• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2018, Vol. 54 ›› Issue (20): 271-278.doi: 10.3901/JME.2018.20.271

• 控制元件与系统 • 上一篇    下一篇

基于迭代学习算法的电液比例伺服控制

彭熙伟1, 何砚高2   

  1. 1. 北京理工大学自动化学院 北京 100081;
    2. 中国航空工业集团公司西安飞行自动控制研究所 西安 710065
  • 收稿日期:2017-09-19 修回日期:2018-03-01 出版日期:2018-10-20 发布日期:2018-10-20
  • 通讯作者: 何砚高(通信作者),男,1992年出生。主要研究方向为检测与智能控制。E-mail:heyangao@126.com
  • 作者简介:彭熙伟,男,1966年出生,教授,硕士研究生导师。主要研究方向为检测与智能控制、测试与检测技术。E-mail:pengxiweiwcy@bit.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61304026)。

Electro-hydraulic Proportional Servo Control Based on Iterative Learning Algorithm

PENG Xiwei1, HE Yangao2   

  1. 1. School of Automation, Beijing Institute of Technology, Beijing 100081;
    2. AVIC Xi'an Flight Automatic Control Research Institute, Xi'an 710065
  • Received:2017-09-19 Revised:2018-03-01 Online:2018-10-20 Published:2018-10-20

摘要: 为解决电液比例控制系统的非线性、时变性、变流量死区及变流量增益等对系统位置控制精度的影响,提高电液比例控制系统的控制精度,针对系统的非线性特性,设计不严格依赖于系统精确数学模型且有较强抗干扰能力的迭代学习算法,同时针对系统的变死区特性,设计能够基于误差和误差变化率在线调整死区补偿量的模糊死区补偿算法。迭代学习算法和模糊死区补偿算法的综合使用是根据当前的控制经验灵活调整控制量,从而有效地改善由于系统非线性及时变性所带来的影响。试验结果表明,不加入模糊死区补偿时,系统位置跟踪存在明显的滞后,最大位移跟踪误差达到6 mm,而同时采用迭代学习算法和模糊死区补偿算法极大的提高系统的控制性能,系统达到稳定跟踪后,最大位移跟随误差在1 mm以内。

关键词: 迭代学习, 阀控液压缸, 非线性, 死区

Abstract: In order to solve the problem of nonlinearity, time-varying, variable flow dead zone and variable flow gain of electro-hydraulic proportional control system, and improve the control precision of electro-hydraulic proportional control system, an iterative learning algorithm that does not strictly depend on the exact mathematical model of the system is designed. At the same time, according to the variable dead zone characteristic of the system, a fuzzy dead-band compensation algorithm is proposed to adjust the dead zone compensation based on error and error rate of change. The combination of iterative learning algorithm and fuzzy dead zone compensation algorithm is based on the current control experience to flexibly adjust the control volume, thus effectively improving the system's control performance. The experimental results show that there is a significant lag in system location tracking without fuzzy dead-band compensation, the maximum displacement tracking error is 6 mm; the control performance of the system is improved by using of the iterative learning algorithm and the fuzzy dead-band compensation algorithm. After the system achieved stable tracking, the maximum displacement tracking error is less than 1 mm.

Key words: dead zone, iterative learning, nonlinear, valve-controlled hydraulic cylinders

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