Hydraulic control system of large-tonnage frame type hydraulic machine

Abstract

The invention relates to the technical field of automatic control, in particular to a large-tonnage frame type hydraulic machine hydraulic control system which comprises a load fluctuation detection module, a local nonlinear modeling module, a filter design module, a real-time feedback module and a gain adjustment module. According to the method, by monitoring and analyzing the load fluctuation data of the hydraulic machine in real time, the characteristics of load change can be accurately recognized, the pressure and flow of the hydraulic system can be automatically adjusted, and after the amplitude, frequency and period information of load fluctuation is extracted, the nonlinear effect in the system can be effectively controlled by combining dynamic behavior modeling of the hydraulic machine; operation errors are reduced, the filter is adaptively adjusted, gain control is performed, the response speed and precision of the hydraulic machine to load fluctuation are improved, pressure and flow control is optimized, stable operation of the system under different working conditions is ensured, and the stability and precision of the control system are improved.

Description

Technical Field

[0001] The invention relates to the field of automatic control technology, and in particular to a hydraulic control system for a large-tonnage frame type hydraulic press. Background Art

[0002] The field of automation control technology includes the use of control theory, computer technology, sensor technology, and actuator technology to complete the automated operation and management of various machines, equipment, and systems. Its core content is to achieve real-time monitoring, adjustment, and optimization of the equipment operation process through automated control systems. The technical application in this field involves the design and implementation of various automated control systems, including industrial production process control, robot control, hydraulic control, and automated control of intelligent equipment. Through intelligent control systems, the efficiency, accuracy, and stability of equipment operation can be improved, human intervention can be reduced, and the level of production automation can be improved.

[0003] Among them, the hydraulic control system of a large-tonnage frame hydraulic press refers to a hydraulic control technology applied to a large-tonnage hydraulic press. This patent subject proposes a hydraulic control solution for the control system of a hydraulic press, especially the control problem of the hydraulic system of a large-tonnage frame hydraulic press during operation. The hydraulic control system ensures the stable operation of the hydraulic press under high load conditions by precisely adjusting the hydraulic flow and pressure. The system achieves real-time control and optimization of the mechanical behavior of a large-tonnage hydraulic press during operation through the fine coordination of hydraulic control elements and automatic adjustment of the pressure of the hydraulic oil according to load changes.

[0004] Existing technologies cannot adjust the pressure and flow of the hydraulic system in real time when the load changes greatly, resulting in delayed response and affecting working accuracy. Traditional control strategies rely on fixed set values ​​and lack accurate identification and adjustment of the nonlinear effects of the system, resulting in instability during the working process. Insufficient gain control makes it difficult for hydraulic presses to cope with rapidly changing working conditions, affecting the operating efficiency and accuracy of the equipment, increasing the need for human intervention, and reducing the level of production automation. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a hydraulic control system for a large-tonnage frame type hydraulic press.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A hydraulic control system of a large-tonnage frame hydraulic press comprises:

[0007] The load fluctuation detection module collects the load variation data of the hydraulic press under different working conditions through the load fluctuation sensor of the hydraulic press, discretizes the data, extracts the characteristic information including the load fluctuation amplitude, frequency and period, analyzes the data fluctuation, and generates the load fluctuation characteristic value;

[0008] The local nonlinear modeling module establishes a dynamic behavior model of the hydraulic press based on the load change data, analyzes the nonlinear behaviors of the hydraulic press including valve control and oil flow, compares each working condition, identifies the nonlinear effects caused by load fluctuations, and generates nonlinear characteristic values ​​of the hydraulic press;

[0009] The filter design module designs an adaptive filter based on the nonlinear characteristic value of the hydraulic press, dynamically adjusts the amplitude and frequency of load fluctuations, selects the frequency range according to the influence of response time and accuracy, adjusts the filter parameters, and generates adaptive filter parameter values;

[0010] The real-time feedback module monitors the pressure and flow changes of the hydraulic press through the real-time feedback mechanism of the hydraulic press control mechanism, and adjusts the filter response by comparing the deviation between the expected behavior and the actual behavior of the hydraulic press control mechanism in combination with the adaptive filter parameter value, and corrects the hydraulic press control strategy in real time to generate the filter correction parameter value;

[0011] The gain adjustment module adjusts the hydraulic press gain control based on the filter correction parameter value, optimizes the pressure and flow control through gain adjustment in response to changes in load fluctuations, dynamically compares the deviation between the hydraulic press output signal and the target control value, adjusts the gain response speed in combination with the load change trend, and generates a gain adjustment value.

[0012] The load fluctuation detection module includes load fluctuation amplitude, frequency, and period; the local nonlinear modeling module includes valve control, oil flow, and nonlinear effect; the filter design module includes adaptive filter, frequency range, and filter parameters; the real-time feedback module includes pressure, flow change, deviation, and filter correction parameters; the gain adjustment module includes gain control, pressure control, flow control, and gain adjustment value.

[0013] As a further solution of the present invention, the step of obtaining the load fluctuation characteristic value is specifically as follows:

[0014] The load fluctuation sensor of the hydraulic press is used to collect the load variation data of the hydraulic press under different working conditions, and the data is discretized into numerical points, and the fluctuation amplitude, frequency and period value are calculated to obtain the load fluctuation feature set;

[0015] Based on the load fluctuation feature set, by analyzing multiple feature values, parameters associated with the fluctuation amplitude, frequency and period are extracted, and the load fluctuation data set that meets the conditions is screened to obtain the key features of the load fluctuation;

[0016] Combined with the key characteristics of the load fluctuation, the oscillation processing method based on frequency domain analysis is used to calculate the fluctuation energy on the differentiated frequency band, using the formula:

[0017]

[0018] The frequency spectrum characteristic value of the load fluctuation is obtained by calculation, and the load fluctuation characteristic value is generated by combining the fluctuation amplitude and period;

[0019] Among them, F load Represents the load fluctuation characteristic value, A i represents the amplitude of the ith frequency band, f i represents the frequency of the ith frequency band, t represents the time variable, φ i represents the phase of the i-th frequency band, B i Represents the width of the frequency bandwidth, C i Normalization process used to adjust the energy within the bandwidth.

[0020] As a further solution of the present invention, the step of obtaining the nonlinear characteristic value of the hydraulic press is specifically as follows:

[0021] According to the load variation data, by analyzing the nonlinear behavior of valve control and oil flow, a dynamic behavior model of the hydraulic press is set, nonlinear characteristics associated with load fluctuations are extracted, indicators associated with the nonlinear effects caused by load fluctuations in the hydraulic press system are calculated, and initial values ​​of nonlinear effects of the hydraulic press are generated;

[0022] Based on the initial value of the nonlinear effect of the hydraulic press, the dynamic behavior model of the hydraulic press is compared under working conditions, and the nonlinear characteristic values ​​under multiple working conditions are analyzed one by one. By comparing with the standard value, the nonlinear effect caused by the load fluctuation on the hydraulic press is identified, and the nonlinear effect change value is obtained;

[0023] Combined with the nonlinear effect change value, the impact of load fluctuation on multiple nonlinear effects of the hydraulic press is evaluated, the range, mean and variance in the load fluctuation data are calculated, and the threshold method is used to determine the working conditions with significant load fluctuation impact, using the formula:

[0024]

[0025] Calculate and generate nonlinear characteristic values ​​of hydraulic press;

[0026] Where f represents the nonlinear characteristic value of the hydraulic press, L i represents the load fluctuation data of the i-th working condition,

[0027] L avg represents the average value of load fluctuation data, α is the adjustment coefficient of load fluctuation, n is the number of working conditions, L j is the load data of the jth working condition.

[0028] As a further solution of the present invention, the step of obtaining the adaptive filter parameter value is specifically:

[0029] Analyze the nonlinear characteristic value of the hydraulic press, extract the amplitude and frequency of load fluctuation, divide the differentiation interval according to the load fluctuation frequency, and calculate the characteristic value fluctuation range of multiple intervals to obtain an interval parameter set;

[0030] Based on the interval parameter set, combined with the response time and accuracy requirements of the load fluctuation, the interval frequency range is dynamically adjusted, the corresponding filter coefficients are calculated, and the filter is initially set to generate initial parameters of the adaptive filter;

[0031] The adaptive filter is screened according to the initial parameters of the adaptive filter and the set target response time and accuracy value, and the frequency range during the filter parameter adjustment process is compared, and the formula is used:

[0032]

[0033] Calculate the optimal parameters of the filter and generate adaptive filter parameter values;

[0034] Among them, P final represents the adaptive filter parameters, w i represents the weight of the filter coefficient, f i represents the frequency of the ith interval, f0 represents the standard frequency, α1 is the adjustment coefficient, T j Represents the response time, T target represents the target response time, β1 is the accuracy adjustment coefficient, and γ1 is the weighted adjustment coefficient of the parameter.

[0035] As a further solution of the present invention, the step of obtaining the filter correction parameter value is specifically as follows:

[0036] By monitoring the deviation between the actual behavior and the expected behavior of the hydraulic press control mechanism, the deviation value of the hydraulic press control behavior is calculated, and the deviation is judged whether it exceeds the control range according to the set threshold value, and the difference between the deviation value and the threshold value is analyzed. If the deviation value exceeds the set threshold value, data collection and feedback calculation are triggered to generate the deviation value of the hydraulic press control behavior;

[0037] Based on the real-time feedback mechanism and the hydraulic press pressure and flow data, combined with the adaptive filter parameter value, the actual feedback signal is collected and data processed, the pressure and flow change trends are monitored and analyzed, and a correction parameter set is obtained;

[0038] The hydraulic press control behavior deviation value is compared with the correction parameter set, the correction parameters of the filter are updated, and the correction amount of the hydraulic press control behavior is calculated in combination with the change trend of pressure and flow rate, using the formula:

[0039]

[0040] Get the filter correction parameter value;

[0041] Among them, P corr Represents the filter correction parameter value, w i Represents the weight coefficient of each control feedback parameter, D i represents the behavioral difference data associated with the deviation, and is a constant to avoid zero division, ΔP i represents the hydraulic press control behavior deviation value, α represents the adjustment coefficient, P max , P min Represents the maximum and minimum pressure values ​​of the hydraulic press control, and n represents the total number of feedback parameters in the hydraulic press control process.

[0042] As a further solution of the present invention, the step of obtaining the gain adjustment value is specifically as follows:

[0043] Based on the filter correction parameter value, the control error is calculated, the deviation between the target control value and the output signal of the hydraulic press is compared, the error distribution law is analyzed, the key error mode is identified, and the control error data set is summarized and generated;

[0044] Analyze the control error data set, perform load fluctuation trend analysis, extract key fluctuation parameters, identify load change mode, calculate gain correction coefficient in combination with gain response speed adjustment factor, adjust the control error correction process, and obtain gain adjustment coefficient;

[0045] The gain adjustment coefficient and the control error data set are used to dynamically compare the deviation between the hydraulic press output signal and the target control value, using the formula:

[0046]

[0047] Execute a gain adjustment operation to calculate a gain adjustment value;

[0048] Among them, G adj Represents the gain adjustment value, E ctrl represents the control error, α is the gain correction coefficient, ΔP is the pressure change value, β is the gain response adjustment index, γ is the load fluctuation response coefficient, ΔF represents the load change value, and θ is the load fluctuation correction index.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, by real-time monitoring and analysis of the load fluctuation data of the hydraulic press, the characteristics of load changes can be accurately identified and the pressure and flow of the hydraulic system can be automatically adjusted. After the amplitude, frequency and period information of the load fluctuation are extracted, combined with the dynamic behavior modeling of the hydraulic press, the nonlinear effects in the system can be effectively controlled and the operation errors can be reduced. The adaptive adjustment filter and gain control improve the response speed and accuracy of the hydraulic press to load fluctuations, optimize the control of pressure and flow, ensure the stable operation of the system under different working conditions, and improve the stability and accuracy of the control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a system flow chart of the present invention;

[0052] Figure 2 A flow chart of the steps for obtaining the load fluctuation characteristic value of the present invention;

[0053] Figure 3 A flowchart of the steps for obtaining the nonlinear characteristic value of the hydraulic press of the present invention;

[0054] Figure 4 A flowchart of the steps for obtaining the parameter values ​​of the adaptive filter of the present invention;

[0055] Figure 5 A flow chart of the steps for obtaining the filter correction parameter value of the present invention;

[0056] Figure 6 This is a flow chart of the steps for obtaining the gain adjustment value of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] Embodiment 1

[0060] See also Figure 1, a large-tonnage frame hydraulic press hydraulic control system includes:

[0061] The load fluctuation detection module collects the load variation data of the hydraulic press under different working conditions through the load fluctuation sensor of the hydraulic press, discretizes the data, extracts the characteristic information including the load fluctuation amplitude, frequency and period, analyzes the data fluctuation, and generates the load fluctuation characteristic value;

[0062] The local nonlinear modeling module builds a dynamic behavior model of the hydraulic press based on load change data, analyzes the nonlinear behavior of the hydraulic press, including valve control and oil flow, and compares each working condition to identify the nonlinear effects caused by load fluctuations and generate nonlinear characteristic values ​​of the hydraulic press.

[0063] The filter design module designs an adaptive filter based on the nonlinear characteristic value of the hydraulic press, dynamically adjusts the amplitude and frequency of load fluctuations, selects the frequency range based on the impact of response time and accuracy, adjusts the filter parameters, and generates adaptive filter parameter values;

[0064] The real-time feedback module monitors the pressure and flow changes of the hydraulic press through the real-time feedback mechanism of the hydraulic press control mechanism, and adjusts the filter response by comparing the deviation between the expected behavior and the actual behavior of the hydraulic press control mechanism in combination with the adaptive filter parameter value, thereby correcting the hydraulic press control strategy in real time and generating the filter correction parameter value;

[0065] The gain adjustment module adjusts the hydraulic press gain control based on the filter correction parameter value. It optimizes the pressure and flow control through gain adjustment in response to changes in load fluctuations, dynamically compares the deviation between the hydraulic press output signal and the target control value, adjusts the gain response speed based on the load change trend, and generates a gain adjustment value.

[0066] The load fluctuation detection module includes load fluctuation amplitude, frequency, and period; the local nonlinear modeling module includes valve control, oil flow, and nonlinear effects; the filter design module includes adaptive filter, frequency range, and filter parameters; the real-time feedback module includes pressure, flow change, deviation, and filter correction parameters; the gain adjustment module includes gain control, pressure control, flow control, and gain adjustment value.

[0067] See also Figure 2 , the specific steps for obtaining the load fluctuation characteristic value are:

[0068] The load fluctuation sensor of the hydraulic press is used to collect the load variation data of the hydraulic press under different working conditions, and the data is discretized into numerical points, and the fluctuation amplitude, frequency and period value are calculated to obtain the load fluctuation feature set;

[0069] First, the collected data needs to be discretized by dividing the continuous time series data into several intervals for subsequent analysis. Then, the fluctuation amplitude of each data interval is calculated, that is, the difference between the maximum and minimum values ​​in each time period is calculated, and it is calibrated to confirm the numerical range of its fluctuation amplitude. Then the frequency and period of the data are calculated. The frequency calculation can be obtained by Fourier transform or by calculating the number of changes per second based on the data points per second. The period calculation can be obtained by detecting the time difference between the peak values ​​of the data fluctuation. Finally, the values ​​of these three parameters are combined to obtain a preliminary feature set including fluctuation amplitude, frequency, and period. For example, assuming that the maximum value in a data interval is 120N and the minimum value is 100N, the fluctuation amplitude is 20N, and the frequency is calculated by the sampling frequency and the time interval of the data points. Assuming the sampling frequency is 100Hz, the period can be calculated by observing the time interval of the periodic change of the peak value, thereby obtaining the load fluctuation feature set.

[0070] Based on the load fluctuation feature set, the parameters associated with the fluctuation amplitude, frequency and period are extracted by analyzing multiple feature values, and the qualified load fluctuation data sets are screened to obtain the key features of the load fluctuation.

[0071] First, filter out the intervals with fluctuation amplitudes higher than the preset threshold. The threshold can be obtained by statistical analysis of historical data. Set a suitable fluctuation amplitude range. For example, a fluctuation amplitude greater than 15N is a high fluctuation interval. Frequency screening can select a suitable frequency range based on the fluctuation characteristics. For example, the high frequency band may be defined as an interval with a frequency greater than 1Hz. Then, by statistically analyzing the periodic data, identify the intervals with obvious period lengths. Then, based on the relationship between fluctuation amplitude, frequency and period, establish a screening rule to filter out data that meets the preset threshold and obtain the main characteristics of load fluctuations. Assuming that the fluctuation amplitude threshold obtained by statistical analysis is 15N and the frequency threshold is 1Hz, then the intervals larger than this range will be considered to be the main characteristics, and the key characteristics of load fluctuations will be obtained.

[0072] Combining the key characteristics of load fluctuations, the oscillation processing method based on frequency domain analysis is used to calculate the fluctuation energy on the differentiated frequency bands using the formula:

[0073]

[0074] The frequency spectrum characteristic value of the load fluctuation is obtained by calculation, and the load fluctuation characteristic value is generated by combining the fluctuation amplitude and period;

[0075] Among them, F load Represents the load fluctuation characteristic value, A i represents the amplitude of the ith frequency band, f irepresents the frequency of the ith frequency band, t represents the time variable, φ i represents the phase of the i-th frequency band, B i Represents the width of the frequency bandwidth, C i Normalization process used to adjust the energy within the bandwidth.

[0076] formula:

[0077]

[0078] Among them, F load Represents the load fluctuation characteristic value, A i represents the amplitude of the ith frequency band, f i represents the frequency of the ith frequency band, t represents the time variable, φ i represents the phase of the i-th frequency band, B i and C i Represents frequency bandwidth and gain coefficient, frequency bandwidth B i and gain factor C i is obtained through statistical analysis and calculation of frequency domain data, where B i It can be obtained by dividing the interval of frequency domain data, C i It is obtained by weighting the amplitude of each frequency band.

[0079] Calculation process:

[0080] Assume that the following data is obtained from frequency domain analysis:

[0081] A1=5, f1=0.5Hz, φ1=0, B1=0.2, C1=1, A2=3, f2=1.0Hz, φ2

[0082] =π / 4, B2=0.3, C2=1.2.

[0083] Then, according to the formula, first calculate the energy of each frequency band:

[0084]

[0085] Then add these two results to get the total load fluctuation spectrum characteristic value:

[0086]

[0087] Calculation example:

[0088] Assume t = 1s, then:

[0089]

[0090]

[0091] The spectral characteristic value of load fluctuation is:

[0092]

[0093] The results show that the load fluctuation characteristic value obtained by frequency domain analysis is F load ≈5.03, which represents the load fluctuation spectrum characteristics of the hydraulic press under the current working conditions. Combined with the actual working conditions, it can be used to further optimize the load management of the hydraulic system.

[0094] See also Figure 3 , the specific steps for obtaining the nonlinear characteristic value of the hydraulic press are:

[0095] According to the load change data, by analyzing the nonlinear behavior of valve control and oil flow, the dynamic behavior model of the hydraulic press is set, the nonlinear characteristics associated with load fluctuations are extracted, the indicators associated with the nonlinear effects caused by load fluctuations in the hydraulic press system are calculated, and the initial values ​​of the nonlinear effects of the hydraulic press are generated;

[0096] First, it is necessary to monitor the load change data, record the load fluctuation under each working condition, classify the data according to the working condition, identify the load fluctuation amplitude and trend under each working condition, and further analyze the nonlinear response of the hydraulic press valve control and oil flow under different load conditions. Next, match the load fluctuation under each working condition with the valve control system and oil flow characteristics, calculate the nonlinear characteristics of the hydraulic press, and evaluate whether the system exhibits obvious nonlinear behavior under different operating conditions by calculating the nonlinear characteristics of the hydraulic press. During the data monitoring and analysis process, compare the amplitude range of the load fluctuation with the actual monitored value, and calculate the nonlinear characteristics of the hydraulic press in combination with the dynamic behavior model of the hydraulic press. Based on the dynamic response of the system, identify the nonlinear influence range of the load fluctuation and obtain the initial value of the nonlinear effect of the hydraulic press.

[0097] Based on the initial value of the nonlinear effect of the hydraulic press, the dynamic behavior model of the hydraulic press is compared under different working conditions, and the nonlinear characteristic values ​​under multiple working conditions are analyzed one by one. By comparing with the standard value, the nonlinear effect caused by load fluctuation on the hydraulic press is identified, and the change value of the nonlinear effect is obtained.

[0098] First, the load fluctuation data of each working condition is obtained. According to the actual test data under different working conditions, the load change of each working condition is calculated. After sorting these data, the impact of load fluctuation on the performance of the hydraulic press under each working condition is calculated and compared with the standard value. In order to more accurately evaluate the nonlinear effect of load fluctuation in the hydraulic press system, it is necessary to use a data model to compare the nonlinear characteristic values ​​under each working condition with the standard value to identify the fluctuation effect beyond the normal range. In this process, by comparing the load fluctuation characteristics under different working conditions, the specific impact of load fluctuation on the performance of the hydraulic press is further clarified, and the nonlinear characteristic changes under each working condition of the hydraulic press are obtained. Then, the nonlinear effect caused by load fluctuation can be identified, forming a basis for adjusting and optimizing the hydraulic press system.

[0099] Combined with the change value of nonlinear effect, the influence of load fluctuation on multiple nonlinear effects of hydraulic press is evaluated, the range, mean and variance in load fluctuation data are calculated, and the threshold method is used to determine the working conditions with significant influence of load fluctuation. The formula is:

[0100]

[0101] Calculate and generate nonlinear characteristic values ​​of hydraulic press;

[0102] Where f represents the nonlinear characteristic value of the hydraulic press, L i Represents the load fluctuation data of the i-th working condition, L avg represents the average value of load fluctuation data, α is the adjustment coefficient of load fluctuation, n is the number of working conditions, L j is the load data of the jth working condition.

[0103] formula:

[0104]

[0105] The benefit of the formula is that, by combining the load fluctuation with the nonlinear adjustment coefficient α, the formula can effectively reflect the impact of load fluctuation on the dynamic behavior of the hydraulic press under different working conditions.

[0106] Detailed explanation of the formula and the process of formula calculation and derivation:

[0107] f represents the nonlinear characteristic value of the hydraulic press, indicating the nonlinear behavior of the hydraulic press under specific working conditions.

[0108] L i Represents the load fluctuation data of the i-th working condition.

[0109] L avg Represents the average value of load fluctuation data, which is calculated through the load data of all working conditions. The formula is:

[0110]

[0111] α is the adjustment coefficient of load fluctuation, which represents the weighted influence of load fluctuation on the nonlinear characteristics of the hydraulic press. It needs to be set according to the hydraulic press design and test results. The coefficient is generally obtained through experiments.

[0112] n is the number of operating conditions.

[0113] It is the variance of the load fluctuation data, which reflects the degree of discreteness of the data.

[0114] Assume that the hydraulic press load data obtained from the experiment are as follows: L1 = 150, L2 = 160, L3

[0115] =145,L4=155 (unit: N).

[0116] First calculate the mean:

[0117]

[0118] Then calculate the variance:

[0119]

[0120] Substitute into the formula to calculate f, assuming α = 0.05:

[0121]

[0122] f≈0.223·0.000553+0.672·0.000335+0.672·0.000453+0.223·0.000439;

[0123] f≈0.000123+0.000225+0.000304+0.000098≈0.00075;

[0124] The results show that the nonlinear characteristic value f of the hydraulic press under the current working condition is about 0.00075, which indicates that the nonlinear behavior of the hydraulic press under load fluctuation is relatively mild. Based on this result, the hydraulic press control system can be further adjusted.

[0125] See also Figure 4 , the steps for obtaining the adaptive filter parameter values ​​are as follows:

[0126] Analyze the nonlinear eigenvalues ​​of the hydraulic press, extract the amplitude and frequency of load fluctuations, divide the differentiation intervals according to the load fluctuation frequency, and calculate the eigenvalue fluctuation ranges of multiple intervals to obtain the interval parameter set;

[0127] First, collect the load data of the hydraulic press through sensors or monitoring equipment, and then analyze the amplitude of the load fluctuation. The common way is to measure the amplitude by the difference between the peak and valley values ​​of the signal. The frequency can be obtained by the fast Fourier transform (FFT) method to obtain the frequency component of the load fluctuation, and the division of each frequency interval is determined based on the frequency domain analysis. For example, after collecting the load fluctuation data within a certain period of time, its spectrum distribution can be calculated. If the frequency range is 0-10Hz, 10-20Hz, and 20-30Hz, the corresponding load fluctuation characteristic value can be assigned to each interval respectively. Furthermore, according to the actual application needs, according to the amplitude of the load fluctuation, the characteristic value fluctuation range of each frequency interval is assigned, and a set of interval parameters is generated to ensure that the characteristic value of each interval is suitable for subsequent analysis.

[0128] Based on the interval parameter set, combined with the response time and accuracy requirements of load fluctuations, the interval frequency range is dynamically adjusted, the corresponding filter coefficients are calculated, and the initial settings of the filter are performed to generate the initial parameters of the adaptive filter;

[0129] First, extract the frequency information of each interval from the interval parameter set, and determine the adjustment plan for the filter in combination with the response time and accuracy requirements, including the frequency range setting and filter gain value of the filter. By applying the bandpass filter principle, select the appropriate frequency range for filter design, use the calculation formula to calculate the filter coefficients for each frequency band, and use these coefficients to set the filter gain value to ensure effective filtering within the dynamic range of load fluctuations. For example, if the filter needs to optimize the fluctuation response time of a certain frequency band, the filter coefficients can be adjusted to achieve precise adjustment effects. The initial parameters of the adaptive filter generated through this process provide a basis for subsequent optimization.

[0130] The adaptive filter is screened according to the initial parameters and the set target response time and accuracy value, and the frequency range during the filter parameter adjustment process is compared, using the formula:

[0131]

[0132] Calculate the optimal parameters of the filter and generate adaptive filter parameter values;

[0133] Among them, P final represents the adaptive filter parameters, w i represents the weight of the filter coefficient, f i represents the frequency of the ith interval, f0 represents the standard frequency, α1 is the adjustment coefficient, T j Represents the response time, T target represents the target response time, β1 is the accuracy adjustment coefficient, and γ1 is the weighted adjustment coefficient of the parameter.

[0134] formula:

[0135]

[0136] The benefit of the formula is that, by introducing the interval frequency adjustment coefficient, the target response time and the accuracy coefficient, the parameters of the adaptive filter can be adjusted more finely and dynamically, thereby improving the response accuracy of the filter under different load conditions.

[0137] Detailed explanation of the formula and the process of formula calculation and derivation:

[0138] In the formula, Part is the frequency f of each interval i The weight adjustment of the standard frequency f0 and the proportional relationship of the frequencies are optimized by adjusting the coefficient α1. According to each response time T j With the target response time T target The precision coefficient β1 further strengthens the adjustment effect of the process on the response time, and γ1 is the overall adjustment coefficient, which is used to comprehensively consider the balance between frequency and response time. Assume that the frequencies of each interval obtained through data collection are f1=100, f2=150, f3

[0139] =200 and standard frequency f0 = 150, each response time T1 = 10, T2 = 12, T3 = 14 and target response time T target =12, set weights w1=0.4, w2=0.3, w3=0.3, and adjust coefficients α1=1.2, β1=1.5, γ1=0.8, then these values ​​can be put into the formula for calculation:

[0140]

[0141] Calculating:

[0142] The first summation part:

[0143] 0.4·(0.6667) 1.2 =0.4 0.612 = 0.2448;

[0144] 0.3·(1) 1.2 =0.3·1=0.3;

[0145] 0.3·(1.3333) 1.2 =0.3·1.3804=0.4141;

[0146] The sum is: 0.2448+0.3+0.4141=0.9589;

[0147] The second summation part:

[0148]

[0149] The sum is: 0.7594+1+1.3754=3.1348;

[0150] result:

[0151] P final =0.9589·(3.1348) 0.8 =0.9589·2.4234=2.325;

[0152] The results show that by accurately adjusting the filter parameters, the best response within the load fluctuation range can be ensured, and the accuracy and efficiency of the adaptive filter are optimized by dynamically adjusting the frequency and response time in different intervals.

[0153] See also Figure 5 , the specific steps for obtaining the filter correction parameter value are:

[0154] By monitoring the deviation between the actual behavior and the expected behavior of the hydraulic press control mechanism, the deviation value of the hydraulic press control behavior is calculated, and the deviation is judged whether it exceeds the control range according to the set threshold value, and the difference between the deviation value and the threshold value is analyzed. If the deviation value exceeds the set threshold value, data collection and feedback calculation are triggered to generate the deviation value of the hydraulic press control behavior;

[0155] First, the actual data of the hydraulic press during operation is collected through sensor equipment. These data include parameters such as pressure, flow, and movement speed of the hydraulic press. The actual value and the expected value are compared by data, and the deviation value is calculated. For example, the deviation between the actual pressure value of the hydraulic press and the preset standard pressure value can be calculated by a simple difference formula: ΔP = |P actual -P expected |, the obtained deviation value will be compared with the preset threshold value to set a control threshold value (threshold T). When the deviation value is greater than the threshold value T, it means that the control system is abnormal and the data collection and feedback mechanism needs to be triggered. The feedback mechanism will collect the control data of the hydraulic press in real time and compare and analyze it to generate the deviation value of the control behavior of the hydraulic press. This process can not only detect the current deviation value, but also provide a basis for system adjustment by monitoring historical data.

[0156] Based on the real-time feedback mechanism and the hydraulic press pressure and flow data, combined with the adaptive filter parameter value, the actual feedback signal is collected and data processed, the pressure and flow change trends are monitored and analyzed, and the correction parameter set is obtained;

[0157] The pressure and flow changes in the hydraulic press system are collected in real time by sensors, and the collected data will be processed by adaptive filters. Adaptive filters can effectively remove noise and smooth signals, thereby more accurately reflecting the actual control situation. In order to optimize the filtering effect, the parameters of the filter must be adjusted according to the real-time feedback of pressure and flow data. For example, the minimum mean square error (MSE) method can be used to estimate the filter parameters by comparing historically collected data with current data. These parameter adjustments help maintain efficient and stable operation of the hydraulic press in a dynamic environment. When abnormal changes in pressure and flow are monitored, the adjustment strategy based on the correction parameter set will be optimized. Past short-term and long-term trends are analyzed through regression analysis, and the current control strategy is corrected to ensure that the hydraulic press can maintain optimal performance under various working conditions.

[0158] Compare the hydraulic press control behavior deviation value with the correction parameter set, update the filter correction parameters, and calculate the correction amount of the hydraulic press control behavior in combination with the pressure and flow rate change trends. The formula is:

[0159]

[0160] Get the filter correction parameter value;

[0161] Among them, P corr Represents the filter correction parameter value, w i Represents the weight coefficient of each control feedback parameter, D i represents the behavioral difference data associated with the deviation, ∈ is a constant to avoid numerical division by zero, ΔP i represents the hydraulic press control behavior deviation value, α represents the adjustment coefficient, P max , P min Represents the maximum and minimum pressure values ​​of the hydraulic press control, and n represents the total number of feedback parameters in the hydraulic press control process.

[0162] formula:

[0163]

[0164] The benefit of the formula is that, by weighting each control feedback parameter and combining the deviation value and the maximum and minimum values ​​of the hydraulic press control pressure, the control behavior of the hydraulic press can be accurately corrected to ensure that it reaches the expected working state, especially the change trend of pressure and flow, and the correction amount can be fed back in real time, thereby optimizing the control effect.

[0165] Detailed explanation of the formula and the process of formula calculation and derivation:

[0166] P corr : Filter correction parameter value, which represents the correction amount of the hydraulic press control behavior and affects the control feedback behavior.

[0167] w i : The weight coefficient of each control feedback parameter represents the weight of each parameter in the correction process, which is set by the system designer according to the system response characteristics or obtained through experiments.

[0168] D i : The behavior difference data associated with the deviation is the difference between the expected control behavior and the actual control behavior, reflecting the deviation between the actual behavior and the expected behavior of the hydraulic press system.

[0169] ∈: A constant to avoid zero division, which is a very small number (e.g. ∈ = 10 -6 ) to prevent division by zero errors during calculations.

[0170] ΔP i : The hydraulic press control behavior deviation value represents the deviation of the hydraulic press control behavior at each moment, which is obtained through the real-time monitoring system.

[0171] α: Adjustment coefficient, representing the sensitivity to deviation correction. The optimal value is obtained through repeated adjustments in the experiment.

[0172] P max , P min :The maximum and minimum pressure values ​​controlled by the hydraulic press are determined by the design or working environment of the hydraulic press and can be obtained in real time through the pressure sensor.

[0173] n: The total number of feedback parameters in the hydraulic press control process, indicating the number of feedback parameters.

[0174] Assume that the following values ​​are obtained through real-time monitoring:

[0175] w1=0.6, w2=0.4-D1=0.5, D2=0.3, ∈=10 -6 , ΔP1=2.0, ΔP2=1.5,

[0176] P max =150,P min =50-α=0.8,n=2.

[0177] Substituting into the formula:

[0178]

[0179] For i=1:

[0180]

[0181] P corr1 =1.2·2.8=3.36;

[0182] For i=2:

[0183]

[0184] P corr2 =1.333·2.3=3.066;

[0185] Corrected parameter values:

[0186] P corr =P corr1 +P corr2 =3.36+3.066=6.426;

[0187] The result shows that by correcting the control behavior of the hydraulic press, the system feedback correction amount is 6.426, which can ensure that the control behavior of the hydraulic press is closer to the expected value, especially in the dynamic changes of the pressure and flow of the hydraulic system, the corrected behavior will be more stable.

[0188] See also Figure 6 , the specific steps for obtaining the gain adjustment value are:

[0189] Based on the filter correction parameter value, the control error is calculated, the deviation between the target control value and the hydraulic press output signal is compared, the error distribution law is analyzed, the key error mode is identified, and the control error data set is summarized and generated;

[0190] The calculation of control error can be quantified by the following formula: E ctrl =|y target -y output Among them, E ctrl represents the control error, y target is the target control value, y output is the output signal of the hydraulic press. By collecting the deviation data between the output signal of the hydraulic press and the target control value in real time, a series of error values ​​can be obtained. These values ​​will reveal the distribution law of the error through statistical analysis, such as using the mean, standard deviation and other methods to calculate the statistical characteristics of the error. In the process of identifying the control error, it is first necessary to identify which errors are systematic errors and which are random errors caused by the external environment or other factors. By analyzing the error pattern, potential problems of the control system can be better identified, providing data support for subsequent gain correction. By summarizing these control error data, a complete control error data set can be established as the basis for subsequent load fluctuation trend analysis. This error data set will help engineers understand the behavior of the control system and optimize the control strategy.

[0191] Analyze the control error data set, perform load fluctuation trend analysis, extract key fluctuation parameters, identify load change mode, calculate the gain correction coefficient in combination with the gain response speed adjustment factor, adjust the control error correction process, and obtain the gain adjustment coefficient;

[0192] First, it is necessary to analyze the trend of load fluctuation based on historical data. Usually, the load fluctuation pattern can be observed by drawing a time series graph of load changes. By performing trend analysis on load fluctuations, key fluctuation parameters can be extracted. These parameters may include the fluctuation amplitude, frequency and change speed of the load, which are important factors affecting the control accuracy of the hydraulic press. Using these parameters, the pattern of load change, such as sudden change of load, periodic fluctuation, etc., can be identified. The gain correction coefficient needs to be calculated in combination with the gain response speed adjustment factor. This step requires regression analysis or modeling calculation of the relationship between the load fluctuation parameter and the gain response speed to obtain the gain correction coefficient. This coefficient is used to dynamically adjust the gain during the control process of the hydraulic press to compensate for the control error caused by load fluctuations. The acquisition and calculation process of the load fluctuation parameters can be achieved by collecting and analyzing the actual operation data of the hydraulic press. Typical tools may include data acquisition systems, sensors and data analysis software. The results of the load fluctuation analysis will provide the necessary input data for the gain correction process to ensure the stability and accuracy of the hydraulic press control system.

[0193] Using the gain adjustment coefficient and the control error data set, the deviation between the hydraulic press output signal and the target control value is dynamically compared using the formula:

[0194]

[0195] Execute a gain adjustment operation to calculate a gain adjustment value;

[0196] Among them, G adj Represents the gain adjustment value, E ctrl represents the control error, α is the gain correction coefficient, ΔP is the pressure change value, β is the gain response adjustment index, γ is the load fluctuation response coefficient, ΔF represents the load change value, and θ is the load fluctuation correction index.

[0197] formula:

[0198]

[0199] The benefit of the formula is that by introducing the control error (E ctrl ), gain correction coefficient (α), pressure change value (ΔP), gain response adjustment index (β), load change value (ΔF) and load fluctuation response coefficient (γ) and other parameters can dynamically adjust the gain so that the hydraulic press control system can adapt to different load fluctuation conditions, thereby improving the control accuracy and system stability.

[0200] Detailed explanation of the formula and the process of formula calculation and derivation:

[0201] In this formula, Gadj Represents the gain adjustment value, and the control error is E ctrl , which represents the difference between the target control value and the actual output signal of the hydraulic press; α is the gain correction coefficient, which is used to adjust the size of the gain; ΔP represents the pressure change value, which is the pressure fluctuation range inside the hydraulic system; β is the gain response adjustment index, which represents the response sensitivity of the gain to the pressure change; γ is the load fluctuation response coefficient, which represents the influence of the load change on the control system; ΔF is the load change value, which represents the fluctuation amount of the load; θ is the load fluctuation correction index, which is used to describe the influence characteristics of the load change on the gain correction.

[0202] Assume that the parameters of a hydraulic press control system are as follows:

[0203] Control error E ctrl =5;

[0204] Gain correction coefficient α = 0.8;

[0205] Pressure change value ΔP = 3.5;

[0206] Gain response adjustment index β = 1.2;

[0207] Load change value ΔF = 10;

[0208] Load fluctuation response coefficient γ = 0.5;

[0209] Load fluctuation correction index θ = 0.9;

[0210] Substituting the above values ​​into the formula:

[0211]

[0212] First calculate the part in brackets:

[0213] Then, calculate 1.142857 1.2 :1.142857 1.2 ≈1.2184;

[0214] Next, calculate the load fluctuation part: |10| 0.9 =10 0.9 ≈7.943;

[0215] Therefore, the load fluctuation response term is: 1+0.5·7.943=1+3.9715=4.9715;

[0216] Finally, substitute these calculations into the gain adjustment formula: (gain adjustment value);

[0217] The result shows that the system gain value after gain adjustment is 2.241, which means that the system gain has been adjusted to adapt to changes in load fluctuations and control errors. This result further improves the stability and accuracy of the hydraulic press control system under load fluctuations and helps optimize the performance of the control system.

[0218] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A hydraulic control system for a large-tonnage frame hydraulic press, characterized in that: The system comprises: The load fluctuation detection module collects the load variation data of the hydraulic press under different working conditions through the load fluctuation sensor of the hydraulic press, discretizes the data, extracts the characteristic information including the load fluctuation amplitude, frequency and period, analyzes the data fluctuation, and generates the load fluctuation characteristic value; The local nonlinear modeling module establishes a dynamic behavior model of the hydraulic press based on the load change data, analyzes the nonlinear behavior of the hydraulic press including valve control and oil flow, compares each working condition, identifies the nonlinear effect caused by load fluctuation, and generates nonlinear characteristic values ​​of the hydraulic press; The filter design module designs an adaptive filter based on the nonlinear characteristic value of the hydraulic press, dynamically adjusts the amplitude and frequency of load fluctuations, selects the frequency range according to the influence of response time and accuracy, adjusts the filter parameters, and generates adaptive filter parameter values; The real-time feedback module monitors the pressure and flow changes of the hydraulic press through the real-time feedback mechanism of the hydraulic press control mechanism, and adjusts the filter response by comparing the deviation between the expected behavior and the actual behavior of the hydraulic press control mechanism in combination with the adaptive filter parameter value, and corrects the hydraulic press control strategy in real time to generate the filter correction parameter value; The gain adjustment module adjusts the hydraulic press gain control based on the filter correction parameter value, optimizes the pressure and flow control through gain adjustment in response to changes in load fluctuations, dynamically compares the deviation between the hydraulic press output signal and the target control value, adjusts the gain response speed in combination with the load change trend, and generates a gain adjustment value.

2. The hydraulic control system of a large-tonnage frame hydraulic press according to claim 1 is characterized in that: The load fluctuation detection module includes load fluctuation amplitude, frequency, and period; the local nonlinear modeling module includes valve control, oil flow, and nonlinear effect; the filter design module includes adaptive filter, frequency range, and filter parameters; the real-time feedback module includes pressure, flow change, deviation, and filter correction parameters; the gain adjustment module includes gain control, pressure control, flow control, and gain adjustment value.

3. The hydraulic control system of a large-tonnage frame hydraulic press according to claim 2 is characterized in that: The steps for obtaining the load fluctuation characteristic value are specifically as follows: The load fluctuation sensor of the hydraulic press is used to collect the load variation data of the hydraulic press under different working conditions, and the data is discretized into numerical points, and the fluctuation amplitude, frequency and period value are calculated to obtain the load fluctuation feature set; Based on the load fluctuation feature set, by analyzing multiple feature values, parameters associated with the fluctuation amplitude, frequency and period are extracted, and the load fluctuation data set that meets the conditions is screened to obtain the key features of the load fluctuation; Combined with the key characteristics of the load fluctuation, the oscillation processing method based on frequency domain analysis is used to calculate the fluctuation energy on the differentiated frequency band, using the formula: The frequency spectrum characteristic value of the load fluctuation is obtained by calculation, and the load fluctuation characteristic value is generated by combining the fluctuation amplitude and period; Among them, F load Represents the load fluctuation characteristic value, A i represents the amplitude of the ith frequency band, f i represents the frequency of the ith frequency band, t represents the time variable, φ i represents the phase of the i-th frequency band, B i Represents the width of the frequency bandwidth, C i Normalization process used to adjust the energy within the bandwidth.

4. The hydraulic control system of a large-tonnage frame hydraulic press according to claim 3 is characterized in that: The steps for obtaining the nonlinear characteristic value of the hydraulic press are specifically as follows: According to the load variation data, by analyzing the nonlinear behavior of valve control and oil flow, a dynamic behavior model of the hydraulic press is set, nonlinear characteristics associated with load fluctuations are extracted, indicators associated with the nonlinear effects caused by load fluctuations in the hydraulic press system are calculated, and initial values ​​of nonlinear effects of the hydraulic press are generated; Based on the initial value of the nonlinear effect of the hydraulic press, the dynamic behavior model of the hydraulic press is compared under working conditions, and the nonlinear characteristic values ​​under multiple working conditions are analyzed one by one. By comparing with the standard value, the nonlinear effect caused by the load fluctuation on the hydraulic press is identified, and the nonlinear effect change value is obtained; Combined with the nonlinear effect change value, the impact of load fluctuation on multiple nonlinear effects of the hydraulic press is evaluated, the range, mean and variance in the load fluctuation data are calculated, and the threshold method is used to determine the working conditions with significant load fluctuation impact, using the formula: Calculate and generate nonlinear characteristic values ​​of hydraulic press; Where f represents the nonlinear characteristic value of the hydraulic press, L i represents the load fluctuation data of the i-th working condition, L avg represents the average value of load fluctuation data, α is the adjustment coefficient of load fluctuation, n is the number of working conditions, L j is the load data of the jth working condition.

5. The hydraulic control system of a large-tonnage frame hydraulic press according to claim 4 is characterized in that: The step of obtaining the adaptive filter parameter value is specifically as follows: Analyze the nonlinear characteristic value of the hydraulic press, extract the amplitude and frequency of load fluctuation, divide the differentiation interval according to the load fluctuation frequency, and calculate the characteristic value fluctuation range of multiple intervals to obtain an interval parameter set; Based on the interval parameter set, combined with the response time and accuracy requirements of the load fluctuation, the interval frequency range is dynamically adjusted, the corresponding filter coefficients are calculated, and the filter is initially set to generate initial parameters of the adaptive filter; The adaptive filter is screened according to the initial parameters of the adaptive filter and the set target response time and accuracy value, and the frequency range during the filter parameter adjustment process is compared, and the formula is used: Calculate the optimal parameters of the filter and generate adaptive filter parameter values; Among them, P final represents the adaptive filter parameters, w i represents the weight of the filter coefficient, f i represents the frequency of the ith interval, f0 represents the standard frequency, α1 is the adjustment coefficient, T j Represents the response time, T target represents the target response time, β1 is the accuracy adjustment coefficient, and γ1 is the weighted adjustment coefficient of the parameter.

6. The hydraulic control system of a large-tonnage frame hydraulic press according to claim 5 is characterized in that: The steps for obtaining the filter correction parameter value are specifically as follows: By monitoring the deviation between the actual behavior and the expected behavior of the hydraulic press control mechanism, the deviation value of the hydraulic press control behavior is calculated, and the deviation is judged whether it exceeds the control range according to the set threshold value, and the difference between the deviation value and the threshold value is analyzed. If the deviation value exceeds the set threshold value, data collection and feedback calculation are triggered to generate the deviation value of the hydraulic press control behavior; Based on the real-time feedback mechanism and the hydraulic press pressure and flow data, combined with the adaptive filter parameter value, the actual feedback signal is collected and data processed, the pressure and flow change trends are monitored and analyzed, and a correction parameter set is obtained; The hydraulic press control behavior deviation value is compared with the correction parameter set, the correction parameters of the filter are updated, and the correction amount of the hydraulic press control behavior is calculated in combination with the change trend of pressure and flow rate, using the formula: Get the filter correction parameter value; Among them, P corr Represents the filter correction parameter value, w i Represents the weight coefficient of each control feedback parameter, D i represents the behavioral difference data associated with the deviation, and is a constant to avoid zero division, ΔP i represents the hydraulic press control behavior deviation value, α represents the adjustment coefficient, P max , P min Represents the maximum and minimum pressure values ​​of the hydraulic press control, and n represents the total number of feedback parameters in the hydraulic press control process.

7. The hydraulic control system of a large-tonnage frame hydraulic press according to claim 6, characterized in that: The step of obtaining the gain adjustment value is specifically as follows: Based on the filter correction parameter value, the control error is calculated, the deviation between the target control value and the output signal of the hydraulic press is compared, the error distribution law is analyzed, the key error mode is identified, and the control error data set is summarized and generated; Analyze the control error data set, perform load fluctuation trend analysis, extract key fluctuation parameters, identify load change mode, calculate gain correction coefficient in combination with gain response speed adjustment factor, adjust the control error correction process, and obtain gain adjustment coefficient; The gain adjustment coefficient and the control error data set are used to dynamically compare the deviation between the hydraulic press output signal and the target control value, using the formula: Execute a gain adjustment operation to calculate a gain adjustment value; Among them, G adj Represents the gain adjustment value, E ctrl represents the control error, α is the gain correction coefficient, ΔP is the pressure change value, β is the gain response adjustment index, γ is the load fluctuation response coefficient, ΔF represents the load change value, and θ is the load fluctuation correction index.

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