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CN-121995847-A - Self-adaptive identification and compensation method for friction nonlinear characteristics of feeding system of numerical control machine tool

CN121995847ACN 121995847 ACN121995847 ACN 121995847ACN-121995847-A

Abstract

The invention discloses a self-adaptive identification and compensation method for friction nonlinear characteristics of a feeding system of a numerical control machine tool. The method comprises the steps of collecting an actual position signal, an actual speed signal, an actual current signal and an instruction position signal of a feeding system, initializing a LuGre friction model parameter, carrying out friction parameter self-adaptive identification based on a motion state section, updating a coulomb friction parameter and a viscous friction parameter by adopting a least square method in a uniform speed stage, updating a bristle stiffness parameter, a bristle damping parameter and a Stribeck speed parameter by adopting an extended Kalman filter in an acceleration stage and a deceleration stage, generating a friction force estimated value according to the identified friction parameter, converting the friction force estimated value into a compensation current signal, and adding the compensation current signal into a current instruction of a servo driver after phase lead compensation. The invention can adaptively identify the friction characteristic change on line, effectively inhibit tracking error and vibration caused by friction nonlinearity and improve the processing precision and stability of the numerical control machine tool.

Inventors

  • XIONG ZHANG
  • XU MENGYI
  • Mai Xiaohua

Assignees

  • 同行智能(广东)数控机床有限公司

Dates

Publication Date
20260508
Application Date
20251229

Claims (10)

  1. 1. The self-adaptive identification and compensation method for the friction nonlinear characteristics of the feeding system of the numerical control machine tool is characterized by comprising the following steps of: Step 1, motion data of a feeding system are collected, actual position signals of a workbench are collected through an encoder, numerical differentiation processing is carried out on the actual position signals to obtain actual speed signals, actual current signals of a servo motor are collected through a servo driver, and command position signals of the feeding system are read from a numerical control system; Initializing friction nonlinear characteristic model parameters, and setting initial parameters of a LuGre model, wherein the initial parameters comprise bristle stiffness parameters, bristle damping parameters, coulomb friction parameters, viscous friction parameters and Stribeck speed parameters; Step 3, based on the self-adaptive identification of friction parameters of the motion state segmentation, judging the motion state of the feeding system according to an actual speed signal, wherein the motion state is divided into an acceleration stage, a deceleration stage and a uniform speed stage, wherein a least square method is adopted to update coulomb friction parameters and viscous friction parameters in the uniform speed stage, and an extended Kalman filter is adopted to update bristle stiffness parameters, bristle damping parameters and Stribeck speed parameters in the acceleration stage and the deceleration stage; step 4, friction compensation signal generation and system dynamic compensation, calculating a friction force estimated value of the LuGre model based on the currently identified friction parameter and the actual speed signal, converting the friction force estimated value into a compensation current signal, performing phase lead compensation on the compensation current signal, and adding the compensation current signal into a current instruction of a servo driver; And 5, monitoring the parameter identification and compensation effect, calculating the tracking error of the actual position signal and the instruction position signal, triggering the parameter to be reinitialized when the root mean square value of the tracking error exceeds a set threshold value, and adjusting the updating rate of the self-adaptive identification algorithm when the change rate of the friction parameter exceeds a stable threshold value.
  2. 2. The method for adaptively identifying and compensating the friction nonlinear characteristics of the feeding system of the numerical control machine tool according to claim 1, wherein in the step 1, the actual speed signal is obtained by performing numerical differentiation processing on the actual position signal, specifically, a first-order backward difference method is adopted, and the actual speed value is obtained by dividing the difference between the position value of the current sampling time and the position value of the previous sampling time by the sampling time interval; The sampling time interval is set according to the maximum movement speed of the feeding system, so that the sampling frequency is ensured to be higher than the dynamic response frequency of the feeding system; All the collected signals are synchronously stored in the memory of the controller.
  3. 3. The method for adaptively identifying and compensating the friction nonlinear characteristics of the feeding system of the numerical control machine tool according to claim 1, wherein in the step 2, the initial parameters of the LuGre model are set based on the offline experimental data of the feeding system, the offline experiment is performed by driving the feeding system to move at a constant speed, measuring the steady friction force, and fitting to obtain the initial values of the parameters; The initial parameters are stored in the controller as initial values for the first recognition.
  4. 4. The method for adaptively identifying and compensating the frictional nonlinear characteristics of the feeding system of the numerical control machine tool according to claim 1, wherein in the step 3, the motion state of the feeding system is judged according to the actual speed signal, and the specific process comprises the step of judging an acceleration stage when the change rate of the actual speed signal is larger than a positive threshold value; When the change rate of the actual speed signal is smaller than a negative threshold value, judging as a deceleration stage; when the change rate of the actual speed signal is between the negative threshold value and the positive threshold value, judging as a constant speed stage; The rate of change is calculated by numerical differentiation of the actual speed signal, the positive and negative thresholds being set based on the feed system maximum acceleration.
  5. 5. The method for adaptively identifying and compensating the friction nonlinear characteristics of the feeding system of the numerical control machine tool according to claim 1, wherein in the step 3, the coulomb friction parameter and the viscous friction parameter are updated by a least square method, and the specific process comprises the following steps: Constructing a friction force estimated value, wherein the friction force estimated value is equal to an actual current signal multiplied by a torque constant, and then subtracting an inertial force, wherein the inertial force is obtained by multiplying an actual acceleration signal by the total mass of the system, and the actual acceleration signal is calculated by the numerical differentiation of an actual speed signal; establishing a linear relation between a friction force estimated value and an actual speed signal, and solving a coulomb friction parameter and a viscous friction parameter by using a recursive least square method; and in the recursive least square method, the forgetting factor is adaptively adjusted according to the motion state, and the forgetting factor is set to be a larger value in a uniform speed stage.
  6. 6. The method for adaptively identifying and compensating the frictional nonlinear characteristics of the feeding system of the numerical control machine tool according to claim 1, wherein in the step 3, the bristle stiffness parameter, the bristle damping parameter and the Stribeck speed parameter are updated by adopting an extended kalman filter in an acceleration stage and a deceleration stage, and the specific process comprises the following steps: Discretizing the LuGre model into a state space equation, wherein state variables comprise bristle deformation and bristle deformation rate; Taking an actual speed signal as an input and an actual current signal as an observed value; predicting state variables and parameters by an extended Kalman filter, and updating parameter estimation values according to the observation errors; the process noise covariance and observed noise covariance of the extended kalman filter are set based on the feed system motion data statistics.
  7. 7. The method for adaptively identifying and compensating the frictional nonlinear characteristics of the feeding system of the numerical control machine tool according to claim 1, wherein in the step 4, the frictional force estimated value of the LuGre model is calculated based on the currently identified frictional parameters and the actual speed signals, specifically, the frictional force estimated value is calculated by bristle deformation, bristle deformation rate, actual speed signals and frictional parameters, and the bristle deformation and bristle deformation rate are recursively obtained by a state equation of the LuGre model; The conversion of the friction estimate into a compensation current signal, in particular the compensation current signal, is equal to the friction estimate divided by the torque constant.
  8. 8. The method for adaptively identifying and compensating the friction nonlinear characteristics of the feeding system of the numerical control machine tool according to claim 1, wherein in the step 4, the phase lead compensation is performed on the compensation current signal, specifically, the phase compensation is performed by adopting a first-order lead network, the transfer function of the first-order lead network is set according to the open-loop frequency response of the feeding system, and the cut-off frequency is higher than the bandwidth of the feeding system; and adding the compensation current signal after the phase lead compensation into a current instruction of a servo driver to realize friction compensation.
  9. 9. The method for adaptively identifying and compensating the frictional nonlinear characteristics of the feeding system of the numerical control machine according to claim 1, wherein in the step 5, tracking errors of actual position signals and command position signals are calculated, and the tracking errors are stored in a time sequence form; The set threshold is set based on the machining precision requirement; And triggering parameter reinitialization when the root mean square value of the tracking error exceeds a set threshold, and particularly, reinitializing the friction nonlinear characteristic model parameter initialization process from the step 2.
  10. 10. The method for adaptively identifying and compensating the friction nonlinear characteristics of the feeding system of the numerical control machine tool according to claim 1, wherein in the step 5, when the change rate of the friction parameter exceeds a stability threshold, the update rate of the adaptive identification algorithm is adjusted; When the change rate of the friction parameter exceeds a stable threshold, reducing the update rate of the self-adaptive identification algorithm; And when the change rate of the friction parameter is lower than the stability threshold, the updating rate of the self-adaptive identification algorithm is improved.

Description

Self-adaptive identification and compensation method for friction nonlinear characteristics of feeding system of numerical control machine tool Technical Field The invention relates to the technical field of numerical control machine tools, in particular to a self-adaptive identification and compensation method for friction nonlinear characteristics of a feeding system of a numerical control machine tool. Background The numerical control machine tool is used as core equipment in the modern manufacturing industry, and the performance of a feeding system directly determines the machining precision and efficiency. The feeding system is usually composed of a servo motor, a ball screw, a guide rail, a workbench and other parts, and during the movement process, the friction among the parts can generate nonlinear characteristics, so that the tracking error, the creeping phenomenon and the vibration problem of the system occur, and the processing quality is seriously affected. Friction nonlinearity is mainly represented by a complex combination of static friction, coulomb friction and viscous friction, and dynamically changes with changes in temperature, wear and lubrication conditions, making the traditional fixed parameter compensation method difficult to effectively cope with. In the prior art, friction compensation methods are mainly divided into two types, namely feedforward compensation and adaptive compensation based on a model. Model-based feedforward compensation methods rely on pre-established friction models, such as the LuGre model or the Stribeck model, to obtain model parameters through offline experiments, and to introduce compensation signals into the control system. However, this method requires accurate model parameters, and in actual operation, due to factors such as temperature rise, component wear, and lubricant performance change, the friction characteristics drift, so that the model with fixed parameters cannot accurately describe the real-time friction behavior, and the compensation effect gradually deteriorates. The self-adaptive compensation method can adjust parameters on line, but most methods rely on a simplified model or assume that friction characteristics change slowly, and in a high-speed high-precision processing scene, the problems of low identification precision and poor instantaneity exist in the methods due to the fact that the dynamic response of a feeding system is fast and the load change is frequent. For example, some adaptive methods adopt a recursive least square method or a neural network to perform parameter identification, but have high computational complexity and are difficult to realize in real time in an industrial controller, and other adaptive methods perform indirect estimation based on position or speed signals, but lack direct measurement of friction force, so that an identification result is greatly influenced by external interference. In addition, the parameter identification process in the prior art often needs to inject additional test signals, such as sine waves or pseudo random sequences, which interfere with the normal processing process and reduce the production efficiency. Meanwhile, the dynamic coupling effect of the system is not considered when the compensation signal is generated, phase lag or high-frequency oscillation can be introduced, and the system performance is deteriorated. Therefore, a method for adaptively identifying the friction nonlinear characteristics on line without interrupting the processing process and matching the compensation signal with the system dynamics is urgently needed, so as to improve the control precision and stability of the feeding system of the numerical control machine tool. Disclosure of Invention Based on the above purpose, the invention provides a method for adaptively identifying and compensating friction nonlinear characteristics of a feeding system of a numerical control machine tool, which comprises the following steps: Step 1, motion data of a feeding system are collected, actual position signals of a workbench are collected through an encoder, numerical differentiation processing is carried out on the actual position signals to obtain actual speed signals, actual current signals of a servo motor are collected through a servo driver, and command position signals of the feeding system are read from a numerical control system; Initializing friction nonlinear characteristic model parameters, and setting initial parameters of a LuGre model, wherein the initial parameters comprise bristle stiffness parameters, bristle damping parameters, coulomb friction parameters, viscous friction parameters and Stribeck speed parameters; Step 3, based on the self-adaptive identification of friction parameters of the motion state segmentation, judging the motion state of the feeding system according to an actual speed signal, wherein the motion state is divided into an acceleration stage, a deceleration stage and a uniform s