65°C to 72°C, and from 72°C to 95°C. In particular, there are large differences in the heating and cooling processes. Since the heating block has heat exchange with the room temperature air, there is a tendency to cool down. If the same parameters are used for cooling and heating, a large overshoot (~10°C) will be caused. In addition, since fuzzy control will give different control parameters according to different errors, and the size of different errors is relative in different temperature change processes, for example, an error of 1°C is very small for the process from room temperature to 95°C, but very large for the process from 65°C to 72°C. Therefore, it is difficult to ensure fast response, good stability, and small overshoot in each temperature change process by using the same set of fuzzy control parameters. 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 technical deficiencies of the existing fuzzy PID temperature control system in the application of rapid PCR instruments include: 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 the rapid heating and cooling process 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 problem in the prior art that, when a fuzzy PID temperature control system is applied to a rapid PCR instrument, the fuzzy control parameters cannot be applied to multiple temperature intervals, and the performance degradation of the heat dissipation module during rapid temperature rise and fall 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 intervals, and perform parameter compensation when the performance of the heat dissipation module is degraded 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 rise and fall 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 scheme, 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 suitable for multiple temperature ranges, so as to improve 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] Wherein, 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 individually 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 the second fuzzy rule, the input temperature control parameter data is divided into a plurality of 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 the fuzzy reasoning results are output;

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

[0023] Among them, PID parameters represent fuzzy control parameters, including proportional term coefficient K p , integral 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 represents the differential time, triangularfunction(E,DE) represents the K returned by the membership function for a given error E and error change rate DE. p , K i , K d Membership value.

[0030] In the above technical scheme, different fuzzy rules are set for fuzzy reasoning, so that the obtained PID parameters can be effectively applied to multiple temperature ranges; the centroid method is used for defuzzification calculation, which 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 rapid PCR instrument.

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

[0032] According to the temperature rise and fall state or the cycle 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 when the temperature rising error crosses zero and an integral adjustment mechanism when the temperature falling error crosses zero are set;

[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 drop error crosses zero.

[0035] Further, 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 drop 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 passes through 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 scheme, the integral item 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 rise error passes through zero and the integral adjustment mechanism when the temperature drop 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 when applied to a 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 calculated according to the temperature control gain compensation mechanism. p Dynamic adjustment is performed to compensate 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 parameters in the cyclic 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 scheme, 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 fast PCR instrument based on a fuzzy PID temperature control system, the fast PCR instrument is used to realize 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 back the temperature to the host.

[0054] A computer-readable storage medium stores a computer program, which 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, segmented fuzzy control can be performed on fuzzy PID temperature control systems with different set temperatures, so that the parameters of the fuzzy PID temperature control system are 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 rise and fall 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 rise and fall 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 fast PCR instrument. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0058] Figure 2A schematic 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 a conventional fuzzy PID system and a conventional PID system provided in an embodiment of the present application;

[0060] Figure 4 The effect diagram of the segmented fuzzy control rule provided in the embodiment of the present application in the temperature control of the rapid PCR instrument;

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

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

[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] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Preferred embodiments of the present invention are provided in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[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 related listed items.

[0067] Embodiment 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: Obtaining input temperature control parameter data of the fuzzy PID temperature control system;

[0070] Step S2: According to the input temperature control parameter data, fuzzy control parameters are individually designed for different temperature rise and fall processes;

[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 the error |e| represents the set temperature T set 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 the first fuzzy rule and the 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 reasoning result, and PID parameters are output;

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

[0083] Specifically, in step S21, a first fuzzy rule is designed for large-range temperature adjustment (>20°C), and a second fuzzy rule is designed for small-range temperature adjustment (<20°C). 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 rule is designed, and the value range of each subset is appropriately expanded according to the empirical values ​​of the parameters of the traditional PID; the rules are shown in Table 1, Table 2 and Table 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 represents the differential time, triangularfunction(E,DE) represents the K returned by the membership function for a given error E and error change rate DE. 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 centroid 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 comprises 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 when the temperature rising error crosses zero and an integral adjustment mechanism when the temperature falling error crosses zero are set;

[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 drop 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 drop 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 item threshold can effectively monitor the fuzzy PID temperature control system according to the PID parameters of the fuzzy PID temperature control system, so as to 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 rise 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 when applied to the fast PCR instrument.

[0111] As a preferred embodiment, the method further comprises 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 Dynamic adjustment is performed to compensate 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 parameters in the cyclic 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 represents the gain coefficient, which is set according to the speed at which the heat sink performance decreases and the desired degree of compensation; n represents the number of cycles, K′ p Indicates the proportional term coefficient after gain compensation.

[0119] Exemplarily, 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 to be 0.005.

[0120] The PID controller outputs temperature control information according to the above 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 up 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.

[0122] In the embodiment, 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; according to the temperature rising and falling 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 rising and falling 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] Embodiment 2:

[0125] This embodiment provides corresponding experimental verification data based on the first embodiment, 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: Segmental 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. 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 limit mechanism is usually used to prevent the error from accumulating too much and causing the controller output to exceed the threshold, but it is used here to reduce overshoot. Therefore, an integral threshold is set. When the integral term reaches this threshold, it will no longer accumulate, which can effectively reduce overshoot. As can be seen from the figure, after adding the integral limit mechanism, the overshoot is reduced from 3.91℃ to below 2.0℃.

[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 passes through zero: Figure 5 It can be seen from the figure that when the actual temperature reaches the set temperature, the actual temperature will still rise and be 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 the 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 reached 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 occurs.

[0136] Embodiment three:

[0137] This embodiment provides a fast PCR instrument based on a fuzzy PID temperature control system, the fast PCR instrument 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 back the temperature to the host.

[0141] Embodiment 4:

[0142] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement 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 specification 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 rise and fall 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.

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; Wherein, 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 is characterized in that: The process of designing fuzzy control parameters for different heating and cooling processes includes: 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; According to the set first fuzzy rule or the second fuzzy rule, the input temperature control parameter data is divided into a plurality of 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 the fuzzy reasoning results are output; Defuzzification is performed based on the fuzzy reasoning results, and PID parameters are output; Among them, PID parameters represent fuzzy control parameters, including proportional term coefficient K p , integral coefficient K i , differential term coefficient K d .

4. The control method of the fuzzy PID temperature control system according to claim 3 is 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 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 represents the differential time, triangularfunction(E,DE) represents the K returned by the membership function for a given error E and error change rate DE. p , K i , K d Membership value.

5. 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 the cycle 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 when the temperature rising error crosses zero and an integral adjustment mechanism when the temperature falling error crosses zero are set; 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 drop error crosses zero; Among them, PID parameters represent fuzzy control parameters, including proportional term coefficient K p , integral coefficient K i , differential term coefficient K d .

6. The control method of the fuzzy PID temperature control system according to claim 5, 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 passes through 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.

7. The control method of the fuzzy PID temperature control system according to claim 1, characterized in that: The method also The following steps are involved: 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 calculated according to the temperature control gain compensation mechanism. p Perform dynamic adjustment to compensate the gain of the fuzzy PID temperature control system; Among them, PID parameters represent fuzzy control parameters, including proportional term coefficient K p , integral coefficient K i , differential term coefficient K d .

8. The control method of the fuzzy PID temperature control system according to claim 7, characterized in that: 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 parameters in the cyclic 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.

9. A fast PCR instrument based on a fuzzy PID temperature control system, characterized in that: The fast 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 8, and the PCR instrument comprises: 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 back the temperature to the host.

10. 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 8.

Images