CN117930633B - Automatic control optimization method for fuel gas conveying system - Google Patents
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Abstract
The invention provides an automatic control optimization method of a fuel gas conveying system, which belongs to the technical field of automatic control of fuel gas conveying and comprises the following steps: s1, improving defense range control factor of zebra optimization algorithm defense strategyUsing improved defensive range control factorsUpdating a zebra optimization algorithm defense strategy mathematical model; s2, establishing a mathematical model of the fuel gas conveying system as an optimization objective function of a zebra optimization algorithm; s3, improving an optimizing mechanism of a zebra optimization algorithm, and adding a probability mechanismBalancing foraging and defense strategies of the zebra optimization algorithm; s4, simulating foraging and defending strategies of the improved zebra optimization algorithm, and setting PID (proportion integration differentiation) control of the fuel gas conveying systemAndParameters, the optimization of a PID controller of the fuel gas conveying system is realized; s5, controlling the gas conveying system by using the PID controller of the optimized gas conveying system, and realizing accurate control of the gas conveying amount.
Description
Technical Field
The invention relates to the field of control of fuel gas conveying systems, in particular to an automatic control optimization method of a fuel gas conveying system.
Background
The automatic control of the intelligent fuel gas delivery quantity mainly depends on a SCADA system (monitoring and data acquisition system). The system can monitor and analyze various parameters in the gas conveying process, such as pressure, flow, temperature and the like in real time, and automatically adjust the gas conveying amount according to a preset threshold value or algorithm. Through automatic control, the system can automatically schedule the natural gas delivery according to the actual running condition of the pipeline, thereby realizing effective and efficient natural gas delivery. The automatic control system can improve the conveying efficiency and reduce the energy waste, and meanwhile, the automatic control system can realize accurate control on the pipeline system, so that unnecessary energy waste and equipment loss are avoided. This not only reduces the operating costs, but also increases the economic benefits of the overall system.
PID (proportional-integral-derivative) controllers are widely used in automatic control systems for gas delivery. The PID controller has the advantages of simple structure, small calculation workload, convenient parameter setting and stronger adaptability and flexibility. In the automatic control system for gas delivery, a PID controller is usually used together with a sensor and an executing mechanism to realize accurate control of gas delivery. The sensor is responsible for monitoring parameters such as gas flow, pressure and the like in real time and transmitting the data to the PID controller; the PID controller calculates the control quantity according to the set value and the actual value, and sends a control signal to the executing mechanism; the actuating mechanism adjusts the gas conveying quantity according to the control signal, so that the stable operation of the system is maintained.
The gas delivery system has non-linear, time-varying, and uncertainty characteristics that make it difficult to build accurate mathematical models. Whereas PID controllers are typically designed based on linear models, they may not adapt well to these complex systems, resulting in poor control. Meanwhile, the design of the PID controller needs to be balanced between rapidity and overshoot, and if rapid response is pursued, the overshoot of the system can be caused; while if less overshoot is pursued, the rapidity of the system is sacrificed. Thus, in gas delivery control, it is a challenge to balance these two aspects.
The Zebra Optimization Algorithm (ZOA) is a novel bionic meta heuristic algorithm; the basic inspiration comes from the behavior of the zebra, the ZOA simulates the foraging behavior of the zebra and the defending strategy of the zebra on the attack of predators, and a mathematical mode is established to realize the optimization of target performance. The standard zebra optimization algorithm is applied in the strategy of the foraging stage and the defending stage according to the steps of foraging before defending, each iteration is the same, the mechanism is unfavorable for the balance of global searching and local development of the zebra optimization algorithm, and the standard optimization algorithm has the risk of being easy to fall into local optimum.
Disclosure of Invention
The invention aims to provide an automatic control optimization method for a fuel gas conveying system, which is used for solving the problem that in the prior art, a traditional PID controller cannot be well adapted to the problem of poor balance between rapidity and overshoot in the environment of the fuel gas conveying system, and meanwhile, solving the problem that a zebra optimization algorithm is poor in balance in a foraging stage and a defending stage, and improving the optimizing speed and precision of the zebra optimization algorithm on parameters of the PID controller of the fuel gas conveying system.
In order to solve the technical problems, the invention adopts the following technical scheme: the automatic control optimizing method of the fuel gas conveying system improves the accuracy and the robustness of the zebra optimizing algorithm on the PID control of the fuel gas conveying system by improving the zebra optimizing algorithm, so that the automatic control performance of the fuel gas conveying system is optimized.
S1, improving defense range control factor of zebra optimization algorithm defense strategyUsing improved defensive range control factorsUpdating the mathematical model of the defense strategy of the zebra optimization algorithm.
S2, establishing a mathematical model of the fuel gas conveying system as an optimization objective function of the zebra optimization algorithm.
S3, improving an optimizing mechanism of a zebra optimization algorithm, and adding a probability mechanismBalancing foraging and defense strategies of the zebra optimization algorithm; the probability mechanism specifically comprises the following steps: calculate this iterationIf the value of (1)And executing the foraging strategy of the zebra optimization algorithm, otherwise, executing the defending strategy of the zebra optimization algorithm.
S4, simulating foraging and defending strategies of the improved zebra optimization algorithm, and setting PID (proportion integration differentiation) control of the fuel gas conveying systemAndAnd the parameters are used for realizing the optimization of the PID controller of the fuel gas conveying system.
S5, controlling the gas conveying system by using the PID controller of the optimized gas conveying system, and realizing accurate control of the gas conveying amount.
Preferably, the ZOA is a population-based optimizer, the zebra is a member of the population in the algorithm, and each zebra is a PID controller of the gas delivery system after the mathematical model is builtAndOne set of parameters.
Preferably, the defense range control factor of the defense strategy of the ZOAA fixed value is adopted in a standard zebra optimization algorithm, but the performance of the zebra optimization algorithm is changed along with the change of the iteration number in the optimizing process, so that a fixed range control factor is adoptedThe optimization requirement of the algorithm cannot be well met; in the early stage of iteration, the zebra optimization algorithm should mainly execute foraging strategies, expand the foraging range and consider surrounding dangerous factors less, so that the range control factors of the defense strategies of the zebra optimization algorithmShould have a small value; at the later stage of iteration, the zebra optimization algorithm should mainly execute a defense strategy to avoid being attacked by hunters, and the zebra defense range should be expanded to avoid being hunted and eaten, so that the range control factorShould have a larger value.
Preferably, the defense range control factor of the zebra optimization algorithm defense strategy is improved based on the zebra behavior characteristics and the zebra optimization algorithm strategyImproved defensive range control factorThe mathematical model formula is:；
In the method, in the process of the invention, To take on random values within 0,1,For the current iterationThe worst fitness value of the group position,For the size of the zebra population,For the maximum number of iterations of the zebra optimization algorithm,And calculating the fitness value of all individual zebra positions of the current iteration.
Preferably, the improved zebra optimization algorithm is utilized to optimize the PID controller of the gas delivery system, and the Improved Zebra Optimization Algorithm (IZOA) parameters are first initialized, including: algorithm optimizing maximum iteration numberProblem dimension of algorithm optimizationUpper bound for algorithm optimizationAnd lower boundary valueAnd the initial position of the zebra population, and the improved defense range control factorAnd an energy factorZebra population size。
Preferably, the improved zebra optimization algorithm is utilized to optimize the PID controller of the gas conveying system, a mathematical model formula is firstly set to judge whether the PID controller achieves the target optimization effect, and the improved zebra optimization algorithm is designed to set the PID control of the gas conveying systemAndThe objective function of the parameter is taken as the adaptability function of algorithm optimization, and the objective function is taken into consideration to solve the balance problem of the PID controller between control rapidity and overshootThe mathematical model formula is:
；
In the method, in the process of the invention, The maximum iteration number of the zebra optimization algorithm;， And The weight coefficients are 0.3,0.5,0.2 respectively; response time for the PID controller control of the gas delivery system; The overshoot is released for the fuel gas; Is the maximum amplitude; is the difference between the target gas release amount and the real-time gas release amount at the current iteration t.
Preferably, after parameter initialization of the improved zebra optimization algorithm is completed, the initial position of the zebra population is compared with the PID controller of the fuel gas conveying systemAndThe parameter set establishes a mapping relation, and when the individual positions of the zebra population are optimized and transformed by a zebra optimization algorithm after the improvement, the PID controller of the gas conveying systemAndThe parameter set is updated accordingly.
Preferably, the strategy application of the standard zebra optimization algorithm in the foraging stage and the defending stage is carried out according to the steps of foraging before defending, each iteration is carried out, the mechanism is unfavorable for the balance of global searching and local development of the zebra optimization algorithm, a probability mechanism M is introduced, the capacity of global searching and local development is balanced, the global searching stage aims at exploring different areas of a searching space to find a possible optimal solution, the local development stage aims at carrying out fine searching nearby the current optimal solution to find a more accurate optimal solution, good balance between the global searching and the local development can be achieved through alternately introducing the probability mechanism M, and therefore premature sinking into the local optimal solution is avoided, and meanwhile the global searching capacity and the convergence speed of the algorithm are improved.
Preferably, the foraging and defending strategies of the improved zebra optimization algorithm are simulated, and the PID control of the fuel gas conveying system is setAndThe parameters comprise the following specific steps:
S41, initializing parameters of an Improved Zebra Optimization Algorithm (IZOA);
s42, combining the initial position of the zebra population with a PID controller of the fuel gas conveying system AndEstablishing a mapping relation by the parameter set;
s43, calculating the fitness value of the current individual zebra position through the objective function The position of the individual with the minimum fitness value is reserved, the individual position corresponding to the minimum fitness value of the last iteration is compared, and the zebra position corresponding to the smaller fitness value is reserved;
S44, simulating foraging and defense strategies of the improved zebra optimization algorithm, establishing a position updating mathematical model, and updating individual positions of the zebra population;
S45, judging the current iteration times Whether or not to meetIf yes, exiting from the optimizing, and decoding the iteration position of the zebra population into the PID controller of the fuel gas conveying systemAndAnd if not, returning to the step S41 to continue execution.
Preferably, foraging and defense strategies of the improved zebra optimization algorithm are simulated, and the method comprises the following steps:
S441, calculating the iterative probability mechanism If the value of (1)Step S442 is executed, otherwise step S443 is executed;
s442, in the first stage, guiding other population members to the position of the precursor zebra, wherein the position of the precursor zebra is the optimal position of the current population, and the position of the precursor zebra is The position of the precursor zebra is taken as the center to carry out foraging, so that mathematical modeling can be carried out by using a formula (2) when the position of the zebra in the foraging stage is updated;
（2）；
In the method, in the process of the invention, Is the first stageZebra individual at the firstThe position of the dimension to be updated,Is the firstZebra individual at the firstThe current position of the dimension is determined,To be within the rangeA random number that increases as the number of iterations increases,For the current location of the precursor zebra,，Is a random number in interval [0,1 ];
S443, simulating a lion attack zebra, a zebra selection escape strategy and other predators attack zebra by using a defense strategy in IZOA, wherein the zebra selection attack strategy is subjected to mathematical modeling by using a formula (3);
（3）；
In the method, in the process of the invention, Is the second stageZebra individual at the firstThe position of the dimension to be updated,In order to improve the defensive range control factor,The probability of selecting one of two strategies randomly generated in interval 0,1 as an energy factor,As the location of the hacked zebra,For the maximum number of iterations of the zebra optimization algorithm,The current iteration number.
Preferably, the probability mechanismDesigned such that, in the early phase of the iteration, compared to the later phase of the iteration,Has a larger value, which, as a function of iteration,The nonlinear self-adaptive reduction of the value is realized, and the mathematical model formula is as follows:
。
compared with the prior art, the invention has the following beneficial effects:
The invention improves the defense range control factor of the zebra optimization algorithm defense strategy The position updating strategy optimizing performance of the zebra optimization algorithm in the defending stage is improved, and the improved range control factor is improvedThe method has a smaller value in the early iteration stage, is convenient for the algorithm to execute foraging strategy, improves the global searching performance of the algorithm, has a larger value in the later iteration stage, and improves the local convergence performance of the algorithm; joining probability mechanismsThe foraging and defending strategies of the zebra optimization algorithm are balanced, the capability of the algorithm for avoiding sinking into local optimum is improved, and the improved zebra optimization algorithm sets PID control of the fuel gas conveying systemAndThe parameters solve the problem that the traditional PID controller cannot be well adapted to the environment of a gas conveying system and has poor balance between rapidity and overshoot.
Drawings
FIG. 1 is a flow chart of steps of an automatic control optimization method for a fuel gas delivery system.
FIG. 2 is a flow chart of an improved zebra optimization algorithm optimizing mechanism.
FIG. 3 is a graph of the performance of the improved zebra optimization algorithm compared to other optimization algorithms.
FIG. 4 is a diagram of a PID controller for tuning a gas delivery system using a pre-and post-retrofit zebra optimization algorithmAndAnd comparing the adaptability of the parameters with a graph.
FIG. 5 is a diagram of a PID controller for tuning a gas delivery system using a pre-and post-retrofit zebra optimization algorithmAndA variation graph of the parameters.
FIG. 6 is a graph comparing the rapidity and overshoot performance of a PID controller of a fuel gas delivery system optimized by a zebra optimization algorithm before and after modification.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-6, the present invention provides a technical solution:
The automatic control optimizing method of the fuel gas conveying system improves the accuracy and the robustness of the zebra optimizing algorithm on the PID control of the fuel gas conveying system by improving the zebra optimizing algorithm, so that the automatic control performance of the fuel gas conveying system is optimized, and the method is shown in figure 1 and comprises the following steps.
S1, improving defense range control factor of zebra optimization algorithm defense strategyUsing improved defensive range control factorsUpdating the mathematical model of the defense strategy of the zebra optimization algorithm.
S2, establishing a mathematical model of the fuel gas conveying system as an optimization objective function of the zebra optimization algorithm.
S3, improving an optimizing mechanism of a zebra optimization algorithm, and adding a probability mechanismBalancing foraging and defense strategies of the zebra optimization algorithm; the probability mechanism specifically comprises the following steps: calculate this iterationIf the value of (1)And executing the foraging strategy of the zebra optimization algorithm, otherwise, executing the defending strategy of the zebra optimization algorithm.
S4, simulating foraging and defending strategies of the improved zebra optimization algorithm, and setting PID (proportion integration differentiation) control of the fuel gas conveying systemAndAnd the parameters are used for realizing the optimization of the PID controller of the fuel gas conveying system.
S5, controlling the gas conveying system by using the PID controller of the optimized gas conveying system, and realizing accurate control of the gas conveying amount.
Preferably, the ZOA is a population-based optimizer, the zebra is a member of the population in the algorithm, and each zebra is a PID controller of the gas delivery system after the mathematical model is builtAndOne set of parameters.
Preferably, the defense range control factor of the defense strategy of the ZOAA fixed value is adopted in a standard zebra optimization algorithm, but the performance of the zebra optimization algorithm is changed along with the change of the iteration number in the optimizing process, so that a fixed range control factor is adoptedThe optimization requirement of the algorithm cannot be well met; in the early stage of iteration, the zebra optimization algorithm should mainly execute foraging strategies, expand the foraging range and consider surrounding dangerous factors less, so that the range control factors of the defense strategies of the zebra optimization algorithmShould have a small value; at the later stage of iteration, the zebra optimization algorithm should mainly execute a defense strategy to avoid being attacked by hunters, and the zebra defense range should be expanded to avoid being hunted and eaten, so that the range control factorShould have a larger value.
Preferably, the defense range control factor of the zebra optimization algorithm defense strategy is improved based on the zebra behavior characteristics and the zebra optimization algorithm strategyImproved defensive range control factorThe mathematical model formula is:；
In the method, in the process of the invention, To take on random values within 0,1,For the current iterationThe worst fitness value of the group position,For the size of the zebra population,For the maximum number of iterations of the zebra optimization algorithm,And calculating the fitness value of all individual zebra positions of the current iteration.
Preferably, the improved zebra optimization algorithm is utilized to optimize the PID controller of the gas delivery system, and the Improved Zebra Optimization Algorithm (IZOA) parameters are first initialized, including: maximum iteration number of zebra optimization algorithmProblem dimension of algorithm optimizationUpper bound for algorithm optimizationAnd lower boundary valueAnd the initial position of the zebra population, and the improved defense range control factorAnd an energy factorZebra population size。
Preferably, the improved zebra optimization algorithm is utilized to optimize the PID controller of the gas conveying system, a mathematical model formula is firstly set to judge whether the PID controller achieves the target optimization effect, and the improved zebra optimization algorithm is designed to set the PID control of the gas conveying systemAndThe objective function of the parameter is taken as the adaptability function of algorithm optimization, and the objective function is taken into consideration to solve the balance problem of the PID controller between control rapidity and overshootThe mathematical model formula is:
；
In the method, in the process of the invention, The maximum iteration number of the zebra optimization algorithm;， And The weight coefficients are 0.3,0.5,0.2 respectively; response time for the PID controller control of the gas delivery system; The overshoot is released for the fuel gas; Is the maximum amplitude; is the difference between the target gas release amount and the real-time gas release amount at the current iteration t.
Preferably, after parameter initialization of the improved zebra optimization algorithm is completed, the initial position of the zebra population is compared with the PID controller of the fuel gas conveying systemAndThe parameter set establishes a mapping relation, and when the individual positions of the zebra population are optimized and transformed by a zebra optimization algorithm after the improvement, the PID controller of the gas conveying systemAndThe parameter set is updated accordingly.
Preferably, the strategy application of the standard zebra optimization algorithm in the foraging stage and the defending stage is carried out according to the steps of foraging before defending, each iteration is carried out, the mechanism is unfavorable for the balance of global searching and local development of the zebra optimization algorithm, a probability mechanism M is introduced, the capacity of global searching and local development is balanced, the global searching stage aims at exploring different areas of a searching space to find a possible optimal solution, the local development stage aims at carrying out fine searching nearby the current optimal solution to find a more accurate optimal solution, good balance between the global searching and the local development can be achieved through alternately introducing the probability mechanism M, and therefore premature sinking into the local optimal solution is avoided, and meanwhile the global searching capacity and the convergence speed of the algorithm are improved.
Preferably, the foraging and defending strategies of the improved zebra optimization algorithm are simulated, and the PID control of the fuel gas conveying system is setAndThe parameters comprise the following specific steps:
S41, initializing parameters of an Improved Zebra Optimization Algorithm (IZOA);
s42, combining the initial position of the zebra population with a PID controller of the fuel gas conveying system AndEstablishing a mapping relation by the parameter set;
s43, calculating the fitness value of the current individual zebra position through the objective function The position of the individual with the minimum fitness value is reserved, the individual position corresponding to the minimum fitness value of the last iteration is compared, and the zebra position corresponding to the smaller fitness value is reserved;
S44, simulating foraging and defense strategies of the improved zebra optimization algorithm, establishing a position updating mathematical model, and updating individual positions of the zebra population;
S45, judging the current iteration times Whether or not to meetIf yes, exiting from the optimizing, and decoding the iteration position of the zebra population into the PID controller of the fuel gas conveying systemAndAnd if not, returning to the step S41 to continue execution.
Preferably, foraging and defense strategies of the improved zebra optimization algorithm are simulated, and as shown in fig. 2, the method comprises the following steps:
S441, calculating the iterative probability mechanism If the value of (1)Step S442 is executed, otherwise step S443 is executed;
s442, in the first stage, guiding other population members to the position of the precursor zebra, wherein the position of the precursor zebra is the optimal position of the current population, and the position of the precursor zebra is The position of the precursor zebra is taken as the center to carry out foraging, so that mathematical modeling can be carried out by using a formula (2) when the position of the zebra in the foraging stage is updated;
（2）；
In the method, in the process of the invention, Is the first stageZebra individual at the firstThe position of the dimension to be updated,Is the firstZebra individual at the firstThe current position of the dimension is determined,To be within the rangeA random number that increases as the number of iterations increases,For the current best zebra position,，Is a random number in interval [0,1 ];
S443, simulating a lion attack zebra, a zebra selection escape strategy and other predators attack zebra by using a defense strategy in IZOA, wherein the zebra selection attack strategy is subjected to mathematical modeling by using a formula (3);
（3）；
In the method, in the process of the invention, Is the second stageZebra individual at the firstThe position of the dimension to be updated,In order to improve the defensive range control factor,The probability of selecting one of two strategies randomly generated in interval 0,1 as an energy factor,As the location of the hacked zebra,For the maximum number of iterations of the zebra optimization algorithm,The current iteration number.
Preferably, the probability mechanismDesigned such that, in the early phase of the iteration, compared to the later phase of the iteration,Has a larger value, which, as a function of iteration,The nonlinear self-adaptive reduction of the value is realized, and the mathematical model formula is as follows:
。
Initializing Zebra Optimization Algorithm (ZOA), improved Zebra Optimization Algorithm (IZOA), raccoon optimization algorithm (COA) and cheetah optimization algorithm (CO) in Matalb, and uniformly setting population scale Total number of test iterationsThe Improved Zebra Optimization Algorithm (IZOA) and the rest 4 algorithms are subjected to performance test by adopting an F5 reference function, the performance test result is shown in figure 3, and according to the principle that the smaller the optimal value is, the better the performance is, the better the Improved Zebra Optimization Algorithm (IZOA) can be obviously found in the optimizing speed, and the minimum optimal value can be reached at the fastest change speed in the initial iteration stage.
In Matlab, an objective function is usedCalculating the fitness value of the zebra position, and setting PID (proportion integration differentiation) control of the fuel gas conveying system by a standard Zebra Optimization Algorithm (ZOA) and an Improved Zebra Optimization Algorithm (IZOA)AndThe smaller the value of the objective function J, the better trade-off between the rapidity and overshoot of the PID controller of the gas delivery system, as shown in fig. 4, the faster the fitness of IZOA drops from about 5 iterations, indicating that IZOA is more efficient than the ZOA algorithm; the fitness of IZOA began to stabilize around 10 iterations, and ZOA continued to slowly decrease after about 15 iterations, with both ZOA and IZOA stabilized and IZOA had a lower fitness, indicating IZOA tuning of the PID control of the gas delivery systemAndThe parameters have better performance.
Setting the maximum iteration numberAlgorithm optimizing upper boundLower bound ofAs shown in fig. 5, the PID control of the gas delivery system is tuned for the pre-and post-retrofit zebra optimization algorithmAndA variation graph of the parameters.
Building a gas conveying system simulation model in Simulink, wherein the simulation model comprises a target gas release amount input module, a real-time gas release amount acquisition input module, an improved zebra optimization algorithm module, a PID controller module and a system response result module; setting target fuel gas release amount, and controlling PID of optimal fuel gas conveying systemAndThe parameters are input into an improved zebra optimization algorithm module, applied to a PID controller module and output a system response result.
Testing the overshoot and response time of the PID control of the gas delivery system, wherein the response time represents the time required for the gas delivery system to reach a stable state from input to output; the overshoot represents the maximum deviation experienced by the gas delivery system output before reaching steady state; from the specification, in the attached figure 6, the response speed of the classical PID is the fastest, but the overshoot phenomenon appears after the response speed approaches to the set value, the response speed of the ZOA-PID is slower than that of the classical PID, the overshoot is smaller, the stability time is longer than that of the classical PID, the response speed of the IZOA-PID is not the fastest, but the response speed is very fast approaching to the steady state without the overshoot, the minimum error is kept in the whole process, and the overshoot and the response time are better balanced.
Claims (2)
1. The automatic control optimizing method of the fuel gas conveying system improves the accuracy and the robustness of the zebra optimizing algorithm on the PID control of the fuel gas conveying system by improving the zebra optimizing algorithm, so as to optimize the automatic control performance of the fuel gas conveying system, and is characterized by comprising the following steps:
s1, improving a defense range control factor R of a zebra optimization algorithm defense strategy, and updating a zebra optimization algorithm defense strategy mathematical model by using the improved defense range control factor R'; the improved defense range control factor R' is expressed by the following mathematical model formula:
Wherein, rand is a random value with a value within [0,1], fit end is the worst fitness value of the population position at the current iteration T, N is the zebra population scale, T is the maximum iteration times of the zebra optimization algorithm, and sum is the fitness value of all zebra individual positions of the current iteration;
s2, establishing a mathematical model of the fuel gas conveying system as an optimization objective function of a zebra optimization algorithm;
S3, improving an optimizing mechanism of the zebra optimization algorithm, adding a probability mechanism M, and balancing foraging and defense strategies of the zebra optimization algorithm; the probability mechanism specifically comprises the following steps: calculating the value of the iteration M, if M is more than or equal to 0.4, executing the foraging strategy of the zebra optimization algorithm, otherwise, executing the defending strategy of the zebra optimization algorithm; and in the early iteration stage, M has a larger value compared with the later iteration stage, and the M value is reduced in a nonlinear self-adaptive manner along with the iteration change, so that a mathematical model formula is as follows:
Wherein T is the current iteration number, T is the maximum iteration number of the zebra optimization algorithm, and r is the range A random number that increases as the number of iterations increases;
S4, simulating foraging and defense strategies of the improved zebra optimization algorithm, and setting k p and k d parameters of PID control of the gas conveying system to realize optimization of a PID controller of the gas conveying system; the method comprises the following specific steps:
S441, calculating the value of the iterative probability mechanism M, if M is more than or equal to 0.4, executing a step S442, otherwise, executing a step S443;
S442, in the first stage, guiding other population members to the position of the precursor zebra, wherein the position of the precursor zebra is PZ j, and foraging is performed by taking the position of the precursor zebra as the center, so that the position of the precursor zebra in the foraging stage is updated and mathematical modeling is performed by using a formula (2);
In the method, in the process of the invention, For the position to be updated of the ith zebra individual in the j-th dimension in the first stage, X i,j is the current position of the ith zebra individual in the j-th dimension, and r is the range/>The random number increased with the increase of the iteration number, PZ j is the current position of the precursor zebra, i=1+rand, rand is a random number in the interval [0,1 ];
S443, simulating a lion attack zebra in a modified zebra optimization algorithm, wherein the zebra selects an escape strategy and other predators attack the zebra, and the zebra selects an attack strategy and carries out mathematical modeling by using a formula (3);
In the method, in the process of the invention, For the position to be updated of the ith zebra individual in the j-th dimension in the second stage, R' is an improved defense range control factor, p s is an energy factor, probability of one of two strategies randomly generated in the interval [0,1] is selected, AZ j is the position of the attacked zebra, T is the maximum iteration number of the zebra optimization algorithm, and T is the current iteration number;
S5, controlling the gas conveying system by using the PID controller of the optimized gas conveying system, and realizing accurate control of the gas conveying amount.
2. The automatic control optimization method of a gas delivery system according to claim 1, wherein the objective function J in step S2 is designed by solving the trade-off problem between control rapidity and overshoot of the PID controller, and the mathematical model formula of the objective function J is:
Wherein T is the maximum iteration number of the zebra optimization algorithm; alpha, beta and gamma are weight coefficients which are 0.3,0.5,0.2 respectively; tr is the response time of the PID controller control of the gas delivery system; k is the gas release overshoot; k max is the maximum amplitude; e (t) is the difference between the target gas release amount and the real-time gas release amount at the current iteration t.
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