Enhanced generalized predictive control with disturbance compensation

Kwek Lee Chung, Alan Tan Wee Chiat, W. E. Kiong
{"title":"Enhanced generalized predictive control with disturbance compensation","authors":"Kwek Lee Chung, Alan Tan Wee Chiat, W. E. Kiong","doi":"10.1109/ICED.2014.7015848","DOIUrl":null,"url":null,"abstract":"This paper presents an improved generalized predictive control (GPC) scheme integrated with a disturbance compensation scheme that combines iterative learning control (ILC) and real-time feedback control (RFC). A least mean square error (LMSE) estimator has been used to estimate the output error caused by repeatable disturbances. The use of this estimated error information in the ILC component aims to reduce the effect of real-time disturbances in the learning process. On the other hand, the inclusion of the current cycle error information, handled by the RFC component, allows the controller to make more immediate corrections with respect to disturbances occurring during the on-going operation. The proposed GPC-ILC-RFC-LMSE method is simulated on a two-link planar robotic manipulator that is to track a circular trajectory repeatedly. A discrete-time model of the robotic manipulator is used to predict the system output over a prediction horizon such that optimal control inputs that minimize the angular position and velocity trajectory errors can be determined. The proposed GPC-ILC-RFC-LMSE scheme succeeds to reduce the trajectory tracking errors significantly where the average MSE values is merely 40% of that of the GPC-ILC counterpart. In addition, the proposed controller is more robust if compared to the existing GPC learning methods where smoother control input profiles has been achieved.","PeriodicalId":143806,"journal":{"name":"2014 2nd International Conference on Electronic Design (ICED)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 2nd International Conference on Electronic Design (ICED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICED.2014.7015848","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

This paper presents an improved generalized predictive control (GPC) scheme integrated with a disturbance compensation scheme that combines iterative learning control (ILC) and real-time feedback control (RFC). A least mean square error (LMSE) estimator has been used to estimate the output error caused by repeatable disturbances. The use of this estimated error information in the ILC component aims to reduce the effect of real-time disturbances in the learning process. On the other hand, the inclusion of the current cycle error information, handled by the RFC component, allows the controller to make more immediate corrections with respect to disturbances occurring during the on-going operation. The proposed GPC-ILC-RFC-LMSE method is simulated on a two-link planar robotic manipulator that is to track a circular trajectory repeatedly. A discrete-time model of the robotic manipulator is used to predict the system output over a prediction horizon such that optimal control inputs that minimize the angular position and velocity trajectory errors can be determined. The proposed GPC-ILC-RFC-LMSE scheme succeeds to reduce the trajectory tracking errors significantly where the average MSE values is merely 40% of that of the GPC-ILC counterpart. In addition, the proposed controller is more robust if compared to the existing GPC learning methods where smoother control input profiles has been achieved.
具有扰动补偿的增强广义预测控制
本文提出了一种结合迭代学习控制(ILC)和实时反馈控制(RFC)的改进的广义预测控制(GPC)方案和扰动补偿方案。最小均方误差(LMSE)估计器用于估计可重复扰动引起的输出误差。在ILC分量中使用这种估计误差信息的目的是减少学习过程中实时干扰的影响。另一方面,包含由RFC组件处理的当前周期错误信息,允许控制器对正在进行的操作期间发生的干扰进行更直接的纠正。以重复跟踪圆形轨迹的两连杆平面机械臂为实验对象,对所提出的GPC-ILC-RFC-LMSE方法进行了仿真。利用机械臂的离散时间模型在预测范围内预测系统输出,从而确定使角度位置和速度轨迹误差最小的最优控制输入。提出的GPC-ILC- rfc - lmse方案成功地降低了轨迹跟踪误差,平均MSE值仅为GPC-ILC方案的40%。此外,与现有的GPC学习方法相比,所提出的控制器具有更强的鲁棒性,而现有的GPC学习方法可以实现更平滑的控制输入轮廓。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信