Segmentation filtering iterative learning control method of motor servo system

A technology of iterative learning control and servo system, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as bad learning transient, and achieve the effect of avoiding bad learning transient and improving tracking accuracy

Active Publication Date: 2015-09-09
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] The present invention overcomes the disadvantage of poor learning transient in the prior art, and provides a segmented filter iter

Method used

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  • Segmentation filtering iterative learning control method of motor servo system
  • Segmentation filtering iterative learning control method of motor servo system
  • Segmentation filtering iterative learning control method of motor servo system

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Embodiment Construction

[0033] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0034] consider as figure 2 In the ILC simulation system shown, the motor servo control system G(z) is as figure 1 shown. Take the learning gain c 1 、c 2 600 and 50 respectively, the zero-phase filter Q(z) uses a second-order Butterworth low-pass filter. Motor torque constant K s =0.687N·m / V, the moment of inertia J of the motor s =6.234×10 -5 kg·m 2 , the viscosity coefficient B of the motor s =2.127×10 -3 kg·m 2 / s, motor transmission ratio k=0.4, other parameters K p =1.089×10 -1 V s / mm, K i＝ 3.716V / mm, T i = 0.0001s. according to figure 2 The rules for obtaining the ILC are as follows:

[0035] u k (z)=c 1 e k (z)+c 2 Q(z)e k-1 (z)+u k-1 (z)

[0036] the y k (z)=G(z)u k (z)

[0037] G ( z ) = 3.701 × ...

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Abstract

Provided is a segmentation filtering iterative learning control method of a motor servo system. The method comprises steps of firstly determining learning law convergence frequency domain scope is 0-f<c0> when a zero-phase filter, i.e., Q(z)=1, is not introduced; then performing reference trajectory track of the primary iteration-free loop so as to obtain an error signal e0(t), applying the EMD algorithm to decompose the e0(t) into limited number of intrinsic mode functions, and obtaining time sequence characteristics of the error signal e0(t) via the Hilbert conversion; then using frequency f<c0> as the segmentation reference frequency; segmenting the error signal e0(t), and distinguishing time segments where instant frequency of the error signal e0(t) is higher than the f<c0> and lower than the f<c0>; designing cut-off frequency of a zero phase according to the Hilbert time sequence characteristics of the e0(t); in the time segment where the instant frequency of the error signal is higher than the f<c0>, f<c>=f<c0>+1/2(f<max>-f<c0>) is adopted as the cut-off frequency of the filter, wherein fmax represents the highest frequency in this segment; in other time segments, f<c0> is adopted as the cut-off frequency of the filter; and at last, applying the designed segmentation zero phase filter to the following iterative learning control processes.

Description

(1) Technical field [0001] The invention belongs to an iterative learning control method applied to a motor servo system. (2) Background technology [0002] Iterative Learning Control (ILC) uses the control experience and deviation information of previous batches of the control system to improve the control performance of the current batch and achieve precise tracking of the desired trajectory. It provides an effective control method for the motor servo system and its application equipment (such as industrial robots, CNC machine tools, etc.) with high-speed and high-precision requirements. [0003] The basic idea of ​​ILC appeared in a Japanese research paper published by Japanese scholar Uchiyama in 1978. Until 1984, Arimoto and others perfected the basic idea of ​​iterative learning, established a practical algorithm, and gradually formed the theory of iterative learning control. In the actual application of ILC, due to the existence of difficult-to-identify parasitic vi...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 徐建明易芬孙明轩欧林林邢科新何德峰
Owner ZHEJIANG UNIV OF TECH
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