Robust Monotonic Convergence Point-to-Point Iterative Learning Control Method for Single-axis Feed System

Abstract

The invention discloses a robust monotonic convergence point-to-point iterative learning control method for a single-axis feed system, and relates to the field of single-axis feed systems. Discrete the state-space model and obtain the input-output matrix model; select M preset time points, and obtain the point-to-point uncertainty dynamic equation through the Toeplitz matrix satisfying the preset conditions, and obtain the output of the current running batch at the preset time point Vector; Determine the tracking error at the current running batch at the preset time point by the output vector at the current running batch at the preset time point; Iteratively update the input vector of the current running batch through the iterative learning control update law until the tracking error is no longer is greater than the preset value, the single-axis feed system is controlled by the input vector of the current running batch, and the monotonic convergence of point-to-point tracking error is realized.

Description

Robust monotonically convergent point-to-point iterative learning control method for single-axis feed systems technical field The present invention relates to the field of uniaxial feed system, especially a kind of robust monotonic convergence point-to-point of uniaxial feed system Iterative learning control method. Background technique [0002] The single-axis feed system is a drive system commonly used in the machinery industry, such as laser cutting machines, CNC machines Beds, wire-cutting machines, water jet machines, etc. all need to use a single-axis feed system, and in actual use, it is necessary to drive a single-axis The feeding system moves according to a specific trajectory, but in actual operation, the single-axis feeding system tracks the specific trajectory. The ability to move is not strong, and there is often a certain error. In order to correct this error and improve the tracking accuracy of the single-axis feed system, the The generation learning c...

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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