CN-122018289-A - Hybrid control method integrating feedforward compensation and self-adaptive fuzzy PID

CN122018289ACN 122018289 ACN122018289 ACN 122018289ACN-122018289-A

Abstract

The invention discloses a hybrid control method integrating feedforward compensation and self-adaptive fuzzy PID, and relates to the technical field of automatic control. The hybrid control method for fusing feedforward compensation and self-adaptive fuzzy PID comprises the steps of acquiring real-time data, acquiring position errors and change rates of all shafts, calculating feedforward compensation, generating feedforward compensation quantity through kinematic inverse solution, performing fuzzy reasoning, calculating PID parameter increment, predicting compensation, establishing a multi-shaft linkage prediction model, generating prospective prediction compensation, and synthesizing control quantity. The hybrid control method for fusing feedforward compensation and self-adaptive fuzzy PID acquires the following error data of each driving shaft in real time, combines a kinematic model to reversely deduce a contour error source, constructs a multi-axis linkage prediction model, and realizes accurate prediction of the movement error of the tool nose point in a multi-axis linkage state through Matlab/Simulink simulation.

Inventors

ZHANG YING
ZHANG JINCHAO
Pan anyuan
LI ZHENGXIN
CHEN YUJUN
YUAN YUKE
WANG XIANG

Assignees

浙江德欧电气技术股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260413

Claims (4)

1. A hybrid control method for fusing feedforward compensation and self-adaptive fuzzy PID is characterized by comprising the following steps: Step one, real-time data acquisition, namely acquiring the position error of each shaft Rate of change ; Step two, feedforward compensation calculation, which comprises a feedforward compensation module, wherein feedforward compensation quantity is generated through kinematic inverse solution ; Step three, fuzzy reasoning comprises a self-adaptive fuzzy PID module, and PID parameter increment is calculated , , ; Step four, predictive compensation, namely establishing a multi-axis linkage predictive model, and generating a prospective predictive compensation u comp based on the prospective of the step one, the step two and the step three; Step five, control amount synthesis: Wherein: In order to adapt the fuzzy PID output, For the amount of feed-forward compensation, Compensating for look-ahead predictions; and step six, performing closed loop execution, namely outputting the control quantity and updating the system state.
2. The hybrid control method for fusing feedforward compensation and adaptive fuzzy PID of claim 1, wherein the feedforward compensation module builds an error mapping model based on multi-axis kinematic reverse analysis, including error reverse derivation and nonlinear compensation model; Error reverse derivation: Error of contour Decomposing to each axis: ; for jacobian, the differential relationship of knife tip error to each axis error is described: ; Wherein: Is a generalized coordinate vector The device comprises a linear axis and a rotary axis, wherein x, y and z are linear axis coordinate vectors, and a and c are rotary axis coordinate vectors; is the error vector of the tool nose point ; Nonlinear compensation model: ; In the formula, Compensating the gain for the reverse gap; is the coulomb coefficient of friction; Is a friction speed sensitive factor; as the estimated value of the elastic deformation between the shafts, The output range is (-1, 1) and has a smooth S-shaped curve as a hyperbolic tangent function.
3. The method for hybrid control of fusion feedforward compensation and adaptive fuzzy PID of claim 1, wherein the adaptive fuzzy PID module is configured to collect position errors in real time Rate of change On-line adjustment is performed through 49 fuzzy rules, including fuzzy processing, fuzzy rule base and parameter updating; and (3) blurring: Input normalization: , ; Triangle membership function: In the formula (I), in the formula (II), For a center point (nb= -1, pb=1), ; Fuzzy rule base: typical rule example: IF is NB AND is NB THEN is PB, is NB, is PM; Weighted average deblurring: , ; Parameter updating: The update expression is expressed as: ; in the formula, the Clip function is defined mathematically as: 。
4. The method for hybrid control with fused feedforward compensation and adaptive fuzzy PID of claim 1, wherein establishing a multi-axis linkage prediction model includes construction of a state space prediction model and look-ahead compensation; Building a state space prediction model: the core components are as follows: ; In the formula, , Is a system matrix; the weight is coupled between shafts; is a nonlinear disturbance term; describing the time error on the system as a linear dynamic term Influence on the next moment; for controlling input items, for quantifying controlled-quantity variation Correction capability for errors; is an inter-axis coupling term; Q represents generalized coordinates, a vector describing the position of each axis of the machine tool, including the displacement of the axis of the linear axis X, Y, Z and the rotation angle of the axis of rotation a, c or b, expressed as Q is a generalized velocity, and represents a vector of motion velocities of the respective axes; And (3) forward-looking compensation: ; In the formula, To compensate the gain matrix, a 3-step look-ahead is set.

Description

Hybrid control method integrating feedforward compensation and self-adaptive fuzzy PID Technical Field The invention relates to the technical field of automatic control, in particular to a hybrid control method for fusing feedforward compensation and self-adaptive fuzzy PID. Background At present, a high-performance five-axis linkage high-end numerical control machine tool, in particular to a precision machine tool for machining complex curved surface parts in the aerospace field. The basic framework of the machine tool is generally composed of three linear axes (X/Y/Z) and two rotating shafts (A/C or B/C), is provided with a high-rigidity gantry structure, a linear motor or a double-drive synchronization technology, adopts a fully-closed-loop grating ruler feedback system, adopts an electric spindle technology to realize high rotating speed of over 20000rpm, adopts a multi-channel multi-axis linkage control framework, and is provided with an RTCP (rotary cutter center point) function to realize precise control of the cutter point track. However, the following needs still exist for the core technology of the existing high-performance five-axis linkage high-end numerical control machine tool at present: The complex kinematic conversion of the five-axis linkage machine tool can amplify the influence of each axis following error on the contour precision, and the contour error needs to be accurately decomposed to each axis through feedforward compensation; when thin-wall parts such as impellers, casings and the like are processed, the self-adaptive fuzzy PID can effectively elastically deform caused by the change of cutting force, and the flutter is restrained by parameter self-setting; the reverse gap and nonlinear friction of the rotating shaft can generate periodic contour errors during spatial interpolation, and the prediction model is required to be compensated in advance. Aiming at the technical requirements, the invention provides a hybrid control algorithm integrating feedforward compensation and self-adaptive fuzzy PID. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a hybrid control method for fusing feedforward compensation and self-adaptive fuzzy PID, which solves the defects and the shortcomings in the prior art. In order to achieve the above purpose, the invention is realized by the following technical scheme: a hybrid control method for fusing feedforward compensation and self-adaptive fuzzy PID comprises the following steps: Step one, real-time data acquisition, namely acquiring the position error of each shaft Rate of change; Step two, feedforward compensation calculation, which comprises a feedforward compensation module, wherein feedforward compensation quantity is generated through kinematic inverse solution; Step three, fuzzy reasoning comprises a self-adaptive fuzzy PID module, and PID parameter increment is calculated,,; Step four, predictive compensation, namely establishing a multi-axis linkage predictive model, and generating a prospective predictive compensation u comp based on the prospective of the step one, the step two and the step three; Step five, control amount synthesis: Wherein: In order to adapt the fuzzy PID output, For the amount of feed-forward compensation,Compensating for look-ahead predictions; and step six, performing closed loop execution, namely outputting the control quantity and updating the system state. Preferably, the feedforward compensation module builds an error mapping model based on multi-axis kinematic reverse analysis, including error reverse derivation and nonlinear compensation model; Error reverse derivation: Error of contour Decomposing to each axis: ; for jacobian, the differential relationship of knife tip error to each axis error is described: ; Wherein: Is a generalized coordinate vector The device comprises a linear axis and a rotary axis, wherein x, y and z are linear axis coordinate vectors, and a and c are rotary axis coordinate vectors; is the error vector of the tool nose point ; Nonlinear compensation model: ; In the formula, Compensating the gain for the reverse gap; is the coulomb coefficient of friction; Is a friction speed sensitive factor; as the estimated value of the elastic deformation between the shafts, The output range is (-1, 1) and has a smooth S-shaped curve as a hyperbolic tangent function. Preferably, the adaptive fuzzy PID module is used for acquiring the position error in real timeRate of changeOn-line adjustment is performed through 49 fuzzy rules, including fuzzy processing, fuzzy rule base and parameter updating; and (3) blurring: Input normalization: ,; Triangle membership function: In the formula (I), in the formula (II), For a center point (nb= -1, pb=1),; Fuzzy rule base: typical rule example: IF is NB AND is NB THEN is PB, is NB, is PM; Weighted average deblurring: ,; Parameter updating: The update expression is expressed as: ; In the formula, the Clip function (also c