Robust monotonic convergence point-to-point iterative learning control method for single-axis feeding system

Abstract

The invention discloses a robust monotonic convergence point-to-point iterative learning control method for a single-axis feeding system, and relates to the field of single-axis feeding systems. The method comprises the steps that expressing the single-axis feeding system through a kinetic equation, converting the kinetic equation into a discrete state space model, and acquiring an input and output matrix model; selecting m preset time points and acquiring the point-to-point uncertainty kinetic equation through the Toeplitz matrix meeting preset conditions, and acquiring the output vectors of the preset time points in the current operation batch; determining a tracking error of the preset time point in the current operation batch through the output vector of the preset time point in the current operation batch; and iteratively updating the input vector of the current operation batch through an iterative learning control updating law until the tracking error is not greater than a preset value, and controlling the single-axis feeding system through the input vector of the current operation batch to realize point-to-point tracking error monotonic convergence.

Description

technical field [0001] The invention relates to the field of single-axis feed systems, in particular to a robust monotone convergence point-to-point iterative learning control method for the single-axis feed system. Background technique [0002] The single-axis feed system is a drive system commonly used in the machinery industry. For example, laser cutting machines, CNC machine tools, wire electric discharge machines, water jet machines, etc. all need to use single-axis feed systems. In actual use, it is necessary to Drive the single-axis feed system to move according to a specific trajectory, but in the actual operation process, the ability of the single-axis feed system to track the specific trajectory is not strong, and there are often certain errors. In order to correct this error, improve the single-axis The tracking accuracy of the feed system, the iterative learning control method has been greatly developed, the iterative learning control method is suitable for syste...

AI Technical Summary

Problems solved by technology

[0003] In most cases, the purpose of the iterative learning control method is to solve the problem of tracking error. However, in the application of many iterative learning methods, it is often not necessary to track the complete trajectory movement, and only need to meet the tracking requirements at some key time points. However, the existing iterative learning control method cannot meet this requirement. At the same time, when the established simulation model is not accurate enough, the robustness will be a serious problem, and the convergence speed of the iterative learning control method will be slowed down, and even cannot be used for Provides an efficient solution to the original control problem

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