This work builds upon a framework for improving trajectory flexibility in systems controlled by Iterative Learning Control (ILC). Here we focus on positioning systems, decomposing a class of trajectories into motion primitives, termed basis tasks. The correct input signal for each basis task is identified in a training routine with ILC. The main development of this paper is a framework to intelligently apply these basis task specific input signals using an adaptation of bumpless transfer techniques. Bumpless transfer is reoriented to seamlessly transition between open-loop ILC signals without attenuating signal content away from the transition points. Experimental results display the effectiveness of the proposed approach on a serial planar positioning robot. Two conditions on basis task sequencing are tested. One which satisfies constraints imposed by previous work, and a relaxed trajectory constraint case designed to further explore trajectory flexibility. Bumpless transfer recovers some of the performance lost by constraint relaxation.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Number of pages7
StatePublished - 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011


Other2011 American Control Conference, ACC 2011
Country/TerritoryUnited States
CitySan Francisco, CA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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