Fractional-order PID control system and method for heating furnace temperature based on artificial bee colony algorithm

Abstract

The present invention discloses a fractional-order PID control system and method for heating furnace temperature based on artificial bee colony algorithm, which belongs to the technical field of furnace combustion control in steel rolling industry, and includes a temperature sensor, an A / D converter, a fractional-order PID controller optimized by artificial bee colony, a D / A converter, and a control actuator. The present invention is improved on the basis of traditional PID control, and the fractional-order idea is introduced for control; in addition, in order to obtain a better control effect, the artificial bee colony is used to optimize the various parameters of the fractional-order PID, thereby realizing stable control of the furnace temperature of the heating furnace; compared with the traditional PID optimization control, the present invention can control the furnace temperature of the heating furnace in real time, effectively solve the problems of large inertia, large lag and slow response existing in the current heating furnace temperature control, improve the stability of temperature regulation, shorten the regulation response time, and realize efficient regulation of furnace temperature, which is worthy of being promoted and used.

Description

Technical Field

[0001] The invention relates to the technical field of furnace combustion control in steel rolling industry, and in particular to a fractional-order PID control system and method for heating furnace temperature based on an artificial bee colony algorithm. Background Art

[0002] As an important part of the steel rolling process, the heating furnace continuously produces and heats hot air. The steel rolling heating furnace is a major energy consumer in the steel rolling process, and its heating furnace fuel consumption accounts for 80% to 85% of the total energy medium consumption in this process. In addition, the air temperature provided by the heating furnace will directly affect the thermal efficiency of the chemical reaction in the subsequent steel rolling process. Therefore, it is an inevitable trend to continuously study and explore new control solutions for heating furnaces and implement them.

[0003] The generation of hot air mainly comes from the combustion reaction of air and oxygen, and its production efficiency depends on the air-fuel ratio in the heating furnace, that is, the ratio of the two reactants. In addition to the hardware structure of the heating furnace, the most commonly used method is to adjust and control through the optimization control algorithm. By adjusting the input gas volume, the temperature in the heating furnace can be kept constant for a long time, which provides a guarantee for the sufficient and efficient heating of the steel rolling process production line and creates considerable economic benefits for the enterprise.

[0004] The current heating furnace combustion technology mainly includes calculating the input and output calorific values ​​or flow rates for adjustment and introducing RBF neural network learning model control. The first method has high hardware requirements and requires high precision for calorific value meters, thermometers and other equipment. The latter has been around for a long time but is still in the research stage. Currently, very few companies have tested or put it into use.

[0005] At present, many domestic steel rolling enterprises have not updated their heating furnace equipment, and the technology is backward. They still use manual control and adjustment methods for industrial production, and their accuracy cannot be guaranteed. In addition, a large amount of manpower and material resources are required to maintain the operation of the heating furnace. The personnel have different qualifications and experiences, which has great limitations. Therefore, a fractional-order PID control system for the heating furnace temperature based on the artificial bee colony algorithm is proposed. Summary of the invention

[0006] The technical problem to be solved by the present invention is: how to solve the problems of large inertia, large lag and slow response existing in the current temperature control of a heating furnace, and provide a fractional-order PID control system for the temperature of a heating furnace based on an artificial bee colony algorithm, wherein a temperature sensor is selected as a data acquisition module, and PID is selected as a main control system, and the idea of ​​fractional order is integrated into the PID controller, so that the integral link and the differential link are expanded to fractional-order integral and differential; at the same time, an artificial bee colony algorithm, namely, the ABC algorithm, is introduced to optimize the parameters of the fractional-order PID controller, and relatively optimal parameters are obtained, thereby obtaining the optimal control result.

[0007] The present invention solves the above technical problems through the following technical scheme, which includes a temperature sensor, an A / D converter, a fractional-order PID controller optimized by an artificial bee colony, a D / A converter, and a control actuator; the temperature inside the current heating furnace is monitored by the temperature sensor, the monitoring result is converted into a digital signal by the A / D converter, a digital control signal is sent out by the fractional-order PID controller optimized by the artificial bee colony, the digital control signal is converted into an analog control signal by the D / A converter, the control actuator controls the gas intake of the heating furnace according to the analog control signal, and then the temperature inside the heating furnace is closed-loop controlled.

[0008] The present invention also provides a fractional-order PID control method for the temperature of a heating furnace based on an artificial bee colony algorithm. The control system described above can be used to control the temperature inside the heating furnace, effectively alleviating the problems of slow adjustment and large temperature overshoot in traditional control methods, and has good control effect and the characteristics of long-term linearity, comprising the following steps:

[0009] S1: Collect the variable data of the heating furnace combustion process and establish a mathematical model of the combustion process;

[0010] S2: Stop the operation of the heating furnace and replace the original PID controller with a fractional-order PID controller;

[0011] S3: Introduce the artificial bee colony algorithm to select the parameters of the fractional-order PID controller, and then enter the fractional-order PID controller after obtaining the optimal parameters;

[0012] S4: When the temperature inside the heating furnace reaches a relatively stable state, that is, the difference between the actual temperature inside the furnace and the expected temperature is within the set range, the control system will suspend its action, that is, maintain dynamic stability and not make adjustments for the time being;

[0013] S5: The heating is continued, and steps S3 to S4 are repeated to perform closed-loop control on the heating process.

[0014] Furthermore, in the step S1, the variable data of the heating combustion process includes: a control quantity and a controlled quantity; the control quantity includes the input gas quantity; the controlled quantity includes the temperature inside the heating furnace.

[0015] Furthermore, in step S1, the process of establishing the mathematical model of the combustion process includes the following steps:

[0016] S11: According to the combustion process variable data, its transfer function is simplified as:

[0017]

[0018] Among them: K1 is the gas intake flow coefficient; K2 is the heating temperature conversion coefficient; T1 is the gas intake time; T2 is the heating time in the furnace; t is the function independent variable;

[0019] S12: Get the relationship between the input and output of the control system:

[0020]

[0021] Where: r(t) is the system control input; P(t) refers to the effect of the fractional-order PID controller; y(t) is the system control output.

[0022] Furthermore, in step S2, the control transfer function of the original PID controller is:

[0023]

[0024] Where: K p is the proportionality coefficient; K i is the integral coefficient; K d is the differential coefficient.

[0025] Furthermore, in step S2, the control transfer function of the fractional-order PID controller is:

[0026] in, is the integral term; t μ is the differential term; K p is the proportionality coefficient; K i is the integral coefficient; K d is the differential coefficient.

[0027] Furthermore, in step S3, the process of selecting the parameters of the fractional-order PID controller and obtaining the optimal parameters and then inserting them into the fractional-order PID controller includes the following steps:

[0028] S31: Initialize the population, initialize the PID parameter values ​​to be optimized and the values ​​of the variables related to the artificial bee colony algorithm;

[0029] S32: The employed bees conduct mining around the current food source and evaluate the new solutions generated;

[0030] S33: using the selection probability formula to calculate the probability of each food source being selected, the follower bee selects a food source according to the probability value, and further explores near the selected food source;

[0031] S34: If no better food source is found after a limited search, it is considered that the current bee colony optimization has entered a local extreme value, and the scout bee randomly generates a new food source to replace this position;

[0032] S35: Determine whether the algorithm meets the stop condition. If not, enter step S32 and repeat the cycle. If so, the process is completed and the final result is output.

[0033] Furthermore, in step S31, the PID parameter values ​​include K p , K i , K d , λ, μ; the values ​​of artificial bee colony algorithm related variables include population size and number of iterations.

[0034] Compared with the prior art, the present invention has the following advantages: the fractional-order PID control system of the heating furnace temperature based on the artificial bee colony algorithm is improved on the basis of traditional PID control, and the fractional-order concept is introduced for control; in addition, in order to obtain better control effect, the artificial bee colony is used to optimize the various parameters of the fractional-order PID, thereby realizing stable control of the furnace temperature of the heating furnace; compared with the traditional PID optimization control, the present invention can control the furnace temperature of the heating furnace in real time, effectively solve the problems of large inertia, large lag and slow response in the current heating furnace temperature control, improve the stability of temperature regulation, shorten the regulation response time, and realize efficient regulation of furnace temperature, which is worthy of popularization and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the structure of the heating furnace temperature control system in the first embodiment of the present invention;

[0036] Figure 2 It is a schematic diagram of a heating furnace temperature control method in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0037] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0038] Embodiment 1

[0039] like Figure 1 , 2 As shown, this embodiment provides a technical solution: a heating furnace temperature control system combining an artificial bee colony algorithm with a fractional-order PID, comprising: a temperature sensor, an A / D converter, a fractional-order PID controller, a D / A converter, and a control actuator. The heating furnace temperature control system monitors the current furnace temperature (i.e., the temperature inside the furnace) of the heating furnace through a temperature sensor, and the result is converted into a digital signal through an A / D converter, and the system gas intake is controlled through a fractional-order PID controller optimized by an artificial bee colony, thereby forming the entire heating furnace temperature control system.

[0040] This embodiment also provides a heating furnace temperature control method combining an artificial bee colony algorithm with a fractional-order PID, comprising the following steps:

[0041] Step 1, initializing the parameters of the original traditional PID controller of the heating furnace (i.e., the original PID controller of the present invention), including target temperature, rated furnace pressure, various coefficients of the controller, blower frequency conversion parameters, air-fuel ratio and other data;

[0042] Step 2: Collect the variable data of the heating furnace combustion process and establish a mathematical model of the combustion process;

[0043] Step 3: Stop the operation of the heating furnace and replace the original traditional PID controller with a fractional-order PID controller;

[0044] Step 4: Introduce the artificial bee colony (ABC) algorithm to select the parameters of the fractional-order PID controller, and then input the relatively optimal parameters into the PID controller;

[0045] Step 5: The temperature in the steel rolling heating furnace reaches a relatively stable state, that is, it maintains a small fluctuation around the expected temperature value. The present invention sets the expected temperature to 1200°C with a deviation of ±4.8°C, and the control system pauses, that is, it maintains dynamic stability and does not adjust temporarily;

[0046] Step 6: The heating is continued, and steps 4 to 5 are repeated to perform closed-loop control on the heating process;

[0047] In this embodiment, the variable data of the heating and combustion process in step 2 include: a control quantity and a controlled quantity; the control quantity includes the input gas quantity; the controlled quantity includes the temperature inside the heating furnace.

[0048] In this embodiment, the process of establishing the mathematical model of the combustion process in step 2 includes the following steps:

[0049] Step 2.1: Based on the combustion process variable data, the transfer function can be simplified as follows:

[0050]

[0051] Among them: K1 is the gas intake flow coefficient; K2 is the heating temperature conversion coefficient; T1 is the gas intake time; T2 is the heating time in the furnace; t is the function independent variable, which has no specific meaning;

[0052] Step 2.2: According to the design idea of ​​the present invention, the relationship between input and output is obtained:

[0053]

[0054] Where: r(t) is the system control input; P(t) here refers to the (fractional order) PID controller effect; y(t) is the system control output.

[0055] In this embodiment, the traditional PID controller mentioned in step 3 corresponds to one of the most classic and mature control strategies, which is widely used in machinery, chemistry, metallurgy and other industries.

[0056] The traditional PID control transfer function is:

[0057]

[0058] Where: K p is the proportionality coefficient; K i is the integral coefficient; K d is the differential coefficient.

[0059] In order to make the controller design more flexible, the performance better, and better guarantee the dynamic performance of the system, the fractional order idea is integrated into the PID controller in this embodiment, and the integral and differential links of the integer solution PID controller are extended to fractional order integral and differential, and a new PID control transfer function is obtained:

[0060]

[0061] in, is the integral term; t μ is the differential term; K p is the proportionality coefficient; K i is the integral coefficient; K d is the differential coefficient.

[0062] In this embodiment, the artificial bee colony (ABC) algorithm is introduced in step 4 to select the parameters of the fractional-order PID controller, and the fractional-order PID controller is introduced after obtaining the relatively optimal parameters. The specific steps are as follows:

[0063] Step 4.1, initialize the population, initialize the PID parameter values ​​to be optimized and the values ​​of algorithm-related variables, the parameter values ​​include: K p , K i , K d , λ, μ; algorithm-related variables include population size, number of iterations, etc.;

[0064] Step 4.2: The employed bees mine around the current food source and evaluate the new solutions generated;

[0065] Step 4.3: Use the selection probability formula to calculate the probability of each food source being selected. Follower bees select a food source based on the probability value and conduct further exploration near the selected food source.

[0066] Step 4.4: If no better food source is found after a limited search, it is considered that the current bee colony optimization has entered a local extreme point. At this time, the scout bee randomly generates a new food source to replace this position.

[0067] Step 4.5: Determine whether the algorithm meets the stopping condition. If not, enter step 4.2 and repeat the cycle. If it is satisfied, the process is completed and the final result is output.

[0068] The control system in this embodiment is used as a control system. In addition to the adjustment time, overshoot and other data, the error integral criterion is also an important measure of the control system performance. In order to verify its quality, the present invention selects the time multiplied by the absolute error integral criterion, that is, the ITAE index as a reference:

[0069]

[0070] Among them, J(ITAE i ) is the integral of the absolute value of the error and time;

[0071] The corresponding fitness is:

[0072]

[0073] Among them: fit refers to the fitness function.

[0074] Embodiment 2

[0075] In order to compare the optimization performance, in this embodiment, Matlab program simulation software is used to introduce ABC (artificial bee colony) and PSO (particle swarm) algorithms for the same PID parameters for comparison. p , K i and K d The search space ranges are set to 0-40, 0-10 and 0-2 respectively, the initial population size is defined as 40, the maximum number of iterations is set to 100, the inertia weight of the particle swarm algorithm is set to 0.6, the acceleration coefficient c1=c2=2, the dimension D=3, the maximum limit search number is 120, and the two algorithms are run 10 times each to compare the fitness values ​​and simulation step responses.

[0076] By comparing the simulation diagrams, it can be concluded that the solution obtained by the PSO algorithm has better dynamic characteristics, but the corresponding solution of the ABC algorithm performs better in steady-state performance.

[0077] In order to verify the effectiveness of the control, the present invention simulates the output curve graph through Matlab program simulation software, and the specific steps are as follows:

[0078] Step 4.6, select the step signal as the reference input, and set the simulation time to 10s;

[0079] Step 4.7, set and adjust the PID parameters until the output curve is similar to the original control chart;

[0080] Step 4.8, introduce artificial bee colony to optimize the PID parameters, the specific operation is as described in steps 4.1 to 4.5 above;

[0081] Step 4.9: The control system continues to run and outputs the effect diagram at the same time.

[0082] In order to verify the application effect of the present invention in actual engineering, the method was introduced into a 1780mm unit walking beam heating furnace of a certain rolling mill for verification. The results showed that the method of the present invention can control the temperature inside the heating furnace, effectively alleviating the problems of slow adjustment and large temperature overshoot of traditional control methods, and has good control effect and the characteristics of maintaining linearity for a long time.

[0083] To sum up, the fractional-order PID control system of the heating furnace temperature based on the artificial bee colony algorithm in the above-mentioned embodiment is improved on the basis of traditional PID control, and the fractional-order idea is introduced for control; in addition, in order to obtain better control effect, the artificial bee colony is used to optimize the parameters of the fractional-order PID, so as to realize stable control of the furnace temperature of the heating furnace; compared with the traditional PID optimization control, the present invention can control the furnace temperature of the heating furnace in real time, effectively solve the problems of large inertia, large lag and slow response existing in the current heating furnace temperature control, improve the stability of temperature regulation, shorten the regulation response time, and realize efficient regulation of furnace temperature, which is worthy of popularization and use.

[0084] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A fractional-order PID control method for heating furnace temperature based on artificial bee colony algorithm, which uses a fractional-order PID control system for heating furnace temperature based on artificial bee colony algorithm to control the temperature inside the heating furnace, characterized in that: The following steps are involved: S1: Collect the variable data of the heating furnace combustion process and establish a mathematical model of the combustion process; S2: Stop the operation of the heating furnace and replace the original PID controller with a fractional-order PID controller; S3: Introduce the artificial bee colony algorithm to select the parameters of the fractional-order PID controller, and then enter the fractional-order PID controller after obtaining the optimal parameters; S4: When the temperature inside the heating furnace reaches a relatively stable state, that is, the difference between the actual temperature inside the furnace and the expected temperature is within the set range, the control system will suspend its action, that is, maintain dynamic stability and not make adjustments for the time being; S5: Heating continues, and steps S3 to S4 are repeated to perform closed-loop control on the heating process; The fractional-order PID control system for the temperature of the heating furnace based on the artificial bee colony algorithm comprises: a temperature sensor, an A / D converter, a fractional-order PID controller optimized by an artificial bee colony, a D / A converter, and a control actuator; the temperature sensor is used to monitor the temperature in the current heating furnace, the monitoring result is converted into a digital signal by the A / D converter, a digital control signal is sent out by the fractional-order PID controller optimized by the artificial bee colony, the digital control signal is converted into an analog control signal by the D / A converter, the control actuator controls the gas intake of the heating furnace according to the analog control signal, and then the temperature in the heating furnace is closed-loop controlled; In step S1, the variable data of the heating combustion process includes: a control quantity and a controlled quantity; the control quantity includes the input gas quantity; the controlled quantity includes the temperature inside the heating furnace; In step S1, the process of establishing a mathematical model of the combustion process includes the following steps: S11: According to the combustion process variable data, its transfer function is simplified as: Among them: K1 is the gas intake flow coefficient; K2 is the heating temperature conversion coefficient; T1 is the gas intake time; T2 is the heating time in the furnace; t is the function independent variable; S12: Get the relationship between the input and output of the control system: Where: r(t) is the system control input; P(t) refers to the effect of the fractional-order PID controller; y(t) is the system control output.

2. The fractional-order PID control method for heating furnace temperature based on artificial bee colony algorithm according to claim 1 is characterized in that: In step S2, the control transfer function of the original PID controller is: Where: K p is the proportionality coefficient; K i is the integral coefficient; K d is the differential coefficient.

3. The fractional-order PID control method for heating furnace temperature based on artificial bee colony algorithm according to claim 2 is characterized in that: In step S2, the control transfer function of the fractional-order PID controller is: in, is the integral term; t μ is the differential term; K p is the proportionality coefficient; K i is the integral coefficient; K d is the differential coefficient; λ,μ>0.

4. The fractional-order PID control method for heating furnace temperature based on artificial bee colony algorithm according to claim 3 is characterized in that: In step S3, the process of selecting the parameters of the fractional-order PID controller and obtaining the optimal parameters and then inserting them into the fractional-order PID controller includes the following steps: S31: Initialize the population, initialize the PID parameter values ​​to be optimized and the values ​​of the variables related to the artificial bee colony algorithm; S32: The employed bees conduct mining around the current food source and evaluate the new solutions generated; S33: using the selection probability formula to calculate the probability of each food source being selected, the follower bee selects a food source according to the probability value, and further explores near the selected food source; S34: If no better food source is found after a limited search, it is considered that the current bee colony optimization has entered a local extreme value, and the scout bee randomly generates a new food source to replace this position; S35: Determine whether the algorithm meets the stop condition. If not, enter step S32 and repeat the cycle. If so, the process is completed and the final result is output.

5. The fractional-order PID control method for heating furnace temperature based on artificial bee colony algorithm according to claim 4 is characterized in that: In step S31, the PID parameter values ​​include K p , K i , K d , λ, μ; the values ​​of artificial bee colony algorithm related variables include population size and number of iterations.

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