Flutter signal parameter optimization method and system based on improved myxomycete optimization algorithm

Abstract

The invention discloses a flutter signal parameter optimization method and system based on an improved myxomycete optimization algorithm, and relates to the technical field of electro-hydraulic servo system control signal optimization, and the method comprises the steps: superposing a flutter signal in a control signal outputted by a PID controller of an electro-hydraulic servo system, and determining the frequency and amplitude of the optimized flutter signal; constructing a fitness function according to the parametric variable and the average hysteresis width of the system, determining the frequency and amplitude range of the flutter signal according to the specification attribute information of the related equipment of the system, and constructing the overshoot constraint condition of the system and the upper and lower limit constraint conditions of the parametric variable; constructing 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 myxomycete optimization algorithm, inputting an interval corresponding to a parameter variable into the flutter signal parameter optimization model, and obtaining an optimized parameter variable value. The system control precision is improved.

Description

Technical Field

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

[0002] Electro-hydraulic servo systems have been widely used in hydraulic robots, active suspensions and other products and equipment due to their advantages such as high output power, fast response speed and high system stiffness. However, the hydraulic cylinder body and piston in the electro-hydraulic servo system are affected by the combined effects of static friction, dynamic friction and viscous friction. The nonlinear characteristics of friction, such as Figure 2 As shown in the figure, when the input signal changes, the output signal does not change immediately with the change of the input signal. Only when the change amplitude of the input signal reaches a certain level, the output signal will change accordingly. Moreover, when the input signal changes in the opposite direction, there is a certain lag in the change of the output signal, and finally a ring-shaped image is presented. This situation will cause the hysteresis problem of the electro-hydraulic servo system, which will affect the dynamic performance of the system.

[0003] In order to reduce the nonlinear effect of friction in the electro-hydraulic servo system, in the related research field of nonlinear friction, flutter compensation is the simplest and most effective compensation and control method. In the related technology, in the existing electro-hydraulic servo system, the common flutter signal parameter adjustment method currently mainly relies on manual experience, such as manually adjusting the signal frequency and amplitude. This method is not only time-consuming and labor-intensive, but also lacks consistency and stability in the adjustment results due to individual differences and subjective judgments of operators. Summary of the invention

[0004] The present 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 achieve automatic parameter optimization of the flutter signal, reduce manual intervention and reduce time costs, and further improve the efficiency and accuracy of nonlinear friction compensation and control.

[0005] In a first aspect, the present application provides a method for optimizing vibration signal parameters based on an improved slime mold optimization algorithm, comprising: Superimposing a vibration signal on a control signal output by a PID controller of an electro-hydraulic servo system, and determining parameter variables and a frequency upper limit value that need to be optimized for the vibration signal, wherein the parameter variables include a signal frequency and an amplitude; Constructing a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system; According to the upper frequency limit value and the specification attribute information of the related equipment of the electro-hydraulic servo system, the overshoot constraint condition and the upper and lower limit constraint conditions of the parameter variables of the electro-hydraulic servo system are constructed; According to the fitness function, the overshoot constraint condition, and the upper and lower limit constraints, and based on the improved slime mold optimization algorithm, a flutter signal parameter optimization model is constructed; The interval corresponding to the parameter variable is input into the vibration signal parameter optimization model to obtain the optimized parameter variable value.

[0006] According to some embodiments of the present application, superimposing a chatter signal on a control signal output by a PID controller of an electro-hydraulic servo system, and determining parameter variables and a frequency upper limit value that need to be optimized for the chatter signal include: Taking a triangular wave signal as an input signal, an experiment is conducted based on the entire stroke of the hydraulic cylinder in the electro-hydraulic servo system. The flutter signal is superimposed on the control signal output by the PID controller of the electro-hydraulic servo system, and the upper frequency limit of the flutter signal is determined in order from high frequency to low frequency.

[0007] According to some embodiments of the present application, constructing a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system includes: Inputting a continuously changing periodic given signal into the electro-hydraulic servo system, and outputting displacement data corresponding to different point values ​​in the periodic given signal, wherein the periodic given signal is used to characterize the periodic current signal input into the electro-hydraulic servo system; Determine an ascending curve and a descending curve in the same coordinate system according to the periodic current signal and the displacement data; According to the rising curve and the falling curve in the same coordinate system, based on different hysteresis widths at positions corresponding to a plurality of current point values, the average hysteresis width is determined.

[0008] According to some embodiments of the present application, constructing a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system includes: The calculation expression of the average hysteresis loop 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; According to some embodiments of the present application, inputting an interval corresponding to a parameter variable into the flutter signal parameter optimization model to obtain an optimized parameter variable value 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 frequency and amplitude of the chatter signal; Randomly generate initial positions of slime molds, wherein each position of slime molds corresponds to a solution, and calculate fitness based on the fitness function, The fitness is sorted to select the best position of the slime mold, and a weight and a position selection range are calculated for each position according to the fitness; Further position updates are performed in the vicinity of the optimal position of the slime mold to determine the optimal solution of the parameter variables within the interval.

[0009] According to some embodiments of the present application, the further updating of the position in the vicinity of the optimal position of the slime mold to determine the optimal solution of the parameter variable within the interval includes: The position update calculation expression is: , In the formula, is a random number between 0 and 1. is the upper limit value of the parameter to be optimized; is the lower limit of the parameter to be optimized, W is the current weight, and Two positions are randomly selected from the position selection range, is the current best position, is the maximum number of iterations, is the nonlinear variation parameter in [-a, a], is a 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.

[0010] According to some embodiments of the present application, the further updating of the position in the vicinity of the optimal position of the slime mold to determine the optimal solution of the parameter variable within the interval includes: The calculation expression of the judgment variable is: , In the formula, for The fitness of is the best fitness in the current iteration, The calculation expression of the nonlinear change parameter value a is: , In the formula, is the current iteration number, is the maximum number of iterations.

[0011] Compared with the prior art, the technical solution provided in the first aspect of the present application includes at least the following beneficial effects or advantages: The method of the present application combines the fitness function, overshoot constraint conditions, and upper and lower limit constraints with the improved slime mold optimization algorithm to construct a flutter signal parameter optimization model. The flutter signal parameters are optimized through the model to avoid the disadvantages of being influenced by human subjective experience, reduce manual intervention and reduce time costs, effectively reduce the system hysteresis area and hysteresis width, and further improve the efficiency and accuracy of nonlinear friction compensation and control. In addition, the improved slime mold optimization algorithm combined with the model proposes a new nonlinear decreasing strategy in the traditional slime mold optimization algorithm. This strategy can dynamically adjust the search range according to the iterative process, significantly improving the algorithm's global exploration ability 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 number of iterations is combined in the algorithm, so that the optimal solution of the flutter signal parameters can be obtained more quickly.

[0012] In a second aspect, the present application provides a vibration signal parameter optimization based on an improved slime mold optimization algorithm, including: A parameter determination module is configured to superimpose a chatter signal on a control signal output by a PID controller of an electro-hydraulic servo system, and determine parameter variables and a frequency upper limit value that need to be optimized for the chatter signal, wherein the parameter variables include a signal frequency and an amplitude; A fitness function building module is configured to build a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system; A constraint condition building module is configured to build an overshoot constraint condition and upper and lower limit constraint conditions of a parameter variable of the electro-hydraulic servo system according to the upper frequency limit value and specification attribute information of related equipment of the electro-hydraulic servo system; An optimization model building module, which builds a flutter signal parameter optimization model according to the fitness function, the overshoot constraint condition, and the upper and lower limit constraints, and based on an improved slime mold optimization algorithm; The parameter optimization module inputs the interval corresponding to the parameter variable into the vibration signal parameter optimization model to obtain the optimized parameter variable value.

[0013] In a third aspect, the present application further provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed 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 perform the steps of the vibration signal parameter optimization method based on the improved slime mold optimization algorithm provided in the first aspect above.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the steps of the vibration signal parameter optimization method based on the improved slime mold optimization algorithm provided in the first aspect above.

[0015] It can be understood that the beneficial effects of the technical solutions provided in the second, third and fourth aspects can be found in the relevant description of the first aspect, and will not be repeated here.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a flow chart of a method for optimizing vibration signal parameters based on an improved slime mold optimization algorithm according to an embodiment of the present application; Figure 2 is a coordinate diagram of a hysteresis image according to an embodiment of the present application; Figure 3 is a comparison diagram of the effect of optimizing the chatter signal parameters on the system hysteresis after the improved slime mold optimization algorithm shown in the embodiment of the present application; Figure 4 is a block diagram of a vibration signal parameter optimization system based on an improved slime mold optimization algorithm according to an embodiment of the present application; Figure 5 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application are described in detail below. The embodiments described with reference to the accompanying 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.

[0020] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific implementations and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] See also Figure 1 This embodiment provides a method for optimizing vibration signal parameters based on an improved slime mold optimization algorithm, including: Step S100: superimposing a vibration signal on a control signal output by a PID controller of an electro-hydraulic servo system, and determining parameter variables and a frequency upper limit value that need to be optimized for the vibration signal, wherein the parameter variables include a signal frequency and an amplitude; In some embodiments, in order to reduce the nonlinear effect caused by friction in the electro-hydraulic servo system, the system PID controller is combined with an adjustable dither signal to effectively compensate for the system being affected by friction, hysteresis, etc., and the parameter variables of the dither signal generally include amplitude, frequency, and phase, etc. Therefore, in order to reduce the nonlinear effect caused by the electro-hydraulic servo system, first, it is necessary to determine the parameter variables affecting the dither signal. In this step, the parameter variables of the dither signal that need to be optimized include the frequency of the dither signal and the amplitude of the dither signal; It should be understood that the dither signal is a sinusoidal periodic signal, that is, the input dither signal is a continuous signal rather than taking a certain point value therein.

[0022] 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. Experiments are carried out according to the full stroke of the hydraulic cylinder in the electro-hydraulic servo system. In order to find the optimal parameter variable, it is necessary to determine the value range of the parameter variable. Generally speaking, the lower limit of the signal frequency is zero, and the upper limit of the signal frequency can be determined by superimposing the vibration signal on the control signal output by the PID controller. The upper limit of the frequency of the vibration signal can be determined in order from high frequency to low frequency according to the experimental results of the electro-hydraulic servo system.

[0023] It can be understood that according to the frequency response curve of the electro-hydraulic servo valve, the higher the frequency of the chatter signal, the smaller the frequency response amplitude of the servo valve (with -3dB as the starting position). The amplitude of the signal can be obtained according to the factory specifications of the electro-hydraulic servo system, where the lower limit of the amplitude of the signal is zero, and the upper limit of the amplitude is the rated value of the drive signal. Taking the MOOG761 series servo valve as an example, the rated currents of different models are 8, 15, 40mA, etc.

[0024] Step S200: constructing a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system; In some embodiments, a continuously changing periodic given signal is input into the electro-hydraulic servo system, and displacement data corresponding to different point values ​​within the periodic given signal is output, wherein the periodic given signal is used to characterize a periodic current signal input into the electro-hydraulic servo system; based on the periodic current signal and the displacement data, a rising curve and a falling curve in the same coordinate system are determined; based on the rising curve and the falling curve in the same coordinate system, an average hysteresis width is determined based on different hysteresis widths at corresponding positions of multiple current point values.

[0025] Exemplarily, the electro-hydraulic servo system outputs a position based on 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, the input value gradually decreases, and the output value also gradually decreases. The input value is the horizontal axis and the output value is the vertical axis to determine the rise and fall of the curve in the same coordinate system. Due to the nonlinear influence of friction, the output value has a certain lag, and eventually a hysteresis curve is formed.

[0026] See also Figure 2 In order to obtain the average hysteresis width, in a coordinate system with input current I / mA as the horizontal axis and output displacement Xp / mm as the vertical axis, when the input signal starts to increase from the initial value, the output signal remains unchanged at first until the input reaches a certain threshold, and the output begins to rise, forming an upward curve. When the input signal subsequently decreases, the output does not return along the original upward curve. Only when the input decreases to another lower threshold does the output begin to decrease, producing a downward curve different from the upward curve. The two curves do not overlap, forming a hysteresis curve. Take N points on the horizontal axis of the system hysteresis curve, combined with Figure 2 As shown, the two outputs of the system corresponding to the i-th point are recorded as and , the average hysteresis width of the system is calculated by calculating the hysteresis widths of N positions.

[0027] Optionally, the average hysteresis width can be calculated as: 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 fitness function is constructed by the obtained average hysteresis loop width, where the calculation expression of the fitness function is: In the formula, is the number of iterations; is the maximum number of iterations; It should be noted that the fitness function is mainly used to judge the control effect of the controlled object, among which the number of iterations to screen out the optimal parameter variable combination is the number of iterations of the algorithm (the controlled system will also run once the algorithm iterates once).

[0028] It can be understood that the average hysteresis width reflects the control accuracy of the electro-hydraulic servo system. When the system hysteresis area and the hysteresis width are larger, the system control accuracy is lower. In this embodiment, it is actually necessary to optimize the vibration signal parameters with the help of an improved algorithm to avoid the disadvantages of being influenced by human subjective experience, effectively reduce the system hysteresis area and the hysteresis width, and thus improve the system control accuracy.

[0029] Step S300: constructing overshoot constraint conditions and upper and lower limit constraint conditions of parameter variables of the electro-hydraulic servo system according to the upper frequency limit value and specification attribute information of related equipment of the electro-hydraulic servo system; In this step, the relevant equipment is mainly the electro-hydraulic servo valve and hydraulic cylinder. The specification attribute information mainly includes the hydraulic cylinder piston area Ap, hydraulic cylinder piston displacement , Hydraulic cylinder piston rise time And the ideal output flow ,in, The overshoot constraint condition is: In the formula, is the piston area of ​​the hydraulic cylinder, is the displacement of the hydraulic cylinder piston, is the rise time, is the ideal output flow rate, p is the overshoot control range and is generally a constant of 0.2.

[0030] Set the upper and lower limits of the optimization parameters as follows: In the formula, is the frequency of the chatter signal; The lower frequency limit of the chatter signal, usually 0Hz; is the upper frequency limit of the chatter signal; is the amplitude of the chatter signal; The lower limit of the amplitude of the vibration signal is 0mA; is the upper limit of the amplitude of the chatter signal (taking a servo valve of the MOOG 761 series as an example, is 120Hz, is 15mA).

[0031] Step S400: constructing a chatter signal parameter optimization model according to the fitness function, the overshoot constraint condition, and the upper and lower limit constraints, and based on an improved slime mold optimization algorithm; In this step, the improvement of the slime mold optimization algorithm mainly includes the following contents: 1) In order to improve the global search capability, in the traditional slime mold optimization algorithm, the parameters The value range is [ ] is relatively simple, which can easily lead to the algorithm falling into the local optimum too early in high-dimensional problems. Therefore, a new nonlinear decreasing strategy is proposed, which can dynamically adjust the search range according to the iterative process and significantly improve the global exploration ability of the algorithm in complex search space: In the formula, a is the nonlinear change parameter value, is the current iteration number; is the maximum number of iterations; Since the search strategy in the slime mold optimization algorithm is prone to fall into local optimality in high-dimensional problems, the search strategy of the moth-to-flame algorithm is combined, and the position of the improved slime mold is updated as follows: , In the formula, is a random number between 0 and 1. is the upper limit value of the parameter to be optimized; is the lower limit of the parameter to be optimized, W is the current weight, and Two positions are randomly selected from the position selection range, is the current best position, is the maximum number of iterations, is the nonlinear variation parameter in [-a, a], is a 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, which is usually 0.03.

[0032] 2) Convergence speed is enhanced. In order to enhance the late convergence of the slime mold algorithm, a method of dynamically adjusting the individual selection range according to the number of iterations is proposed. and It will be updated to randomly select two positions from the range RG, and the selection range parameters are described as follows: In the formula, is the maximum number of iterations, t is the current number of iterations, and range is the range of the slime mold population.

[0033] Step S500: inputting intervals corresponding to parameter variables into the vibration signal parameter optimization model to obtain optimized parameter variable values.

[0034] In some embodiments, the number of slime mold populations, dimensions, and iteration times of the improved slime mold optimization algorithm are initialized, and an interval corresponding to the frequency and amplitude of the chatter signal is input. It should be understood that the interval corresponding to the frequency and amplitude of the chatter signal is continuous data between the upper and lower limits determined by the aforementioned steps. An initial position of the slime mold is randomly generated in the algorithm, wherein the position of each slime mold corresponds to a solution, and the position fitness of each slime mold is calculated based on the fitness function, the fitness is sorted to select the best position of the slime mold, and the weight and position selection range are calculated for each position according to the fitness, and the position is further updated in the area near the best position of the slime mold to determine the optimal solution of the parameter variable within the interval.

[0035] Optionally, the process of optimizing the chatter signal parameters by the chatter signal parameter optimization model may include the following steps: Step S510: Initialize the algorithm slime mold population size to 30, the dimension to 2, the number of iterations to 50, the upper and lower limits of the chatter signal frequency to [0, 120], and the upper and lower limits of the chatter signal amplitude to [0, 15]; Step S520: Randomly generate the initial position of the slime mold and calculate the fitness. Each slime mold position corresponds to a solution. The initial position formula is as follows: , In the formula, To optimize the upper limit of the parameters, To optimize the lower limit of the parameter, A random number between 0 and 1; Among them, a d-dimensional vector is used to represent the slime mold information; the slime mold information includes the frequency f and amplitude A of the chatter signal, and each slime mold information is represented as follows: In the formula, represents the position of the slime mold i in the dth dimension. Of course, d can be 2. ; Step S530: Calculate the fitness of each slime mold position, where the fitness function is calculated as: In the formula, is the number of iterations, is the hysteresis loop width, is the maximum number of iterations; Step S540: sorting the fitness obtained by the fitness formula to select the best position, and calculating the weight and position selection range RG for each position according to the fitness; wherein, In the formula, is the best fitness in the current iteration, is the worst fitness in the current iteration, Sort the current iteration fitness, is the range of the slime mold population, for Adaptability; A random number in [0,1].

[0036] Step S550: Calculate the nonlinear variation parameter in [-a, a] , linearly decreasing parameter in [0,1] , and the judgment variable p, further position updates are performed near the optimal position of the slime mold to ensure that the optimal solution is found within the constraints. The position update is as follows: In the formula, is a random number between 0 and 1. is the upper limit value of the parameter to be optimized; is the lower limit of the parameter to be optimized, W is the current weight, and Two positions are randomly selected from the position selection range, is the current best position, is the maximum number of iterations, is the nonlinear variation parameter in [-a, a], is a 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.

[0037] Among them, the calculation expression of the judgment variable is: , In the formula, for The fitness of is the best fitness in the current iteration; The calculation expression of the nonlinear change parameter value a is: , In the formula, is the current iteration number, is the maximum number of iterations.

[0038] Step S560: judging the steady-state error of the overall operation of the system (the deviation generated from the start to the stop of the system) by the overshoot, and the overshoot constraint is: In the formula, is the piston area of ​​the hydraulic cylinder, is the displacement of the hydraulic cylinder piston, is the rise time, is the ideal output flow rate, p is the overshoot control range and is generally a constant of 0.2.

[0039] In the above method steps, the fitness function, overshoot constraint conditions, and upper and lower limit constraints are combined with the improved slime mold optimization algorithm to construct a flutter signal parameter optimization model. The flutter signal parameters are optimized through the model to avoid the disadvantages of being influenced by human subjective experience, reduce manual intervention and reduce time costs, effectively reduce the system hysteresis area and hysteresis width, and further improve the efficiency and accuracy of nonlinear friction compensation and control. In addition, the improved slime mold optimization algorithm combined with the model proposes a new nonlinear decreasing strategy in the traditional slime mold optimization algorithm. This strategy can dynamically adjust the search range according to the iterative process, significantly improving the algorithm's global exploration ability 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, the method of dynamically adjusting the individual selection range according to the number of iterations is combined in the algorithm, so that the optimal solution of the flutter signal parameters can be obtained more quickly.

[0040] See also Figure 3 , Figure 3 The hysteresis diagram of the system corresponding to different adjustment methods in the current-displacement coordinate system is shown. The parameters optimized by the algorithm in the above steps are applied to the electro-hydraulic servo system, and the effect of the optimized chatter signal on the system operation is compared with the original system operation effect and the manually adjusted chatter signal. Compared with the parameter adjustment without chatter signal and the traditional chatter signal parameter adjustment, the hysteresis area of ​​the improved SMA chatter signal parameter adjustment is significantly reduced.

[0041] See also Figure 4 , Figure 4 The block diagram of the chatter signal parameter optimization system based on the improved slime mold optimization algorithm provided by the present embodiment is shown. The chatter signal parameter optimization system 200 based on the improved slime mold optimization algorithm includes: The parameter determination module 210 is configured to superimpose the 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 frequency limit value that need to be optimized for the chatter signal, wherein the parameter variables include the signal frequency and amplitude; A fitness function construction module 220 is configured to construct a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system; The constraint condition building module 230 is configured to build 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; An optimization model building module 240 builds a flutter signal parameter optimization model according to the fitness function, the overshoot constraint condition, and the upper and lower limit constraints, and based on an improved slime mold optimization algorithm; The parameter optimization module 250 inputs the interval corresponding to the parameter variable into the vibration signal parameter optimization model to obtain the optimized parameter variable value.

[0042] It can be understood that, in the implementation of the vibration signal parameter optimization system 200 based on the improved slime mold optimization algorithm in this embodiment, each module runs the above Figure 1 The steps of a vibration signal parameter optimization method based on an improved slime mold optimization algorithm in the corresponding embodiment, and the technical effects that can be achieved can be referred to the above Figure 1 The description of the technical effects achieved in the corresponding embodiments will not be repeated here.

[0043] See also Figure 5 , Figure 5 5 is a block diagram of an electronic device provided in 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 of a method for optimizing vibration signal parameters based on an improved slime mold optimization algorithm. When the processor 501 executes the computer program 503, the steps of the method for optimizing vibration signal parameters based on an improved slime mold optimization algorithm in the above-mentioned embodiments are implemented, such as Figure 1 The corresponding embodiment of step S100 to step S500. Alternatively, the processor 501 executes the computer program 503 to implement the above Figure 4 The functions of each module in the corresponding embodiment are, for example, Figure 4 For details on the functions of the modules shown (eg, parameter determination module 210), please refer to Figure 4 The relevant descriptions in the corresponding embodiments are not repeated here.

[0044] Exemplarily, the computer program 503 may be divided into one or more units, one or more units are stored in the memory 502, and are executed by the processor 501 to complete the technical solution provided in the above embodiment. One or more units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 503 in the server 500.

[0045] The electronic device may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that Figure 5 It is only an example of the server 500 in the electronic device and does not constitute a limitation of the server 500. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the turntable terminal device may also include an input and output terminal device, a network access terminal device, a bus, etc.

[0046] The processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0047] The memory 502 may be an internal storage unit of the server 500, such as a hard disk or memory of the server 500. The memory 502 may also be an external storage terminal device of the server 500, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the server 500. Further, the memory 502 may also include both an internal storage unit of the server 500 and an 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 may also be used to temporarily store data that has been output or is to be output.

[0048] In some embodiments, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the vibration signal parameter optimization method based on the improved slime mold optimization algorithm as described in the above embodiment are implemented.

[0049] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0050] 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, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, 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.

[0051] The terms "first", "second", "third", etc. in the specification and claims of the present application and the drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a series of steps or units are included, or optionally, steps or units not listed are included, or optionally, other steps or units inherent to these processes, methods, products or devices are included.

[0052] Only the part relevant to the present application is shown in the accompanying drawings, but not all of the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processing or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. When its operation is completed, the process can be terminated, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0053] The terms "component", "module", "system", "unit", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination 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 be executed from various computer-readable media having various data structures stored thereon. Units can communicate through local and / or remote processes, for example, based on signals having one or more data packets (e.g., data from a second unit interacting with another unit in a local system, a distributed system, and / or a network. For example, the Internet interacts with other systems via signals).

[0054] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0055] Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Mentioning "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present embodiment application. The appearance of this phrase in various positions in the specification is not necessarily the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It can be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0056] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0057] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the disclosure disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

Claims

1. A method for optimizing vibration signal parameters based on an improved slime mold optimization algorithm, characterized in that: include: Superimposing a vibration signal on a control signal output by a PID controller of an electro-hydraulic servo system, and determining parameter variables and a frequency upper limit value that need to be optimized for the vibration signal, wherein the parameter variables include a signal frequency and an amplitude; Constructing a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system; According to the upper frequency limit value and the specification attribute information of the related equipment of the electro-hydraulic servo system, the overshoot constraint condition and the upper and lower limit constraint conditions of the parameter variables of the electro-hydraulic servo system are constructed; According to the fitness function, the overshoot constraint condition, and the upper and lower limit constraints, and based on the improved slime mold optimization algorithm, a flutter signal parameter optimization model is constructed; The interval corresponding to the parameter variable is input into the vibration signal parameter optimization model to obtain the optimized parameter variable value.

2. The method for optimizing vibration signal parameters based on the improved slime mold optimization algorithm according to claim 1, characterized in that: The flutter signal is superimposed on the control signal output by the PID controller of the electro-hydraulic servo system, and the parameter variables and the upper frequency limit value that need to be optimized of the flutter signal are determined, wherein the parameter variables include the signal frequency and amplitude, including: Taking a triangular wave signal as an input signal, an experiment is conducted based on the entire stroke of the hydraulic cylinder in the electro-hydraulic servo system. The flutter signal is superimposed on the control signal output by the PID controller of the electro-hydraulic servo system, and the upper frequency limit of the flutter signal is determined in order from high frequency to low frequency.

3. The method for optimizing vibration signal parameters based on the improved slime mold optimization algorithm according to claim 1, characterized in that: The step of constructing a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system comprises: Inputting a continuously changing periodic given signal into the electro-hydraulic servo system, and outputting displacement data corresponding to different point values ​​in the periodic given signal, wherein the periodic given signal is used to characterize the periodic current signal input into the electro-hydraulic servo system; Determine an ascending curve and a descending curve in the same coordinate system according to the periodic current signal and the displacement data; According to the rising curve and the falling curve in the same coordinate system, based on different hysteresis widths at positions corresponding to a plurality of current point values, the average hysteresis width is determined.

4. The method for optimizing vibration signal parameters based on the improved slime mold optimization algorithm according to claim 3 is characterized in that: The step of constructing a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system comprises: The calculation expression of the average hysteresis loop 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.

5. The method for optimizing vibration signal parameters based on the improved slime mold optimization algorithm according to claim 1, characterized in that: The inputting the interval corresponding to the parameter variable into the vibration signal parameter optimization model to obtain the optimized parameter variable value 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 frequency and amplitude of the chatter signal; Randomly generating initial positions of slime molds, wherein each position of the slime mold corresponds to a solution, and calculating fitness based on the fitness function; The fitness is sorted to select the best position of the slime mold, and a weight and a position selection range are calculated for each position according to the fitness; Further position updates are performed in the vicinity of the optimal position of the slime mold to determine the optimal solution of the parameter variables within the interval.

6. The method for optimizing vibration signal parameters based on the improved slime mold optimization algorithm according to claim 5, characterized in that: The further updating of the position in the vicinity of the optimal position of the slime mold to determine the optimal solution of the parameter variable within the interval includes: The position update calculation expression is: ， In the formula, is a random number between 0 and 1. is the upper limit value of the parameter to be optimized; is the lower limit of the parameter to be optimized, W is the current weight, and Two positions are randomly selected from the position selection range, is the current best position, is the maximum number of iterations, is the nonlinear variation parameter in [-a, a], is a 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.

7. The method for optimizing vibration signal parameters based on the improved slime mold optimization algorithm according to claim 6, characterized in that: The further updating of the position in the vicinity of the optimal position of the slime mold to determine the optimal solution of the parameter variable within the interval includes: The calculation expression of the judgment variable is: ， In the formula, for The fitness of is the best fitness in the current iteration, The calculation expression of the nonlinear change parameter value a is: ， In the formula, is the current iteration number, is the maximum number of iterations.

8. A vibration signal parameter optimization system based on an improved slime mold optimization algorithm, characterized in that: include: A parameter determination module is configured to superimpose a chatter signal on a control signal output by a PID controller of an electro-hydraulic servo system, and determine parameter variables and a frequency upper limit value that need to be optimized for the chatter signal, wherein the parameter variables include a signal frequency and an amplitude; A fitness function building module is configured to build a fitness function according to the parameter variables and the average hysteresis width of the electro-hydraulic servo system; A constraint condition building module is configured to build an overshoot constraint condition and upper and lower limit constraint conditions of a parameter variable of the electro-hydraulic servo system according to the upper frequency limit value and specification attribute information of related equipment of the electro-hydraulic servo system; An optimization model building module, which builds a flutter signal parameter optimization model according to the fitness function, the overshoot constraint condition, and the upper and lower limit constraints, and based on an improved slime mold optimization algorithm; The parameter optimization module inputs the interval corresponding to the parameter variable into the vibration signal parameter optimization model to obtain the optimized parameter variable value.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, 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 a vibration signal parameter optimization method based on an improved slime mold optimization algorithm according to any one of claims 1 to 7.

10. 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 vibration signal parameter optimization method based on an improved slime mold optimization algorithm according to any one of claims 1 to 7 are implemented.

Images