Learning Time-optimal Anti-swing Trajectories for Overhead Crane Systems

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• 关键词：Overhead crane systems ; Minimum ; time trajectory planning ; Machine learning ; Regression techniques
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9719
• 期：1
• 页码：338-345
• 全文大小：778 KB
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• 作者单位：Xuebo Zhang (16)
  Ruijie Xue (16)
  Yimin Yang (17)
  Long Cheng (18)
  Yongchun Fang (16)
  
  16. Institute of Robotics and Automatic Information System (IRAIS) and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, 300071, China
  17. Department of Electrical and Computer Engineering, University of Windsor, Windsor, N9B3P4, Canada
  18. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
  
• 丛书名：Advances in Neural Networks ¨C ISNN 2016
• ISBN：978-3-319-40663-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9719

文摘

Considering both state and control constraints, minimum-time trajectory planning (MTTP) can be implemented in an ‘offline’ way for overhead crane systems [1]. In this paper, we aim to establish a real-time trajectory planning model by using machine learning approaches to approximate those results obtained by MTTP. The fusion of machine learning regression approaches into the trajectory planning module is new and the application is promising for intelligent mechatronic systems. In particular, we first reformulate the considered trajectory planning problem in a three-segment form, where the acceleration and deceleration segments are symmetric. Then, the offline MTTP is applied to generate a database of minimum-time trajectories for the acceleration stage, based on which several regression approaches including Extreme Learning Machine (ELM) and Backpropagation Neural Network (BP) are adopt to approximate MTTP results with high accuracy. More important, the resulting model only contains a set of parameters, rather than a large volume of offline data, and thus machine learning based approaches could be implemented in low-cost digital signal processing chips required by industrial applications. Comparative evaluation results are provided to show the superior performance of the selected regression approach.