Water flow velocity control method for hydraulic execution system

Abstract

The invention provides a water flow velocity control method for a hydraulic execution system, and belongs to the field of water flow velocity control, and the method comprises the steps: S1, collecting the water flow velocity and pressure change data of the hydraulic execution system, and building a water flow velocity control model based on data driving; s2, constructing an improved eagle-fish optimization algorithm, and adopting the improved eagle-fish optimization algorithm to adjust proportion, integral and differential parameters of a position type PID control algorithm on line; s3, the position type PID control algorithm is used for a water flow speed controller of the hydraulic execution system, and an output signal of the water flow speed controller is adjusted; the output signal is input into a water flow velocity control model to obtain the water flow velocity of the real-time hydraulic execution system; and S4, calculating a difference value between the target water flow velocity and the real-time water flow velocity, inputting the difference value into a water flow velocity controller, and adjusting the water flow velocity of the real-time hydraulic execution system by using a position type PID control algorithm until the difference value is lower than a target threshold value, thereby realizing water flow velocity control optimization. And the control precision and response speed of the system are improved.

Description

Technical Field

[0001] The present application relates to the field of water flow rate control, and in particular to a water flow rate control method for a hydraulic execution system. Background Art

[0002] In various hydraulic execution systems, such as liquid transportation pipelines, water pressure regulating equipment, pump-valve linkage systems, and fire water systems, water flow rate control is a core link that directly affects the stability and response performance of the system operation. In order to achieve precise control of flow rate, the traditional proportional-integral-differential (PID) control algorithm is currently widely used to adjust the hydraulic system; the traditional PID controller adjusts the flow rate error through real-time feedback, generates a control signal based on the proportion, accumulation, and change rate of the error, and thus adjusts the opening of the water pump or valve to achieve automatic control of the water flow rate; the proportional, integral, and differential parameters of the traditional PID controller are difficult to adjust quickly for different hydraulic systems or dynamic load environments, and rely on experience settings, which cannot meet the response requirements under complex working conditions; the response speed is limited, and it is easy to produce overshoot and oscillation: when facing system disturbances or set point changes, the control response is often delayed, which can easily lead to large fluctuations in water pressure or water speed and a long stabilization time.

[0003] HawkFish Optimization Algorithm (HFOA) is a swarm intelligence optimization algorithm inspired by the unique biological habits of hawkfish (fish with gender-changing behavior) in nature. When dealing with complex problems, especially water flow rate control problems with high control requirements, the standard HawkFish optimization algorithm is prone to converge prematurely and fall into local extreme points. With the increase of the number of iterations, the individual positions may tend to be consistent, resulting in a significant decrease in population diversity, thereby affecting the global search performance of the algorithm. After the introduction of the optimization algorithm, the computational complexity is improved compared to the traditional single PID algorithm, and higher requirements are placed on the real-time processing capability of the water flow rate control system in practical applications. Summary of the invention

[0004] In response to the problems existing in the above-mentioned background technology, the present application proposes a self-regulating feedback control method for water flow rate control of a hydraulic execution system, aiming to utilize the traditional position-based PID control algorithm and the intelligent optimization algorithm to optimize the water flow rate controller of the hydraulic execution system to improve the control accuracy and control speed of the controller. The control accuracy is achieved by controlling the overshoot, and the control speed is achieved by adjusting the response time. The water flow rate control optimization method for a hydraulic execution system proposed in the present application significantly improves the control accuracy and response speed of the system by combining traditional PID control with an intelligent optimization algorithm. The method is suitable for a variety of water application scenarios, and can effectively improve the efficiency of the hydraulic execution system, providing a new solution for intelligent hydraulic application optimization.

[0005] The present invention proposes a water flow rate control method for a hydraulic actuator system, and optimizes a water flow rate controller of the hydraulic actuator system. The specific steps are as follows: S1. Collect water flow rate and pressure change data of the hydraulic execution system and establish a water flow rate control model based on data drive; S2. Construct an improved hawkfish optimization algorithm, and use the improved hawkfish optimization algorithm to adjust the proportional, integral and differential parameters of the position PID control algorithm online; the improved hawkfish optimization algorithm improves the hawkfish individual movement rules and learning coefficients of the standard hawkfish optimization algorithm; specifically: S21. The distribution state of the hawkfish population in the search space is regarded as a spatial distribution field with a topological structure, and a topological folding strategy is defined to adaptively change the learning coefficient; S22. A probabilistic transition movement strategy is introduced, and the historical differential trend of the agent individual fitness is used as the trigger condition for the transition to improve the hawkfish individual movement rule; S3, the position PID control algorithm is used for the water flow rate controller of the hydraulic execution system to adjust the output signal of the water flow rate controller; the output signal is input into the water flow rate control model to obtain the real-time water flow rate of the hydraulic execution system; S4. Calculate the difference between the target water flow rate and the real-time water flow rate, input the difference into the water flow rate controller, and use the position PID control algorithm to adjust the water flow rate of the real-time hydraulic execution system until the difference is lower than the target threshold, thereby achieving water flow rate control optimization.

[0006] Preferably, the hydraulic execution system embodies AI intelligent control by automatically controlling the water flow. The hydraulic execution system includes a data acquisition unit, a data-driven water flow rate control model unit, a PID controller unit and an execution unit; the input data includes water flow rate and pressure change data; a water flow rate control model is established based on the real-time collected water flow rate and pressure change data; the real-time water flow rate data and the target water flow rate data are input into the data acquisition unit, and the water flow rate difference is output. The water flow rate difference is input into the PID controller unit, and the water flow rate difference is processed by an online adjusted position PID control algorithm to output a control signal First, the water pump of the hydraulic execution system is acted on to adjust the water pressure in real time. When the water pressure changes, the water flow rate changes accordingly through the water flow rate control model based on the mapping relationship between the real-time water flow rate and the water pressure, ultimately achieving precise control of the actual water flow rate.

[0007] Preferably, the pressure control of the hydraulic actuator system is a dynamic process. The present invention adopts a typical second-order inertia link model to represent it. The second-order inertia link model is a time domain model, and the input is a control signal , the output is the real-time water pressure of the hydraulic execution system; the real-time water pressure is converted into the real-time water flow rate through the water flow rate control model, and the water flow rate control model is: ; In the formula, For the The water flow rate at any time, and are model parameters, which are determined through online data identification. For the Real-time water pressure at all times, For the The water flow rate at the moment.

[0008] Preferably, when the standard hawkfish optimization algorithm is used to adjust the parameters of the position PID control algorithm, it is difficult to accurately capture the actual state of the proxy individual in the search space, resulting in poor exploration and development capabilities of the group and easy to fall into the local optimum; when the improved hawkfish optimization algorithm is used to adjust the parameters of the position PID control algorithm online, the hawkfish individual of the improved hawkfish optimization algorithm is used as the proxy individual, and the position of the proxy individual is updated by the hawkfish individual movement rule and the hawkfish learning rule, wherein the position of the proxy individual is mapped to the proportional, integral and differential parameters of the position PID control algorithm of the water flow rate controller of the hydraulic execution system, and the mapped position of the proxy individual is a three-dimensional value, which is expressed by a mathematical model as: \left ( {{K}^{n}_{p},{K}^{n}_{i},{K}^{n}_{d}} \right )=\left [ {{X}^{1}_{n},{X}^{2}_{n},{X}^{3}_{n}} \right ] ;in, , and are the proportional parameter, integral coefficient and differential parameter corresponding to the position value of the nth agent; , and are the position values ​​of the 1st, 2nd and 3rd dimensions of the nth agent individual.

[0009] Preferably, the present invention breaks through the representation of simple distance in the existing standard hawkfish optimization algorithm and proposes a topological folding strategy. Specifically, the method regards the distribution state of the hawkfish population in the search space as a spatial distribution field with a topological structure and defines a topological folding strategy. The topological folding strategy reflects the geometric characteristics and distribution density of the population position distribution. Through the topological folding strategy, the improved hawkfish optimization algorithm can keenly capture the subtle changes in the group state and drive the learning coefficient with the underlying geometric characteristics. Adaptive changes; the mathematical model is: S211, the eagle fish group of the tth iteration The distribution in the search space is defined as the topological folding strategy , the mathematical model is: ; In the formula, is the topological folding strategy of the tth iteration, N is the maximum size of the agent individual, n≠j; The Euclidean distance between the agent individuals in the t-th iteration population; S212, introduce the topological folding strategy and propose a mathematical model for adaptive control of learning coefficients: ; In the formula, is the minimum value of the learning coefficient, is the maximum value of the learning coefficient, μ is the sensitivity adjustment factor, and the sensitivity adjustment factor starts from the fitness ranking ratio. The changing trend of the relative ranking of the optimal agent individual in the population essentially reflects the search status of the group, which is automatically fed back to the sensitivity adjustment factor. The mathematical model is: ; in, is the sensitivity adjustment factor of the tth iteration, is the ranking of the current best agent in the population.

[0010] Preferably, the sin term reflects the folding state of the topological space, and the distance ratio Combined with the sine function, the topological folding strategy can reflect both the distance and the nonlinear topological characteristics. The folding strategy reflects the closeness of the agent group and the complexity of the topological structure. When the value is large, the population topology is complex, indicating that the distribution of agent positions is dispersed, that is, the distribution of solutions is dispersed. When the value is small, the topological structure is simple, indicating that the positions of the agent individuals are concentrated, that is, the solution has the risk of falling into the local optimum; arctan() is a nonlinear smooth mapping function that can naturally realize the adaptive regulation of the learning coefficient as the topological potential changes; when the population When the population changes dramatically, the learning coefficient is adaptively increased to enhance the global exploration or rapid focusing capabilities; When it tends to be stable, the learning coefficient is automatically reduced to strengthen the local fine search.

[0011] Preferably, in the individual movement rules of the hawk fish, the present invention innovatively introduces a probabilistic transition movement strategy based on the gradual update of the individual movement routine of the hawk fish. When the hawk fish is trapped in the local search area for a long time, a transition is triggered with a certain probability (trigger transition value), and a cross-space jump movement is implemented to effectively avoid the algorithm from falling into the local optimum prematurely; the strategy no longer uses the traditional simple regulation of distance or fitness, but uses the historical differential trend of the fitness of the proxy individual as the trigger condition for the transition; The mathematical model is: ; in, is the position of the nth agent at the t+1th iteration, is the position of the nth agent at the tth iteration, is the step length of the nth agent, is the j-th dimension of the direction vector of the n-th agent individual, is the centroid position of the current population, is the current global optimal agent individual position; is the trigger transition value of the tth iteration, rand is a random number from 0 to 1 that follows a uniform distribution, is the pseudo-pulse transition sensitivity parameter, which controls the degree of change of transition probability. The mathematical model is: ; In the formula, is the position of the nth agent individual in the t+1th iteration, is the maximum number of iterations, randn(0,1) is a standard normal distribution random number; Among them, the trigger transition factor is used to judge that the eagle fish is trapped in the local search area for a long time. The mathematical model of the trigger transition factor is: ; In the formula, and is the fitness value of the i-th agent in two consecutive iterations.

[0012] Preferably, the improved Eagle Fish optimization algorithm is used to adjust the proportional, integral and differential parameters of the position PID control algorithm online. During the adjustment process, the improved Eagle Fish optimization algorithm is guided by the fitness function to optimize the parameters and find the global optimal solution, that is, the agent individual position corresponding to the minimum value of the fitness function. The specific steps are: S31. Initialize the population agent individual positions of the improved eagle-fish optimization algorithm , each agent's individual position D-dimensional vector; set the maximum number of iterations and maximum population size ; S32, divide the hawkfish individuals into male and female proxy individuals, define the availability of food resources in the environment as d(t) and design a gender transition mechanism model; S33, calculate the fitness value of each agent individual in the current iteration, the fitness value is the fitness function value of each agent individual position, and record the agent individual position corresponding to the current minimum fitness value as ; S34, calculating the trigger transition value of the t-th iteration, and executing the mathematical model of the hawk-fish individual movement rule through the trigger transition value to update the proxy individual position; S35, dynamically dividing subgroups using the Euclidean distance matrix, performing dynamic clustering and determining the position of the subgroup leader, where the subgroup leader position is the best proxy individual position in the subgroup; S36, constructing the eagle-fish learning rule by adaptively regulating the learning coefficient through the topology folding strategy, and using the eagle-fish learning rule to update the position of the agent individual; S37, the agent individual adjusts the search range according to the gender, sets a threshold, and if the fitness value of the agent individual exceeds the threshold, the agent individual changes gender and updates the agent individual position according to the dynamic visual rule; S38. Limit the agent individual position within the range of [lb, ub], increase the current number of iterations by one, and determine whether the current number of iterations satisfies t+1=Tmax; if so, exit the optimization process, output the current global optimal agent individual position, and parse it into the proportional, integral, and differential parameter values ​​of the position PID control algorithm; otherwise, return to execute S33.

[0013] Preferably, the specific process of the gender transition mechanism is as follows: when the improved hawkfish optimization algorithm is started, the initial population of all hawkfish individuals is a single-sex proxy individual, and the average fitness value of the current population is used as the threshold. If the fitness value of the proxy individual exceeds the threshold, a part of female individuals are selected from the current proxy individual population for gender transition. After the gender transition is completed, the improved hawkfish optimization algorithm re-updates the gender ratio in the hawkfish population and records the current male-female ratio for subsequent iterations.

[0014] Preferably, after the position of the proxy individual is updated by the improved Eagle Fish optimization algorithm, the position of the proxy individual When it is less than the lower boundary lb or greater than the upper boundary ub, if the position of the proxy individual is greater than the upper boundary ub, the individual position is directly set to the upper boundary value ub; if the position of the proxy individual is less than the lower boundary lb, the individual position is directly set to the lower boundary value lb; if the position of the proxy individual is between the upper and lower boundaries, it remains unchanged.

[0015] Preferably, in S35, the eaglefish learning rule is designed based on the best agent individual position in the subgroup and the learning coefficient of adaptive regulation, and the mathematical model is: ; In the formula, is the j-th dimension value of the position of the n-th agent individual in the t+1-th iteration, is the j-th dimension value of the position of the n-th agent individual in the t-th iteration, To adaptively control the learning coefficient, is the j-th dimension value of the best agent individual position in the t-th iteration.

[0016] Compared with the existing methods, the beneficial effects and innovations of the present invention are as follows: the present invention breaks through the purely distance-based characterization method in the standard hawkfish optimization algorithm, and innovatively proposes a learning coefficient adaptive control method based on a topological folding strategy. The topological folding strategy keenly captures the geometric characteristics of the population position distribution, drives the dynamic change of the learning coefficient, and enables the algorithm to quickly respond to small changes in the optimization environment, more effectively avoids the algorithm from falling into the local optimum, and thus optimizes the proportional, integral, and differential parameter values ​​of the optimal position PID control algorithm; at the same time, the probabilistic transition movement strategy is innovatively introduced, and the historical differential trend of the fitness of the agent individual is used as the basis for the transition trigger. When the agent individual is trapped in the local search area for a long time, the algorithm implements cross-regional transitions with a certain probability, which enhances the global search capability of the algorithm and effectively prevents premature convergence; the present invention combines the traditional position PID control with the improved hawkfish optimization algorithm, and the improved hawkfish optimization algorithm can optimize the PID controller parameters in real time, so that the control system can maintain good adaptability and stability in different types of water application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the water flow rate control optimization method of the hydraulic execution system.

[0018] Figure 2 It is a schematic diagram of the improved Eagle Fish optimization algorithm for online adjustment of position PID control algorithm parameters.

[0019] Figure 3 It is a comparison chart of the fitness value changes during the optimization process of the existing algorithm and the algorithm of the present invention.

[0020] Figure 4 This is a graph showing the results of online adjustment of position PID control algorithm parameters using the algorithm of the present invention.

[0021] Figure 5 The figure is a comparison diagram of the control effect of the method of the present invention and the existing method on optimizing the water flow rate controller of the hydraulic execution system. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that the following detailed descriptions are exemplary and are intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those generally understood by ordinary technicians in the technical field to which the present invention belongs. In the absence of conflict, in order to make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, 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.

[0023] The present invention is based on intelligent optimization algorithm and PID control technology to realize a water flow rate control method for a hydraulic execution system, and optimizes the water flow rate controller of the hydraulic execution system. The specific steps are as follows: Figure 1 shown.

[0024] S1. Collect water flow rate and pressure change data of the hydraulic execution system and establish a data-driven water flow rate control model.

[0025] Specifically, the water flow rate and pressure change data are collected in real time with a sampling interval of 0.5 seconds, and a data sample is obtained in each sampling period\left [ {P\left ( {τ} \right ),v\left ( {τ} \right )} \right ] , the data sample set is obtained as: ; The water flow rate and pressure change data of the collected hydraulic execution system are constructed in Excel. It is found that there is a linear proportional relationship between the real-time water flow rate and the real-time water pressure. Therefore, the real-time data collection and online least squares fitting method of the present invention determine the parameters. The specific implementation model is: ; Where T is the maximum running time, which is set to 20 to minimize The best model parameters can be obtained and .

[0026] Specifically, the real-time water pressure is converted into the real-time water flow rate through the water flow rate control model, and the water flow rate control model is: ; In the formula, For the The water flow rate at any time, and are model parameters, which are determined through online data identification. For the Real-time water pressure at all times, For the The water flow rate at the moment.

[0027] Specifically, complete the model subfiles and complete the code in MATLAB; P=randn(1, T); % Initial water pressure data v=randn(1, T); % Initial water flow rate data % Initialize historical water flow rate and remove the initial state v_prev=[0, v(1:end-1)]; % The data of the water flow rate at the previous moment %Define the objective function J(a, b) J=@(a, b)sum((v(2:end)-a*P(2:end)-b*v_prev(2:end)).^2); %Use fminsearch to minimize J(a, b) and obtain the optimal parameters a and b initial_guess=[0, 0]; % Initial a and b options=optimset('Display', 'off'); % Turn off output optimal_params=fminsearch(@(params)J(params(1), params(2)), initial_guess, options); % Output optimal parameters a and b a_optimal = optimal_params(1); b_optimal=optimal_params(2); %Fitting water flow rate according to optimal parameters v_fitted=a_optimal*P(2:end)+b_optimal*v_prev(2:end).

[0028] S2. Construct an improved eagle fish optimization algorithm, and use the improved eagle fish optimization algorithm to online adjust the proportional, integral and differential parameters of the position PID control algorithm; the improved eagle fish optimization algorithm improves the eagle fish individual movement rules and learning coefficients of the standard eagle fish optimization algorithm; specifically: S21. The distribution state of the eagle fish population in the search space is regarded as a spatial distribution field with a topological structure, and a topological folding strategy is defined to adaptively change the learning coefficient; S22. A probabilistic transition movement strategy is introduced, and the historical differential trend of the agent individual fitness is used as the trigger condition for the transition to improve the eagle fish individual movement rules.

[0029] Specifically, a mathematical model of the standard hawkfish optimization algorithm is constructed, and the distribution state of the hawkfish population in the search space is regarded as a spatial distribution field with a topological structure. A topological folding strategy is defined, which reflects the geometric characteristics and distribution density of the population position distribution. Through the topological folding strategy, the improved hawkfish optimization algorithm can keenly capture the subtle changes in the group state and drive the learning coefficient with the underlying geometric characteristics. Adaptive changes; the implementation mathematical model is: S211, the eagle fish group of the tth iteration The distribution in the search space is defined as the topological folding strategy , the mathematical model is: ; In the formula, is the topology folding strategy for the tth iteration, N is the maximum size of the agent individual, n≠j; it is set to 30 in the program; The Euclidean distance between the agent individuals in the t-th iteration population; S212, introduce the topological folding strategy and propose a mathematical model for adaptive control of learning coefficients: ; In the formula, is the minimum value of the learning coefficient, which is set to 0 in practice; is the maximum value of the learning coefficient, which is set to 2 in practice; μ is the sensitivity adjustment factor. The sensitivity adjustment factor starts from the fitness ranking ratio. The changing trend of the relative ranking of the optimal agent individual in the population essentially reflects the search status of the group, which is automatically fed back to the sensitivity adjustment factor. The mathematical model is: ; in, is the sensitivity adjustment factor of the tth iteration, is the ranking of the current best agent in the population.

[0030] Specifically, in the individual movement rules of eagle fish, on the basis of the gradual update of the individual movement routine of eagle fish, a probabilistic jump movement strategy is innovatively introduced. When the eagle fish is trapped in the local search area for a long time, a jump is triggered with a certain probability, and a cross-space jump movement is implemented to effectively avoid the algorithm from falling into the local optimum prematurely; the strategy no longer uses the traditional simple regulation of distance or fitness, but uses the historical differential trend of the fitness of the proxy individual as the trigger condition for the jump; the implementation mathematical model is: ; in, is the position of the nth agent individual in the t+1th iteration, is the position of the nth agent at the tth iteration, is the step length of the nth agent, is the j-th dimension of the direction vector of the n-th agent individual, is the centroid position of the current population, is the current global optimal agent individual position; is the trigger transition value of the tth iteration, rand is a random number from 0 to 1 that follows a uniform distribution, is the pseudo-pulse transition sensitivity parameter, which controls the degree of change of transition probability. The mathematical model is: ; In the formula, is the position of the nth agent individual in the t+1th iteration, is the maximum number of iterations, set to 20, randn(0,1) is a standard normal distribution random number; Among them, the trigger transition factor is used to judge that the eagle fish is trapped in the local search area for a long time. The mathematical model of the trigger transition factor is: ; In the formula, and is the fitness value of the i-th agent in two consecutive iterations.

[0031] Specifically, a relationship is established between the improved eagle-fish optimization algorithm and the position PID algorithm of the hydraulic execution system. The eagle-fish individual of the improved eagle-fish optimization algorithm is used as an agent individual. The position of the agent individual is updated through the eagle-fish individual movement rule and the eagle-fish learning rule. The position of the agent individual is mapped to the proportional, integral and differential parameters of the position PID control algorithm of the water flow rate controller of the hydraulic execution system. The mapped position of the agent individual is a three-dimensional value, which is expressed by a mathematical model as follows: \left ( {{K}^{n}_{p},{K}^{n}_{i},{K}^{n}_{d}} \right )=\left [ {{X}^{1}_{n},{X}^{2}_{n},{X}^{3}_{n}} \right ] ;in, , and are the proportional parameter, integral coefficient and differential parameter corresponding to the position value of the nth agent; , and is the position value of the 1st, 2nd and 3rd dimensions of the nth agent individual. The MATLAB code is: [HFOA_score,HFOA_pos,HFOA _ curve , HFOA_PID]=HFOA(N,T,lb,ub,D,f); [WHFOA_score,WHFOA_pos,WHFOA _ curve,WHFOA_PID]=WHFOA(N,T,lb,ub,D,f); Among them, HFOA and WHFOA functions are sub-file codes of the standard Eagle Fish optimization algorithm and the improved Eagle Fish optimization algorithm respectively. The results of running will be assigned to WHFOA_score, WHFOA_pos and WHFOA _curve, WHFOA_PID, are all three-dimensional variables, among which WHFOA_score is the position value, WHFOA_pos is the optimal control parameter value, and WHFOA _ curve is the fitness value, and WHFOA_PID is the control parameter value of each iteration.

[0032] S3. The position PID control algorithm is used for the water flow rate controller of the hydraulic execution system to adjust the output signal of the water flow rate controller; the output signal is input into the water flow rate control model to obtain the real-time water flow rate of the hydraulic execution system.

[0033] Specifically, Figure 2 As shown in the figure, the improved Eagle Fish optimization algorithm is used to adjust the proportional, integral and differential parameters of the position PID control algorithm online. During the adjustment process, the improved Eagle Fish optimization algorithm is guided by the fitness function to optimize the parameters and find the global optimal solution, that is, the agent individual position corresponding to the minimum value of the fitness function. The specific steps are as follows: S31. Initialize the population agent individual positions of the improved eagle-fish optimization algorithm , each agent's individual position D-dimensional vector; set the maximum number of iterations and maximum population size ; S32, divide the hawkfish individuals into male and female proxy individuals, define the availability of food resources in the environment as d(t) and design a gender transition mechanism model; S33, calculate the fitness value of each agent individual in the current iteration, the fitness value is the fitness function value of each agent individual position, and record the agent individual position corresponding to the current minimum fitness value as ; S34, calculating the trigger transition value of the t-th iteration, and executing the mathematical model of the hawk-fish individual movement rule through the trigger transition value to update the proxy individual position; S35, dynamically dividing subgroups using the Euclidean distance matrix, performing dynamic clustering and determining the position of the subgroup leader, where the subgroup leader position is the best proxy individual position in the subgroup; S36, constructing the eagle-fish learning rule by adaptively regulating the learning coefficient through the topology folding strategy, and using the eagle-fish learning rule to update the position of the agent individual; S37, the agent individual adjusts the search range according to the gender, sets a threshold, and if the fitness value of the agent individual exceeds the threshold, the agent individual changes gender and updates the agent individual position according to the dynamic visual rule; S38. Limit the agent individual position within the range of [lb, ub], increase the current number of iterations by one, and determine whether the current number of iterations satisfies t+1=Tmax; if so, exit the optimization process, output the current global optimal agent individual position, and parse it into the proportional, integral, and differential parameter values ​​of the position PID control algorithm; otherwise, return to execute S33.

[0034] More specifically, a fitness function is designed according to the implementation mathematical model; ; In the formula, is the fitness function, is the total running time, set to 20; is the control signal, is the control signal at the previous moment, The water flow rate difference is used to calculate the actual water flow rate measured in real time. Difference from target water flow rate: , where the target water flow rate is determined according to the set water pressure, the initial value of the set water pressure is set to 8, and the final value is set to 4, to verify the mobility and stability of the method of the present invention after the target water flow rate changes.

[0035] More specifically, the mathematical model implemented by S35 is: S351. Calculate the distance matrix between two agent individuals: ; S352, based on the distance matrix, the agent individual population is divided into k clusters C1, C2, ..., Ck, and the mathematical model is: , where p = 1, 2, ..., k; S353, the cluster center position is the current optimal individual position, and the mathematical model is: ; In the formula, is the location of the cluster center at the tth iteration, is the fitness value of the nth agent individual position.

[0036] More specifically, the S37 dynamic visual rule updates the individual agent positions, and the specific implementation model is: ; In the formula, is the position of the nth agent at the t+1th iteration, is the position of the nth agent at the tth iteration, step is the step size, and is set to 0.5 in the implementation.

[0037] S4. Calculate the difference between the target water flow rate and the real-time water flow rate, input the difference into the water flow rate controller, and use the position PID control algorithm to adjust the water flow rate of the real-time hydraulic execution system until the difference is lower than the target threshold, thereby achieving water flow rate control optimization.

[0038] Specifically, the optimal proportional, integral, and differential parameter values ​​obtained by optimization are used in the position PID control algorithm. The mathematical model of the position PID control algorithm is: ; in, is the scale parameter, is the integration parameter, is the differential parameter, is the water velocity difference, is the water flow velocity difference at the previous moment.

[0039] Furthermore, the control signal and real-time water pressure are represented by a typical second-order inertia link model during the implementation process. The second-order inertia link model is a time domain model, and the input is the control signal , the output is the real-time water pressure of the hydraulic execution system; the mathematical model is: ; in, is the second-order inertia time constant, reflecting the hysteresis inertia characteristics of the water pressure response process. is the damping ratio, which reflects the oscillation during the water pressure response process. is the static gain coefficient of the system, which indicates the proportional relationship between the control input signal and the water pressure output; complete the code in MATLAB: % Time vector time = 0:dt:T; % Initialize variables P = zeros(1, length(time)); % water pressure; u = WHFOA_score; % control signal, WHFOA_score is the control signal output value after mapping; e = zeros(1, length(time)); % Error; % Initial conditions P(1) = 0; % Initial water pressure; e(1) = 10; % Assume the initial error is 10; u(1) = 0; % The initial control signal is 0; % State variables of the second-order inertial model P_dot = 0; % Rate of change of water pressure; P_ddot = 0; % Acceleration of water pressure % PID control and water flow rate calculation for t = 2:length(time) e(t) = 10 - v(t-1); % The target water flow rate is 10; u(t) = Kp * e(t) + Ki * sum(e(1:t)) * dt + Kd * (e(t) - e(t-1)) / dt; % Second-order inertia link model P_ddot = K * u(t) - 2 * xi * beta * P_dot - beta^2 * P(t); % acceleration of water pressure; P_dot = P_dot + P_ddot * dt; % Update the rate of change of water pressure; P(t) = P(t-1) + P_dot * dt; % Update water pressure.

[0040] Further, the conversion to S-domain transfer function is Expression, used in Siumulink simulation model: ; in, For time The S-domain parameters, Set to 0.8, Set to 1.5, Set to 5; get the transfer function of the Simulink simulation model: .

[0041] Furthermore, the mathematical model in the above embodiment is implemented in Matlab and converted into a code form, including: the standard eagle fish optimization algorithm and the improved eagle fish optimization algorithm code of the present invention, the water flow rate control model and the position PID control algorithm code, as well as the main function and the fitness function code, wherein the main function code implements the mapping of the proportion, integral, and differential parameters of the standard and improved eagle fish optimization algorithms and the position PID control algorithm and the mapping with the fitness function value; the fitness function is mapped with the objective function of the Siumulink simulation model, the optimal proportion, integral, and differential parameter values ​​obtained by optimization are input into the PID module of the simulation model, and the maximum number of iterations of the standard and improved eagle fish optimization algorithms is set. and maximum population size , problem dimension D = 3; agent individual position lower bound lb = [0 0.05 0], upper bound ub = [2020 10]; run the main program, and get the following Figures 3 to 5 Result graph.

[0042] Furthermore, if Figure 3 The comparison diagram of fitness value changes in the optimization process of the existing algorithm and the algorithm of the present invention is shown. The smaller the fitness value, the higher the accuracy of the solution, and the better the water flow rate control effect for the hydraulic execution system. It can be clearly seen from the curve trend that the fitness value of the algorithm of the present invention decreases rapidly after the first few iterations, and quickly reaches a lower value in the 9th iteration, reflecting the fast convergence characteristics; while the convergence speed of the existing algorithm, that is, the standard Eagle Fish optimization algorithm, is significantly slower, although it decreases in the early stage, the amplitude is small, and it basically remains stable in the later stage, and fails to continue to optimize, and the convergence speed is obviously not as good as the algorithm of the present invention; Combining the above factors, it can be clearly seen that the overall performance of the algorithm of the present invention is significantly better than the existing algorithm; the convergence speed of the algorithm of the present invention is faster, the optimization accuracy is higher, and it has better stability and robustness; the existing algorithm has obvious deficiencies in all aspects, the optimization effect is limited, and there are performance bottlenecks in practical applications.

[0043] Furthermore, after the fitness value of the algorithm of the present invention reaches the minimum value and stabilizes at the 9th iteration, the corresponding Figure 4 As shown, the best proportional, integral and differential parameter values ​​are obtained in the 9th iteration, with the proportional parameter being 0.79642, the integral parameter being 0.26329 and the differential parameter being 0.53419.

[0044] Furthermore, if Figure 5 As shown in the figure, from the control overshoot, when the water pressure transitions from 0 to the initial target value 8, the existing control method has obvious overshoot, exceeding 10, and in the subsequent process of decreasing from 8 to the target value 4, obvious overshoot occurs again, showing poor stability; the overshoot of the control method of the present invention is significantly reduced, and the water pressure control is basically stable at the target value in the initial rising stage, without significantly exceeding the target water pressure, and no obvious overshoot phenomenon occurs in the subsequent process of decreasing to 4; it can be seen from the curve that the water pressure response of the control method of the present invention is rapid and stable, and the target setting value is reached quickly, showing good response characteristics. Although the initial response speed of the existing control method is also fast, it is accompanied by severe overshoot and repeated oscillations. The overall control speed is affected by the oscillations, and the response is not stable.

Claims

1. A water flow rate control method for a hydraulic execution system, characterized in that: The specific steps for optimizing the water flow rate controller of the hydraulic actuator system are as follows: S1. Collect water flow rate and pressure change data of the hydraulic execution system and establish a water flow rate control model based on data drive; S2. Construct an improved hawkfish optimization algorithm, and use the improved hawkfish optimization algorithm to adjust the proportional, integral and differential parameters of the position PID control algorithm online; the improved hawkfish optimization algorithm improves the hawkfish individual movement rules and learning coefficients of the standard hawkfish optimization algorithm; specifically: S21. The distribution state of the hawkfish population in the search space is regarded as a spatial distribution field with a topological structure, and a topological folding strategy is defined to adaptively change the learning coefficient; S22. A probabilistic transition movement strategy is introduced, and the historical differential trend of the agent individual fitness is used as the trigger condition for the transition to improve the hawkfish individual movement rule; S3, the position PID control algorithm is used for the water flow rate controller of the hydraulic execution system to adjust the output signal of the water flow rate controller; the output signal is input into the water flow rate control model to obtain the real-time water flow rate of the hydraulic execution system; S4. Calculate the difference between the target water flow rate and the real-time water flow rate, input the difference into the water flow rate controller, and use the position PID control algorithm to adjust the water flow rate of the real-time hydraulic execution system until the difference is lower than the target threshold, thereby achieving water flow rate control optimization.

2. A water flow rate control method for a hydraulic execution system according to claim 1, characterized in that: The water flow rate control model is represented by a typical second-order inertia link model. The second-order inertia link model is a time domain model, and the input is the control signal , the output is the real-time water pressure of the hydraulic execution system; the real-time water pressure is converted into the real-time water flow rate through the water flow rate control model, and the water flow rate control model is: ； In the formula, For the The water flow rate at any time, and are model parameters, which are determined through online data identification. For the Real-time water pressure at all times, For the The water flow rate at the moment.

3. A water flow rate control method for a hydraulic execution system according to claim 2, characterized in that: The improved Eaglefish optimization algorithm captures subtle changes in the state of the agent group through a topological folding strategy, and drives the learning coefficients with the underlying geometric characteristics. Adaptive changes; the mathematical model is: S211, the eagle fish group of the tth iteration The distribution in the search space is defined as the topological folding strategy , the mathematical model is: ； In the formula, is the topological folding strategy of the tth iteration, N is the maximum size of the agent individual, n≠j; The Euclidean distance between the agent individuals in the t-th iteration population; S212, introduce the topological folding strategy and propose a mathematical model for adaptive control of learning coefficients: ； In the formula, is the minimum value of the learning coefficient, is the maximum value of the learning coefficient, μ is the sensitivity adjustment factor, and the sensitivity adjustment factor starts from the fitness ranking ratio. The changing trend of the relative ranking of the optimal agent individual in the population essentially reflects the search status of the group, which is automatically fed back to the sensitivity adjustment factor. The mathematical model is: ； in, is the sensitivity adjustment factor of the tth iteration, is the ranking of the current best agent in the population.

4. A water flow rate control method for a hydraulic execution system according to claim 3, characterized in that: The probabilistic transition movement strategy triggers the transition with the trigger transition value when the agent individual is trapped in the local search area for a long time, and implements a cross-space jump movement to effectively avoid the algorithm from falling into the local optimum prematurely; the strategy uses the historical differential trend of the agent individual fitness as the trigger condition for the transition, and the mathematical model is: ； in, is the position of the nth agent individual in the t+1th iteration, is the position of the nth agent at the tth iteration, is the step length of the nth agent, is the j-th dimension of the direction vector of the n-th agent individual, is the centroid position of the current population, is the current global optimal agent individual position; is the trigger transition value of the tth iteration, rand is a random number from 0 to 1 that follows a uniform distribution, is the pseudo-pulse transition sensitivity parameter, which controls the degree of change of transition probability. The mathematical model is: ； In the formula, is the position of the nth agent individual in the t+1th iteration, is the maximum number of iterations, randn(0,1) is a standard normal distribution random number; Among them, the trigger transition factor is used to judge that the eagle fish is trapped in the local search area for a long time. The mathematical model of the trigger transition factor is: ； In the formula, and is the fitness value of the i-th agent in two consecutive iterations.

5. A water flow rate control method for a hydraulic execution system according to claim 4, characterized in that: The improved Eagle Fish optimization algorithm is used to adjust the proportional, integral, and differential parameters of the position PID control algorithm online. The specific steps are as follows: S31. Initialize the population agent individual positions of the improved eagle-fish optimization algorithm , each agent's individual position is a D-dimensional vector; Set the maximum number of iterations and maximum population size ; S32, divide the hawkfish individuals into male and female proxy individuals, define the availability of food resources in the environment as d(t) and design a gender transition mechanism model; S33, calculate the fitness value of each agent individual in the current iteration, the fitness value is the fitness function value of each agent individual position, and record the agent individual position corresponding to the current minimum fitness value as ; S34, calculating the trigger transition value of the t-th iteration, and executing the mathematical model of the hawk-fish individual movement rule through the trigger transition value to update the proxy individual position; S35, dynamically dividing subgroups using the Euclidean distance matrix, performing dynamic clustering and determining the position of the subgroup leader, where the subgroup leader position is the best proxy individual position in the subgroup; S36, constructing the eagle-fish learning rule by adaptively regulating the learning coefficient through the topology folding strategy, and using the eagle-fish learning rule to update the position of the agent individual; S37, the agent individual adjusts the search range according to the gender, sets a threshold, and if the fitness value of the agent individual exceeds the threshold, the agent individual changes gender and updates the agent individual position according to the dynamic visual rule; S38, limiting the position of the agent individual within the range of [lb, ub], increasing the current number of iterations by one, and determining whether the current number of iterations satisfies t+1=Tmax; If satisfied, then exit the optimization process, output the current global optimal agent individual position, and resolve it into the proportional, integral, and differential parameter values ​​of the position PID control algorithm; otherwise, return to execute S33.

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