Generalized norm optimal iterative learning control with intermediate point and sub-interval tracking

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• 作者：David H. Owens ; Chris T. Freeman ; Bing Chu
• 关键词：Iterative learning control ; learning control ; optimization ; linear systems ; robotics
• 刊名：International Journal of Automation and Computing
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：12
• 期：3
• 页码：243-253
• 全文大小：764 KB
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• 作者单位：David H. Owens (1) (2) (3)
  Chris T. Freeman (2)
  Bing Chu (2)
  
  1. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
  2. School of Electronics and Computer Science, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
  3. Advanced Robotics Division, Italian Institute of Technology, Genoa, Italy
  
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Computer Applications
  Computer-Aided Engineering and Design
  Chinese Library of Science
  
• 出版者：Institute of Automation, Chinese Academy of Sciences, co-published with Springer-Verlag GmbH
• ISSN：1751-8520

文摘

Norm optimal iterative learning control (NOILC) has recently been applied to iterative learning control (ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix “point to point-movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC’s well-defined convergence properties. Design guidelines and supporting experimental results are included.