Feed-forward compensation control of nonlinear friction for high acceleration motion system

Jing CUI; Si-yu WANG; Zhong-yi CHU

doi:10.3788/OPE.20182601.0077

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Feed-forward compensation control of nonlinear friction for high acceleration motion system

Micro/Nano Technology and Fine Mechanics | 更新时间：2020-08-13

- Feed-forward compensation control of nonlinear friction for high acceleration motion system
- Optics and Precision Engineering Vol. 26, Issue 1, Pages: 77-85(2018)
- 作者机构：
  
  1.北京工业大学机械工程与应用电子技术学院, 北京 100124
  2.北京航空航天大学仪器科学与光电工程学院, 北京 100191
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182601.0077
  CLC： TP273;TH117.1
- Received：28 April 2017，
  
  Accepted：14 July 2017，
  
  Published：25 January 2018
- 稿件说明：
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Jing CUI, Si-yu WANG, Zhong-yi CHU. Feed-forward compensation control of nonlinear friction for high acceleration motion system[J]. Optics and precision engineering, 2018, 26(1): 77-85.
DOI：

Jing CUI, Si-yu WANG, Zhong-yi CHU. Feed-forward compensation control of nonlinear friction for high acceleration motion system[J]. Optics and precision engineering, 2018, 26(1): 77-85. DOI： 10.3788/OPE.20182601.0077.

摘要

高加速度运动系统中非线性摩擦的建模补偿对提高轨迹跟踪性能至关重要。本文针对传统参数化模型难以准确预估高加速度运动启停阶段摩擦过冲等非线性摩擦的问题，在传统模型结构的基础上，结合扩展Stribeck模型，提出一种扩展参数化模型，模型参数的训练和学习样本源于高精度迭代学习控制获取的有限轨迹下非线性摩擦前馈补偿数据，并采用Levenberg-Marquardt算法拟合模型参数。最后，在音圈电机驱动的高加速定位平台上针对不同运动轨迹进行了实验验证。结果表明，该方法能够克服传统参数化模型难以消除高加速度启停阶段摩擦过冲等非线性摩擦对轨迹跟踪精度的影响；且与迭代学习控制的轨迹跟踪精度接近，有效避免了迭代学习泛化性差等问题，可实现工作空间下任意轨迹的摩擦补偿。

Abstract

Modeling compensation of nonlinear friction is vital to improve the trajectory tracking performance of high acceleration motion systems. To overcome the problem of inaccurate estimation of the start-stop stage nonlinear friction (including friction overshoot) associated with the traditional parametric model for high acceleration motion systems

this paper describes a novel extended parametric model combining the traditional model structure with the extended Stribeck model. The training data for identifying the model parameters are obtained using the high-precise Iterative Learning Control (ILC) approach

which supplies the nonlinear friction feed-forward compensation data with limited trajectories in the workspace. The data are fitted with the Levenberg-Marquardt algorithm. Finally

the proposed model is validated with different trajectories on a high acceleration position platform driven by a Voice Coil Motor (VCM). The experimental results indicate that the proposed method can overcome the influence of nonlinear friction associated with the traditional model

including the friction overshoot in the start-stop stage. Moreover

the accuracy is comparable with the result of ILC

but offers the advantage that the proposed model can avoid the problem of poor generalization in ILC to realize the friction compensation of an arbitrary trajectory in the workspace.

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references

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