Multi-hardness material-oriented shear parameter adaptive control method and system

Abstract

The invention relates to the technical field of industrial control, in particular to a shear parameter self-adaptive control method and system for a multi-hardness material, and the method comprises the steps: carrying out the pre-pressing of a to-be-processed material, applying a micro-amplitude disturbance signal, and extracting the complex impedance spectrum characteristics of a dynamic mechanical response to construct a frequency domain fingerprint of an internal heterogeneous structure; based on the fingerprint, predicting load abrupt change, and generating a virtual control track taking the displacement of the shearing tool rest as a benchmark; calculating a feed-forward torque compensation amount by using a prediction trajectory planner, and superposing the feed-forward torque compensation amount with a position feedback adjustment amount to generate an optimal blade gap and shearing speed cooperative motion instruction adaptively matched with the current hardness; according to the method, nondestructive detection of the internal hardness mutation of the material is achieved through the frequency domain fingerprint technology, the driving torque and the mechanical gap are synchronously adjusted before shear mutation occurs by means of a feedforward compensation mechanism based on inverse dynamics, and the problems of response lag and easy tipping in heterogeneous material processing are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial control, in particular to a shear parameter adaptive control method and system for multi-hardness materials. BACKGROUND

[0002] In the shear processing of heterogeneous or multi-hardness materials, due to the strong time-varying, randomness and unobservability of the physical properties of the controlled object, the existing open-loop or fixed parameter control system cannot respond to the instantaneous load mutation in real time, resulting in poor robustness of the control system, easy to cause overload damage, jamming failure of the actuator, and low energy efficiency.

[0003] Specifically, due to the heterogeneous characteristics existing in the controlled object, a black box model is formed, and the traditional control method lacks a feedforward prediction mechanism and can only rely on feedback error signals after a significant deviation of the controlled variable; however, limited by the inherent transmission delay and dynamic response bandwidth of the actuator, this lagging feedback adjustment cannot establish an effective control quantity matching within the millisecond transient window of the disturbance signal input, resulting in insufficient phase margin of the closed-loop control system and serious decline in dynamic tracking performance.

[0004] This lack of model prediction capability directly leads to poor robustness of the control strategy and inability to achieve optimal control: if high gain or conservative control parameters are used to suppress potential maximum disturbances, the system will be in an over-damped or low-efficiency operation state with high energy consumption; if conventional control parameters are used, once a random parameter disturbance occurs, the control input will be saturated or oscillated, resulting in system instability or actuator damage due to overload.

[0005] Therefore, a shear parameter adaptive control method and system for multi-hardness materials are proposed. SUMMARY

[0006] The purpose of the present application is to provide a shear parameter adaptive control method and system for multi-hardness materials, which realizes adaptive control of shear parameters by predicting internal hardness mutations.

[0007] To achieve the above purpose, the present application provides the following technical solutions: The material to be processed is pre-pressed, a micro-perturbation signal is applied during pre-pressing, the dynamic mechanical response of the micro-perturbation signal of the material to be processed is collected, the complex impedance spectrum characteristics of the dynamic mechanical response are extracted by fast Fourier transform, and the frequency domain fingerprint of the internal heterogeneous structure distribution of the material to be processed is constructed based on the complex impedance spectrum characteristics. The virtual control generator is constructed based on the frequency domain fingerprint to predict the load mutation time and peak intensity caused by the material hardness mutation; the predicted load mutation time and peak intensity are converted into discrete characteristic nodes based on the vertical displacement of the shear tool holder, and the discrete characteristic nodes are connected by using a spline interpolation algorithm to generate a virtual control trajectory; The virtual control trajectory is input into a predictive trajectory planner; a feedforward torque compensation required to overcome the hardness change resistance is calculated, and the feedforward torque compensation is superimposed with a feedback adjustment based on real-time position error to generate an optimal blade gap and shear speed cooperative motion instruction sequence that matches the current hardness, to drive the hydraulic servo valve and the gap adjustment motor to perform shear parameter control.

[0008] Preferably, the micro-perturbation signal applied during pre-pressing is specifically: The material to be processed is applied and maintained by the pressure device. After the static pre-pressure is stable, a sinusoidal scanning excitation signal with linearly increasing frequency within a preset bandwidth is superimposed on the driving system of the pressure device; the amplitude of the sinusoidal scanning excitation signal is set to be a preset percentage of the static pre-pressure, the stress amplitude generated by the micro-perturbation signal is lower than the micro-yield strength threshold corresponding to the current hardness of the material to be processed, and the dynamic mechanical response of the micro-perturbation signal of the material to be processed is collected.

[0009] Preferably, the dynamic mechanical response is a real-time excitation force time domain signal and a real-time displacement deformation time domain signal collected by the sensor synchronously during the loading of the sinusoidal scanning excitation signal; Wherein, the real-time excitation force time domain signal represents the actual driving force waveform applied to the material, and the real-time displacement deformation time domain signal represents the follow-up elastic deformation waveform of the material.

[0010] Preferably, the virtual control generator uses a generative mapping model based on inverse dynamics, and the specific construction process includes: establishing a nonlinear mapping relationship between the complex impedance modulus peak in the frequency domain fingerprint and the shear strength distribution of the material to be processed along the thickness direction; An inverse dynamics equation is constructed using the kinematic parameters of the shear mechanism, the shear strength distribution is substituted into the inverse dynamics equation as a boundary condition, the theoretical resistance evolution curve of the shear tool required to overcome the shear strength distribution under constant speed working condition is deduced reversely, and the extreme value point and its corresponding time domain position are extracted from the theoretical resistance evolution curve as the peak intensity and load mutation time.

[0011] Preferably, the conversion into discrete characteristic nodes based on the vertical displacement of the shear tool holder is specifically: The shear speed value under the constant speed working condition is multiplied by the predicted load mutation time to convert a corresponding shear tool holder vertical stroke displacement value; the shear tool holder vertical stroke displacement value is taken as a reference position, and the peak intensity is taken as a target load value at the reference position, and the two are paired to form a set of characteristic data; a plurality of sets of the characteristic data are arranged in order from small to large according to the shear tool holder vertical stroke displacement value to form the discrete characteristic node.

[0012] Preferably, the prediction trajectory planner includes a shear mechanism dynamics prediction model and a rolling optimization solver, the shear mechanism dynamics prediction model establishes the correlation between the hydraulic servo valve output torque and the shear tool holder motion state, and predicts the shear tool holder displacement response; The rolling optimization solver takes the virtual control trajectory as a reference benchmark, and in each control cycle, optimizes and calculates to minimize the deviation between the predicted shear tool holder displacement response and the virtual control trajectory, calculates the feedforward torque compensation required for balancing the material of the hardness, and synchronously outputs the corresponding blade gap adjustment instruction.

[0013] Preferably, the specific process of generating the adaptive optimal blade gap and shear speed cooperative motion instruction sequence matching the current hardness is as follows: A position closed-loop tracking controller is constructed to calculate the tracking error between the actual position of the shear tool holder and the theoretical position set in the virtual control trajectory in real time, and generate a feedback adjustment amount based on the tracking error; The feedforward torque compensation and the feedback adjustment amount are superimposed to synthesize a total control amount, and the total control amount is converted into a current control signal for driving the hydraulic servo valve; at the same time, the blade gap adjustment instruction is analyzed into a target position code and converted into a pulse control signal for driving the gap adjustment motor; The current control signal and the pulse control signal are output in the same control cycle, and the blade gap dynamic adjustment and the shear tool holder variable speed cutting are cooperatively actuated.

[0014] A shear parameter adaptive control system for multi-hardness materials, comprising: A fingerprint construction module: pre-pressing the material to be processed, applying a micro-perturbation signal during pre-pressing, collecting the dynamic mechanical response of the material to be processed to the micro-perturbation signal, extracting the complex impedance frequency spectrum features of the dynamic mechanical response through fast Fourier transform, and constructing the frequency domain fingerprint of the internal heterogeneous structure distribution of the material to be processed based on the complex impedance frequency spectrum features; Trajectory generation module: the virtual control generator is constructed to predict the load mutation moment and peak intensity caused by material hardness mutation based on the frequency domain fingerprint; the predicted load mutation moment and peak intensity are converted into discrete feature nodes based on the vertical displacement of the shear tool holder, the discrete feature nodes are connected by using a spline interpolation algorithm, and a virtual control trajectory is generated; Feedforward compensation module: inputting the virtual control trajectory into a predictive trajectory planner; calculating the feedforward torque compensation required to overcome the hardness change resistance; Parameter control module: superimposing the feedforward torque compensation and the feedback adjustment based on the real-time position error to generate an optimal blade gap and shear speed cooperative motion instruction sequence that matches the current hardness, driving the hydraulic servo valve and the gap adjustment motor to perform shear parameter control.

[0015] Compared with the prior art, the application has the following beneficial effects: 1、The application constructs the frequency domain fingerprint of the material by applying sinusoidal scanning micro-perturbation in the pre-pressing stage and using fast Fourier transform to extract complex impedance spectrum characteristics, and uses the frequency response difference of different hardness components to dynamic excitation, which non-destructively maps the originally closed, random and unobservable internal hardness distribution of the material into quantifiable frequency domain characteristic signals, effectively solves the blindness of the traditional control method for the internal structure of the black box, and enables the control system to perceive the distribution rule of the internal heterogeneous structure of the material in advance before the shear action starts, thereby providing decisive prior data support for subsequent precise prediction control.

[0016] 2、The application innovatively introduces a virtual control generator based on inverse dynamics, which converts the hardness mutation predicted by the frequency domain fingerprint into a virtual control trajectory based on the vertical displacement of the shear tool holder. The feedforward torque compensation required to overcome the hardness mutation is calculated in advance based on the trajectory by using a rolling optimization solver, which can output a matching driving torque at the moment when the tool contacts the hard point, instead of adjusting after the speed decreases. This feedforward compensation mechanism fundamentally overcomes the defects of existing open-loop or pure PID feedback control in terms of response lag and large overshoot when facing strong time-varying load, significantly improving the dynamic response speed and robustness when dealing with extreme hardness changes.

[0017] 3, The application not only controls the shear torque, but also superimposes the feedforward torque and the position feedback error, and synchronously generates the optimal blade gap adjustment instruction; in the same control cycle, the system can adjust the hydraulic servo valve (force / speed) and the gap adjustment motor (geometric parameters) according to the hardness characteristics of the current position, and the multivariable cooperative mechanism ensures that high precision and high efficiency are maintained when cutting soft materials, and the gap is increased and the torque is increased to avoid tool collapse or jam failure when encountering extremely hard materials. By upgrading the open-loop fixed parameter shear to a full-closed-loop adaptive cooperative shear, the problem of unstable processing quality caused by the randomness of the physical properties of the controlled object is effectively solved, and the tool life is prolonged. BRIEF DESCRIPTION OF DRAWINGS

[0018] Fig. 1 It is a flowchart of a shear parameter adaptive control method for multi-hardness materials; Fig. 2 It is a perception-prediction-compensation-execution closed-loop process of the application; Fig. 3 It is a structural diagram of a shear parameter adaptive control system for multi-hardness materials. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the application will be described below in detail with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0020] Embodiment one: Please refer to Figs. 1 to 3 The application provides a shear parameter adaptive control method and system for multi-hardness materials, and the technical solutions are as follows: A shear parameter adaptive control method for multi-hardness materials, comprising: Pre-pressing the material to be processed, applying a micro-perturbation signal during pre-pressing, collecting the dynamic mechanical response of the micro-perturbation signal of the material to be processed, extracting the complex impedance spectrum characteristics of the dynamic mechanical response by fast Fourier transform, and constructing the frequency domain fingerprint of the internal heterogeneous structure distribution of the material to be processed based on the complex impedance spectrum characteristics; A virtual control generator is constructed to predict the load mutation time and peak intensity caused by material hardness mutation based on the frequency domain fingerprint; the predicted load mutation time and peak intensity are converted into discrete feature nodes with the shear tool holder vertical stroke displacement as the reference, the discrete feature nodes are connected by using a spline interpolation algorithm, and a virtual control trajectory is generated; The virtual control trajectory is input to a predicted trajectory planner; a feedforward torque compensation required to overcome the hardness change resistance is calculated, and the feedforward torque compensation is superimposed with a feedback adjustment based on real-time position error to generate an optimal blade gap and shear speed cooperative motion instruction sequence that matches the current hardness, to drive the hydraulic servo valve and gap adjustment motor to perform shear parameter control.

[0021] The micro-perturbation signal applied during pre-pressing is specifically: The material pressing device applies and maintains a constant static pre-pressing force to the material to be processed, and after the static pre-pressing force is stabilized, a sinusoidal scanning excitation signal with a linearly increasing frequency within a preset bandwidth is superimposed to the driving system of the material pressing device; the amplitude of the sinusoidal scanning excitation signal is set to a preset percentage of the static pre-pressing force, the stress amplitude generated by the micro-perturbation signal is lower than the micro-yield strength threshold corresponding to the current hardness of the material to be processed, and the dynamic mechanical response of the micro-perturbation signal of the material to be processed is collected.

[0022] Specifically, an instruction is sent to the material pressing device of the shear machine to drive it to move downward and contact the surface of the material to be processed, apply and maintain a constant static pre-pressing force, and monitor the pressure value in real time through the built-in pressure sensor; when it is detected that the pressure fluctuation value converges to a preset error range and maintains for a certain time, it is determined that the material pressing device enters a steady state; at this time, the excitation module is started, and a sinusoidal scanning excitation signal with a linearly increasing frequency within a preset bandwidth is superimposed to the servo driving system of the material pressing device; the frequency of the sinusoidal scanning excitation signal starts from the initial frequency and linearly increases to the terminal frequency over time, covering the possible inherent frequency response interval of the material to be processed; During the application of the excitation signal, the energy intensity of the signal is strictly limited; the amplitude of the sinusoidal scanning excitation signal is set to a preset percentage (5% in this embodiment) of the static pre-pressing force, and through this small force loading change, it is ensured that the stress amplitude generated by the micro-perturbation signal is lower than the micro-yield strength threshold corresponding to the current hardness of the material to be processed, and this limitation condition is to ensure that the material only undergoes elastic deformation when disturbed, and is always in the linear elastic interval where Hooke's law is applicable, thereby avoiding the destruction of the material structure or the interference with the subsequent impedance analysis accuracy due to plastic deformation; while applying the perturbation, the dynamic mechanical response of the micro-perturbation signal of the material to be processed is synchronously collected by using a high-frequency force sensor and a micro-displacement sensor.

[0023] The method of superimposing a micro-sine scanning excitation signal on a static pre-pressure is adopted to realize the wide-band non-destructive detection of the internal heterogeneous structure of multi-hardness materials. On the one hand, the dynamic impedance characteristics of the materials at different frequency bands can be effectively excited and captured through linearly increasing frequency scanning, avoiding the possible feature omission in single frequency detection. On the other hand, the disturbance is strictly controlled within the linear elastic range, which not only physically eliminates the micro-plastic damage or surface indentation of the materials caused by the detection process, ensuring the quality of the finished product, but also mathematically ensures the linearity of the mechanical response, significantly improving the signal-to-noise ratio and hardness identification accuracy of the subsequent frequency domain fingerprint extraction.

[0024] The dynamic mechanical response is a real-time excitation force time-domain signal and a real-time displacement deformation time-domain signal synchronously collected by a sensor during the loading of the sine scanning excitation signal. The real-time excitation force time-domain signal represents the actual driving force waveform applied to the material, and the real-time displacement deformation time-domain signal represents the follow-up elastic deformation waveform of the material.

[0025] Specifically, when the sine scanning excitation signal starts to be loaded to the pressing device driving system, a hardware trigger interrupt mechanism is used to simultaneously activate a high-frequency dynamic force sensor installed on the end effector of the pressing device and a high-precision micro-displacement sensor installed beside the mechanical motion pair. These two sensors are controlled by a unified synchronous clock signal at a sampling rate much higher than the highest cutoff frequency of the sine scanning excitation signal (in this embodiment, it is set to more than 10 times the upper limit of the excitation frequency). The physical quantities are continuously sampled at high speed, so that the real-time excitation force time-domain signal and the real-time displacement deformation time-domain signal are synchronously collected. The real-time excitation force time-domain signal records the force value sequence superimposed on the static pre-pressure reference with dynamic changes in amplitude and frequency over time, representing the actual driving force waveform applied to the material as an input excitation function. The real-time displacement deformation time-domain signal records the instantaneous micro-compression and rebound displacement of the surface of the material to be processed in the vertical direction under the action of the alternating excitation force, representing the follow-up elastic deformation waveform of the material as an output response function to the excitation. After strict time stamp alignment and denoising of the two groups of time-domain signals, they are packaged and stored as dual-channel time sequence data as the basic data source for subsequent calculation of complex impedance and extraction of frequency domain fingerprints. By using the hardware level trigger interrupt and unified clock synchronization mechanism, the microsecond-level synchronous collection of excitation force (input) and displacement deformation (output) is realized, completely eliminating the phase delay error between multi-channel signals. Combined with the high-multiplying oversampling strategy, signal aliasing and high-frequency feature loss are effectively prevented.

[0026] The virtual control generator adopts an inverse dynamics-based generative mapping model, and the specific construction process includes: establishing a nonlinear mapping relationship between the complex impedance modulus peak value in the frequency domain fingerprint and the shear strength distribution of the material to be processed along the thickness direction; An inverse dynamics equation is constructed using the kinematic parameters of the shearing mechanism, the shear strength distribution is substituted as a boundary condition into the inverse dynamics equation, the theoretical resistance evolution curve of the shear cutter required to overcome the shear strength distribution under constant speed working conditions is deduced reversely, and the extreme value point and the corresponding time domain position thereof are extracted from the theoretical resistance evolution curve as the peak strength and the load mutation time.

[0027] Specifically, the quantitative relationship between the frequency domain fingerprint and the mechanical properties of the material is established, including constructing a nonlinear mapping relationship between the complex impedance modulus peak value in the frequency domain fingerprint and the shear strength distribution of the material to be processed along the thickness direction; the establishment of the nonlinear mapping relationship is to perform the same micro-perturbation excitation and frequency domain fingerprint extraction on reference samples with different thickness distribution hardness to form a sample library, and to use a multilayer perception machine learning model, based on the frequency domain fingerprint feature vector of each reference sample as input, and based on the thickness direction hardness distribution (obtained by actual measurement data or metallographic microscopic analysis) corresponding thereto as a training target, to complete offline training of the mapping model, and to output a one-dimensional array whose length is equal to the number of thickness segmentation of the material, and each array element represents the relative shear strength value at the thickness position; the key features in the frequency domain fingerprint obtained in the previous step are extracted, in particular, the resonance peak value of the complex impedance modulus in a specific frequency band and the corresponding frequency offset; accordingly, the shear strength profile data of the material to be processed in the longitudinal depth is output, which accurately describes whether there are inclusions or hardened layers with high hardness in the material, and the specific distribution position of these high-hardness regions in the thickness direction; An inverse dynamics equation is constructed using the kinematic parameters of the shearing mechanism, the shear strength distribution is substituted as a boundary condition into the inverse dynamics equation, the theoretical resistance evolution curve of the shear cutter required to overcome the shear strength distribution under constant speed working conditions is deduced reversely, and the extreme value point and the corresponding time domain position thereof are extracted from the theoretical resistance evolution curve as the peak strength and the load mutation time. By constructing an inverse dynamics model containing key mechanical parameters such as moment of inertia and friction coefficient, a precise physical mapping between the micro-shear strength distribution of the material and the macro-hydraulic driving force of the equipment is established, so that the generated control command is no longer an empirical estimate, but an exact value that conforms to the laws of physics, thereby providing a highly accurate torque compensation benchmark for feedforward control.

[0028] Through the above reverse solving process, the theoretical resistance evolution curve required by the shear cutter to overcome the shear strength distribution under constant speed working condition is deduced inversely, which simulates the theoretical driving force waveform required to be provided by the hydraulic system if the cutter cuts the heterogeneous material at a constant speed; numerical analysis is performed on the theoretical resistance evolution curve, and the peak position of the resistance sharp rise in the curve is identified by using an extreme value search algorithm (first derivative zero crossing judgment method in this embodiment), and the extreme value points and their corresponding time domain positions are extracted, which represent the key nodes where the cutter will contact the hardness mutation layer (such as hard alloy inclusions or welds) of the material, which are marked as the peak strength and the load mutation time respectively, as the core basis for generating subsequent feedforward compensation instructions; The present application adopts a generative mapping model based on inverse dynamics to establish a physical connection from micro-frequency domain fingerprint to macro-mechanical load; by nonlinearly mapping the abstract complex impedance characteristics to specific shear strength distribution and combining with mechanical kinematic parameters for reverse deduction, the exact position in time domain and peak strength of the hardness mutation point inside the material can be accurately predicted before the shear action occurs; this feedforward prediction mechanism overcomes the hysteresis defect of traditional feedback control when dealing with sudden load, and can adjust the hydraulic output and motion parameters in advance to specifically suppress the impact vibration caused by hardness change, thereby ensuring the flatness of the shear section while significantly reducing the risk of cutter edge collapse and equipment mechanical wear.

[0029] The specific process of converting into discrete characteristic nodes based on the vertical displacement of the shear tool holder is as follows: The shear speed value under the constant speed working condition is multiplied by the predicted load mutation time to obtain the corresponding vertical displacement value of the shear tool holder; the vertical displacement value of the shear tool holder is taken as the reference position, and the peak strength is taken as the target load value at the reference position, and the two are paired to form a group of characteristic data; the characteristic data is arranged in order from small to large according to the vertical displacement value of the shear tool holder, and the discrete characteristic nodes are formed.

[0030] Specifically, since the load mutation time is a relative parameter in the time domain, in order to adapt to the position-based servo characteristics of the shearing machine, a space-time mapping transformation is performed, that is, each predicted load mutation time is multiplied by the shearing speed value. Through this linear transformation calculation (the calculation formula is: displacement = speed × time), the corresponding shearing tool holder vertical stroke displacement value is converted, which represents the absolute physical coordinates of the tool holder in the Z-axis direction relative to the starting point when the shearing blade contacts the internal hardness mutation point of the material; A multi-dimensional feature vector is constructed, the shearing tool holder vertical stroke displacement value is taken as the reference position (horizontal coordinate), and the peak intensity (i.e. the resistance extreme value extracted in the previous step) is taken as the target load value under the reference position (vertical coordinate), and the two are paired to form a group of feature data. Repeat the pairing process until all extracted mutation points are processed. In order to meet the monotonicity requirement of the subsequent trajectory planning algorithm for input data, arrange the multiple groups of feature data in order of the shearing tool holder vertical stroke displacement value from small to large; Through this sorting operation, unordered time points are converted into ordered space sequences distributed along the tool cutting path, forming the discrete feature nodes. This sequence is not just a simple collection of data points, but essentially constitutes a digital spatial map of the internal hardness distribution of the material to be processed, providing accurate geometric control points for subsequent generation of continuous and smooth virtual control trajectories through spline interpolation.

[0031] By accurately mapping the load prediction based on the time domain to the tool holder stroke displacement based on the space domain, a material hardness spatial distribution map independent of the time variable is successfully constructed; This time-space transformation mechanism effectively eliminates the sensitivity of traditional time-based control to actual shearing speed fluctuations, ensuring that even under dynamic shearing speed adjustment conditions, the feed torque compensation instruction can still maintain strict zero-phase alignment with the physical position of the internal hardness mutation layer of the material; At the same time, through the ordered arrangement and monotonicity processing of the feature nodes, geometric constraints conforming to mathematical logic are provided for subsequent trajectory planning, avoiding oscillation or singular points in the interpolation algorithm, significantly improving the robustness and control accuracy of the adaptive control system under complex non-steady-state conditions.

[0032] The predicted trajectory planner includes a dynamics prediction model of the shearing mechanism and a rolling optimization solver, the dynamics prediction model of the shearing mechanism establishes the correlation between the output torque of the hydraulic servo valve and the motion state of the shearing tool holder, and predicts the displacement response of the shearing tool holder; The rolling optimization solver takes the virtual control trajectory as a reference benchmark, and in each control cycle, it performs optimization calculation to minimize the deviation between the predicted shear tool holder displacement response and the virtual control trajectory, and solves the required feedforward torque compensation for balancing the material hardness, and synchronously outputs the corresponding blade gap adjustment instructions.

[0033] The dynamic prediction model of the shearing mechanism is established based on the physical characteristics of the hydraulic and mechanical system, adopts a discrete-time state-space equation form, and establishes the correlation between the output torque of the hydraulic servo valve and the motion state of the shear tool holder. In this model, the real-time displacement, velocity, and acceleration of the shear tool holder are defined as state variables, the control current of the hydraulic servo valve is defined as a control input variable, and the physical parameters such as the equivalent mass, viscous damping coefficient, and bulk modulus of the hydraulic oil of the system are combined to simulate the dynamic behavior of the system through matrix operations, thereby being able to predict the evolution trend of the shear tool holder displacement response in the future finite time domain on a mathematical level under given input; The rolling optimization solver adopts a model predictive control algorithm architecture, takes the virtual control trajectory as a reference benchmark, and reads it into the buffer in each control cycle (set to 1 millisecond in this embodiment). At the current sampling time, the solver performs multi-step iterative deduction based on the current state feedback using the dynamic prediction model, constructs a quadratic programming mathematical problem, the core of which is to construct an objective function that performs optimization calculation to minimize the deviation between the predicted shear tool holder displacement response and the virtual control trajectory, while introducing a control increment constraint term to ensure system stability. The quadratic programming mathematical problem is: to construct a minimized objective function containing two weighted terms: the first part is the tracking error term, which is the weighted cumulative value of the sum of squares of the deviation between the shear tool holder displacement response at all predicted time points within the set prediction time domain and the corresponding theoretical position in the virtual control trajectory, used to ensure control accuracy; the second part is the control stability term, which is the weighted cumulative value of the sum of squares of the control instruction change (i.e., control increment) between adjacent control cycles within the set control time domain, used to suppress the drastic fluctuations of the control input. The solver aims to find an optimal control increment sequence that minimizes the sum of the above two parts. The control increment constraint term is to strictly limit the control increment value allowed to be output in each control cycle within the preset maximum positive increment threshold and maximum negative increment threshold; that is, the change amplitude of the driving signal of the hydraulic servo valve in a single adjustment should not exceed the smooth limit that the physical system can withstand, so as to prevent hydraulic impact or system oscillation caused by excessive adjustment; The multi-step iterative deduction and quadratic programming algorithm are used to realize the predictive high-precision tracking of the virtual control trajectory, effectively suppress the control input oscillation caused by the pursuit of fast response, and ensure the dynamic response speed of the system while considering the running stability and robustness of the actuator.

[0034] A set of future optimal control input sequences is obtained by solving the above optimization problem, and only the first element of the sequence is taken as the control instruction at the current time. The instruction is parsed into two parts. One part is to calculate the feedforward torque compensation required to balance the hardness of the material, which represents the additional driving force that the hydraulic system needs to output in advance to resist the upcoming hardness mutation resistance. The other part is to query the pre-set shear force-optimal gap process database according to the mapping relationship between the calculated torque and the current material position, and output the corresponding blade gap adjustment instruction. The two sets of instructions are sent to the underlying actuator simultaneously to ensure that the tool has enough cutting force and is in the best shear gap state at the instant of contact with the hard point. The rolling optimization mechanism based on MPC effectively overcomes the response lag of the hydraulic system, realizes the fundamental change of the control mode from post-correction to pre-compensation, and through the millisecond-level feedforward torque compensation and synchronous adjustment of the blade gap, ensures that the tool always maintains constant speed and optimal process when dealing with hardness mutations, thereby significantly improving the shear section quality and effectively reducing mechanical wear and the risk of blade collapse.

[0035] The specific process of generating the optimal blade gap and shear speed cooperative motion instruction sequence that adaptively matches the current hardness is as follows: A position closed-loop tracking controller is constructed to calculate the tracking error between the actual position of the shear tool holder and the theoretical position set in the virtual control trajectory in real time, and generate a feedback adjustment based on the tracking error; The feedforward torque compensation and the feedback adjustment are superimposed to synthesize the total control amount, and the total control amount is converted into a current control signal for driving the hydraulic servo valve. At the same time, the blade gap adjustment instruction is parsed into a target position code and converted into a pulse control signal for driving the gap adjustment motor; The current control signal and the pulse control signal are output in the same control cycle, and the blade gap dynamic adjustment and shear tool holder variable speed cutting are cooperatively actuated.

[0036] Specifically, in the motion controller, the position closed-loop tracking controller reads the high-precision displacement sensor feedback signal installed on the shear slider at a high sampling rate (1 kHz in this embodiment) to obtain the actual position of the shear cutter holder and compare it with the theoretical position corresponding to the current time in the virtual control trajectory generated in the previous step to calculate the deviation between the two as the tracking error. The controller calculates the tracking error according to the preset proportional, integral, and differential gain parameters to generate a feedback adjustment amount that aims to eliminate the steady-state position deviation caused by non-modeled disturbances such as friction and hydraulic oil temperature drift. The composite control signal is subjected to a synthesis process to superimpose the feedforward torque compensation amount and the feedback adjustment amount, and the calculation formula is total control torque = feedforward compensation amount + feedback adjustment amount. The total control amount obtained by calculation is converted into a current control signal for driving the hydraulic servo valve through the field bus interface, and the current signal directly determines the opening size and direction of the servo valve core, thereby accurately controlling the driving force and speed of the hydraulic cylinder. In the parallel processing thread, the blade gap adjustment instruction is parsed into a target position code; according to the mechanical transmission ratio of the shear cutter holder gap adjustment mechanism, the target physical gap value is converted into the required rotation encoder value of the servo motor and further converted into a pulse control signal for driving the gap adjustment motor. To ensure the strict synchronization of the dynamic compensation and the geometric adjustment in time, the current control signal and the pulse control signal are output to the hydraulic servo amplifier and the motor driver in the same control cycle by using the synchronous clock interrupt function of the real-time operating system. Through this hard real-time synchronization mechanism, the system realizes the coordinated action of the dynamic adjustment of the blade gap and the variable-speed cutting of the shear cutter holder, that is, at the moment when the cutting tool contacts the different hardness layers of the material, the hydraulic system automatically outputs a matching variable force and variable speed drive, and the cutter holder mechanism is simultaneously fine-tuned to the optimal lateral gap, thereby ensuring that the process parameters are always in the optimal matching state throughout the shearing process. By constructing a composite control architecture of feedforward torque compensation + feedback position correction and combining a multi-axis synchronous output mechanism, the technical problems of insufficient force and fixed gap of traditional shears when dealing with non-homogeneous materials are solved. The superposition of feedforward and feedback ensures that the system can quickly respond to load shocks caused by sudden changes in hardness and eliminate accumulated errors; and the millisecond-level synchronous coordination of the hydraulic drive and the motor gap adjustment adjusts the cutting force and engagement gap in real time according to the hardness change of each micron depth of the material, thereby ensuring efficient shearing while maximizing the improvement of the perpendicularity and smoothness of the cross section and prolonging the service life of the core components of the equipment.

[0037] The present application realizes the perspective nondestructive identification of the internal inhomogeneous structure of the material to be processed by introducing the micro-perturbation detection and frequency domain fingerprint construction technology in the pre-pressing stage, effectively solves the technical problem that the traditional shearing process cannot predict the hardness mutation in the material, and further uses the feedforward torque compensation mechanism based on the virtual control trajectory to upgrade the traditional post-feedback correction to the pre-prediction compensation, which can automatically match the optimal driving torque and blade gap within the millisecond window before the tool contact hard point, completely eliminates the mechanical impact and system response lag caused by load mutation, and significantly improves the flatness and perpendicularity of the sheared section of the complex material while effectively reducing the tool chipping risk and greatly prolonging the service life of the core components of the shearing equipment.

[0038] Embodiment two: This embodiment takes the shearing of high-strength steel plates containing laser welding seams in the scrap car disassembly production line as an example. Since the hardness of the weld area is much higher than that of the base material, and the distribution position is random, the traditional shearing is prone to tool chipping or jamming. The system solves this problem through the cooperative work of the following four modules: fingerprint construction module: pre-pressing the material to be processed, applying a micro-perturbation signal during pre-pressing, collecting the dynamic mechanical response of the micro-perturbation signal of the material to be processed, extracting the complex impedance frequency spectrum characteristics of the dynamic mechanical response through fast Fourier transform, and constructing the frequency domain fingerprint of the internal inhomogeneous structure distribution of the material to be processed based on the complex impedance frequency spectrum characteristics; Trajectory generation module: the virtual control generator predicts the load mutation time and peak intensity caused by the hardness mutation of the material based on the frequency domain fingerprint; converts the predicted load mutation time and peak intensity into discrete feature nodes based on the vertical displacement of the shearing tool holder, connects the discrete feature nodes using the spline interpolation algorithm, and generates a virtual control trajectory; Feedforward compensation module: input the virtual control trajectory into the predictive trajectory planner; calculate the feedforward torque compensation required to overcome the hardness change resistance; Parameter control module: superimpose the feedforward torque compensation and the feedback adjustment based on the real-time position error to generate an optimal blade gap and shearing speed cooperative motion instruction sequence that matches the current hardness, drive the hydraulic servo valve and gap adjustment motor, and execute the shearing parameter control; The fingerprint construction module first controls the hydraulic pressure foot of the heavy gantry shearing machine to press down, fixes the high-strength steel plate to be sheared at a static pre-pressure of 20 MPa, after the pressure is stabilized, the fingerprint construction module activates the piezoelectric ceramic exciter integrated in the pressure foot to apply a sinusoidal scanning micro-disturbance signal with a frequency linearly increasing from 50 Hz to 500 Hz to the steel plate, the amplitude is controlled at 5% of the static pre-pressure (i.e. 1 MPa), ensuring that no plastic indentation is generated; at the same time, the real-time exciting force and displacement deformation response of the steel plate to the disturbance are synchronously collected by the high-frequency force sensor and the laser displacement sensor, the data is processed by using fast Fourier transform, and it is found by system analysis that a significant complex impedance modulus peak value appears in a specific high-frequency band of the frequency spectrum, and the peak value characteristics constitute the frequency domain fingerprint for identifying the position of the hidden high-hardness weld inside the steel plate; The trajectory generation module receives the above-mentioned frequency domain fingerprint data, analyzes it by using the built-in inverse dynamics-based generative mapping model, identifies that the complex impedance peak value corresponds to the laser weld hardened layer at Z=15mm to Z=18mm in the thickness direction of the steel plate. Map this hardness mutation to a sharp rise in theoretical shearing resistance, and predict a load peak strength up to 3 times the normal value; then, this time-based load mutation prediction is combined with the set shearing speed of 50 mm / s to convert into discrete feature nodes based on the vertical displacement of the shearing tool holder; by using the spline interpolation algorithm, these nodes are smoothly connected to generate a virtual control trajectory containing the expected load mutation, which describes where the tool holder will encounter the weld resistance in the entire downward stroke; The feedforward compensation module inputs the generated virtual control trajectory into the predictive trajectory planner, and uses a rolling optimization solver to predict the tool holder state forward in each 1 millisecond control period; it is found by calculation that if the original hydraulic input is maintained, the tool holder will instantaneously decrease in speed when it contacts the weld at Z=15mm. Therefore, the feedforward torque compensation required to balance this weld hardness mutation is calculated, i.e. the hydraulic system pressure needs to be increased in advance when the tool holder reaches Z=14.8mm; at the same time, according to the process database, the blade gap needs to be temporarily adjusted from the current 0.5mm to 0.8mm to prevent chipping for this weld hardness, and the corresponding blade gap adjustment instructions are output synchronously accordingly; The parameter control module superimposes the calculated feedforward torque compensation and the real-time error adjustment amount fed back by the position sensor during the shearing process to generate a total control current to drive the hydraulic servo valve, so that the oil cylinder bursts with high pressure at the moment of contacting the weld, maintaining a constant shearing speed; in the same control period, the blade gap adjustment instructions are converted into pulse signals to drive the gap adjustment motor to quickly fine-tune the tool holder position, and the tool holder descends at high speed, at the moment of cutting into the weld, the hydraulic power is automatically enhanced and the blade gap is automatically slightly expanded, and after cutting off the weld, the original parameters are quickly restored, realizing adaptive and smooth shearing under variable hardness conditions.

[0039] The embodiment is aimed at the extreme working condition of high-strength steel and laser welding seam coexisting in the dismantling of scrapped cars. Through the non-destructive micro disturbance frequency domain fingerprint technology, the precise perspective prediction of the hidden welding seam position is realized, and the blindness of the traditional cutting surface to the random hardness mutation is effectively solved. Using the feedforward compensation and gap cooperative control mechanism, the preparation of high-pressure explosion and tool gap expansion is completed within the millisecond window before the tool contact hardening layer, which not only guarantees the energy required to cut the weld seam with 3 times the load strength, but also avoids the tool collapse or jam failure caused by hard collision through the optimization of physical gap, significantly improving the processing efficiency of non-homogeneous scrap steel and the service life of core components.

[0040] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for adaptive control of shear parameters for multi-hardness materials, characterized in that, The method comprises the following steps: pre-pressing the material to be processed, applying a micro-perturbation signal during pre-pressing, collecting the dynamic mechanical response of the material to be processed to the micro-perturbation signal, extracting the complex impedance frequency spectrum characteristics of the dynamic mechanical response by fast Fourier transform, and constructing the frequency domain fingerprint of the internal heterogeneous structure distribution of the material to be processed based on the complex impedance frequency spectrum characteristics; constructing a virtual control generator to predict the load mutation time and peak intensity caused by the material hardness mutation based on the frequency domain fingerprint; converting the predicted load mutation time and peak intensity into discrete characteristic nodes based on the vertical displacement of the shear tool holder; connecting the discrete characteristic nodes by using a spline interpolation algorithm to generate a virtual control trajectory; inputting the virtual control trajectory into a predictive trajectory planner; calculating the feedforward torque compensation required to overcome the hardness change resistance, superimposing the feedforward torque compensation and the feedback adjustment based on the real-time position error, generating an optimal blade gap and shear speed cooperative motion instruction sequence that matches the current hardness, and driving the hydraulic servo valve and the gap adjustment motor to execute the shear parameter control.

2. The method of claim 1, wherein, The micro-perturbation signal applied during pre-pressing is specifically: controlling the material pressing device to apply and maintain a constant static pre-pressing force to the material to be processed, superimposing a sinusoidal scanning excitation signal with a linearly increasing frequency within a preset bandwidth to the driving system of the material pressing device after the static pre-pressing force stabilizes; the amplitude of the sinusoidal scanning excitation signal is set to a preset percentage of the static pre-pressing force, the stress amplitude generated by the micro-perturbation signal is lower than the micro-yield strength threshold corresponding to the current hardness of the material to be processed, and the dynamic mechanical response of the material to the micro-perturbation signal is collected.

3. The method of claim 2, wherein, The dynamic mechanical response is the real-time excitation force time domain signal and the real-time displacement deformation time domain signal collected by the sensor during the loading of the sinusoidal scanning excitation signal; wherein the real-time excitation force time domain signal represents the actual driving force waveform applied to the material, and the real-time displacement deformation time domain signal represents the follow-up elastic deformation waveform of the material.

4. The method of claim 1, wherein, The virtual control generator uses a generative mapping model based on inverse dynamics, and the specific construction process includes: establishing a nonlinear mapping relationship between the complex impedance modulus peak in the frequency domain fingerprint and the shear strength distribution of the material to be processed along the thickness direction; inverse dynamics equation is constructed by using the kinematic parameters of the shear mechanism, the shear strength distribution is substituted into the inverse dynamics equation as a boundary condition, the theoretical resistance evolution curve of the shear tool required to overcome the shear strength distribution under constant speed working condition is deduced reversely, and the extreme point and its corresponding time domain position are extracted from the theoretical resistance evolution curve as the peak intensity and load mutation time.

5. The method of claim 4, wherein, The conversion into discrete characteristic nodes based on the vertical displacement of the shear tool holder is specifically: Obtaining the shear speed value under the constant speed working condition, multiplying the predicted load mutation time by the shear speed value to convert the corresponding shear tool holder vertical stroke displacement value; taking the shear tool holder vertical stroke displacement value as the reference position, taking the peak intensity as the target load value at the reference position, and pairing them to form a set of characteristic data; arranging multiple sets of characteristic data in order from small to large according to the shear tool holder vertical stroke displacement value to form the discrete characteristic node.

6. The method of claim 1, wherein, The predicted trajectory planner includes a shear mechanism dynamics prediction model and a rolling optimization solver, the shear mechanism dynamics prediction model establishes the correlation between the hydraulic servo valve output torque and the shear tool holder motion state, and predicts the shear tool holder displacement response; The rolling optimization solver takes the virtual control trajectory as a reference benchmark, and in each control cycle, optimizes the calculation to minimize the deviation between the predicted shear tool holder displacement response and the virtual control trajectory, and calculates the feedforward torque compensation required to balance the hardness of the material, and synchronously outputs the corresponding blade gap adjustment instruction.

7. The method of claim 6, wherein the shear parameter adaptive control method is oriented to a multi-hardness material, and The specific process of generating the optimal blade gap and shear speed cooperative motion instruction sequence adaptive to the current hardness is: A position closed-loop tracking controller is constructed to calculate the tracking error between the actual position of the shear tool holder and the theoretical position set in the virtual control trajectory in real time, and a feedback adjustment amount is generated based on the tracking error; The feedforward torque compensation and the feedback adjustment amount are superimposed to synthesize the total control amount, and the total control amount is converted into a current control signal for driving the hydraulic servo valve; at the same time, the blade gap adjustment instruction is analyzed into a target position code, which is converted into a pulse control signal for driving the gap adjustment motor; The current control signal and the pulse control signal are output in the same control cycle, and the blade gap dynamic adjustment and the shear tool holder variable speed cutting are cooperated.

8. A shear parameter adaptive control system for multi-hardness materials, characterized by, It includes: A fingerprint construction module: pre-pressing the material to be processed, applying a micro disturbance signal during pre-pressing, collecting the dynamic mechanical response of the material to be processed to the micro disturbance signal, extracting the complex impedance frequency spectrum features of the dynamic mechanical response by fast Fourier transform, and constructing the frequency domain fingerprint of the internal heterogeneous structure distribution of the material to be processed based on the complex impedance frequency spectrum features; A trajectory generation module: a virtual control generator is constructed to predict the load mutation time and peak intensity caused by the hardness mutation of the material based on the frequency domain fingerprint; the predicted load mutation time and peak intensity are converted into discrete characteristic nodes with the shear tool holder vertical stroke displacement as the reference, and the discrete characteristic nodes are connected by using a spline interpolation algorithm to generate a virtual control trajectory; A feedforward compensation module: the virtual control trajectory is input to a predicted trajectory planner; The feedforward torque compensation required to overcome the hardness change resistance is calculated; A parameter control module: superimposing the feedforward torque compensation and the feedback adjustment amount based on the real-time position error to generate an optimal blade gap and shear speed cooperative motion instruction sequence adaptive to the current hardness, driving the hydraulic servo valve and the gap adjustment motor, and executing the shear parameter control.

Images