Flutter Signal Parameter Optimization Method and System Based on Improved Slime Mould Optimization Algorithm

Abstract

The present application discloses a method and system for optimizing the parameters of a flutter signal based on an improved slime mold optimization algorithm, which relates to the technical field of optimizing control signals for electro-hydraulic servo systems. It includes superimposing a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system to determine the frequency and amplitude of the optimized flutter signal; constructing a fitness function according to the parameter variables and the average hysteresis width of the system, determining the frequency and amplitude ranges of the flutter signal according to the specification attribute information of the relevant equipment of the system, constructing the overshoot constraint condition of the system and the upper and lower limit constraint conditions of the parameter variables, and based on the fitness function, the overshoot constraint condition and the upper and lower limit constraint conditions, and an improved slime mold optimization algorithm, constructing a flutter signal parameter optimization model, inputting the interval corresponding to the parameter variables into the flutter signal parameter optimization model, and obtaining the optimized values of the parameter variables. The method can effectively reduce the hysteresis area and hysteresis width of the system and improve the control accuracy of the system.

Description

Technical Field

[0001] This application relates to the technical field of electro-hydraulic servo system control signal optimization, and particularly relates to a method and system for optimizing the parameters of a flutter signal based on an improved slime mold optimization algorithm. Background Art

[0002] Due to its advantages such as large output power, fast response speed, and high system stiffness, the electro-hydraulic servo system has been widely used in product equipment such as hydraulic robots and active suspensions. However, between the cylinder block and the piston of the hydraulic cylinder in the electro-hydraulic servo system, the combined effects of static friction, dynamic friction, and viscous friction are present. The non-linear characteristics of the friction force, as Figure 2 shown, when the input signal changes, the output signal does not immediately change with the change of the input signal. Only when the change amplitude of the input signal reaches a certain degree, will the output signal change accordingly. Moreover, when the input signal changes in the reverse direction, there is a certain lag in the change of the output signal, and finally a loop image is presented. This situation will cause the hysteresis problem of the electro-hydraulic servo system, and then affect the dynamic performance of the system.

[0003] To reduce the non-linear effect brought by the friction force in the electro-hydraulic servo system, in the related research field of non-linear friction, flutter compensation is the simplest and most effective compensation and control method. In the related technologies, in the existing electro-hydraulic servo systems, the common methods for adjusting the parameters of the flutter signal mainly rely on manual experience at present. For example, the signal frequency and amplitude are determined by manual adjustment. This method not only takes time and effort, but also due to the individual differences and subjective judgments of the operators, the adjustment results lack consistency and stability. Summary of the Invention

[0004] This application aims to solve at least one of the problems existing in the above-mentioned prior art. Based on this, a method and system for optimizing the parameters of a flutter signal based on an improved slime mold optimization algorithm are proposed to realize the automatic parameter optimization of the flutter signal, reduce manual intervention and lower the time cost, and further improve the efficiency and accuracy of non-linear friction compensation and control.

[0005] In a first aspect, this application provides a method for optimizing the parameters of a flutter signal based on an improved slime mold optimization algorithm, including:

[0006] Superimpose a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system, and determine the parameter variables to be optimized of the flutter signal and the upper limit value of the frequency. The parameter variables include the signal frequency and the amplitude;

[0007] Construct a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system;

[0008] According to the upper frequency limit value and the specification attribute information of the relevant equipment of the electro-hydraulic servo system, the overshoot constraint condition of the electro-hydraulic servo system and the upper and lower limit constraint conditions of the parameter variables are constructed;

[0009] According to the fitness function, the overshoot constraint condition and the upper and lower limit constraint conditions, and based on the improved slime mold optimization algorithm, a flutter signal parameter optimization model is constructed;

[0010] Input the interval corresponding to the parameter variable into the flutter signal parameter optimization model to obtain the optimized value of the parameter variable.

[0011] According to some embodiments of the present application, superimposing a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system to determine the parameter variable and the upper frequency limit value that need to be optimized for the flutter signal, includes:

[0012] Taking a triangular wave signal as the input signal, conducting experiments based on the entire stroke of the hydraulic cylinder in the electro-hydraulic servo system, superimposing the flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system, and determining the upper frequency limit value of the flutter signal in the order from high frequency to low frequency.

[0013] According to some embodiments of the present application, constructing the fitness function according to the parameter variable and the average hysteresis width of the electro-hydraulic servo system, includes:

[0014] Input a continuously changing periodic given signal into the electro-hydraulic servo system, and output displacement data corresponding to different point values within the periodic given signal, where the periodic given signal is used to represent the periodic current signal input into the electro-hydraulic servo system;

[0015] According to the periodic current signal and the displacement data, determine the rising curve and the falling curve in the same coordinate system;

[0016] According to the rising curve and the falling curve in the same coordinate system, and based on the different hysteresis widths corresponding to the positions of multiple current point values, determine the average hysteresis width.

[0017] According to some embodiments of the present application, constructing the fitness function according to the parameter variable and the average hysteresis width of the electro-hydraulic servo system, includes:

[0018] The calculation expression of the average hysteresis width is:

[0019] ,

[0020] In the formula, is the first output value corresponding to the i-th point; is the second output value corresponding to the i-th point; is the number of sampling points;

[0021] The calculation expression of the fitness function is:

[0022] ,

[0023] In the formula, is the number of iterations; is the maximum number of iterations;

[0024] According to some embodiments of the present application, inputting the interval corresponding to the parameter variable into the flutter signal parameter optimization model to obtain the optimized value of the parameter variable includes:

[0025] Initializing the slime mold population size, dimension, and number of iterations of the improved slime mold optimization algorithm, and inputting the intervals corresponding to the flutter signal frequency and amplitude;

[0026] Randomly generating the initial positions of the slime molds, where the position of each slime mold corresponds to a solution, and calculating the fitness based on the fitness function,

[0027] Sorting the fitness values to select the best position of the slime molds, and calculating the weight and position selection range for each position according to the fitness;

[0028] Performing further position updates in the area near the best position of the slime molds to determine the optimal solution of the parameter variable within the interval.

[0029] According to some embodiments of the present application, the further position updates in the area near the best position of the slime molds to determine the optimal solution of the parameter variable within the interval include:

[0030] The calculation expression for the position update is:

[0031] ,

[0032] In the formula, is a random number from 0 to 1, is the upper limit value of the parameter to be optimized; is the lower limit value of the parameter to be optimized, W is the current weight, and are respectively two positions randomly selected from the position selection range, is the current best position, is the maximum number of iterations, is the non-linear variation parameter in [-a, a], is the linearly decreasing parameter in [0, 1], Let be the current position, p be the judgment variable, r be a random number in [0,1], and z be an adjustable parameter.

[0033] According to some embodiments of the present application, the further position update in the area near the optimal position of the slime mold to determine the optimal solution of the parameter variable within the interval includes:

[0034] The calculation expression of the judgment variable is:

[0035] ,

[0036] In the formula, is the fitness of, is the best fitness in the current iteration,

[0037] The calculation expression of the non-linear change parameter value a is:

[0038] ,

[0039] In the formula, is the current iteration number, is the maximum iteration number.

[0040] Compared with the prior art, the technical solution provided in the first aspect of the present application at least includes the following beneficial effects or advantages:

[0041] The method of the present application combines the fitness function, overshoot constraint conditions and upper and lower limit constraint conditions to improve the slime mold optimization algorithm to construct a flutter signal parameter optimization model, and optimizes the flutter signal parameters through the model, avoiding the disadvantages caused by the influence of human subjective experience, reducing manual intervention and time cost, effectively reducing the system hysteresis area and hysteresis width, further improving the efficiency and accuracy of non-linear friction compensation and control. Moreover, the improved slime mold optimization algorithm combined with the model proposes a new non-linear decreasing strategy in the traditional slime mold optimization algorithm. This strategy can dynamically adjust the search range according to the iteration process, significantly improving the global exploration ability of the algorithm in the complex search space of flutter signal parameter variables. At the same time, in order to enhance the late convergence of the slime mold algorithm, a method of dynamically adjusting the individual selection range according to the iteration number is combined in the algorithm, so that the optimal solution of the flutter signal parameters can be obtained more quickly.

[0042] In the second aspect, the present application provides a flutter signal parameter optimization based on an improved slime mold optimization algorithm, including:

[0043] A parameter determination module configured to superimpose a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system to determine the parameter variables and the upper frequency limit value that need to be optimized for the flutter signal, and the parameter variables include the signal frequency and amplitude;

[0044] A fitness function construction module, configured to construct a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system;

[0045] A constraint condition construction module, configured to construct the overshoot constraint condition of the electro-hydraulic servo system and the upper and lower limit constraint conditions of the parameter variables according to the upper frequency limit value and the specification attribute information of the related equipment of the electro-hydraulic servo system;

[0046] An optimization model construction module, according to the fitness function, the overshoot constraint condition and the upper and lower limit constraint conditions, and based on the improved slime mold optimization algorithm, constructs a flutter signal parameter optimization model;

[0047] A parameter optimization module, inputs the interval corresponding to the parameter variables into the flutter signal parameter optimization model, and obtains the optimized values of the parameter variables.

[0048] In a third aspect, the present application further provides an electronic device, including:

[0049] At least one processor; and

[0050] A memory communicatively connected to the at least one processor; wherein,

[0051] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the method for optimizing flutter signal parameters provided in the first aspect above.

[0052] In a fourth aspect, the present application further provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the steps of the method for optimizing flutter signal parameters provided in the first aspect above are implemented.

[0053] It can be understood that the beneficial effects of the technical solutions provided in the second, third, and fourth aspects above can refer to the relevant descriptions in the first aspect above, and will not be repeated here.

[0054] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0056] Figure 1 is a flowchart of a flutter signal parameter optimization method based on an improved slime mold optimization algorithm shown according to an embodiment of the present application;

[0057] Figure 2 is a coordinate diagram of a hysteresis loop image shown according to an embodiment of the present application;

[0058] Figure 3 is a comparison diagram of the effects on the system hysteresis loop after optimizing the flutter signal parameters by the improved slime mold optimization algorithm shown according to an embodiment of the present application;

[0059] Figure 4 is a block diagram of a flutter signal parameter optimization system based on an improved slime mold optimization algorithm shown according to an embodiment of the present application;

[0060] Figure 5 is a block diagram of the structure of an electronic device shown according to an embodiment of the present application. Specific Embodiments

[0061] The following details the embodiments of the present application. The embodiments described with reference to the drawings are exemplary. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] It should be noted that unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms used in the specification of the present application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0063] Please refer to Figure 1 , this embodiment provides a flutter signal parameter optimization method based on an improved slime mold optimization algorithm, including:

[0064] Step S100: Superimpose a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system, determine the parameter variables to be optimized for the flutter signal and the upper limit value of the frequency, and the parameter variables include the signal frequency and the amplitude;

[0065] In some embodiments, to reduce the non - linear effects brought by friction in the electro - hydraulic servo system, by using the system PID controller in combination with an adjustable flutter signal, the situation where the system is affected by friction, hysteresis, etc. can be effectively compensated. The parameter variables of the flutter signal usually include amplitude, frequency, and phase, etc. Therefore, to reduce the non - linear effects in the electro - hydraulic servo system, first, the parameter variables affecting the flutter signal need to be determined. In this step, the parameter variables that need to be optimized for the flutter signal include the frequency and amplitude of the flutter signal;

[0066] It should be understood that the flutter signal is a sinusoidal periodic signal, that is, the input flutter signal is a continuous signal, rather than taking a certain point value.

[0067] It should be noted that the triangular wave signal is a periodic signal with a continuously changing slope. The electro - hydraulic servo system uses the triangular wave signal as the input position given signal and conducts experiments over the entire stroke of the hydraulic cylinder in the electro - hydraulic servo system. To find the optimal parameter variables, the value range of the parameter variables needs to be determined. Generally speaking, the lower limit value of the frequency of the signal is zero, and for the upper limit value of the frequency of the signal, by superimposing the flutter signal on the control signal output by the PID controller and according to the experimental results of the electro - hydraulic servo system in the order from high frequency to low frequency, the upper limit of the frequency of the flutter signal can be determined.

[0068] It can be understood that according to the frequency response curve of the electro - hydraulic servo valve, the higher the frequency of the flutter signal, the smaller the frequency response amplitude of the servo valve (starting from - 3dB). For the amplitude of the signal, it can be obtained according to the factory specifications of the electro - hydraulic servo system. Among them, the lower limit value of the amplitude of the signal is zero, and for the upper limit value of the amplitude, the upper limit value is the rated value of the drive signal. For example, taking the MOOG761 series servo valve as an example, the rated currents of different models are 8, 15, 40mA, etc.

[0069] Step S200: Construct a fitness function according to the parameter variables and the average hysteresis width of the electro - hydraulic servo system;

[0070] In some embodiments, a continuously changing periodic given signal is input to the electro - hydraulic servo system, and displacement data corresponding to different point values within the periodic given signal is output, where the periodic given signal is used to represent the periodic current signal input to the electro - hydraulic servo system; according to the periodic current signal and the displacement data, the rising curve and the falling curve in the same coordinate system are determined; according to the rising curve and the falling curve in the same coordinate system, based on the different hysteresis widths at the positions corresponding to multiple current point values, the average hysteresis width is determined.

[0071] Exemplarily, the electro-hydraulic servo system outputs a position according to an input position given signal. As the input value gradually increases, the output value also gradually increases. When the input value reaches the maximum value, as the input value gradually decreases, the output value also gradually decreases. Taking the input value as the horizontal axis and the output value as the vertical axis, the rise and fall of the curve in the same coordinate system are determined. Due to the non-linear influence of friction, the output value has a certain lag, and finally a hysteresis loop curve is formed.

[0072] Please refer to Figure 2 , in order to obtain the average hysteresis loop width, in the coordinate system with the input value current I / mA as the horizontal axis and the output value displacement Xp / mm as the vertical axis, when the input signal increases from the initial value, the output signal initially remains unchanged until the input reaches a certain threshold, and then the output begins to rise, forming an ascending curve. When the input signal then decreases, the output does not return along the original ascending curve. Only when the input decreases to another lower threshold, the output begins to fall, generating a descending curve different from the ascending curve. The two curves do not coincide, forming a hysteresis loop curve. Take N points on the abscissa of the system hysteresis loop curve, and combined with Figure 2 shown, the two outputs corresponding to the i-th point of the system are respectively denoted as and , and the average hysteresis loop width of the system is calculated by calculating the hysteresis loop widths at N positions.

[0073] Optionally, the calculation expression for the average hysteresis loop width is:

[0074]

[0075] In the formula, is the first output value corresponding to the i-th point; is the second output value corresponding to the i-th point; is the number of sampling points;

[0076] A fitness function is constructed through the obtained average hysteresis loop width, where the calculation expression for the fitness function is:

[0077]

[0078] In the formula, is the number of iterations; is the maximum number of iterations;

[0079] It should be noted that the role of the fitness function is mainly to judge the control effect of the controlled object. Among them, the number of iterations for screening out the optimal parameter variable combination is the number of iterations of the algorithm (when the algorithm iterates once, the controlled system also runs once).

[0080] It can be understood that the average hysteresis width reflects the control accuracy of the electro-hydraulic servo system. When the hysteresis area and width of the system are larger, the control accuracy of the system is lower. In this embodiment, it is actually necessary to optimize the flutter signal parameters by means of an improved algorithm, avoid the disadvantages caused by the influence of subjective experience, effectively reduce the hysteresis area and width of the system, and thus improve the control accuracy of the system.

[0081] Step S300: According to the upper limit value of the frequency and the specification attribute information of the relevant equipment of the electro-hydraulic servo system, construct the overshoot constraint condition and the upper and lower limit constraint conditions of the parameter variables of the electro-hydraulic servo system;

[0082] In this step, the relevant equipment is mainly the electro-hydraulic servo valve and the hydraulic cylinder, and the specification attribute information mainly includes the piston area Ap of the hydraulic cylinder, the piston displacement of the hydraulic cylinder , the piston rising time of the hydraulic cylinder and the ideal output flow , where

[0083] The overshoot constraint condition is:

[0084]

[0085] In the formula, is the piston area of the hydraulic cylinder, is the piston displacement of the hydraulic cylinder, is the rising time, is the ideal output flow, and p is the overshoot control range, generally a constant of 0.2.

[0086] Set the upper and lower limit constraint conditions of the optimization parameters as:

[0087]

[0088] In the formula, is the frequency of the flutter signal; is the lower limit value of the frequency of the flutter signal, generally 0Hz; is the upper limit value of the frequency of the flutter signal; is the amplitude of the flutter signal; is the lower limit value of the amplitude of the flutter signal, which is 0mA; is the upper limit value of the amplitude of the flutter signal (taking a certain servo valve of the MOOG 761 series as an example, is 120Hz, is 15mA).

[0089] Step S400: According to the fitness function, the overshoot constraint condition and the upper and lower limit constraint conditions, and based on the improved slime mold optimization algorithm, construct an optimization model for flutter signal parameters;

[0090] In this step, the improvement of the slime mold optimization algorithm mainly includes the following parts:

[0091] 1) To enhance the global search ability, in the traditional slime mold optimization algorithm, the decreasing strategy of the parameter with the value range of is relatively simple, which easily leads to the algorithm falling into local optimum prematurely in high-dimensional problems. Therefore, a new non-linear decreasing strategy is proposed, which can dynamically adjust the search range according to the iteration process, significantly improving the global exploration ability of the algorithm in complex search spaces:

[0092]

[0093] In the formula, a is the non-linear change parameter value, is the current iteration number; is the maximum iteration number;

[0094] Since the search strategy in the slime mold optimization algorithm is prone to falling into local optimum in high-dimensional problems, the search strategy of the moth-flame optimization algorithm is combined, and the position update of the improved slime mold is as follows:

[0095] ,

[0096] In the formula, is a random number from 0 to 1, is the upper limit value of the parameter to be optimized; is the lower limit value of the parameter to be optimized, W is the current weight, and are two positions randomly selected from the position selection range respectively, is the current best position, is the maximum number of iterations, is the non-linear change parameter in [-a, a], is the linear decreasing parameter in [0, 1], is the current position, p is the judgment variable, r is a random number in [0, 1], and z is an adjustable parameter, usually z = 0.03.

[0097] 2) To enhance the convergence speed, to enhance the late convergence of the slime mold algorithm, a method of dynamically adjusting the individual selection range according to the iteration number is proposed, and will be updated to two positions randomly selected from the range RG, and the selection range parameters are described as follows:

[0098]

[0099] In the formula, is the maximum number of iterations, t is the current iteration number, and range is the range of the slime mold population.

[0100] Step S500: Input the interval corresponding to the parameter variables into the flutter signal parameter optimization model to obtain the optimized values of the parameter variables.

[0101] In some embodiments, initialize the slime mold population size, dimension, and number of iterations of the improved slime mold optimization algorithm, and input the intervals corresponding to the flutter signal frequency and amplitude. It should be understood that the intervals corresponding to the flutter signal frequency and amplitude are continuous data between the upper and lower limits determined by the foregoing steps. Randomly generate the initial positions of the slime molds in the algorithm, where the position of each slime mold corresponds to a solution, and calculate the fitness of the position of each slime mold based on the fitness function. Sort the fitness values to select the best position of the slime molds, and calculate the weights and position selection ranges for each position according to the fitness. Perform further position updates in the area near the best position of the slime molds to determine the optimal solution of the parameter variables within the interval.

[0102] Optionally, the process of optimizing the flutter signal parameters for the flutter signal parameter optimization model may include the following steps:

[0103] Step S510: Initialize the slime mold population size of the algorithm to 30, the dimension to 2, the number of iterations to 50, the upper and lower limits of the flutter signal frequency to [0, 120], and the upper and lower limits of the flutter signal amplitude to [0, 15];

[0104] Step S520: Randomly generate the initial positions of the slime molds and calculate the fitness. The position of each slime mold corresponds to a solution. The formula for the initial position is as follows:

[0105] ,

[0106] In the formula, is the upper limit of the optimization parameter, is the lower limit of the optimization parameter, is a random number from 0 to 1;

[0107] Among them, the slime mold information is represented by a d-dimensional vector; the slime mold information includes the frequency f and amplitude A of the flutter signal. Each slime mold information is represented as follows:

[0108]

[0109] In the formula, represents the position of the i-th slime mold in the d-th dimension. Of course, d can take the value of 2, and at this time ;

[0110] Step S530: Calculate the fitness of the position of each slime mold. Among them, the calculation expression of the fitness function is:

[0111]

[0112] In the formula, is the number of iterations, is the hysteresis width, is the maximum number of iterations;

[0113] Step S540: Sort the fitness values obtained from the fitness formula to select the best position, and calculate the weight and the position selection range RG for each position according to the fitness; where,

[0114]

[0115]

[0116]

[0117] In the formula, is the best fitness in the current iteration, is the worst fitness in the current iteration, Sort the fitness values of the current iteration, is the range of the slime mold population, is 's fitness; is a random number in [0,1].

[0118] Step S550: Calculate the non-linear variation parameter in [-a, a], the linearly decreasing parameter in [0,1], and the judgment variable p, and perform further position updates near the best position of the slime mold to ensure finding the optimal solution within the constraints. The position update is as follows:

[0119]

[0120] In the formula, is a random number from 0 to 1, is the upper limit value of the parameter to be optimized; is the lower limit value of the parameter to be optimized, W is the current weight, and are respectively two positions randomly selected from the position selection range, is the current best position, is the maximum number of iterations, is the non-linear variation parameter in [-a, a], is the linearly decreasing parameter in [0,1], is the current position, p is the judgment variable, r is a random number in [0,1], and z is an adjustable parameter, usually 0.03.

[0121] Among them, the calculation expression of the judgment variable is:

[0122] ,

[0123] In the formula, is 's fitness, is the best fitness in the current iteration;

[0124] The calculation expression of the non - linear change parameter value a is:

[0125] ,

[0126] In the formula, is the current iteration number, is the maximum iteration number.

[0127] Step S560: Judge the steady - state error of the overall system operation (the deviation generated from the start to the stop of the system operation) through the overshoot. The overshoot constraint is:

[0128]

[0129] In the formula, is the piston area of the hydraulic cylinder, is the piston displacement of the hydraulic cylinder, is the rise time, is the ideal output flow rate, and p is the overshoot control range, generally a constant 0.2.

[0130] In the above - mentioned method steps, the fitness function, overshoot constraint conditions, and upper and lower limit constraint conditions are combined to improve the slime mold optimization algorithm to construct a flutter signal parameter optimization model. The flutter signal parameters are optimized through the model, avoiding the disadvantages of the influence of subjective human experience, reducing manual intervention and time cost, effectively reducing the system hysteresis area and hysteresis width, and further improving the efficiency and accuracy of non - linear friction compensation and control. Moreover, the improved slime mold optimization algorithm combined with the model proposes a new non - linear decreasing strategy in the traditional slime mold optimization algorithm. This strategy can dynamically adjust the search range according to the iteration process, significantly improving the global exploration ability of the algorithm in the complex search space of flutter signal parameter variables. At the same time, in order to enhance the late convergence of the slime mold algorithm, a method of dynamically adjusting the individual selection range according to the iteration number is combined in the algorithm, so as to obtain the optimal solution of the flutter signal parameters more quickly.

[0131] Please refer to Figure 3 , Figure 3The hysteresis loop diagrams of the system corresponding to different adjustment methods in the current-displacement coordinate system are shown. The parameters optimized by the above algorithm are applied to the electro-hydraulic servo system. The operation effect of the system with the optimized chatter signal is observed and compared with the operation effect of the original system and the operation effect of the system with the chatter signal adjusted manually. Compared with the parameter adjustment without the chatter signal and the traditional chatter signal parameter adjustment, the hysteresis area of the improved SMA chatter signal parameter adjustment is significantly reduced.

[0132] Please refer to Figure 4 , Figure 4 which shows the block diagram of the chatter signal parameter optimization system based on the improved slime mold optimization algorithm provided in this embodiment. The chatter signal parameter optimization system 200 based on the improved slime mold optimization algorithm includes:

[0133] A parameter determination module 210, configured to superimpose a chatter signal on the control signal output by the PID controller of the electro-hydraulic servo system, and determine the parameter variables and the upper limit value of the frequency that need to be optimized for the chatter signal. The parameter variables include the signal frequency and the amplitude;

[0134] A fitness function construction module 220, configured to construct a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system;

[0135] A constraint condition construction module 230, configured to construct the overshoot constraint condition of the electro-hydraulic servo system and the upper and lower limit constraint conditions of the parameter variables according to the upper limit value of the frequency and the specification attribute information of the relevant equipment of the electro-hydraulic servo system;

[0136] An optimization model construction module 240, which constructs a chatter signal parameter optimization model according to the fitness function, the overshoot constraint condition, and the upper and lower limit constraint conditions, and based on the improved slime mold optimization algorithm;

[0137] A parameter optimization module 250, which inputs the interval corresponding to the parameter variables into the chatter signal parameter optimization model to obtain the optimized values of the parameter variables.

[0138] It can be understood that when the chatter signal parameter optimization system 200 based on the improved slime mold optimization algorithm in this embodiment is implemented, each module runs the steps of Figure 1 a chatter signal parameter optimization method based on the improved slime mold optimization algorithm in the corresponding embodiment. The technical effects that can be achieved can be referred to Figure 1 the description of the technical effects achieved in the corresponding embodiment, which will not be elaborated here.

[0139] Please refer to Figure 5 , Figure 5It is a structural block diagram of an electronic device provided by an embodiment of the present application. The server 500 of the electronic device includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501, such as a program for the flutter signal parameter optimization method based on an improved slime mold optimization algorithm. When the processor 501 executes the computer program 503, the steps of the flutter signal parameter optimization method based on the improved slime mold optimization algorithm in the above embodiments are implemented, such as Figure 1 Steps S100 to S500 of the corresponding embodiment. Alternatively, when the processor 501 executes the computer program 503, the functions of the above Figure 4 corresponding modules in the embodiment are implemented. For example, Figure 4 the functions of the modules shown (such as the parameter determination module 210), for details, please refer to Figure 4 the relevant descriptions in the corresponding embodiment, which will not be elaborated here.

[0140] Exemplarily, the computer program 503 can be divided into one or more units. One or more units are stored in the memory 502 and executed by the processor 501 to complete the technical solutions provided in the above embodiments. One or more units can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the computer program 503 in the server 500.

[0141] The electronic device may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art can understand that Figure 5 this is only an example of the server 500 in the electronic device, and does not constitute a limitation on the server 500. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the turntable terminal device may also include an input / output terminal device, a network access terminal device, a bus, etc.

[0142] The so-called processor 501 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0143] The memory 502 can be an internal storage unit of the server 500, such as the hard disk or memory of the server 500. The memory 502 can also be an external storage terminal device of the server 500, such as a plug-in hard disk equipped on the server 500, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 502 can also include both the internal storage unit of the server 500 and the external storage terminal device. The memory 502 is used to store computer programs and other programs and data required by the turntable terminal device. The memory 502 can also be used to temporarily store data that has been output or will be output.

[0144] In some embodiments, a computer-readable storage medium is also provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the flutter signal parameter optimization method based on the improved slime mold optimization algorithm as described in the above embodiments.

[0145] In addition, in each embodiment of the present application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0146] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium can be non-volatile or volatile. Based on this understanding, to implement all or part of the processes in the above embodiments of the method of the present application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0147] In the description and claims of this application and the accompanying drawings, the terms "first", "second", "third", etc. are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a series of steps or units are included, or optionally, steps or units not listed are further included, or optionally other steps or units inherent to these processes, methods, products or apparatuses are further included.

[0148] Only parts related to this application rather than all content are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but there can also be additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0149] The terms "component", "module", "system", "unit", etc. used in this specification are used to denote computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a unit can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or distributed between two or more computers. In addition, these units can execute from various computer-readable media on which various data structures are stored. A unit can communicate, for example, through signals with other systems via local and / or remote processes according to signals having one or more data packets (such as data from a second unit interacting with a local system, a distributed system, and / or a network. For example, the Internet interacting with other systems through signals).

[0150] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example.

[0151] Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. The mention of "embodiments" in this document means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various positions in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. Those skilled in the art can explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0152] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and purposes of the present application. The scope of the present application is defined by the claims and their equivalents.

[0153] Those skilled in the art will readily conceive of other implementations of the present application after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only considered exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

Claims

1. A method for optimizing the parameters of a flutter signal based on an improved slime mold optimization algorithm, characterized in that, Including: Superimpose a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system, and determine the parameter variables and the upper frequency limit value that need to be optimized for the flutter signal. The parameter variables include signal frequency and amplitude; Construct a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system. Specifically, it includes: input a continuously changing periodic given signal to the electro-hydraulic servo system, and output displacement data corresponding to different point values within the periodic given signal. The periodic given signal is used to represent the periodic current signal input to the electro-hydraulic servo system; determine the rising curve and the falling curve in the same coordinate system according to the periodic current signal and the displacement data; determine the average hysteresis width based on the different hysteresis widths at the positions corresponding to multiple current point values according to the rising curve and the falling curve in the same coordinate system. The calculation expression of the average hysteresis width is: ， wherein, is the first output value corresponding to the i-th point; is the second output value corresponding to the i-th point; is the number of sampling points; The calculation expression of the fitness function is: ， In the formula, is the number of iterations; is the maximum number of iterations; Construct the overshoot constraint condition of the electro-hydraulic servo system and the upper and lower limit constraint conditions of the parameter variables according to the upper frequency limit value and the specification attribute information of the related equipment of the electro-hydraulic servo system; Construct a flutter signal parameter optimization model according to the fitness function, the overshoot constraint condition and the upper and lower limit constraint conditions, and based on the improved slime mold optimization algorithm; Input the interval corresponding to the parameter variables into the flutter signal parameter optimization model to obtain the optimized values of the parameter variables.

2. The flutter signal parameter optimization method based on an improved slime mold optimization algorithm according to claim 1, wherein The step of superimposing a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system, and determining the parameter variables and the upper frequency limit value that need to be optimized for the flutter signal. The parameter variables include signal frequency and amplitude, includes: Use a triangular wave signal as the input signal, conduct experiments based on the full stroke of the hydraulic cylinder in the electro-hydraulic servo system, superimpose the flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system, and determine the upper frequency limit value of the flutter signal in the order from high frequency to low frequency.

3. A flutter signal parameter optimization method based on an improved slime mold optimization algorithm according to claim 1, characterized in that, The step of inputting the interval corresponding to the parameter variables into the flutter signal parameter optimization model to obtain the optimized values of the parameter variables, includes: Initialize the slime mold population size, dimension and number of iterations of the improved slime mold optimization algorithm, and input the interval corresponding to the flutter signal frequency and amplitude; Randomly generate the initial positions of the slime molds. Each position of the slime mold corresponds to a solution, and calculate the fitness based on the fitness function; Sort the fitness values to select the best position of the slime mold, and calculate the weight and position selection range for each position according to the fitness; Perform further position updates in the area near the best position of the slime mold to determine the optimal solution of the parameter variables within the interval.

4. The parameter optimization method for flutter signals based on an improved slime mold optimization algorithm according to claim 3, characterized in that The step of performing further position updates in the area near the best position of the slime mold to determine the optimal solution of the parameter variables within the interval, includes: The calculation expression for position update is: ， Wherein, is a random number from 0 to 1, is the upper limit value of the parameter to be optimized; is the lower limit value of the parameter to be optimized, W is the current weight, and respectively select two positions randomly from the position selection range, is the current best position, is the maximum number of iterations, is a non-linear variation parameter in [-a, a], is a linearly decreasing parameter in [0, 1], is the current position, p is a judgment variable, r is a random number in [0, 1], and z is an adjustable parameter.

5. A flutter signal parameter optimization method based on an improved slime mold optimization algorithm according to claim 4, characterized in that The step of performing further position updates in the area near the best position of the slime mold to determine the optimal solution of the parameter variables within the interval, includes: The calculation expression for the judgment variable is: ， In the formula, is 's fitness, is the best fitness in the current iteration, The calculation expression of the non-linear change parameter value a is as follows: ， In the formula, is the current iteration number, is the maximum iteration number.

6. A flutter signal parameter optimization system based on an improved slime mold optimization algorithm, characterized in that, Including: A parameter determination module configured to superimpose a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system, and determine the parameter variables and the upper frequency limit value that need to be optimized for the flutter signal. The parameter variables include signal frequency and amplitude; A fitness function construction module configured to construct a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system; Among them, it includes: inputting a continuously changing periodic given signal to the electro-hydraulic servo system, and outputting displacement data corresponding to different point values within the periodic given signal. The periodic given signal is used to represent the periodic current signal input to the electro-hydraulic servo system; determining an ascending curve and a descending curve in the same coordinate system according to the periodic current signal and the displacement data; determining the average hysteresis width based on the different hysteresis widths at the positions corresponding to multiple current point values according to the ascending curve and the descending curve in the same coordinate system. Among them, the calculation expression of the average hysteresis width is: ， In the formula, is the first output value corresponding to the i-th point; is the second output value corresponding to the i-th point; is the number of sampling points; The calculation expression of the fitness function is: ， In the formula, is the number of iterations; is the maximum number of iterations; A constraint condition construction module configured to construct an overshoot constraint condition for the electro-hydraulic servo system and upper and lower limit constraint conditions for the parameter variables according to the upper frequency limit value and the specification attribute information of the related equipment of the electro-hydraulic servo system; An optimization model construction module constructs a flutter signal parameter optimization model according to the fitness function, the overshoot constraint condition, and the upper and lower limit constraint conditions, and based on an improved slime mold optimization algorithm; A parameter optimization module inputs the interval corresponding to the parameter variables into the flutter signal parameter optimization model to obtain the optimized values of the parameter variables.

7. An electronic device, characterized in that, Including: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor so that the at least one processor can execute the steps of a method for optimizing flutter signal parameters based on an improved slime mold optimization algorithm according to any one of claims 1-5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the steps of a method for optimizing flutter signal parameters based on an improved slime mold optimization algorithm according to any one of claims 1-5 are implemented.

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