A study on iterative learning control for vibration of Stewart platform

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• 作者：Byeongsik Ko ; Jong-Wook Park ; Dong W. Kim
• 关键词：Auto power spectral density ; Halbach permanent magnet array ; iterative learning control ; linear electromagnetic actuator ; normalized RMS error ; Stewart platform
• 刊名：International Journal of Control, Automation and Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：15
• 期：1
• 页码：258-266
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Control, Robotics, Mechatronics;
• 出版者：Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
• ISSN：2005-4092
• 卷排序：15

文摘

This paper presents the replication of a desired vibration response by iterative learning control system for a Stewart platform. The Stewart platform is a multi-input, multi-output system with parameter uncertainties including system nonlinearity and joint nonlinearity. Most vehicle manufacturers are relying on road test simulation facilities in order to reduce development time and to enhance product quality. Road simulation algorithm is essential for developing road test simulation system. With digital signal processing technology, more complex control algorithms including iterative learning control can be utilized. In this paper, a controller based on iterative learning control (ILC) algorithm was developed to produce the desired target response in case of a single actuator as the first experiment after programmed with C language. As a next experiment, the control algorithm was implemented in a road test simulation system using a Stewart platform. A real test was carried out to replicate total six channels of acceleration signals measured at top and left side points of audio player system installed to a car running on Belgian road. The convergence rate and test simulation accuracy higher than 90% showed that the algorithm was acceptable to replicate the target vibration response.