Experimental Validation of Robust Iterative Learning Control on an Overhead Crane Test Setup

详细信息    查看全文

• 作者：Tong Duy Son ^{tong.duyson@mech.kuleuven.be" class="auth_mail" title="E-mail the corresponding author tong.duyson@mech.kuleuven.be" class="auth_mail" title="E-mail the corresponding author} ; Goele Pipeleers ; Jan Swevers
• 关键词：Iterative learning control (ILC) ; Robust control
• 刊名：IFAC-PapersOnLine
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：47
• 期：3
• 页码：5981-5986
• 全文大小：818 K

文摘

This paper presents an experimental validation of a recently proposed robust norm-optimal iterative learning control (ILC). The robust ILC input is computed by minimizing the worst-case value of a performance index under model uncertainty, yielding a convex optimization problem. The proposed robust ILC design is experimentally validated on a lab scale overhead crane system, showing the advantages of the approach over classical ILC designs in monotonic convergence and tracking performance.