High-frequency flow compensation method and system for vibration system of linear friction welding machine

Abstract

The invention discloses a high-frequency flow compensation method and system for a vibration system of a linear friction welding machine. The method comprises the following steps that signals are collected and preprocessed; setting a delay scanning window, and performing time sequence translation on the voltage signal to generate a plurality of groups of candidate delay sequences; taking the piston speed as target output, establishing a regression model containing candidate delay sequences, and identifying candidate valve port flow parameters corresponding to each group of delays by using a least square method; the optimal delay time and the corresponding optimal valve port flow parameter are screened by using the goodness-of-fit index; establishing a dynamic mapping model of the optimal valve port flow parameter changing along with the excitation frequency; the expected flow is determined and calculated through the dynamic mapping model, and the flow error between the expected flow and the actual flow is converted into a control voltage compensation increment which is superposed to an original control signal. The flow modeling precision can be improved, the high-frequency flow compensation effect is achieved, and the problems of vibration track deviation, system output drifting and the like under the medium-high frequency condition are solved.

Description

Technical Field

[0001] This invention belongs to the field of hydraulic servo control and high-frequency vibration control technology, and specifically relates to a high-frequency flow compensation method and system for a linear friction welding machine vibration system. Background Technology

[0002] Linear friction welding is an advanced solid-state joining process that utilizes the frictional heat between two workpieces to soften the interface material, achieving bonding under axial pressure. It offers significant advantages such as no need for solder, a narrow heat-affected zone, and joint strength approaching that of the base material. Unlike traditional rotary friction welding, which is limited to welding axisymmetric components, linear friction welding uses high-frequency reciprocating linear motion as the mechanical energy input. Through the synergistic effect of frictional heat generation and plastic rheology at the workpiece contact surface, it successfully achieves reliable connections of rectangular, polygonal, and even complex irregular cross-section components. Its core equipment is the linear friction welding machine, where the vibration system is crucial for achieving high-quality welding. Currently, mainstream linear friction welding machines use an electro-hydraulic system to drive the slide table for high-frequency vibration. The movement of the hydraulic cylinder is precisely controlled by adjusting the flow valve, thereby achieving stable vibration frequency and amplitude, ensuring the controllability and consistency of the welding process.

[0003] Under high-frequency dynamic loads, due to the inherent nonlinear characteristics of high-frequency vibration, as well as the low bandwidth, small damping ratio, and severe nonlinearity of electro-hydraulic servo systems, the actual system response often exhibits insufficient output flow frequency response, phase delay, and amplitude attenuation under medium-to-high frequency operating conditions. This causes the vibration trajectory to deviate from the predetermined path, thus affecting welding quality. Therefore, a new flow compensation scheme needs to be designed to ensure flow stability under high-frequency operating conditions.

[0004] Chinese invention patent application CN114660948A discloses a high-precision control method for piezoelectric impact-type micro-spray valves. The method considers the hysteresis effect of piezoelectric ceramics and establishes a first dynamic model and a second dynamic model corresponding to the impact pins of the first and second micro-spray valves, respectively. Based on the first and second dynamic models, simulated velocity curves of the impact pins of the first and second micro-spray valves are obtained under the same descent stroke and actual operating frequency. With the goal of minimizing the error between the simulated velocity curves of the second and first micro-spray valves, the least squares method is used to optimize the loading voltage frequency of the second micro-spray valve, obtaining the optimal loading voltage frequency. This ensures high consistency between the simulated velocity curves of the first and second micro-spray valves, ultimately achieving high-precision dispensing and high consistency in dispensing quality for different piezoelectric impact-type micro-spray valves.

[0005] Chinese invention patent application CN110595557A discloses a high-flow-rate compensation algorithm. It consists of an input flow-rate compensation module and an output flow-rate compensation module that collect flow-rate compensation information and transmit it to a compensation algorithm module. A remote computer calculates the compensation flow-rate value according to the compensation algorithm formula and sends it back to the compensation algorithm module. The compensation algorithm module then sends the calculation back to the input flow-rate compensation module and the output flow-rate compensation module, respectively. The input flow-rate compensation module and the output flow-rate compensation module then perform the compensation flow-rate operation according to the compensation flow-rate value.

[0006] While the above solution achieves flow compensation, it still has some shortcomings and limitations that require further improvement. Hydraulic systems experience significant time delays during signal transmission and execution, such as valve core response delay and pipeline inertia. However, the traditional least squares method assumes input-output synchronization, ignoring the actual delay effect. This assumption leads the identification model to forcibly fit mismatched data, causing parameter deviations, especially noticeable in high-speed dynamic responses. Model distortion directly affects subsequent control performance, making it difficult to accurately reflect the system's true dynamic characteristics and limiting its application value in high-performance control scenarios.

[0007] Under high-frequency vibration conditions, hydraulic systems exhibit stronger nonlinear and dynamic hysteresis characteristics, and their flow response is easily affected by frequency changes. Existing technologies use fixed-delay modeling, which cannot effectively reflect the flow attenuation and phase hysteresis caused by frequency changes, leading to a decrease in model accuracy. Especially in high-frequency vibration systems, flow control errors are amplified, affecting system stability and response accuracy, and limiting the adaptability and generalizability of traditional models in complex frequency domains.

[0008] Existing methods often directly apply identified parameters to flow compensation without correcting for potential delays or frequency domain deviations in the model. Under high-frequency operating conditions, this static compensation strategy easily introduces phase errors, leading to a reversed compensation effect or even amplifying the error. The system struggles to achieve effective closed-loop regulation, resulting in a severe disconnect between the control signal and the target response. This can easily cause system oscillations or instability, reducing control accuracy and welding consistency, and failing to meet the real-time regulation requirements of high-frequency dynamic systems. Summary of the Invention

[0009] This invention provides a high-frequency flow compensation method and system for a linear friction welding machine vibration system, aiming to solve problems such as model mismatch, compensation lag, and impact on system stability and response accuracy under high-frequency conditions in existing control methods. Therefore, a control strategy integrating delay adaptive identification, online parameter optimization, and closed-loop feedback compensation is proposed to achieve accurate modeling and real-time correction of high-frequency non-ideal characteristics, thereby improving welding trajectory consistency and welding quality.

[0010] To address the aforementioned technical problems, in a first aspect, this invention proposes a high-frequency flow compensation method for a linear friction welding machine vibration system, comprising the following steps: Collect and preprocess signals of servo proportional valve voltage, system oil supply pressure, load chamber pressure difference, and hydraulic cylinder piston speed; Set a delay scan window, perform time-shifting on the voltage signal, and generate multiple sets of candidate delay sequences; With piston speed as the target output, a regression model containing candidate delay sequences is established, and the least squares method is used to identify the candidate valve flow parameters corresponding to each delay. The optimal delay time and the corresponding optimal valve orifice flow parameters are selected using the goodness-of-fit index. Establish a dynamic mapping model of the optimal valve orifice flow parameters as a function of excitation frequency; The effective flow parameters at the current frequency are determined using the dynamic mapping model to calculate the expected flow rate. The expected flow rate is then compared with the actual flow rate derived from the piston speed. The flow rate error is converted into a control voltage compensation increment and added to the original control signal.

[0011] Preferably, the preprocessing includes high-pass / low-pass filtering, normalization to a zero-mean unit variance sequence, and unified timestamp alignment.

[0012] Preferably, the lower limit of the delayed scanning window is 1 / 3 of the system's natural frequency period, and the upper limit is the maximum observation time of the system response lag; The candidate delay sequence is based on a sampling period of ≤1ms, and the total number of candidate delay inputs generated is not less than 10.

[0013] Preferably, the specific steps for identifying the candidate valve orifice flow parameters corresponding to each group of delays are as follows: A servo proportional valve flow model is constructed, and the servo proportional valve input feature vector containing delay information is calculated based on candidate delay sequences, system pressure, and load chamber pressure difference signals. A hydraulic cylinder flow continuity equation is established that takes into account the actual leakage of the system, as well as the flow required for oil compression and cavity deformation. A quantitative functional relationship between the hydraulic cylinder piston speed, the output flow of the servo proportional valve, and the flow loss term is derived. Substitute the input feature vector of the servo proportional valve into the quantitative function relationship to construct a linear regression model with the hydraulic cylinder piston speed as the response output variable and the input feature vector as the explanatory variable. For each candidate delay within the delay scanning window, the linear regression model is solved based on the least squares criterion to obtain the corresponding valve orifice flow parameter estimate. Each set of candidate delays is then associated with its corresponding valve orifice flow parameter and recorded to form a delay-coefficient candidate combination sequence.

[0014] Preferably, the method for calculating the input feature vector of the servo proportional valve is as follows:

[0015] In the formula, Input feature vector for servo proportional valve, The input voltage of the servo proportional valve after delay processing. For the density of the flowing oil, For the system oil supply pressure, For the pressure difference in the load chamber, t represents the displacement of the servo proportional valve spool, and t represents time.

[0016] Preferably, the method for solving the linear regression model based on the least squares criterion includes: A residual sum of squares loss function is constructed to characterize the sum of squared errors between the measured speed of the hydraulic cylinder piston and the predicted speed calculated by the linear regression model. An identification equation is established with the goal of minimizing the sum of squared residuals, and the identification equation is solved to obtain the estimated value of the valve orifice flow parameter under the current delay.

[0017] Preferably, the step of using a goodness-of-fit index to screen the optimal delay time and the corresponding optimal valve flow parameters specifically includes: The coefficient of determination is used as the goodness-of-fit index to calculate the coefficient of determination of the regression model corresponding to each group of candidate delays. Determine whether the maximum value of all determination coefficients exceeds a preset threshold; If so, the candidate delay corresponding to the maximum determination coefficient value is selected as the optimal delay time, and the corresponding candidate valve orifice flow parameter is selected as the optimal valve orifice flow parameter. If not, the current identification is deemed invalid, triggering the system to enter parameter re-evaluation mode for resampling.

[0018] A second aspect of the present invention also provides a high-frequency flow compensation system for a linear friction welding machine vibration system, the system being used to implement the high-frequency flow compensation method as described in the first aspect of the present invention, comprising: The signal acquisition and preprocessing module is used to acquire servo proportional valve voltage, system oil supply pressure, load chamber pressure difference and hydraulic cylinder piston speed signals in real time, and perform preprocessing operations on the acquired signals. The delay scanning and sequence generation module is used to set the delay scanning window, perform time-shifting on the preprocessed servo proportional valve voltage signal, and generate multiple sets of candidate delay sequences. The least squares identification module is used to establish a regression model containing the candidate delay sequence with the hydraulic cylinder piston speed as the target output, and to identify the candidate valve flow parameters corresponding to each group of delays in the delay scanning window using the least squares method. The optimal parameter filtering module is used to calculate the goodness-of-fit index of each group of identification results, and thereby filter out the optimal delay time and the corresponding optimal valve flow parameters from the candidate set. The dynamic mapping modeling module is used to analyze the optimal valve orifice flow parameters at different frequencies, and to establish and store the dynamic mapping model of the optimal valve orifice flow parameters as a function of the excitation frequency. The closed-loop compensation control module is used to determine the effective flow parameters at the current frequency during the welding process using the dynamic mapping model to calculate the desired flow rate. The desired flow rate is compared with the actual flow rate derived from the real-time piston speed. Based on the flow error, a control voltage compensation increment is generated and superimposed on the original control signal to drive the servo proportional valve to operate.

[0019] A third aspect of the present invention also provides an electronic device comprising: One or more processors; Memory, used to store one or more computer programs; One or more computer programs stored in the memory are executed by the one or more processors, causing the one or more processors to implement the high-frequency flow compensation method as described in the first aspect of the invention.

[0020] In a fourth aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the high-frequency flow compensation method as described in the first aspect of the present invention.

[0021] Compared with the prior art, the present invention has the following technical effects: 1. The high-frequency flow compensation method proposed in this invention is aimed at the vibration system of a linear friction welding machine. It proposes a high-frequency flow compensation scheme based on the improved least squares method for the vibration system of a linear friction welding machine. Under high-frequency dynamic load conditions, there is a flow attenuation characteristic. This method can improve the accuracy of flow modeling, thereby achieving high-frequency flow compensation effect and solving the problems of vibration trajectory deviation and system output drift under medium and high frequency conditions of traditional methods.

[0022] 2. The high-frequency flow compensation method proposed in this invention introduces a delay adaptive scanning mechanism based on sampling period stepping. This mechanism can automatically construct multiple time-shifted versions of the input signal within a preset time window, match them one by one with the hydraulic cylinder speed signal, and build candidate models. In conjunction with this, the invention embeds the delay parameter into a least-squares modeling framework, constructs a regression matrix under each delay, and completes parameter identification. Using the coefficient of determination as a performance indicator, the optimal delay-parameter combination with the smallest modeling error and the strongest physical interpretability is selected. This fusion modeling mechanism not only effectively compensates for input-output asynchrony issues but also improves modeling accuracy and dynamic response capabilities.

[0023] 3. The high-frequency flow compensation method proposed in this invention analyzes the variation trend of the flow coefficient at different frequencies and introduces a method to equivalently map flow attenuation to model parameters. This method does not require the reconstruction of a complex frequency-domain nonlinear model. Instead, it substantially and equivalently reflects the changes in the physical characteristics of the system by adjusting the coefficients in the fitted model. This allows the controller to directly generate compensation signals based on the corrected parameters, enabling the control system to perceive and respond to high-frequency attenuation. This mapping mechanism has clear logic, low computational cost, and greatly improves online compensation efficiency and control accuracy.

[0024] 4. The high-frequency flow compensation method proposed in this invention constructs a complete closed-loop compensation process, from data acquisition, delay matching, model identification to compensation generation and feedback correction, achieving automated processing. The system can automatically complete delay identification and control signal correction based on real-time measured signals without human intervention, significantly improving the system's intelligence level. Especially in complex welding processes, the system can learn the response patterns under different working conditions online, adaptively adjust the compensation strategy, and ensure the stability and trajectory consistency of the welding process. It is suitable for high-precision vibration control systems in intelligent manufacturing and unmanned workshops. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the compensation method described in this invention; Figure 2 This is an improved least squares fitting curve at an excitation frequency of 1Hz in an embodiment of the present invention; Figure 3 This is an improved least squares fitting curve at a 5Hz excitation frequency in an embodiment of the present invention; Figure 4 This is an improved least squares fitting curve at an excitation frequency of 10Hz in an embodiment of the present invention; Figure 5 This is an improved least squares fitting curve at an excitation frequency of 15Hz in an embodiment of the present invention; Figure 6 This is an improved least squares fitting curve at an excitation frequency of 20Hz in an embodiment of the present invention; Figure 7 This is the improved least squares fitting curve at an excitation frequency of 25Hz in this embodiment of the invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present application and with reference to the accompanying drawings.

[0027] Example 1 This embodiment describes a high-frequency flow compensation method for a linear friction welding machine vibration system, such as... Figure 1 As shown, it includes the following steps one through six: Step 1: Acquire the voltage of the servo proportional valve System oil supply pressure Pressure difference in the load chamber and hydraulic cylinder piston speed The signal is preprocessed, where t is the current sampling time.

[0028] In this embodiment, the preprocessing includes high-pass / low-pass filtering, normalization to a zero-mean, unit-variance sequence, and unified timestamp alignment. This ensures that all data participates in modeling and feedback control within a unified time-domain framework.

[0029] Under welding conditions, signals such as the input voltage of the servo proportional valve, the system oil supply pressure, the pressure difference of the load chamber, and the piston speed of the hydraulic cylinder are collected, with the sampling period set to [value missing]. .

[0030] Step 2: Set the delayed scan window for the voltage signal. A time-series shift is performed to generate multiple candidate delayed sequences. The delayed scan window interval is... ,by Generate multiple candidate delays for the step size For the control input signals respectively Perform delayed translation to construct a delayed input sequence. .

[0031] The candidate delay sequence is based on the sampling period. ≤1ms, and the total number of candidate delayed inputs generated is no less than 10.

[0032] In this embodiment, the lower limit of the delayed scan window It is 1 / 3 of the system's natural frequency period, with an upper limit. This is the maximum observation time for the system response lag; in a specific embodiment of the present invention, Set to 0, Set to 20ms Set to 1ms.

[0033] Step 3, at piston speed To achieve the target output, construct a sequence containing candidate delay sequences. System oil supply pressure and the pressure difference in the load chamber The regression model uses the least squares method to identify the candidate valve flow parameters corresponding to each group of delays. The specific method for identifying the flow parameters is as follows: steps S31 to S34: S31. Construct a servo proportional valve flow model. Based on the candidate delay sequence, system pressure, and load chamber pressure difference signal, calculate the servo proportional valve input feature vector containing delay information.

[0034] Linear friction machines typically employ electro-hydraulic servo vibration systems. These systems use electrical signals to control the movement of a servo proportional valve spool, thereby altering the flow direction of the hydraulic fluid in the hydraulic cylinder to achieve high-frequency vibration. This embodiment uses a double-rod hydraulic cylinder as the actuator to move the welded components. Because the effective areas of the two chambers in a double-rod hydraulic cylinder are equal, and under ideal conditions, the flow rates into and out of the two chambers are equal, it is easier to control reciprocating vibration. The system uses a servo proportional valve as the control element, which combines the advantages of both servo and proportional valves. It offers high control accuracy and response speed while maintaining relatively low cost, enabling precise control of the flow and pressure of the hydraulic system based on the input signal.

[0035] The flow equation for a servo proportional valve is as follows:

[0036] In the formula, For the output flow of the servo proportional valve, For flow coefficient, The throttling area gradient at the valve orifice. The load pressure is the pressure difference between the two chambers of the hydraulic cylinder. ,in, These represent the pressures in the left and right chambers of the hydraulic cylinder, respectively. For the density of the flowing oil, This represents the valve core displacement.

[0037] Meanwhile, the valve core displacement of the servo proportional valve is controlled by the input voltage, so the input voltage is the actual control input, and the flow equation of the servo proportional valve can be expressed as:

[0038] In the formula, Defined as a valve orifice flow parameter, it exists , This refers to the voltage gain of the servo proportional valve.

[0039] The servo proportional valve input feature vector The calculation method is as follows:

[0040] In the formula, The input voltage of the servo proportional valve after delay processing. This indicates the displacement of the servo proportional valve spool.

[0041] S32, establish the hydraulic cylinder flow continuity equation considering the actual leakage of the system as well as the flow required for oil compression and cavity deformation, and derive the quantitative functional relationship between the hydraulic cylinder piston speed and the output flow of the servo proportional valve and the flow loss term.

[0042] To facilitate the estimation of the flow rate of the servo proportional valve, the flow rate is estimated using the piston speed of the hydraulic cylinder. Considering that in a practical electro-hydraulic vibration system, the flow rate entering the hydraulic cylinder through the servo proportional valve includes not only the flow rate driving the piston, but also leakage flow and the flow rate required for oil compression and cavity deformation. Therefore, when estimating the valve orifice flow rate using the piston speed of the hydraulic cylinder, a portion of the attenuated flow rate must be considered. Furthermore, the hydraulic cylinder speed is obtained by smoothing and differentiating the displacement data obtained from the displacement sensor.

[0043] Friction welding vibration systems generally use symmetrical hydraulic cylinders; therefore, the flow continuity equation for symmetrical hydraulic cylinders is:

[0044] In the formula, A is the effective area of ​​the hydraulic cylinder piston. For the displacement of the hydraulic cylinder piston, This is the internal leakage coefficient of the hydraulic cylinder. This is the external leakage coefficient of the hydraulic cylinder. For effective bulk modulus, This represents the initial volume of the hydraulic cylinder's oil inlet chamber. This is the initial volume of the hydraulic cylinder's return oil chamber.

[0045] Define load flow:

[0046] The flow continuity equation for the hydraulic cylinder can be obtained based on the flow continuity equation and the load flow rate:

[0047] because , Therefore:

[0048] Therefore, the following relationship exists:

[0049] In an electro-hydraulic servo vibration system, the hydraulic cylinder piston typically operates near the neutral position, therefore:

[0050] In the formula, V t This refers to the total volume of the hydraulic cylinder.

[0051] Based on the above equation, the simplified equation for the flow continuity of the hydraulic cylinder can be obtained as follows:

[0052] In the formula, This represents the total leakage coefficient of the hydraulic cylinder.

[0053]

[0054] From the simplified flow continuity equation of the hydraulic cylinder, the hydraulic cylinder speed can be obtained. :

[0055] Substituting the servo proportional valve flow equation obtained in step S31 into the above equation, we get:

[0056] in,

[0057]

[0058] In the above formula, The values ​​can all be determined experimentally.

[0059] Furthermore, establish the regression function relationship:

[0060] Step S32 is now complete.

[0061] S33, Substitute the input feature vector of the servo proportional valve into the quantitative function relationship to construct a linear regression model with the hydraulic cylinder piston speed as the response output variable and the input feature vector as the explanatory variable.

[0062] This step, based on the delayed signal obtained from S2, introduces an input delay compensation mechanism to reconstruct the hydraulic cylinder velocity equation, correcting it as follows:

[0063] To achieve high-precision parameter identification, an identification framework based on least squares considering delay is constructed. For each delay value... The corresponding regression input and response Define the dynamic regression matrix and parameter vector space:

[0064]

[0065]

[0066] in, For the regression matrix containing the delayed signal, the valve flow rate parameter in the θ formula... These are the parameters to be identified.

[0067] S34. For each candidate delay within the delay scanning window, the linear regression model is solved based on the least squares criterion to obtain the corresponding valve orifice flow parameter estimate. Each set of candidate delays is associated with its corresponding valve orifice flow parameter and recorded to form a delay-coefficient candidate combination sequence.

[0068] The core task of this step is to analyze each candidate delay value within the delay scan interval. The delayed control input signal is substituted into the constructed flow identification model, and the parameters are identified based on the least squares criterion. A candidate sequence containing delay labels and identification parameters is generated, which provides a basis for subsequent selection of the optimal model.

[0069] Specifically, the method for solving the linear regression model based on the least squares criterion includes: A residual sum of squares loss function is constructed to characterize the sum of squared errors between the measured speed of the hydraulic cylinder piston and the predicted speed calculated by the linear regression model; To identify the objects, the residual sum of squares loss function is constructed as follows:

[0070]

[0071] In the formula, This is the predicted speed value given the current delay.

[0072] An identification equation is established with the objective of minimizing the sum of squared residuals. This equation is then solved to obtain an estimate of the valve flow parameters under the current delay. Finally, for each set of delay values ​​and its corresponding estimated valve flow parameters... Record together, traversing the entire delayed scan interval Repeat the above identification process to complete the parameter identification process for all candidate delays, and obtain the delay-coefficient combination sequence. .

[0073] This sequence is uniformly stored in a buffer or array structure, serving as the basis for model optimization and compensation strategy determination in step four. An example of the improved least squares fitting curve at various excitation frequencies in this embodiment is shown below. Figure 2-7 As shown.

[0074] Step four: Use the goodness-of-fit index to screen the optimal delay time and the corresponding optimal valve orifice flow parameters. The specific method is as follows: Using the coefficient of determination As the goodness-of-fit index, the delay for each group of candidate delays is calculated. The corresponding coefficient of determination of the regression model; through goodness of fit filter and .

[0075] Determine whether the maximum value of all determination coefficients exceeds a preset threshold; If so, the candidate delay corresponding to the maximum determination coefficient value is selected as the optimal delay time, and the corresponding candidate valve flow parameter is selected as the optimal valve flow parameter; in this embodiment, a preset threshold example is 0.80, that is... When, select this The largest corresponding and This is the optimal identification result.

[0076] If not, the current identification is deemed invalid, triggering the system to enter parameter re-evaluation mode for resampling.

[0077] Step 5: Establish a dynamic mapping model of the optimal valve orifice flow parameters as a function of the excitation frequency.

[0078] The dynamic mapping model is formally expressed as:

[0079] In the formula, Indicates the excitation frequency, which refers to the vibration frequency of the linear friction welding machine during its current operation; The effective valve orifice flow parameter refers to the flow rate at a specific frequency. Considering the frequency response characteristics of the servo proportional valve, the actual flow gain coefficient that plays a role is the actual basis for the controller to perform calculations at the current frequency. The reference valve orifice flow parameter refers to the valve orifice flow parameter identified under low-frequency (or quasi-static) conditions. At this frequency, the dynamic response attenuation of the servo valve is negligible, and it represents the valve's nominal flow capacity. This is a frequency decay function used to describe the trend of flow parameters decreasing as frequency increases, reflecting the amplitude-frequency characteristics of the servo system. This mapping is embedded in the controller as an online correction mechanism.

[0080] Specifically, this step will be implemented according to the following process: Select several discrete frequency points within the operating frequency range of the linear friction welding machine (e.g., covering the interval from low frequency to the highest operating frequency). Excite the vibration system at each frequency point and repeat steps S1 to S4. Through these steps, obtain the optimal valve flow parameters corresponding to each discrete frequency point. This yields a set of frequency-flow parameter data pairs. .

[0081] From the above data pairs, the flow parameter corresponding to the lowest frequency point (or the frequency point where the system response is closest to ideal linearity) is selected as the reference valve orifice flow parameter. Then, other frequency points were selected. and By performing ratio calculations, the attenuation factor at each frequency point is obtained, i.e. This converts the physical parameters into normalized decay trend data.

[0082] Using curve fitting algorithms (such as polynomial fitting, exponential fitting, or spline interpolation), the above normalized data points were processed. By fitting the data, a continuous frequency decay function is established. This function can describe the attenuation ratio of flow parameters at any unsampled frequency point.

[0083] Base parameters The function obtained by fitting Determined mapping relationship The algorithm is written as a lookup table program or function and embedded into the real-time execution module of the controller. During the subsequent actual welding process, the controller reads the current set frequency in real time. The current effective flow parameters can be directly calculated using this mapping model. The parameter is then passed to step S6 for the calculation of the desired flow rate, thus eliminating the need for repeated complex least-squares identification during the welding process and enabling rapid online correction of the parameter.

[0084] Step 6: Use the dynamic mapping model to determine the effective flow parameters at the current frequency to calculate the expected flow rate. Compare the expected flow rate with the actual flow rate derived from the piston speed. Convert the flow rate error into a control voltage compensation increment and add it to the original control signal.

[0085] In this step, the system acquires the current excitation frequency during the welding process in real time. The parameter dynamic mapping model pre-built and stored in step five is used. Calculate the effective valve orifice flow parameters applicable to the current frequency operating conditions. This parameter will serve as the baseline coefficient for subsequent flow calculations.

[0086] Based on the flow characteristic equation of the servo proportional valve, the effective valve orifice flow parameters determined above are then used. Combined with the original control voltage signal of the servo proportional valve at the current moment System oil supply pressure and the pressure difference in the load chamber Calculate the theoretically expected flow rate that should be output under the current control command. .

[0087] Utilizing the principle of continuous hydraulic cylinder flow (i.e., the reverse process of the model established in step S32), based on the real-time piston speed signal of the hydraulic cylinder collected by the sensor... After compensating for system leakage and flow loss caused by oil compression, the actual flow rate driving the hydraulic cylinder is calculated. .

[0088] Calculate the deviation between expected flow and actual flow. Based on the inverse model of the servo proportional valve flow model, this flow deviation is... Converted into corresponding control voltage compensation increment Finally, the compensation increment will be... Superimposed on the original control signal Above, generate the corrected control commands. It outputs the signal to the servo driver, thereby dynamically compensating for the flow loss caused by high-frequency response lag or load disturbance, and achieving precise control of vibration amplitude.

[0089] Example 2 This embodiment is a high-frequency flow compensation system for a linear friction welding machine vibration system. The system is used to implement the high-frequency flow compensation method as described in Embodiment 1, including: The signal acquisition and preprocessing module is used to acquire servo proportional valve voltage, system oil supply pressure, load chamber pressure difference and hydraulic cylinder piston speed signals in real time, and perform preprocessing operations on the acquired signals. The delay scanning and sequence generation module is used to set the delay scanning window, perform time-shifting on the preprocessed servo proportional valve voltage signal, and generate multiple sets of candidate delay sequences. The least squares identification module is used to establish a regression model containing the candidate delay sequence with the hydraulic cylinder piston speed as the target output, and to identify the candidate valve flow parameters corresponding to each group of delays in the delay scanning window using the least squares method. The optimal parameter filtering module is used to calculate the goodness-of-fit index of each group of identification results, and thereby filter out the optimal delay time and the corresponding optimal valve flow parameters from the candidate set. The dynamic mapping modeling module is used to analyze the optimal valve orifice flow parameters at different frequencies, and to establish and store the dynamic mapping model of the optimal valve orifice flow parameters as a function of the excitation frequency. The closed-loop compensation control module is used to determine the effective flow parameters at the current frequency during the welding process using the dynamic mapping model to calculate the desired flow rate. The desired flow rate is compared with the actual flow rate derived from the real-time piston speed. Based on the flow error, a control voltage compensation increment is generated and superimposed on the original control signal to drive the servo proportional valve to operate.

[0090] Example 3 This embodiment is an electronic device, including: One or more processors; Memory, used to store one or more computer programs; One or more computer programs stored in the memory are executed by the one or more processors, causing the one or more processors to implement the high-frequency flow compensation method as described in Embodiment 1.

[0091] Example 4 This embodiment is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the high-frequency flow compensation method as described in Embodiment 1.

[0092] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A method for high-frequency flow compensation in a vibration system of a linear friction welding machine, characterized in that, Includes the following steps: Collect and preprocess signals of servo proportional valve voltage, system oil supply pressure, load chamber pressure difference, and hydraulic cylinder piston speed; Set a delay scan window, perform time-shifting on the voltage signal, and generate multiple sets of candidate delay sequences; With piston speed as the target output, a regression model containing candidate delay sequences is established, and the least squares method is used to identify the candidate valve flow parameters corresponding to each delay. The optimal delay time and the corresponding optimal valve orifice flow parameters are selected using the goodness-of-fit index. Establish a dynamic mapping model of the optimal valve orifice flow parameters as a function of excitation frequency; The effective flow parameters at the current frequency are determined using the dynamic mapping model to calculate the expected flow rate. The expected flow rate is then compared with the actual flow rate derived from the piston speed. The flow rate error is converted into a control voltage compensation increment and added to the original control signal.

2. The method according to claim 1, characterized in that, The preprocessing includes high-pass / low-pass filtering, normalization to a zero-mean unit variance sequence, and unified timestamp alignment.

3. The method according to claim 1, characterized in that, The lower limit of the delayed scanning window is 1 / 3 of the system's natural frequency period, and the upper limit is the maximum observation time of the system response lag. The candidate delay sequence is based on a sampling period of ≤1ms, and the total number of candidate delay inputs generated is not less than 10.

4. The method according to claim 1, characterized in that, The specific steps for identifying the candidate valve orifice flow parameters corresponding to each group of delays are as follows: A servo proportional valve flow model is constructed, and the servo proportional valve input feature vector containing delay information is calculated based on candidate delay sequences, system pressure, and load chamber pressure difference signals. A hydraulic cylinder flow continuity equation is established that takes into account the actual leakage of the system, as well as the flow required for oil compression and cavity deformation. A quantitative functional relationship between the hydraulic cylinder piston speed, the output flow of the servo proportional valve, and the flow loss term is derived. Substitute the input feature vector of the servo proportional valve into the quantitative function relationship to construct a linear regression model with the hydraulic cylinder piston speed as the response output variable and the input feature vector as the explanatory variable. For each candidate delay within the delay scanning window, the linear regression model is solved based on the least squares criterion to obtain the corresponding valve orifice flow parameter estimate. Each set of candidate delays is then associated with its corresponding valve orifice flow parameter and recorded to form a delay-coefficient candidate combination sequence.

5. The method according to claim 4, characterized in that, The method for calculating the input feature vector of the servo proportional valve is as follows: In the formula, Input feature vector for servo proportional valve, The input voltage of the servo proportional valve after delay processing. For the density of the flowing oil, For the system oil supply pressure, For the pressure difference in the load chamber, t represents the displacement of the servo proportional valve spool, and t represents time.

6. The method according to claim 4, characterized in that, The method for solving the linear regression model based on the least squares criterion includes: A residual sum of squares loss function is constructed to characterize the sum of squared errors between the measured speed of the hydraulic cylinder piston and the predicted speed calculated by the linear regression model. An identification equation is established with the goal of minimizing the sum of squared residuals, and the identification equation is solved to obtain the estimated value of the valve orifice flow parameter under the current delay.

7. The method according to claim 1, characterized in that, The process of using a goodness-of-fit index to screen the optimal delay time and the corresponding optimal valve orifice flow parameters specifically includes: The coefficient of determination is used as the goodness-of-fit index to calculate the coefficient of determination of the regression model corresponding to each group of candidate delays. Determine whether the maximum value of all determination coefficients exceeds a preset threshold; If so, the candidate delay corresponding to the maximum determination coefficient value is selected as the optimal delay time, and the corresponding candidate valve orifice flow parameter is selected as the optimal valve orifice flow parameter. If not, the current identification is deemed invalid, triggering the system to enter parameter re-evaluation mode for resampling.

8. A high-frequency flow compensation system for a linear friction welding machine vibration system, characterized in that, The system is used to implement the high-frequency traffic compensation method as described in any one of claims 1-7, including: The signal acquisition and preprocessing module is used to acquire servo proportional valve voltage, system oil supply pressure, load chamber pressure difference and hydraulic cylinder piston speed signals in real time, and perform preprocessing operations on the acquired signals. The delay scanning and sequence generation module is used to set the delay scanning window, perform time-shifting on the preprocessed servo proportional valve voltage signal, and generate multiple sets of candidate delay sequences. The least squares identification module is used to establish a regression model containing the candidate delay sequence with the hydraulic cylinder piston speed as the target output, and to identify the candidate valve flow parameters corresponding to each group of delays in the delay scanning window using the least squares method. The optimal parameter filtering module is used to calculate the goodness-of-fit index of each group of identification results, and thereby filter out the optimal delay time and the corresponding optimal valve flow parameters from the candidate set. The dynamic mapping modeling module is used to analyze the optimal valve orifice flow parameters at different frequencies, and to establish and store the dynamic mapping model of the optimal valve orifice flow parameters as a function of the excitation frequency. The closed-loop compensation control module is used to determine the effective flow parameters at the current frequency during the welding process using the dynamic mapping model to calculate the desired flow rate. The desired flow rate is compared with the actual flow rate derived from the real-time piston speed. Based on the flow error, a control voltage compensation increment is generated and superimposed on the original control signal to drive the servo proportional valve to operate.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs; The feature is that one or more computer programs stored in the memory are executed by the one or more processors, causing the one or more processors to implement the high-frequency flow compensation method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the high-frequency flow compensation method as described in any one of claims 1-7.

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