A method for controlling spraying speed of greening wet spraying machine

Abstract

The invention discloses a method for controlling the spraying speed of a greening wet spraying machine, which belongs to the technical field of PID control optimization. The specific steps are as follows: step one, constructing a control system model of the greening wet spraying machine; step two, improving the mathematical model of the aurora optimization algorithm, and the specific improvement strategies are as follows: D1, using an adaptive scaling Levy flight strategy to improve the mathematical model of the aurora elliptical complex variant Ao in the aurora optimization algorithm; D2, using a transition perturbation strategy to improve the mathematical model of the particle collision stage of the aurora optimization algorithm; step three, using the improved aurora optimization algorithm to optimize the PID controller parameters of the control system model of the greening wet spraying machine, and obtaining the optimal three parameters of Kp, Ki, and Kd; step four, using the optimal three parameters of Kp, Ki, and Kd obtained in step three for the spraying speed control of the greening wet spraying machine, and optimizing the control effect of the spraying speed of the greening wet spraying machine.

Description

Technical Field

[0001] The invention belongs to the technical field of PID control optimization, and in particular relates to a spraying speed control method for a greening wet spraying machine. Background Art

[0002] Greening wet spraying machine is a kind of equipment used for vegetation greening, usually used to spray a slurry mixed with seeds, fertilizers, adhesives and water on a large area of ​​land to promote plant growth. The working principle of the wet spraying machine is to spray the mixed slurry to the area requiring greening through a high-pressure pump, and adjust the spraying speed by controlling the motor speed of the high-pressure pump of the wet spraying machine. The PID controller can be used to adjust the spraying speed of the wet spraying machine in real time.

[0003] PID controller is a commonly used automatic control algorithm, widely used in industrial control systems. PID stands for Proportional, Integral and Derivative. Proportional control is adjusted according to the current error, Integral control accumulates past errors to eliminate long-term deviations, and Derivative control predicts the trend of future errors, thereby improving the response speed and stability of the system. By reasonably adjusting these three parameters Kp, Ki, and Kd, the PID controller can accurately adjust the system output to keep it close to or at the target value. Traditional PID has a good control effect on linear control systems, but has poor control performance for complex nonlinear control systems. A lot of experiments are required to set the parameters, and the stability and accuracy are poor. Combining PID control with intelligent optimization algorithm is a good control strategy.

[0004] The Aurora Optimization Algorithm is a meta-heuristic algorithm inspired by the Aurora phenomenon or the Aurora. The algorithm model combines rotational motion and Aurora elliptical walking. The former is conducive to local development, and the latter is conducive to global exploration, achieving a balance between local and global search. In addition, the introduction of particle collision strategy improves the efficiency of the algorithm to jump out of the local optimal solution. Tuning the parameters of the PID controller using the Aurora Optimization Algorithm can reduce the complexity of manual parameter debugging and improve the adaptability of the system. However, the Aurora Optimization Algorithm has the disadvantages of slow optimization speed and low precision in some complex controlled scenarios. Improving the mathematical model of the Aurora Optimization Algorithm can solve these problems well. Summary of the invention

[0005] The purpose of the invention is to improve the standard Aurora optimization algorithm, improve the algorithm's optimization accuracy and speed, enhance the algorithm's adaptability and performance, and apply the improved Aurora optimization algorithm to the tuning of PID controller parameters in the greening wet sprayer control system, which greatly improves the parameter tuning efficiency and response speed, and reduces the overshoot in the control process, thereby improving the robustness of the system. By proposing a greening wet sprayer spraying speed control method, the problems of unstable spraying speed and low accuracy of the greening wet sprayer are effectively solved, and more efficient and precise control is achieved.

[0006] In order to achieve the above object, the present invention adopts the following technical solution.

[0007] A method for controlling the spraying speed of a greening wet spraying machine, the specific steps are as follows.

[0008] Step 1: Construct a greening wet sprayer control system model, which includes a speed conversion module, a PID controller, an improved Aurora optimization algorithm module, a spray pump module, and an encoder speed measurement module.

[0009] Step 2: Improve the mathematical model of the Aurora optimization algorithm. The specific improvement strategies are as follows:

[0010] D1. An adaptive scaling Levy flight strategy is used to improve the mathematical model of the aurora ellipse complex variant Ao in the aurora optimization algorithm. The improved complex variant Ao consists of three parts: adaptive flight step length, average position offset and random offset.

[0011] D2. Use a transition perturbation strategy to improve the mathematical model of the particle collision stage of the Aurora optimization algorithm. Adaptively adjust according to the size of the individual's fitness value. The greater the position gap with the optimal individual, the larger the step size of the transition. At the same time, introduce the Cauchy distribution to randomly perturb the update position.

[0012] Step 3: Use the improved Aurora optimization algorithm to optimize the PID controller parameters of the greening wet spraying machine control system model to obtain the optimal three parameters Kp, Ki, and Kd.

[0013] Step 4: Use the optimal Kp, Ki, and Kd parameters obtained in step 3 to control the spraying speed of the greening wet sprayer to optimize the control effect of the spraying speed of the greening wet sprayer.

[0014] Furthermore, in the step 1, the execution process of each module in the constructed greening wet spraying machine spraying control system model is as follows: first, the desired spraying speed is input into the system, and the spraying speed is converted into the desired motor speed through the speed conversion module. Q is the spraying flow rate, A is the cross-sectional area of ​​the nozzle, and the motor speed k is a fluid-related constant. The error between the expected motor speed and the actual motor speed measured by the encoder speed measurement module is calculated to obtain the real-time error e(t). The error e(t) is input into the PID controller. The control quantity u(t) is calculated according to the value of the error e(t) and the control parameters of the PID controller. The calculation formula of u(t) is shown in formula (1). The values ​​of the control parameters Kp, Ki, and Kd of the PID controller are adjusted by the improved Aurora optimization algorithm model. The control quantity u(t) is input into the spray pump module. The flow rate of the liquid sprayed by the spray pump is controlled by adjusting the motor speed of the spray pump, thereby controlling the spraying speed of the spray pump.

[0015]

[0016] In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error.

[0017] Furthermore, in D1, an adaptive scaling Levy flight strategy is used to improve the mathematical model of the aurora elliptical complex variant Ao in the aurora optimization algorithm, and the improved mathematical model is:

[0018] Ao=LevyStep·(X mean -X(iter))+rand_offset(2);

[0019] In formula (2), Ao represents complex variant, LevyStep represents adaptive flight step length, and the calculation formula is shown in formula (3), X mean represents the average value of all search agent position vectors in the population, X(iter) represents the position of the search agent at the iter iteration, and rand_offset represents the random offset. The calculation formula is shown in formula (4);

[0020]

[0021] In formula (3), α represents a random number between [0, 1], iter represents the current iteration number, Maxiter represents the maximum iteration number, Levy(dim) represents the random step size generated using Levy flight, and dim represents the problem dimension;

[0022]

[0023] In formula (4), lb and ub represent the lower and upper limits of the search space respectively, dim represents the problem dimension, iter represents the current iteration number, Maxiter represents the maximum iteration number, and β represents the attenuation control factor, which is 1.5.

[0024] Furthermore, the adaptive scaling Levy flight strategy has larger Levy step size and random offset value in the early stage of the algorithm, with a wider search range and stronger exploration ability. It gradually shrinks in the middle and late stages of the algorithm, and the search gradually focuses on the local area, adjusting the individual position more finely to enhance convergence. This strategy changes continuously with the number of iterations, and can better adapt to different search stages and improve the performance of the entire algorithm.

[0025] Furthermore, in D2, a transition perturbation strategy is used to improve the mathematical model of the particle collision stage of the Aurora optimization algorithm. The improved mathematical model is:

[0026] X new =X(iter)+r1·(f best -f)·r2·(X best -X(iter))+λ·Cauchy(iter) (5);

[0027] In formula (5), X new represents the updated position of the search agent, X(iter) represents the position of the search agent at the iter iteration, and X best represents the optimal position in the population, r1 and r2 both represent random numbers between [0, 1], and f best represents the optimal fitness value, f represents the fitness value of the current search agent, λ represents the weight factor value of 0.5, and Cauchy(iter) represents the random number generated by the Cauchy distribution to simulate random disturbances.

[0028] Furthermore, the transition perturbation strategy automatically adjusts the moving step size by calculating the gap between the individual position of each iteration and the optimal position area, thereby improving the quality of updating the population position and the optimization speed of the algorithm. Through Cauchy random perturbation, the randomness of the population in the updating process is improved, the global search capability of the algorithm is enhanced, and the algorithm is helped to better escape from the situation of being trapped in the local optimal solution.

[0029] Furthermore, in step three, the PID controller parameters of the greening wet spraying machine control system model are optimized using the improved Aurora optimization algorithm, and the specific steps are as follows:

[0030] S1. Initialize the parameters of the improved Aurora optimization algorithm, including the number of search populations N, the upper limit ub and lower limit lb of the search space, and the maximum number of iterations Maxiter of the algorithm;

[0031] S2, set the problem dimension of the algorithm to 3, corresponding to the three parameters of PID, encode the three parameters Kp, Ki, and Kd as the position vector of the search agent, and initialize the search population position through the initial parameters of the algorithm;

[0032] S3. Set the objective function in the algorithm optimization process. The calculation formula of the objective function is shown in formula (6). The fitness value of the search agent in the initial population is calculated by the objective function, and the optimal position is selected as X. best , the optimal fitness value is f best ;

[0033]

[0034] In formula (6), J represents the objective function value, T represents the total system operation time, DP(t) represents the target output, and AP(t) represents the actual output;

[0035] S4, by improving the mathematical model of the Aurora optimization algorithm to update the population position, each iteration completes the update of the optimal position and the best fitness value in the population;

[0036] S5. Determine whether the current iteration has reached the maximum number of iterations Maxiter. If not, continue to search for the optimal solution through S4. If it has, stop searching for the optimal solution and convert the optimal solution into three parameters of PID for output.

[0037] Furthermore, in S4, the population position is updated by improving the mathematical model of the Aurora optimization algorithm, and the specific steps are:

[0038] S41. Calculate particle speed a represents a random number between [1, 1.5, which is used to calculate the weight of the auroral ellipse walk Calculate the complex variant of the auroral ellipse Ao. The calculation formula of Ao is shown in formula (2);

[0039] S42, updating the position of the aurora ellipse walking phase, the mathematical model of the position update is:

[0040] X new =X(iter)+rand·(W1·V(iter)+W2·Ao) (7);

[0041] In formula (7), X new represents the updated position of the search agent, X(iter) represents the position of the search agent at the iter iteration, rand represents a random number between [0, 1], W1 and W2 represent the weights of the auroral ellipse walk, V(iter) represents the particle speed, Ao represents the auroral ellipse complex variant, and the calculation formula is shown in formula (2);

[0042] S43. Calculate the collision probability of the particle collision stage Set two random numbers r3 and r4 between [0, 1]. If the value of r3 is less than K and the value of r4 is less than 0.05, a particle collision is performed. The mathematical model of particle collision is improved using a transition perturbation strategy as shown in formula (5);

[0043] S44, calculate the fitness value of the updated position, and update the optimal position X in the population best And the best fitness value f best .

[0044] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows.

[0045] The present invention proposes a spraying speed control method for a greening wet spraying machine, uses an adaptive scaling Levy flight strategy and a transition disturbance strategy to improve the standard Aurora optimization algorithm, thereby improving the overall optimization performance of the algorithm, and adjusts the PID controller parameters in the greening wet spraying machine control system by improving the Aurora optimization algorithm, thereby improving the efficiency of parameter adjustment, accelerating the response time of the system, effectively reducing the overshoot in the control process, and enhancing the stability and control accuracy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is a flow chart of a method for controlling the spraying speed of a greening wet spraying machine.

[0047] Figure 2 This is the model diagram of the greening wet spraying machine control system.

[0048] Figure 3 This is a comparison chart of the fitness values ​​of the original Aurora optimization algorithm and the improved Aurora optimization algorithm.

[0049] Figure 4 This is a response comparison curve of the PID controller optimized by the original Aurora optimization algorithm and the improved Aurora optimization algorithm. DETAILED DESCRIPTION

[0050] 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.

[0051] The present invention provides a technical solution: a method for controlling the spraying speed of a greening wet spraying machine, the specific steps are as follows: Figure 1 shown.

[0052] Step 1: Use Simulink simulation software to build a greening wet spraying machine control system model. The model includes speed conversion module, PID controller, improved Aurora optimization algorithm module, spray pump module, encoder speed measurement module, such as Figure 2 shown.

[0053] Step 2: Improve the mathematical model of the Aurora optimization algorithm. The specific improvement strategies are as follows:

[0054] D1. An adaptive scaling Levy flight strategy is used to improve the mathematical model of the aurora ellipse complex variant Ao in the aurora optimization algorithm. The improved complex variant Ao consists of three parts: adaptive flight step length, average position offset and random offset.

[0055] D2. Use a transition perturbation strategy to improve the mathematical model of the particle collision stage of the Aurora optimization algorithm. Adaptively adjust according to the size of the individual's fitness value. The greater the position gap with the optimal individual, the larger the step size of the transition. At the same time, introduce the Cauchy distribution to randomly perturb the update position.

[0056] Step 3: Use the improved Aurora optimization algorithm to optimize the PID controller parameters of the greening wet spraying machine control system model to obtain the optimal three parameters Kp, Ki, and Kd.

[0057] Step 4: Use the optimal Kp, Ki, and Kd parameters obtained in step 3 to control the spraying speed of the greening wet sprayer to optimize the control effect of the spraying speed of the greening wet sprayer.

[0058] Furthermore, in the step 1, the execution process of each module in the constructed greening wet spraying machine spraying control system model is as follows: first, the desired spraying speed is input into the system, and the spraying speed is converted into the desired motor speed through the speed conversion module. Q is the spraying flow rate, A is the cross-sectional area of ​​the nozzle, and the motor speed k is a fluid-related constant. The error between the expected motor speed and the actual motor speed measured by the encoder speed measurement module is calculated to obtain the real-time error e(t). The error e(t) is input into the PID controller. The control quantity u(t) is calculated according to the value of the error e(t) and the control parameters of the PID controller. The calculation formula of u(t) is shown in formula (1). The values ​​of the control parameters Kp, Ki, and Kd of the PID controller are adjusted by the improved Aurora optimization algorithm model. The control quantity u(t) is input into the spray pump module. The flow rate of the liquid sprayed by the spray pump is controlled by adjusting the motor speed of the spray pump, thereby controlling the spraying speed of the spray pump.

[0059]

[0060] In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error.

[0061] Furthermore, in D1, an adaptive scaling Levy flight strategy is used to improve the mathematical model of the aurora ellipse complex variant Ao in the aurora optimization algorithm. In the early stage of the algorithm, the Levy step size and random offset value are large, the search range is wider, and the exploration ability is strong. In the middle and late stages of the algorithm, the search gradually narrows, and the search gradually focuses on the local area, and the individual position is adjusted more finely to enhance convergence. The strategy changes continuously with the change of the number of iterations, which can better adapt to different search stages and improve the performance of the entire algorithm. The improved mathematical model is:

[0062] Ao=LevyStep·(X mean -X(iter))+rand_offfset(2);

[0063] In formula (2), Ao represents complex variant, LevyStep represents adaptive flight step length, and the calculation formula is shown in formula (3), X mean represents the average value of all search agent position vectors in the population, X(iter) represents the position of the search agent at the iter iteration, and rand_offset represents the random offset. The calculation formula is shown in formula (4);

[0064]

[0065] In formula (3), α represents a random number between [0, 1], iter represents the current iteration number, Maxiter represents the maximum iteration number, Levy(dim) represents the random step size generated using Levy flight, and dim represents the problem dimension;

[0066]

[0067] In formula (4), lb and ub represent the lower and upper limits of the search space respectively, dim represents the problem dimension, iter represents the current iteration number, Maxiter represents the maximum iteration number, and β represents the attenuation control factor, which is 1.5.

[0068] Furthermore, in D2, a transition perturbation strategy is used to improve the mathematical model of the particle collision stage of the Aurora optimization algorithm. By calculating the gap between the individual position and the optimal position area of ​​each iteration, the moving step size is automatically adjusted, thereby improving the quality of updating the population position and the optimization speed of the algorithm. By using Cauchy random perturbation, the randomness of the population in the updating process is improved, the global search capability of the algorithm is enhanced, and the algorithm is helped to better escape from the situation of being trapped in the local optimal solution. The improved mathematical model is:

[0069] X new =X(iter)+r1·(f best -f)·r2·(X best -X(iter))+λ·Cauchy(iter) (5);

[0070] In formula (5), X new represents the updated position of the search agent, X(iter) represents the position of the search agent at the iter iteration, and X best represents the optimal position in the population, r1 and r2 both represent random numbers between [0, 1], and f best represents the optimal fitness value, f represents the fitness value of the current search agent, λ represents the weight factor value of 0.5, and Cauchy(iter) represents the random number generated by the Cauchy distribution to simulate random disturbances.

[0071] Furthermore, in step three, the PID controller parameters of the greening wet spraying machine control system model are optimized using the improved Aurora optimization algorithm, and the specific steps are as follows:

[0072] S1. Initialize the parameters of the improved Aurora optimization algorithm, including the number of search populations N, the upper limit ub and lower limit lb of the search space, and the maximum number of iterations Maxiter of the algorithm;

[0073] S2, set the problem dimension of the algorithm to 3, corresponding to the three parameters of PID, encode the three parameters Kp, Ki, and Kd as the position vector of the search agent, and initialize the search population position through the initial parameters of the algorithm;

[0074] S3. Set the objective function in the algorithm optimization process. The calculation formula of the objective function is shown in formula (6). The fitness value of the search agent in the initial population is calculated by the objective function, and the optimal position is selected as X. best , the optimal fitness value is f best ;

[0075]

[0076] In formula (6), J represents the objective function value, T represents the total system operation time, DP(t) represents the target output, and AP(t) represents the actual output;

[0077] S4, by improving the mathematical model of the Aurora optimization algorithm to update the population position, each iteration completes the update of the optimal position and the best fitness value in the population;

[0078] S5. Determine whether the current iteration has reached the maximum number of iterations Maxiter. If not, continue to search for the optimal solution through S4. If it has, stop searching for the optimal solution and convert the optimal solution into three parameters of PID for output.

[0079] Furthermore, in S4, the population position is updated by improving the mathematical model of the Aurora optimization algorithm, and the specific steps are:

[0080] S41. Calculate particle speed a represents a random number between [1, 1.5, which is used to calculate the weight of the auroral ellipse walk Calculate the complex variant of the auroral ellipse Ao. The calculation formula of Ao is shown in formula (2);

[0081] S42, updating the position of the aurora ellipse walking phase, the mathematical model of the position update is:

[0082] X new =X(iter)+rand·(W1·V(iter)+W2·Ao) (7);

[0083] In formula (7), X new represents the updated position of the search agent, X(iter) represents the position of the search agent at the iter iteration, rand represents a random number between [0, 1], W1 and W2 represent the weights of the auroral ellipse walk, V(iter) represents the particle speed, Ao represents the auroral ellipse complex variant, and the calculation formula is shown in formula (2);

[0084] S43. Calculate the collision probability of the particle collision stage Set two random numbers r3 and r4 between [0, 1]. If the value of r3 is less than K and the value of r4 is less than 0.05, a particle collision is performed. The mathematical model of particle collision is improved using a transition perturbation strategy as shown in formula (5);

[0085] S44, calculate the fitness value of the updated position, and update the optimal position X in the population best And the best fitness value f best .

[0086] Furthermore, in order to verify that the spraying speed control method of a greening wet spraying machine proposed in the present invention has better performance than other methods, the present invention conducts simulation experiments through Matlab and Simulink, uses Matlab to rewrite the mathematical model of the Aurora optimization algorithm and the objective function of the algorithm optimization, uses Simulink to build a simulation model of the greening wet spraying machine control system, and sets the controlled object of the model as a second-order transfer function to simulate the actual operating environment. The second-order transfer function is s represents the function variable, the parameters of the initialization algorithm, the population size N is set to 100, the problem dimension dim is set to 3, the maximum number of iterations is set to 20, the upper limit ub of the search space is set to 50, and the lower limit lb is set to 0.

[0087] Furthermore, Figure 3 This is a comparison chart of the fitness values ​​of the original Aurora optimization algorithm and the improved Aurora optimization algorithm. From the figure, it can be seen that the improved Aurora optimization algorithm converges faster in the optimization process and the fitness value of the search agent is smaller. Figure 4 This is a response comparison curve diagram of the PID controller optimized by the original Aurora optimization algorithm and the improved Aurora optimization algorithm. The target value is set to 1 unit. It can be seen from the figure that the response curve of the PID controller optimized by the improved Aurora optimization algorithm has lower overshoot and a shorter time to reach stability, indicating that the parameter values ​​found by the improved Aurora optimization algorithm are better, which makes the control effect of the greening wet sprayer control system better and the spraying speed control more precise.

Claims

1. A method for controlling the spraying speed of a greening wet spraying machine, characterized in that: The specific steps are as follows: Step 1: Build a greening wet spraying machine control system model, which includes a speed conversion module, a PID controller, an improved Aurora optimization algorithm module, a spraying pump module, and an encoder speed measurement module; Step 2: Improve the mathematical model of the Aurora optimization algorithm. The specific improvement strategies are as follows: D1. An adaptive scaling Levy flight strategy is used to improve the mathematical model of the aurora ellipse complex variant Ao in the aurora optimization algorithm. The improved complex variant Ao consists of three parts, namely, adaptive flight step length, average position offset and random offset. The improved mathematical model is: Ao=LevyStep·(X mean -X(iter))+rand_offset(2); In formula (2), Ao represents complex variant, LevyStep represents adaptive flight step length, and the calculation formula is shown in formula (3), X mean represents the average value of all search agent position vectors in the population, X(iter) represents the position of the search agent at the iter iteration, and rand_offset represents the random offset. The calculation formula is shown in formula (4); In formula (3), α represents a random number between [0, 1], iter represents the current iteration number, Maxiter represents the maximum iteration number, Levy(dim) represents the random step size generated using Levy flight, and dim represents the problem dimension; In formula (4), lb and ub represent the lower and upper limits of the search space respectively, dim represents the problem dimension, iter represents the current iteration number, Maxiter represents the maximum iteration number, and β represents the attenuation control factor, which is 1.5; D2. Use a transition perturbation strategy to improve the mathematical model of the particle collision stage of the Aurora optimization algorithm. Adaptively adjust according to the size of the individual fitness value. The greater the position gap with the optimal individual, the greater the step length of the transition. At the same time, introduce the Cauchy distribution to randomly perturb the update position. The improved mathematical model is: X new =X(iter)+r1·(f best -f)·r2·(X best -X(iter))+λ·Cauchy(iter) (5); In formula (5), X new represents the updated position of the search agent, X(iter) represents the position of the search agent at the iter iteration, and X best represents the optimal position in the population, r1 and r2 both represent random numbers between [0,1], and f best represents the best fitness value, f represents the fitness value of the current search agent, λ represents the weight factor value of 0.5, and Cauchy(iter) represents the random number generated by the Cauchy distribution to simulate random disturbances; Step 3: Use the improved Aurora optimization algorithm to optimize the PID controller parameters of the greening wet spraying machine control system model to obtain the optimal Kp, Ki, and Kd parameters; Step 4: Use the optimal Kp, Ki, and Kd parameters obtained in step 3 to control the spraying speed of the greening wet sprayer to optimize the control effect of the spraying speed of the greening wet sprayer.

2. The method for controlling the spraying speed of a greening wet spraying machine according to claim 1, characterized in that: The execution process of each module in the greening wet sprayer spraying control system model constructed in step 1 is as follows: first, the desired spraying speed is input into the system, and the spraying speed is converted into the desired motor speed through the speed conversion module. Q is the spraying flow rate, A is the cross-sectional area of ​​the nozzle, and the motor speed k is a fluid-related constant. The error between the expected motor speed and the actual motor speed measured by the encoder speed measurement module is calculated to obtain the real-time error e(t). The error e(t) is input into the PID controller. The control quantity u(t) is calculated according to the value of the error e(t) and the control parameters of the PID controller. The values ​​of the control parameters Kp, Ki, and Kd of the PID controller are adjusted by the improved Aurora optimization algorithm model. The control quantity u(t) is input into the spray pump module. The flow rate of the liquid sprayed by the spray pump is controlled by adjusting the motor speed of the spray pump, thereby controlling the spraying speed of the spray pump.

3. The method for controlling the spraying speed of a greening wet spraying machine according to claim 2, characterized in that: In step three, the PID controller parameters of the greening wet spraying machine control system model are optimized using the improved Aurora optimization algorithm, and the specific steps are as follows: S1. Initialize the parameters of the improved Aurora optimization algorithm, including the number of search populations N, the upper limit ub and lower limit lb of the search space, and the maximum number of iterations Maxiter of the algorithm; S2. Set the problem dimension of the algorithm to 3, corresponding to the three parameters of PID, encode the three parameters Kp, Ki, and Kd as the position vector of the search agent, and initialize the search population position through the initial parameters of the algorithm; S3, set the objective function in the algorithm optimization process, calculate the fitness value of the search agent in the initial population through the objective function, and select the optimal position as X best , the optimal fitness value is f best ; S4. The population position is updated by improving the mathematical model of the Aurora optimization algorithm. The optimal position and the best fitness value in the population are updated in each iteration. The specific steps are as follows: S41, calculate the particle velocity V(iter), the weights W1 and W2 of the auroral ellipse walking, and the auroral ellipse complex variant Ao, the calculation formula of Ao is shown in formula (2); S42, updating the position of the aurora ellipse walking phase; S43. Calculate the collision probability of the particle collision stage Set two random numbers r3 and r4 between [0,1]. If the value of r3 is less than K and the value of r4 is less than 0.05, a particle collision is performed. The mathematical model of particle collision is improved using a transition perturbation strategy as shown in formula (5); S44, calculate the fitness value of the updated position, and update the optimal position X in the population best And the best fitness value f best ; S5. Determine whether the current iteration has reached the maximum number of iterations Maxiter. If not, continue to search for the optimal solution through S4. If it has, stop searching for the optimal solution and convert the optimal solution into three parameters of PID for output.

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