A Design Method of Robust Inverse Model Learning Gain Based on FIR Filter

Abstract

A robust inverse model learning gain design method based on FIR filtering belongs to the field of ultra-precision motion control. The goal of robust inverse model iterative learning control is to continuously improve the servo accuracy of the motion system through servo error learning, and its learning gain is determined by the inverse low-pass filter H(z) of the nominal model of the closed-loop system and the time advance link z τ Three parts are connected in series; the method adopts an FIR low-pass filter with a linear phase shift characteristic to replace the traditional low-pass filter in the prior art, and realizes zero-phase filtering by compensating the linear phase shift of the FIR low-pass filter. In the disclosed method of the present invention, the FIR low-pass filter can directly realize the specified stop-band initial frequency and stop-band attenuation through design, and the amount of time lead compensation can be directly obtained by calculation, and zero-phase filtering can be realized in a larger frequency range, overcoming It eliminates the blindness of parameter setting, and can effectively ensure that the expected compensation effect is achieved in practical applications.

Description

technical field [0001] The invention belongs to the field of ultra-precision motion control, and in particular relates to a robust inverse model learning gain design method based on FIR filtering. Background technique [0002] The ultra-precision motion system is the core component of the lithography machine, and its tracking performance of the highly dynamic reference motion trajectory directly determines the technical indicators of the whole machine. At present, the servo accuracy and adjustment time requirements of high-end lithography machines for their motion systems have reached the nanometer and millisecond levels, respectively, which puts forward extremely high requirements for the level of feedforward compensation. Iterative learning control is a kind of intelligent control technology, which shows strong feedforward compensation ability in motion control, which is very suitable for complex motion systems such as lithography workpiece table, which are difficult to ac...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the common low-pass filter and the time-leading link of the existing method are difficult to effectively design, provide a robust inverse model learning gain design method based on FIR filtering, and further improve the feedforward compensation of the motion control system ability and improve the servo performance of the lithography machine motion table, which has important engineering application value

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