Abstract
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 language | English (US) |
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Title of host publication | Proceedings of the 2011 American Control Conference, ACC 2011 |
Pages | 4305-4311 |
Number of pages | 7 |
State | Published - 2011 |
Event | 2011 American Control Conference, ACC 2011 - San Francisco, CA, United States Duration: Jun 29 2011 → Jul 1 2011 |
Other
Other | 2011 American Control Conference, ACC 2011 |
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Country/Territory | United States |
City | San Francisco, CA |
Period | 6/29/11 → 7/1/11 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering