采用干扰估计的液压系统自适应鲁棒控制

徐张宝 1; 马大为 1; 姚建勇 1; 马威 2

doi:10.7652/xjtuxb201608020

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采用干扰估计的液压系统自适应鲁棒控制

更新时间：2025-05-08

- 采用干扰估计的液压系统自适应鲁棒控制
- An Adaptive Robust Control Algorithm for Hydraulic Actuators with Disturbance Estimation
- 西安交通大学学报 2016年50卷第8期页码：123-129
- 作者机构：
  
  南京理工大学机械工程学院,南京,210094
- 作者简介：
- 基金信息：
  
  : 2016-02-28。作者简介: 徐张宝(1988—),男,博士生;马大为(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(51305203);中国博士后科学基金资助项目(2014M551593);江苏省自然科学基金资助项目(BK20141402)
- DOI：10.7652/xjtuxb201608020
  中图分类号： O121.8;G558
- 网络首发：2016-08-10，
  
  纸质出版：2016
- 稿件说明：
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徐张宝 1, 马大为 1, 姚建勇 1, 等. 采用干扰估计的液压系统自适应鲁棒控制[J]. 西安交通大学学报, 2016,50(8):123-129.

An Adaptive Robust Control Algorithm for Hydraulic Actuators with Disturbance Estimation[J]. 2016, 50(8): 123-129.
徐张宝 1, 马大为 1, 姚建勇 1, 等. 采用干扰估计的液压系统自适应鲁棒控制[J]. 西安交通大学学报, 2016,50(8):123-129. DOI： 10.7652/xjtuxb201608020.

An Adaptive Robust Control Algorithm for Hydraulic Actuators with Disturbance Estimation[J]. 2016, 50(8): 123-129. DOI： 10.7652/xjtuxb201608020.

摘要

针对液压伺服系统的建模不确定性和未建模不确定性问题

提出基于精确干扰估计的自适应鲁棒控制算法。该算法通过反步方法融合了有限时间干扰观测器和自适应鲁棒控制器; 有限时间干扰观测器用于估计非匹配集中干扰

能够保证有限时间精确估计干扰; 自适应鲁棒控制器用于处理系统参数不确定性并保证闭环系统稳定性。该算法理论上能够保证描述的瞬时控制精度和稳态跟踪误差

并且在某一时间T

后取得渐近跟踪性能。实验结果表明:该算法能够准确估计非匹配干扰

系统稳态跟踪误差最大约为0.06 mm

相对跟踪误差约为0.15%

系统跟踪精度得到了提高。

Abstract

An adaptive robust control algorithm with accurate disturbance estimation is proposed to deal with the modeling uncertainties and unmodeled uncertainties of the hydraulic systems. The algorithm uses the backstepping method to synthesize a finite time disturbance observer and an adaptive robust controller. The finite time disturbance observer is designed to estimate the mismatching concentrated uncertainties

and ensures accurate disturbance estimation in a finite time. The adaptive robust controller is designed to handle parametric uncertainties and stabilizes the closed loop system. In theory

the controller guarantees a prescribed transient tracking performance and final tracking accuracy

and achieves asymptotic tracking performance after a finite time T

. Experimental results show that the maximum static tracking e

rror of the proposed algorithm is about 0.06 mm

and the relative tracking error is about 0.15%

and that the algorithm improves the system tracking accuracy.

关键词

Keywords

references

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