首页 | 本学科首页   官方微博 | 高级检索  
     


A framework for brain learning-based control of smart structures
Affiliation:1. Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, D-99423, Germany;2. Department of Mechanical Engineering, Private Bag 4800, Christchurch 8140, University of Canterbury, New Zealand;3. Institute of Artificial Intelligence and Cognitive Engineering, Faculty of Mathematics and Natural Sciences, University of Groningen, Netherlands;1. Civil and Environmental Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA 01609-2280, USA;2. Civil Engineering, Kwandong University, 522 Naegok, Gangneung, Gangwon 210-710, South Korea;1. Univ. of Maryland, 1173 Martin Hall, 4298 Campus Drive, College Park, MD 20742, USA;2. Univ. of Florida, 300 Weil Hall, 1949 Stadium Road, Gainesville, FL 32611, USA;1. Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran;2. Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran;1. PG Student, Civil Engineering Department, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388306, India;2. Professor and Head, Civil Engineering Department, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388306, India;1. School of Civil Engineering, University of Tehran, Tehran, Iran;2. School of Civil Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran;3. Civil Engineering Department, University of Ottawa, Canada;1. Department of Disaster Mitigation for Structures, College of Civil Engineering, Tongji University, Shanghai 200092, China;2. Department of Civil and Environment Engineering, University of Illinois at Urbana and Champaign, Urbana and Champaign 61801, USA
Abstract:A novel framework for intelligent structural control is proposed using reinforcement learning. In this approach, a deep neural network learns how to improve structural responses using feedback control. The effectiveness of the framework is demonstrated in a case study for a moment frame subjected to earthquake excitations. The performance of the learning method was improved by proposing a state-selector function that prevented the neural network from forgetting key states. Results show that the controller significantly improves structural responses not only to earthquake records on which it was trained but also to earthquake records new to the controller. The controller also has stable performance under environmental uncertainties. This capability distinguishes the proposed approach and makes it more appropriate for the situations in which it is likely that the controller will be exposed to unpredictable external excitations and high degrees of uncertainties.
Keywords:Reinforcement learning  Structural control  Seismic control  Aerospace control  Neural networks  Deep learning  Intelligent control  Smart structures  Structural dynamics  Earthquake
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号