Iterative learning-based waypoint optimization for repetitive path planning, with application to airborne wind energy systems

Mitchell Cobb, Kira Barton, Hosam Fathy, Chris Vermillion

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This paper presents an iterative learning approach for optimizing waypoints in repetitive path following applications. Our proposed algorithm consists of two key features: First, a recursive least squares fit is used to construct an estimate of the behavior of the performance index. Secondly, an iteration-to-iteration waypoint adaptation law is used to update waypoints in the direction of optimal performance. This waypoint update law parallels the mathematical structure of a traditional iterative learning control (ILC) update but replaces the tracking error term with an error between the present and estimated optimal waypoint sequences. The proposed methodology is applied to the crosswind path optimization of an airborne wind energy (AWE) system, where the goal is to maximize the average power output over a figure-8 path. In validating the tools from this work, we introduce a simplified 2-dimensional analog to the more complex 3-dimensional AWE system, which distills the problem to its core elements. Using this model, we demonstrate that the proposed waypoint adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions.

Original languageEnglish (US)
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2698-2704
Number of pages7
ISBN (Electronic)9781509028733
DOIs
StatePublished - Jan 18 2018
Event56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia
Duration: Dec 12 2017Dec 15 2017

Publication series

Name2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Volume2018-January

Other

Other56th IEEE Annual Conference on Decision and Control, CDC 2017
CountryAustralia
CityMelbourne
Period12/12/1712/15/17

All Science Journal Classification (ASJC) codes

  • Decision Sciences (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Control and Optimization

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    Cobb, M., Barton, K., Fathy, H., & Vermillion, C. (2018). Iterative learning-based waypoint optimization for repetitive path planning, with application to airborne wind energy systems. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (pp. 2698-2704). (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2017.8264051