A method for controlling the injection speed of adsorption medium

Abstract

The invention discloses an adsorption medium injection speed control method, which belongs to the technical field of PID control optimization. The specific steps are as follows: step one, constructing an adsorption medium injection speed control system; step two, improving a moss growth optimization algorithm; step three, using the improved moss growth optimization algorithm to perform parameter optimization on a PID controller of the adsorption medium injection speed control system, and obtaining an optimal set of parameter values ​​through algorithm iteration; step four, converting the optimized optimal parameters obtained by optimization into three parameters Kp, Ki, and Kd of PID, and inputting the converted parameters into the adsorption medium injection speed control system to optimize the control effect of the system, and by adjusting the PID controller parameters in the adsorption medium injection speed control system by using the improved moss growth optimization algorithm, the control performance of the PID controller is optimized, the control accuracy of the adsorption medium injection speed is improved, and the robustness and adaptability of the entire control system are enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of PID control optimization, and in particular relates to a method for controlling the injection speed of an adsorption medium. Background Art

[0002] Adsorption media refers to materials or substances that can adsorb gases, liquids or solid particles in physical and chemical processes. These media are usually used to remove pollution, separate specific components or improve the performance of materials. The spraying speed of the adsorption medium directly affects the uniformity and thickness of the coating. Different materials can adapt to different spraying intensities. Therefore, reasonable control of the spraying speed of the adsorption medium can ensure product quality, improve efficiency and improve safety.

[0003] PID control is a feedback control technology used in automatic control systems. Its basic principle is to generate control output by performing proportional, integral and differential operations on the control error, so as to adjust the system to achieve the desired goal. Using PID control technology to control the injection speed of the adsorption medium can quickly improve the response speed and stability of the system and improve the control accuracy. However, traditional PID control technology has problems such as poor adaptability to nonlinear control systems, high sensitivity to disturbances, and complex parameter adjustment. In practical applications, other control strategies can be combined to solve these shortcomings, such as optimizing control parameters through intelligent optimization algorithms.

[0004] The moss growth optimization algorithm was proposed under the inspiration of the growth law of mosses in the natural environment. The algorithm first determines the evolutionary direction of the population through a mechanism called determining wind direction. This mechanism adopts a method of dividing the population, which is inspired by the asexual reproduction, sexual reproduction and vegetative reproduction of mosses. Two new search strategies, spore diffusion search and double reproduction search, are proposed for exploration and development respectively. Finally, the cryptobiotic mechanism changes the method of directly modifying individual solutions of the traditional metaheuristic algorithm, avoiding the algorithm from falling into the local optimum. The moss growth optimization algorithm is used for parameter tuning of PID control, which can improve the stability and control accuracy of the PID controller and reduce the complexity of manual parameter adjustment. Summary of the invention

[0005] The present invention provides an adsorption medium injection speed control method, aiming to solve the problems that the adsorption medium injection speed is unstable and has poor precision within a set range, and the injection speed fluctuation leads to uneven surface injection. By improving the standard moss growth optimization algorithm, the adaptability and precision of the algorithm in the optimization process are improved, the response speed of the algorithm is improved, and when the algorithm falls into a local optimal solution, it can quickly jump out and continue to optimize. The PID controller module of the adsorption medium injection speed control system is parameterized by the improved moss growth optimization algorithm, thereby improving the stability and control precision of the entire system.

[0006] In view of the above problems, the present invention provides a method for controlling the injection speed of an adsorption medium, and the specific steps of the method are as follows.

[0007] Step 1: Construct an adsorption medium injection speed control system, including an error calculation module, a PID controller, a speed actuator, a speed feedback module, and an improved moss growth optimization algorithm module.

[0008] Step 2: Improve the moss growth optimization algorithm. The specific improvement strategies are as follows:

[0009] D1, use the smoothing function to improve the wind force value E, and dynamically adjust the step size step1 in the stable wind condition through the current optimal fitness and average fitness;

[0010] D2. An adaptive step-size perturbation strategy is used to improve the mathematical model of the spore propagation search phase of the moss growth optimization algorithm. The adaptive weight factor ω is used to adjust the step size of the current population position close to the optimal position, and random perturbations are performed through the Cauchy distribution.

[0011] Step 3: Use the improved moss growth optimization algorithm to optimize the parameters of the PID controller of the adsorption medium injection speed control system, and obtain the optimal set of parameter values ​​through algorithm iteration.

[0012] Step 4: Convert the optimal parameters obtained by optimization into three parameters of PID, Kp, Ki, and Kd, where Kp is proportional gain, Ki is integral gain, and Kd is differential gain. Input the converted parameters into the adsorption medium injection speed control system to optimize the control effect of the system.

[0013] Preferably, in the adsorption medium injection speed control system described in step 1, the error calculation module in the system is used to calculate the error e(t) between the actual feedback injection speed and the expected injection speed, the PID controller outputs the corresponding control signal u(t) through the error e(t), the speed actuator obtains the injection speed signal v(t) according to the control signal u(t) output by the PID controller and the actual mathematical model of the system, and finally controls the nozzle to inject the adsorption medium at a certain speed according to the speed signal, the speed feedback module feeds back the actual injection speed of the adsorption medium in real time, and inputs the actual injection speed into the error calculation module, and the improved moss growth optimization algorithm module optimizes the PID controller parameters through the improved moss growth optimization algorithm, the mathematical model of the control signal u(t) is shown in formula (1), and the mathematical model of the speed signal v(t) is shown in formula (2):

[0014] (1);

[0015] In formula (1), represents the proportional gain, represents the integral gain, represents the differential gain, e(t) represents the real-time error value;

[0016] (2);

[0017] In formula (2), represents the flow coefficient of the nozzle, represents the pressure coefficient, u(t) represents the control signal as shown in formula (1), p2 represents the nozzle outlet pressure, and ρ represents the fluid density.

[0018] Preferably, the wind force value E is improved by using a smoothing function as described in D1 in step 2, and the step length step1 in the stable wind condition is dynamically adjusted by the current optimal fitness and the average fitness. First, the improved wind force value E adopts the smoothing function Sigmoid. The wind force value E in the original algorithm is a linear decreasing function. As the number of iterations increases, the wind force value E gradually decreases. The improved wind force value E is a nonlinear function. It changes slowly in the early and late stages of the algorithm and changes quickly in the middle stage. The algorithm can smoothly transition between the global search and the local search stages. The specific mathematical model of the wind force value E is shown in formula (3). The improved step length step1 combines the optimal fitness and the average fitness value in the population to adaptively and dynamically adjust the step length of the update movement. The specific mathematical model is shown in formula (4):

[0019] (3);

[0020] In formula (3), iter represents the current iteration number, max_iter represents the maximum number of iterations of the algorithm, α represents the control slope parameter, and β represents the turning point control parameter;

[0021] (4);

[0022] In formula (4), ω represents the step gain coefficient, r2 represents a random number between [0,1], E represents the wind force value as shown in formula (3), and λ represents the adjustment factor. represents the optimal fitness value in the population, Represents the average fitness value in the population.

[0023] Preferably, by improving the wind force value E, sudden changes in linear attenuation are avoided, the smoothness of the parameters is increased, the algorithm performs a sufficient global search in the early stage, and then smoothly transitions to a local search, which helps to improve the optimization accuracy and convergence speed of the algorithm. By improving the step size step1, the step size can be adaptively adjusted according to the current progress of the algorithm, which helps the algorithm to jump out of the local optimal solution. Through the guidance of the optimal fitness value, it can reach the area near the optimal solution more quickly, thereby improving the response speed of the algorithm.

[0024] Preferably, the mathematical model of the spore propagation search phase of the moss growth optimization algorithm using an adaptive step size perturbation strategy described in step 2 D2 is:

[0025] (5);

[0026] In formula (5), represents the updated position of the i-th individual in the population, q represents the adaptive weight, and the calculation formula is shown in formula (6). Step1 represents the moving step length in stable wind conditions as shown in formula (4), and step2 represents the moving step length of the individual in turbulent wind conditions as shown in formula (7). represents the best individual in the population, represents the current individual in the population, γ represents the disturbance term coefficient, cauchy represents the random number generated by the Cauchy distribution, r1 represents a random number between [0,1], and d1 represents the switching factor value of 0.2;

[0027] (6);

[0028] In formula (6), represents the minimum value of weight, Indicates the maximum value of the weight, iter indicates the current iteration number, and max_iter indicates the maximum number of iterations of the algorithm;

[0029] (7);

[0030] In formula (7), k represents the basic ratio of the constant weight control step size, r3 represents a random number between [0,1], E represents the wind force value calculation formula as shown in formula (3), tanh represents the hyperbolic tangent function, and h represents the step size amplitude parameter.

[0031] Preferably, the adaptive step-size perturbation strategy has a large adaptive weight in the early stage of the algorithm, and the individual is greatly affected by the global optimal solution, which helps to quickly search the entire solution space and accelerate convergence. In the middle and late stages of the algorithm, the adaptive weight gradually decreases, and the individual pays more attention to local fine search, reducing the impact of over-reliance on the global optimal solution, thereby preventing falling into the local optimum. The added Cauchy distribution random perturbation ensures the individual's appropriate exploration ability after the weight adjustment, avoiding all individuals in the group from concentrating in a certain area too early.

[0032] Preferably, in step three, the PID controller of the adsorption medium injection speed control system is optimized using an improved moss growth optimization algorithm, and an optimal set of parameter values ​​is obtained through algorithm iteration. The specific steps are:

[0033] S1. Initialize the parameters of the improved moss growth optimization algorithm, including the number of search agents N in the algorithm search population, the upper limit ub and lower limit lb of the search space, the maximum number of iterations max_iter of the algorithm, the problem dimension dim of the algorithm, and set the initial population of the algorithm through the initial parameters;

[0034] S2, encode the three parameters Kp, Ki, Kd of the PID controller into the position vector of the search agent, and after encoding, the position information of the search agent in the search space corresponds to the parameter values ​​of Kp, Ki, Kd;

[0035] S3. Set the objective function in the algorithm execution process, and use the objective function to calculate the fitness value of the search agent to find the best. The calculation formula of the objective function is shown in formula (8). First, the fitness value of the initial position in the population is calculated, and the population is sorted according to the size of the fitness value. The optimal individual in the population , the optimal fitness value in the population ;

[0036] (8);

[0037] In formula (8), J represents the objective function value, T represents the total system operation time, represents the target injection velocity, Indicates the actual injection speed;

[0038] S4, updating the population position by improving the moss growth optimization algorithm, and updating the optimal individual in the population and the optimal fitness value in the population after each iteration;

[0039] S5. Determine whether the current iteration reaches the maximum number of iterations max_iter. If not, continue to execute S4 to search for the best solution. If it reaches the maximum number of iterations, stop searching for the best solution and convert the optimal solution into three parameters of PID for output.

[0040] Preferably, in S4, the population position is updated by improving the moss growth optimization algorithm, and the specific steps are:

[0041] S41. Calculate the wind direction value for moss growth , the calculation formula is shown in formula (9):

[0042] (9);

[0043] In formula (9), num represents the number of individuals in the population. Represents the distance between the current individual and the optimal individual;

[0044] S42, when the algorithm is in the global search phase, it enters the spore dispersal search phase to update the position of the population. The specific updated mathematical model is shown in formula (5);

[0045] S43, when the algorithm is in the local search phase, it enters the double propagation search phase to update the position of the population. The specific updated mathematical model is shown in formula (10);

[0046] (10);

[0047] Formula (10), represents the updated position of the i-th individual in the population, represents the current individual in the population, represents the best individual in the population, act represents a binary random switch with a value of 1 or 0, r4 represents a random number between [0,1], and d2 is a constant parameter with a value of 0.5;

[0048] S44. Calculate the fitness value of the updated position, and update the optimal individual in the population and the optimal fitness value in the population.

[0049] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows.

[0050] The present invention proposes a method for controlling the injection speed of an adsorption medium. An adaptive step-size disturbance strategy is used to improve the mathematical model of the spore propagation search phase of the moss growth optimization algorithm and to improve the wind force value E and the step size step1 in the stable wind condition. This not only enhances the global search capability of the algorithm for the search space, but also improves the fine adjustment effect in the local optimization phase. The optimized adsorption medium injection speed control system of the improved moss growth optimization algorithm exhibits a faster response speed, can accurately follow the change of the set value, reduces the overshoot phenomenon in the control process, improves the stability and robustness of the entire control system, and the system can maintain a high-precision operation effect in a complex environment, thereby achieving a simultaneous improvement in spraying uniformity and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flow chart of a method for controlling the injection speed of an adsorption medium.

[0052] Figure 2 Comparison of fitness values ​​during the optimization process of the standard moss growth optimization algorithm and the improved moss growth optimization algorithm.

[0053] Figure 3 The change diagram of parameter values ​​Kp, Ki, and Kd during the optimization process of the standard moss growth optimization algorithm.

[0054] Figure 4 The change diagram of parameter values ​​Kp, Ki, and Kd during the optimization process of the improved moss growth optimization algorithm.

[0055] Figure 5 Comparison curves of the responses of the PID controller in the adsorption medium injection velocity control system optimized by the standard moss growth optimization algorithm and the improved moss growth optimization algorithm. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] The present invention provides a technical solution: a method for controlling the injection speed of an adsorption medium, and the specific steps are shown in FIG1 .

[0058] Step 1: Construct an adsorption medium injection speed control system, including an error calculation module, a PID controller, a speed actuator, a speed feedback module, and an improved moss growth optimization algorithm module.

[0059] Furthermore, in the adsorption medium injection speed control system described in step 1, the error calculation module in the system is used to calculate the error e(t) between the actual feedback injection speed and the expected injection speed. The PID controller outputs the corresponding control signal u(t) through the error e(t). The speed actuator obtains the injection speed signal v(t) according to the control signal u(t) output by the PID controller and the actual mathematical model of the system. Finally, the nozzle is controlled to inject the adsorption medium at a certain speed according to the speed signal. The speed feedback module feeds back the actual injection speed of the adsorption medium in real time and inputs the actual injection speed into the error calculation module. The improved moss growth optimization algorithm module optimizes the PID controller parameters through the improved moss growth optimization algorithm. The mathematical model of the control signal u(t) is shown in formula (1), and the mathematical model of the speed signal v(t) is shown in formula (2):

[0060] (1);

[0061] In formula (1), represents the proportional gain, represents the integral gain, represents the differential gain, e(t) represents the real-time error value;

[0062] (2);

[0063] In formula (2), represents the flow coefficient of the nozzle, represents the pressure coefficient, u(t) represents the control signal as shown in formula (1), p2 represents the nozzle outlet pressure, and ρ represents the fluid density.

[0064] Step 2: Improve the moss growth optimization algorithm. The specific improvement strategies are as follows:

[0065] D1, use the smoothing function to improve the wind force value E, and dynamically adjust the step size step1 in the stable wind condition through the current optimal fitness and average fitness;

[0066] D2. An adaptive step-size perturbation strategy is used to improve the mathematical model of the spore propagation search phase of the moss growth optimization algorithm. The adaptive weight factor ω is used to adjust the step size of the current population position close to the optimal position, and random perturbations are performed through the Cauchy distribution.

[0067] Furthermore, the wind force value E is improved by using a smoothing function as described in D1 in step 2, and the step length step1 in the stable wind condition is dynamically adjusted by the current optimal fitness and the average fitness. First, the improved wind force value E adopts the smoothing function Sigmoid. The wind force value E in the original algorithm is a linear decreasing function. As the number of iterations increases, the wind force value E gradually decreases. The improved wind force value E is a nonlinear function. It changes slowly in the early and late stages of the algorithm and changes quickly in the middle stage. The algorithm can smoothly transition between the global search and the local search stages. The specific mathematical model of the wind force value E is shown in formula (3). The improved step length step1 combines the optimal fitness and the average fitness value in the population to adaptively and dynamically adjust the step length of the update movement. The specific mathematical model is shown in formula (4):

[0068] (3);

[0069] In formula (3), iter represents the current iteration number, max_iter represents the maximum number of iterations of the algorithm, α represents the control slope parameter, and β represents the turning point control parameter;

[0070] (4);

[0071] In formula (4), ω represents the step gain coefficient, r2 represents a random number between [0,1], E represents the wind force value as shown in formula (3), and λ represents the adjustment factor. represents the optimal fitness value in the population, Represents the average fitness value in the population.

[0072] Furthermore, the mathematical model of the spore propagation search phase of the moss growth optimization algorithm using an adaptive step size perturbation strategy described in step 2 D2 is as follows:

[0073] (5);

[0074] In formula (5), represents the updated position of the i-th individual in the population, q represents the adaptive weight, and the calculation formula is shown in formula (6). Step1 represents the moving step length in stable wind conditions as shown in formula (4), and step2 represents the moving step length of the individual in turbulent wind conditions as shown in formula (7). represents the best individual in the population, represents the current individual in the population, γ represents the disturbance term coefficient, cauchy represents the random number generated by the Cauchy distribution, r1 represents a random number between [0,1], and d1 represents the switching factor value of 0.2;

[0075] (6);

[0076] In formula (6), represents the minimum value of weight, Indicates the maximum value of the weight, iter indicates the current iteration number, and max_iter indicates the maximum number of iterations of the algorithm;

[0077] (7);

[0078] In formula (7), k represents the basic ratio of the constant weight control step size, r3 represents a random number between [0,1], E represents the wind force value calculation formula as shown in formula (3), tanh represents the hyperbolic tangent function, and h represents the step size amplitude parameter.

[0079] Step 3: Use the improved moss growth optimization algorithm to optimize the parameters of the PID controller of the adsorption medium injection speed control system, and obtain the optimal set of parameter values ​​through algorithm iteration.

[0080] Furthermore, in step three, the PID controller of the adsorption medium injection speed control system is optimized by using the improved moss growth optimization algorithm, and the optimal set of parameter values ​​is obtained by algorithm iteration. The specific steps are:

[0081] S1. Initialize the parameters of the improved moss growth optimization algorithm, including the number of search agents N in the algorithm search population, the upper limit ub and lower limit lb of the search space, the maximum number of iterations max_iter of the algorithm, the problem dimension dim of the algorithm, and set the initial population of the algorithm through the initial parameters;

[0082] S2, encode the three parameters Kp, Ki, Kd of the PID controller into the position vector of the search agent, and after encoding, the position information of the search agent in the search space corresponds to the parameter values ​​of Kp, Ki, Kd;

[0083] S3. Set the objective function in the algorithm execution process, and use the objective function to calculate the fitness value of the search agent to find the best. The calculation formula of the objective function is shown in formula (8). First, the fitness value of the initial position in the population is calculated, and the population is sorted according to the size of the fitness value. The optimal individual in the population , the optimal fitness value in the population ;

[0084] (8);

[0085] In formula (8), J represents the objective function value, T represents the total system operation time, represents the target injection velocity, Indicates the actual injection speed;

[0086] S4, updating the population position by improving the moss growth optimization algorithm, and updating the optimal individual in the population and the optimal fitness value in the population after each iteration;

[0087] S5. Determine whether the current iteration reaches the maximum number of iterations max_iter. If not, continue to execute S4 to search for the best solution. If it reaches the maximum number of iterations, stop searching for the best solution and convert the optimal solution into three parameters of PID for output.

[0088] Furthermore, in S4, the population position is updated by improving the moss growth optimization algorithm, and the specific steps are as follows:

[0089] S41. Calculate the wind direction value for moss growth , the calculation formula is shown in formula (9):

[0090] (9);

[0091] In formula (9), num represents the number of individuals in the population. Represents the distance between the current individual and the optimal individual;

[0092] S42, when the algorithm is in the global search phase, it enters the spore dispersal search phase to update the position of the population. The specific updated mathematical model is shown in formula (5);

[0093] S43, when the algorithm is in the local search phase, it enters the double propagation search phase to update the position of the population. The specific updated mathematical model is shown in formula (10);

[0094] (10);

[0095] Formula (10), represents the updated position of the i-th individual in the population, represents the current individual in the population, represents the best individual in the population, act represents a binary random switch with a value of 1 or 0, r4 represents a random number between [0,1], and d2 is a constant parameter with a value of 0.5;

[0096] S44. Calculate the fitness value of the updated position, and update the optimal individual in the population and the optimal fitness value in the population.

[0097] Step 4: Convert the optimal parameters obtained by optimization into three parameters of PID, Kp, Ki, and Kd, where Kp is proportional gain, Ki is integral gain, and Kd is differential gain. Input the converted parameters into the adsorption medium injection speed control system to optimize the control effect of the system.

[0098] Furthermore, in order to verify that the performance of the adsorption medium injection speed control method proposed in the present invention is better than other methods, the present invention adopts Matlab to improve the standard moss growth optimization algorithm, and adjusts the proportional coefficient KP, integral coefficient Ki, and differential coefficient Kd of the PID controller in the adsorption medium injection speed control system of the improved moss growth optimization algorithm. A system simulation model is built in Simulink, including an error calculation module, a PID controller, a speed actuator, a speed feedback module, and an improved moss growth optimization algorithm module. The number of search agents in the search population of the improved moss optimization algorithm is set to N=50, the problem dimension dim=3, the upper limit ub=25 of the search space, the lower limit lb=0.001, and the maximum number of iterations max_iter=15.

[0099] Furthermore, Figure 2 This is a comparison chart of the fitness values ​​of the standard moss growth optimization algorithm and the improved moss growth optimization algorithm during the optimization process. It can be seen from the figure that the fitness value of the improved moss optimization algorithm is smaller during the optimization process, indicating that the error value between the target value and the optimization process is smaller, thus indicating that the solution found is better. Figure 3 and Figure 4 These are the change diagrams of parameter values ​​Kp, Ki, and Kd during the optimization process of the standard moss growth optimization algorithm and the improved moss optimization algorithm. From the figure, we can see the change process of the algorithm in setting PID parameters. Figure 5 This is a response comparison curve diagram of the PID controller in the adsorption medium injection speed control system optimized by the standard moss growth optimization algorithm and the improved moss growth optimization algorithm. The target value is set to 1 unit. It can be seen from the figure that the optimization of the PID controller by the improved moss optimization algorithm is faster than that of the standard moss optimization algorithm, and can reach a stable state faster, indicating that the adsorption medium injection speed control system optimized by the improved moss optimization algorithm has better stability and robustness.

Claims

1. A method for controlling the injection speed of an adsorption medium, characterized in that: The specific steps are as follows: Step 1: construct an adsorption medium injection speed control system, including an error calculation module, a PID controller, a speed actuator, a speed feedback module, and an improved moss growth optimization algorithm module; Step 2: Improve the moss growth optimization algorithm. The specific improvement strategies are as follows: D1, use the smoothing function to improve the wind force value E, and dynamically adjust the step length step1 in the stable wind condition through the current optimal fitness and average fitness. The improved wind force value E uses the smoothing function Sigmoid. The specific mathematical model of the wind force value E is shown in formula (3). The improved step length step1 combines the optimal fitness and average fitness value in the population to adaptively and dynamically adjust the step length of the update movement. The specific mathematical model is shown in formula (4): In formula (3), iter represents the current iteration number, max_iter represents the maximum number of iterations of the algorithm, α represents the control slope parameter, and β represents the turning point control parameter; In formula (4), ω represents the step gain coefficient, r2 represents a random number between [0,1], E represents the wind force value as shown in formula (3), λ represents the adjustment factor, and F best Represents the optimal fitness value in the population, F avg represents the average fitness value in the population; D2. An adaptive step-size perturbation strategy is used to improve the mathematical model of the spore propagation search phase of the moss growth optimization algorithm. The adaptive weight factor ω is used to adjust the step size of the current population position close to the optimal position, and random perturbations are performed through the Cauchy distribution. The specific mathematical model is: In formula (5), represents the updated position of the i-th individual in the population, q represents the adaptive weight, the calculation formula is shown in formula (6), step1 represents the moving step length in stable wind conditions as shown in formula (4), step2 represents the moving step length of the individual in turbulent wind conditions as shown in formula (7), M best represents the best individual in the population, M i represents the current individual in the population, γ represents the disturbance term coefficient, cauchy represents the random number generated by the Cauchy distribution, r1 represents a random number between [0,1], and d1 represents the switching factor value of 0.2; In formula (6), q min represents the minimum value of the weight, q max Indicates the maximum value of the weight, iter indicates the current iteration number, and max_iter indicates the maximum number of iterations of the algorithm; In formula (7), k represents the basic ratio of the constant weight control step size, r3 represents a random number between [0,1], E represents the wind force value calculation formula as shown in formula (3), tanh represents the hyperbolic tangent function, and h represents the step size amplitude parameter; Step 3: Optimize the parameters of the PID controller of the adsorption medium injection speed control system using the improved moss growth optimization algorithm, and obtain the optimal set of parameter values ​​through algorithm iteration; Step 4: Convert the optimal parameters obtained by optimization into three parameters of PID, Kp, Ki, and Kd, where Kp is proportional gain, Ki is integral gain, and Kd is differential gain. Input the converted parameters into the adsorption medium injection speed control system to optimize the control effect of the system.

2. The method for controlling the injection speed of an adsorption medium according to claim 1, characterized in that: The adsorption medium injection speed control system described in step one, the error calculation module in the system is used to calculate the error e(t) between the actual feedback injection speed and the expected injection speed, the PID controller outputs the corresponding control signal u(t) through the error e(t), the speed actuator obtains the injection speed signal v(t) according to the control signal u(t) output by the PID controller and the actual mathematical model of the system, and finally controls the nozzle to inject the adsorption medium at a certain speed according to the speed signal, the speed feedback module feeds back the actual injection speed of the adsorption medium in real time, and inputs the actual injection speed into the error calculation module, and the improved moss growth optimization algorithm module optimizes the PID controller parameters through the improved moss growth optimization algorithm.

3. The method for controlling the injection speed of an adsorption medium according to claim 2, characterized in that: In the step 3, the PID controller of the adsorption medium injection speed control system is optimized by using the improved moss growth optimization algorithm, and the optimal set of parameter values ​​is obtained by algorithm iteration. The specific steps are: S1. Initialize the parameters of the improved moss growth optimization algorithm, including the number of search agents N in the algorithm search population, the upper limit ub and lower limit lb of the search space, the maximum number of iterations max_iter of the algorithm, the problem dimension dim of the algorithm, and set the initial population of the algorithm through the initial parameters; S2, encode the three parameters Kp, Ki, Kd of the PID controller into the position vector of the search agent, and after encoding, the position information of the search agent in the search space corresponds to the parameter values ​​of Kp, Ki, Kd; S3. Set the objective function in the algorithm execution process, and use the objective function to calculate the fitness value of the search agent to find the best. The calculation formula of the objective function is shown in formula (8). First, calculate the fitness value of the initial position in the population, and sort them according to the size of the fitness value. The optimal individual M in the population best , the optimal fitness value F in the population best ; In formula (8), J represents the objective function value, T represents the total system operation time, and v ref represents the target injection velocity, V act Indicates the actual injection speed; S4. Update the population position by improving the moss growth optimization algorithm. The specific steps of the update are: S41. Calculate the wind direction value D for moss growth wind , the calculation formula is shown in formula (9): In formula (9), num represents the number of individuals in the population, dM i Represents the distance between the current individual and the optimal individual; S42, when the algorithm is in the global search phase, it enters the spore dispersal search phase to update the position of the population. The specific updated mathematical model is shown in formula (5); S43, when the algorithm is in the local search phase, it enters the double propagation search phase to update the position of the population. The specific updated mathematical model is shown in formula (10); Formula (10), represents the updated position of the i-th individual in the population, M i represents the current individual in the population, M best represents the best individual in the population, act represents a binary random switch with a value of 1 or 0, r4 represents a random number between [0,1], and d2 is a constant parameter with a value of 0.5; S44, calculating the fitness value of the updated position, updating the best individual in the population and the best fitness value in the population; S5. Determine whether the current iteration has reached the maximum number of iterations max_iter. If not, continue to execute S4 to search for the best solution. If it has, stop searching for the best solution and convert the optimal solution into three parameters of PID for output.

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