Microbial fermentation temperature control method based on PID (Proportion Integration Differentiation)

Abstract

The invention provides a microbial fermentation temperature control method based on PID (Proportion Integration Differentiation), which relates to the field of temperature control, and specifically comprises the following steps: constructing microbial fermentation temperature control models including a temperature monitoring model, an improved PID control model, a temperature driving model, a temperature error model and a target function model; the maternal line optimization algorithm is improved, and the improved maternal line optimization algorithm is guided through the target function to set the coefficient of the traditional PID control algorithm to obtain an improved PID control algorithm; normalizing the deviation value of the target temperature and the real-time temperature in a range of [0, 1] to obtain a normalized temperature deviation value of the real-time temperature and the target temperature; the normalized temperature deviation value is input into an improved PID control model, a temperature control signal is output after adjustment through an improved PID control algorithm, the temperature control signal controls and adjusts the microbial fermentation temperature through a temperature driving model, and the microbial fermentation temperature is accurately and rapidly adjusted.

Description

Technical Field

[0001] The invention relates to the field of temperature control, and in particular to a microbial fermentation temperature control method based on PID. Background Art

[0002] Temperature is one of the key factors affecting the rate of enzyme reactions. Within a suitable temperature range, the growth rate of microorganisms will accelerate as the temperature rises; however, when the temperature exceeds a certain limit, the catalytic activity of the enzyme will decrease, causing the growth rate of the microorganisms to slow down or even stop.

[0003] PID control is a widely used automatic control technology, including proportional coefficient (P), integral coefficient (I) and differential coefficient (D); in the process of microbial fermentation, when the temperature deviates from the set value, the proportional coefficient will respond immediately, speed up the heating or cooling speed, and quickly return the temperature to the set value. The integral coefficient integrates the deviation, and the integral control will continuously adjust the control output until the deviation is completely eliminated, so that the temperature is stabilized at the set value; in the fermentation process, the temperature change trend can reflect the dynamic characteristics of the fermentation process, and the differential control can adjust the control output according to this trend to make the temperature control more stable and accurate; however, the microbial fermentation process is a nonlinear, time-varying, large lag, uncertain multivariable input and output system involving the growth and reproduction process of living organisms, and the mechanism is very complex. When dealing with such a complex system, the traditional PID control algorithm cannot accurately capture and respond to the dynamic changes in the fermentation process, resulting in poor control effect.

[0004] The Mother Optimization Algorithm (MOA) is a newly proposed meta-heuristic algorithm. Its design is inspired by the interaction between mothers and their children in the three stages of education, advice and training. It constructs mathematical models of two strategies: search and development. This algorithm is particularly suitable for solving complex engineering optimization problems and has demonstrated its superiority in global search and local search capabilities on multiple standard test functions. Summary of the invention

[0005] Based on the above background, temperature is a key parameter in the fermentation process, and its control effect is directly related to the efficiency and quality of fermentation. The present invention proposes a microbial fermentation temperature control method based on PID, introduces a meta-heuristic algorithm to improve the traditional PID control algorithm, compensates for the hysteresis problem between the feedback output and the target value of the microbial fermentation temperature control system and the temperature control overshoot problem, and accurately and quickly adjusts the microbial fermentation temperature.

[0006] To achieve the above object, the present invention provides the following technical solution: a microbial fermentation temperature control method based on PID, the specific steps are as follows:

[0007] S101, constructing a microbial fermentation temperature control model, wherein the microbial fermentation temperature control model includes a temperature monitoring model, an improved PID control model, a temperature driving model, a temperature error model and an objective function model.

[0008] S102. The improved PID control model includes a maternal optimization algorithm and an improved PID control algorithm; the improvement of the maternal optimization algorithm includes: S201. Dynamically improving the search strategy of the maternal optimization algorithm based on the historical search results of the maternal optimization algorithm; S202. Improving the development strategy of the maternal optimization algorithm by constructing an individual position diversity strategy among populations, and the improved development strategy is a segmented type; the improved PID control algorithm includes adjusting the Kp, Ki and Kd coefficients of the traditional PID control algorithm by guiding the improved maternal optimization algorithm through the objective function.

[0009] S103 , normalizing the deviation value between the target temperature and the real-time temperature within the range of [0, 1] to obtain a normalized temperature deviation value between the real-time temperature and the target temperature.

[0010] S104, inputting the normalized temperature deviation value into the improved PID control model, outputting a temperature control signal after adjustment through the improved PID control algorithm, and the temperature control signal controls and adjusts the microbial fermentation temperature through the temperature drive model, thereby achieving accurate and rapid adjustment of the microbial fermentation temperature.

[0011] Preferably, the microbial fermentation temperature control is a closed-loop control method, wherein the temperature monitoring model monitors the temperature value of the closed environment of the microbial fermentation experiment in real time, inputs the temperature of the closed environment of the microbial fermentation experiment into the temperature error model, and calculates the deviation value between the target temperature and the real-time temperature. , the deviation value is normalized within the range of [0,1], and the normalized temperature deviation value of the closed environment of the microbial fermentation experiment is input into the improved PID control model. The improved parent optimization algorithm is used to optimize the traditional PID control algorithm. The optimal proportional coefficient, integral coefficient and differential coefficient are used to regulate the temperature deviation of the closed environment of the microbial fermentation experiment, and the temperature control signal is output. Input the temperature driving model, which controls the temperature of the closed environment of the microbial fermentation experiment; the temperature driving model is a second-order inertial model, which reflects the temperature and temperature control signal of the closed environment of the microbial fermentation experiment The corresponding relationship; the adjusted temperature is monitored by the temperature monitoring model and input into the temperature error model, and the temperature of the closed environment of the microbial fermentation experiment is continued to be regulated by the improved PID control model until the temperature value of the closed environment of the microbial fermentation experiment reaches the target temperature.

[0012] Preferably, the temperature-driven model describes the dynamic changes of heat inside the closed environment of the microbial fermentation experiment, which is obtained from the heat balance equation, the relationship between heat input and output, and the dynamic response principle. Its time domain mathematical model is: ; In the formula, is the temperature value of the closed environment of the microbial fermentation experiment at the tth second, is the natural frequency, reflecting the dynamic response speed of the system, is the gain, which describes the amplification factor of the temperature control signal to the temperature change. It is the temperature control signal.

[0013] Preferably, the improved maternal optimization algorithm includes a search strategy and a development strategy. A position update mathematical model is established through the two strategies. The position of the proxy individual is updated through the position update mathematical model. In the process of updating the position, the improved maternal optimization algorithm is guided by the fitness value to output the best proxy individual position, wherein the best proxy individual position is the proxy individual position corresponding to the minimum fitness value. The smaller the fitness value, the better the proxy individual position, and vice versa.

[0014] Preferably, the fitness value is calculated by an objective function model, and the objective function is obtained by inputting the deviation value between the target temperature and the real-time temperature. and response time Design, the smaller the fitness value, that is, the smaller the output value of the objective function, the smaller the deviation value and response time The smaller it is, the better the temperature control effect is. The objective function reflects the control effect of the improved PID control model on the temperature of the closed environment of the microbial fermentation experiment under the current optimal proportional coefficient, integral coefficient and differential coefficient. The mathematical model of Kp, Ki and Kd mapping with the improved PID control algorithm is: {X}_{j}=\left [ {{X}_{1},{X}_{2},{X}_{3}} \right ]=\left [ {Kp,Ki,Kd} \right ] , then the optimal agent individual position is the optimal Kp, Ki and Kd of the improved PID control algorithm; the objective function Mathematical model: ;in, is the total running time.

[0015] Preferably, the search strategy of the maternal optimization algorithm is dynamically improved based on the historical search results of the maternal optimization algorithm. Specifically, the historical search results are reflected by the fitness value of the current iteration and the fitness value of the previous iteration, and the adaptive factor is designed. , the mathematical model is: ; In the formula, Adaptive factor The value of the kth iteration, is the maximum value of the adaptive factor, is the minimum value of the adaptive factor, is the minimum fitness value of the kth iteration, is the minimum fitness value of the k-1th iteration, The agent size of the improved matriarchal optimization algorithm, .

[0016] Preferably, the adaptive factor In the mathematical model, the fitness value change is transformed logarithmically and exponentially to enhance the sensitivity of dynamic adjustment. The cosine function is used to control the nonlinear periodic change of the adaptive factor, so that the parent optimization algorithm can dynamically balance between search and development and avoid falling into the local optimum. and When the difference is large, When the difference is small, the search ability of the parent optimization algorithm is enhanced. The adaptive factor is introduced into the search strategy of the parent optimization algorithm, and the position update mathematical model of the original search strategy is improved to obtain an improved search strategy, and the mathematical model is: ; Where SBB is the set of the bottom 20 agents in fitness ranking in the current iteration process, is the position value of the jth dimension of the ith agent in the SBB set; is the position value of the j-th dimension of the best agent individual in the k-th iteration, is the position value of the j-th dimension of the i-th agent individual in the k-th iteration, is the updated position value of the j-th dimension of the i-th agent individual.

[0017] Preferably, the inter-population individual position diversity strategy is constructed to improve the development strategy of the maternal optimization algorithm, thereby improving the optimization range and accuracy of the control development strategy. Specifically, the inter-population individual position diversity strategy is as follows: the j-dimensional search space of the i-th proxy individual is \left [ {{ub}_{j},{lb}_{j}} \right ] Divided into M equally spaced intervals\left [ {{lb}_{j}+\frac {m-1} {M}\left ( {{ub}_{j}-{lb}_{j}} \right ),{ub}_{j}+\frac {m} {M}\left ( {{ub}_{j}-{lb}_{j}} \right )} \right ] , count the number of agent individuals in the mth interval of the jth dimension in the population Probability , the mathematical model is: ; Calculate the entropy of the entire population agent in the jth dimension. The mathematical model is: ; It reflects the overall position distribution complexity of the entire population in the jth dimension, takes the weighted average of the distribution differences of all sub-populations, and defines the position diversity of proxy individuals among the entire population. The mathematical model is: ; The entropy values ​​of all D dimensions are averaged to obtain the diversity of the entire agent population, reflecting the complexity of the distribution of the entire population in multidimensional space; Individual position diversity strategy between populations The value range of is determined by the number of equally spaced intervals M, which is \left [ {0,lg\left ( {M} \right )} \right ] .

[0018] Preferably, the position update mathematical model of the original development strategy of the maternal optimization algorithm is improved by using the individual position diversity strategy among populations. When the population diversity is low, the exploration is enhanced to introduce random disturbances and increase the randomness of position adjustment; when the population diversity is high, the development is enhanced to reduce the position adjustment range, so that the population can search the current area more accurately. The mathematical model is: ; In the formula, is the position value of the j-th dimension of the i-th agent individual in the k-th iteration, is the updated position value of the j-th dimension of the ith agent individual, is a random number between 0 and 1. is the upper bound of the position value of the agent's j-th dimension, is the lower bound of the position value of the agent's j-th dimension, is the maximum number of iterations, is the average of the positions of all agents in the kth iteration, is a minimum value, and k is the current iteration number.

[0019] Preferably, the objective function is used to guide the improved parent optimization algorithm to adjust the Kp, Ki and Kd parameters of the traditional PID control algorithm, wherein in the process of adjusting the proportional coefficient, the integral coefficient and the differential coefficient, the normalized temperature deviation value is simulated by a unit step signal, and the specific steps are as follows: S401, initializing the population, including: initializing the population size of the agent individuals and the maximum number of iterations And the upper bound of the agent's position and lower bound ; Among them, the population size , that is, there are N agent individuals, corresponding to N groups of Kp, Ki and Kd coefficients of the PID control algorithm; S402, calculate the fitness value, for each agent in the population, use the coefficient of the corresponding PID control algorithm to improve the PID control model, input the unit step signal into the improved PID control model, calculate the objective function value according to the temperature drive model output, and record the optimal objective function value in the population, which is recorded as and the corresponding PID control parameters are recorded as the optimal Kp, Ki, and Kd coefficients; S403, enter the iteration loop and calculate the adaptation factor of the current iteration Values ​​and individual position diversity among populations, using improved search strategies and improved exploitation strategies to update agent individual positions; S404: Calculate the fitness value of each agent according to the updated position, compare the new position with the old position, retain the agent position with the smaller fitness value, and update the global optimal solution of the current iterative population. and the corresponding objective function value; S405, current number of iterations Is this equal to the maximum number of iterations? , if so, the global optimal solution of the current iterative population Output, resolve to the best combination of Kp, Ki, Kd coefficients, otherwise return to execute S402.

[0020] Compared with the prior art, the advantages and positive effects of the present invention are as follows: the present invention utilizes the improved parent optimization algorithm to intelligently adjust the PID control parameters, so that the temperature system can still achieve rapid response in a nonlinear and large lag environment, and dynamically adjusts the Kp, Ki, and Kd parameters to reduce overshoot and steady-state errors, while accelerating the system stabilization time; the present invention achieves efficient and stable performance improvement in the process of optimizing PID control parameters, and provides an intelligent and high-precision optimization method for solving complex fermentation temperature control problems. This technology has broad application prospects and promotion value in the field of industrial automation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The figure is a flow chart of the temperature control method of microbial fermentation based on PID.

[0022] Figure 2 A flow chart for adjusting the Kp, Ki and Kd coefficients of the traditional PID control algorithm using the improved parent optimization algorithm guided by the objective function.

[0023] Figure 3 It is a diagram of the optimization fitness values ​​of the improved maternal optimization algorithm and the standard maternal optimization algorithm of the present invention.

[0024] Figure 4 This is a diagram showing the Kp, Ki and Kd coefficient setting results of the improved parent optimization algorithm of the present invention.

[0025] Figure 5 This is the Kp, Ki and Kd coefficient adjustment result diagram of the standard parent system optimization algorithm.

[0026] Figure 6 This is the controlled function effect diagram of microbial fermentation temperature control. DETAILED DESCRIPTION

[0027] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below; in order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] The present application provides a PID-based microbial fermentation temperature control method, such as Figure 1 As shown, the method includes: S101, constructing a microbial fermentation temperature control model, wherein the microbial fermentation temperature control model includes a temperature monitoring model, an improved PID control model, a temperature driving model, a temperature error model and an objective function model; S102, the improved PID control model includes a maternal optimization algorithm and an improved PID control algorithm; the improvement of the maternal optimization algorithm includes: S201, dynamically improving the search strategy of the maternal optimization algorithm based on the historical search results of the maternal optimization algorithm; S202, constructing an individual position diversity strategy between populations to improve the development strategy of the maternal optimization algorithm, and the improved development strategy is a segmented type; the improved PID control algorithm includes guiding the improved maternal optimization algorithm to adjust the Kp, Ki and Kd coefficients of the traditional PID control algorithm through the objective function; S103, normalizing the deviation value between the target temperature and the real-time temperature within the range of [0,1] to obtain a normalized temperature deviation value between the real-time temperature and the target temperature; S104, inputting the normalized temperature deviation value into the improved PID control model, outputting a temperature control signal after adjustment through the improved PID control algorithm, and the temperature control signal controls and adjusts the microbial fermentation temperature through the temperature drive model, thereby achieving accurate and rapid adjustment of the microbial fermentation temperature.

[0029] Specifically, the temperature control of microbial fermentation is a closed-loop control method, in which the temperature monitoring model monitors the temperature value of the closed environment of the microbial fermentation experiment in real time, inputs the temperature of the closed environment of the microbial fermentation experiment into the temperature error model, and calculates the deviation value between the target temperature and the real-time temperature. , the deviation value is normalized within the range of [0,1], and the normalized temperature deviation value of the closed environment of the microbial fermentation experiment is input into the improved PID control model. The improved parent optimization algorithm is used to optimize the traditional PID control algorithm. The optimal proportional coefficient, integral coefficient and differential coefficient are used to regulate the temperature deviation of the closed environment of the microbial fermentation experiment, and the temperature control signal is output. Input the temperature driving model, which controls the temperature of the closed environment of the microbial fermentation experiment; the temperature driving model is a second-order inertial model, which reflects the temperature and temperature control signal of the closed environment of the microbial fermentation experiment The corresponding relationship; the adjusted temperature is monitored by the temperature monitoring model and input into the temperature error model, and the temperature of the closed environment of the microbial fermentation experiment is continued to be regulated by the improved PID control model until the temperature value of the closed environment of the microbial fermentation experiment reaches the target temperature.

[0030] Specifically, the difference between the deviation value of the current target temperature and the real-time temperature and the set minimum deviation value is divided by the difference between the deviation value of the current target temperature and the real-time temperature and the set maximum deviation value, and the deviation value of the target temperature and the real-time temperature is normalized within the range of [0,1]; wherein, the minimum deviation value is set to 0.01 and the maximum deviation value is set to 10.

[0031] Specifically, the improvement of the maternal optimization algorithm of S102 is to dynamically improve the search strategy of the maternal optimization algorithm based on the historical search results of the maternal optimization algorithm; the historical search results are reflected by the fitness value of the current iteration and the fitness value of the previous iteration, and the adaptive factor is designed. , the mathematical model is: ; In the formula, Adaptive factor The value of the kth iteration, is the maximum value of the adaptive factor, is the minimum value of the adaptive factor, is the minimum fitness value of the kth iteration, is the minimum fitness value of the k-1th iteration, The agent size of the improved matriarchal optimization algorithm, ; The adaptive factor is introduced into the search strategy of the maternal optimization algorithm, and the position update mathematical model of the original search strategy is improved to obtain an improved search strategy. The mathematical model is: ; Where SBB is the set of the bottom 20 agents in fitness ranking in the current iteration process, is the position value of the jth dimension of the ith agent in the SBB set; is the position value of the j-th dimension of the best agent individual in the k-th iteration, is the position value of the j-th dimension of the i-th agent individual in the k-th iteration, is the updated position value of the j-th dimension of the i-th agent individual.

[0032] Specifically, for the improvement of the maternal optimization algorithm of S102, the development strategy of the maternal optimization algorithm is improved by constructing an individual position diversity strategy between populations. The improved development strategy is segmented; the individual position diversity strategy between populations is specifically: the j-dimensional search space of the i-th proxy individual is \left [ {{ub}_{j},{lb}_{j}} \right ] Divided into M equally spaced intervals\left [ {{lb}_{j}+\frac {m-1} {M}\left ( {{ub}_{j}-{lb}_{j}} \right ),{ub}_{j}+\frac {m} {M}\left ( {{ub}_{j}-{lb}_{j}} \right )} \right ] , count the number of agent individuals in the mth interval of the jth dimension in the population Probability , the mathematical model is: ; Calculate the entropy of the entire population agent in the jth dimension. The mathematical model is: ; It reflects the overall position distribution complexity of the entire population in the jth dimension, takes the weighted average of the distribution differences of all sub-populations, and defines the position diversity of proxy individuals among the entire population. The mathematical model is: ; The entropy values ​​of all D dimensions are averaged to obtain the diversity of the entire agent population, reflecting the complexity of the distribution of the entire population in multidimensional space; Individual position diversity strategy between populations The value range of is determined by the number of equally spaced intervals M, which is \left [ {0,lg\left ( {M} \right )} \right ] .

[0033] Specifically, the position update mathematical model of the original development strategy of the maternal optimization algorithm is improved by using the individual position diversity strategy among populations. When the population diversity is low, the exploration is enhanced to introduce random disturbances and increase the randomness of position adjustment; when the population diversity is high, the development is enhanced to reduce the amplitude of position adjustment, so that the population can search the current area more accurately. The mathematical model is: ; In the formula, is the position value of the j-th dimension of the i-th agent individual in the k-th iteration, is the updated position value of the j-th dimension of the ith agent individual, is a random number between 0 and 1. is the upper bound of the position value of the agent's j-th dimension, is the lower bound of the position value of the agent's j-th dimension, is the maximum number of iterations, is the average of the positions of all agents in the kth iteration, is a minimum value, and k is the current number of iterations.

[0034] Specifically, the Kp, Ki and Kd coefficients of the traditional PID control algorithm are adjusted by guiding the improved parent optimization algorithm through the objective function. In the process of adjusting the proportional coefficient, integral coefficient and differential coefficient, the normalized temperature deviation value is simulated by the unit step signal. The specific implementation steps are as follows: Figure 2 As shown, the best Kp, Ki, and Kd parameters are as follows Figure 4 As shown, Kp=13.2365, Ki=1.17048, Kd=2.72867; S401, initializing the population, including: initializing the population size of the agent individuals and the maximum number of iterations And the upper bound of the agent's position and lower bound ; Among them, the population size , that is, there are N agent individuals, corresponding to N groups of Kp, Ki and Kd coefficients of the PID control algorithm; S402, calculate the fitness value, for each agent in the population, use the coefficient of the corresponding PID control algorithm to improve the PID control model, input the unit step signal into the improved PID control model, calculate the objective function value according to the temperature drive model output, and record the optimal objective function value in the population, which is recorded as and the corresponding PID control parameters are recorded as the optimal Kp, Ki, and Kd coefficients; S403, enter the iteration loop and calculate the adaptation factor of the current iteration Values ​​and individual position diversity among populations, using improved search strategies and improved exploitation strategies to update agent individual positions; S404: Calculate the fitness value of each agent according to the updated position, compare the new position with the old position, retain the agent position with the smaller fitness value, and update the global optimal solution of the current iterative population. and the corresponding objective function value; S405, current number of iterations Is this equal to the maximum number of iterations? , if so, the global optimal solution of the current iterative population Output, resolve to the best combination of Kp, Ki, Kd coefficients, otherwise return to execute S402.

[0035] Specifically, the objective function value is recorded as the fitness value, and the objective function Mathematical model: ; In the formula, For the response time.

[0036] Specifically, the optimal Kp, Ki, and Kd parameters are used in the improved PID control algorithm, the normalized temperature deviation value is input, and the temperature control signal is output after adjustment by the improved PID control algorithm. , executed according to the mathematical model; ; In the formula, is the normalized temperature deviation between the real-time temperature and the target temperature, =0,1,...,t.

[0037] Specifically, the temperature drive model is obtained from the heat balance equation, the relationship between heat input and output, and the dynamic response principle. The input drives the temperature change, and its time domain mathematical model is: ; In the formula, is the temperature value of the closed environment of the microbial fermentation experiment at the tth second, is the natural frequency, reflecting the dynamic response speed of the system. is the gain, which describes the amplification factor of the temperature control signal to the temperature change. It is the temperature control signal.

[0038] Furthermore, the PID-based microbial fermentation temperature control method was simulated in Matlab and Simulink, including simulating the normalized temperature deviation value through a unit step signal to obtain the optimal Kp, Ki, and Kd parameters, and using the optimal Kp, Ki, and Kd parameters in the microbial fermentation temperature control model to obtain the microbial fermentation temperature control effect.

[0039] Furthermore, in Matlab, based on Figure 2 As well as the position update mathematical model of the matrilineal optimization algorithm, the program design of the improved matrilineal optimization algorithm is completed, including setting the population size of the proxy individuals The maximum number of iterations is 40. is 70 and the upper bound of the agent's individual position For the three-dimensional set {100,10,100} and the lower bound The three-dimensional set {0.001, 0, 0.001} is used to initialize the position of each agent individual in the population by random initialization method, and the objective function is mapped to the fitness function, and the objective function value is recorded as the fitness function value; the temperature monitoring model, improved PID control model, temperature drive model, temperature error model and objective function model simulation of the microbial fermentation temperature control system are built in Simulink, where the temperature error model uses a unit step signal to simulate the normalized temperature deviation value, and the complex frequency domain mathematical model of the temperature drive model is used as the controlled function.

[0040] Furthermore, the time domain mathematical model of the temperature driven model is Laplace transformed with the initial condition being zero, and the formula is: ; where s is the time value in the complex frequency domain; Simplified, we get , the transfer function of microbial fermentation temperature control is: ;in, is 4, is 1, and the controlled function is: ; The controlled function reflects the effect of the control method of the present invention. Set the running time to 20 seconds, run the simulation model, and obtain the following Figures 3 to 6 The effect diagram shown.

[0041] Furthermore, the simulation model is run with a running time of 20 seconds, and the following results are obtained: Figures 3 to 6 The effect diagram shown is as follows: Figure 3 The fitness value changes in the process of adjusting the Kp, Ki and Kd coefficients of the traditional PID control algorithm by the method of the present invention and the standard parent optimization algorithm. According to the design of the objective function of the present invention, the smaller the fitness value, the more accurate the Kp, Ki and Kd coefficients are. Figure 1 It can be seen that the fitness value of the method of the present invention tends to be stable when the number of iterations reaches 32 times, and the minimum fitness value is 0.628554; the fitness value of the standard maternal optimization algorithm tends to be stable when the number of iterations reaches 12 times, and the minimum fitness value is 0.834896. In comparison, the fitness value of the method of the present invention in the process of setting the Kp, Ki and Kd coefficients of the traditional PID control algorithm is smaller, indicating that the Kp, Ki and Kd coefficients obtained by the method of the present invention are more accurate in controlling the temperature of microbial fermentation. Figure 4 and Figure 5 The Kp, Ki and Kd coefficients of the traditional PID control algorithm are adjusted by the method of the present invention and the standard parent optimization algorithm. The improved PID control model is input and the complex frequency domain mathematical model of the output temperature control signal acting on the temperature drive model is obtained. Figure 6 Controlled function effect diagram.

[0042] like Figure 6 As shown in the figure, the unit step signal simulates the normalized temperature deviation value, and the dynamic response comparison of the two control methods under the unit step input is output. The MOA+PID control method shows a large overshoot phenomenon. The response value of the microbial fermentation temperature control quickly exceeds the target value (the maximum value is close to 1.5), and then obvious oscillation occurs. The overshoot is large, which can easily cause the temperature to exceed the safe range and affect the stability of the system. It takes a long time to gradually converge to the target value 1, and the stabilization time is long, showing a weaker control effect. The response curve of the method of the present invention is smoother, the overshoot is very small, almost not exceeding the target value 1, there is no obvious oscillation phenomenon, the curve quickly approaches the target value, and is stable in a short time, the steady-state error is close to 0, and the control effect is significantly better than the traditional method.

Claims

1. A PID-based microbial fermentation temperature control method, characterized in that: Specifically include: S101, constructing a microbial fermentation temperature control model, wherein the microbial fermentation temperature control model includes a temperature monitoring model, an improved PID control model, a temperature driving model, a temperature error model and an objective function model; S102, the improved PID control model includes a parent optimization algorithm and an improved PID control algorithm; Improvements to the maternal optimization algorithm include: S201, dynamically improving the search strategy of the maternal optimization algorithm based on historical search results of the maternal optimization algorithm; S202, improving the development strategy of the maternal optimization algorithm by constructing an individual position diversity strategy between populations, wherein the improved development strategy is a segmented type; the improved PID control algorithm includes adjusting the Kp, Ki and Kd coefficients of the traditional PID control algorithm by guiding the improved maternal optimization algorithm through an objective function; S103, normalizing the deviation value between the target temperature and the real-time temperature within the range of [0,1] to obtain a normalized temperature deviation value between the real-time temperature and the target temperature; S104, inputting the normalized temperature deviation value into the improved PID control model, outputting a temperature control signal after adjustment through the improved PID control algorithm, and the temperature control signal controls and adjusts the microbial fermentation temperature through the temperature drive model, thereby achieving accurate and rapid adjustment of the microbial fermentation temperature.

2. A PID-based microbial fermentation temperature control method according to claim 1, characterized in that: The temperature driving model in S101 regulates the temperature of the closed environment of the microbial fermentation experiment. The temperature driving model is a second-order inertial model that reflects the temperature of the closed environment of the microbial fermentation experiment and the temperature control signal. The corresponding relationship; the adjusted temperature is monitored by the temperature monitoring model and input into the temperature error model, and the temperature of the closed environment of the microbial fermentation experiment is continuously regulated by the improved PID control model until the temperature value of the closed environment of the microbial fermentation experiment reaches the target temperature; the temperature driving model is obtained by the heat balance equation, the relationship between heat input and output, and the dynamic response principle, and the time domain mathematical model is: ； In the formula, is the temperature value of the closed environment of the microbial fermentation experiment at the tth second, is the natural frequency, reflecting the dynamic response speed of the system. is the gain, which describes the amplification factor of the temperature control signal to the temperature change. It is the temperature control signal.

3. A PID-based microbial fermentation temperature control method according to claim 2, characterized in that: The improved parent optimization algorithm guided by the objective function is used to adjust the Kp, Ki and Kd coefficients of the traditional PID control algorithm. In the process of adjusting the proportional coefficient, the integral coefficient and the differential coefficient, the normalized temperature deviation value is simulated by a unit step signal. The specific steps are as follows: S401, initializing the population, including: initializing the population size of the agent individuals and the maximum number of iterations And the upper bound of the agent's position and lower bound ; Among them, the population size , that is, there are N agent individuals, corresponding to N groups of Kp, Ki and Kd coefficients of the PID control algorithm; S402, calculate the fitness value, for each agent in the population, use the coefficient of the corresponding PID control algorithm to improve the PID control model, input the unit step signal into the improved PID control model, calculate the objective function value according to the temperature drive model output, and record the optimal objective function value in the population, which is recorded as and the corresponding PID control parameters are recorded as the optimal Kp, Ki, and Kd coefficients; S403, enter the iteration loop and calculate the adaptation factor of the current iteration Values ​​and individual position diversity among populations, using improved search strategies and improved exploitation strategies to update agent individual positions; S404: Calculate the fitness value of each agent according to the updated position, compare the new position with the old position, retain the agent position with the smaller fitness value, and update the global optimal solution of the current iterative population. and the corresponding objective function value; S405, current number of iterations Is this equal to the maximum number of iterations? , if so, the global optimal solution of the current iterative population Output, resolve to the best combination of Kp, Ki, Kd coefficients, otherwise return to execute S402.

4. The PID-based microbial fermentation temperature control method according to claim 1, characterized in that: The above-mentioned improvement of the parent optimization algorithm, the historical search results are reflected by the fitness value of the current iteration and the fitness value of the previous iteration, and the adaptive factor is designed. , the mathematical model is: ； In the formula, Adaptive factor The value of the kth iteration, is the maximum value of the adaptive factor, is the minimum adaptive factor, is the minimum fitness value of the kth iteration, is the minimum fitness value of the k-1th iteration, The agent size of the improved matriarchal optimization algorithm, ; The adaptive factor is introduced into the search strategy of the maternal optimization algorithm, and the position update mathematical model of the original search strategy is improved to obtain an improved search strategy, and the mathematical model is: ； Where SBB is the set of the bottom 20 agents in fitness ranking in the current iteration process, is the position value of the jth dimension of the ith agent in the SBB set; is the position value of the j-th dimension of the best agent individual in the k-th iteration, is the position value of the j-th dimension of the i-th agent individual in the k-th iteration, is the updated position value of the j-th dimension of the i-th agent individual.

5. The PID-based microbial fermentation temperature control method according to claim 1, characterized in that: The individual position diversity strategy among populations is specifically as follows: the i-th proxy individual j-dimensional search space Divided into M equally spaced intervals , count the number of agent individuals in the mth interval of the jth dimension in the population Probability , the mathematical model is: ; Calculate the entropy of the entire population agent in the jth dimension. The mathematical model is: ; It reflects the overall position distribution complexity of the entire population in the jth dimension, takes the weighted average of the distribution differences of all sub-populations, and defines the position diversity of proxy individuals among the entire population. The mathematical model is: ; The entropy values ​​of all D dimensions are averaged to obtain the diversity of the entire agent population, reflecting the complexity of the distribution of the entire population in multidimensional space; Individual position diversity strategy between populations The value range of is determined by the number of equally spaced intervals M, and the range is .

6. A PID-based microbial fermentation temperature control method according to claim 5, characterized in that: The position update mathematical model of the original development strategy of the matrilineal optimization algorithm is improved by using the individual position diversity strategy among populations. The improved development strategy mathematical model is: ； In the formula, is the position value of the j-th dimension of the i-th agent individual in the k-th iteration, is the updated position value of the j-th dimension of the ith agent individual, is a random number between 0 and 1. is the upper bound of the position value of the agent's j-th dimension, is the lower bound of the position value of the agent's j-th dimension, is the maximum number of iterations, is the average of the positions of all agents in the kth iteration, is a minimum value, and k is the current number of iterations.

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