A data-driven indirect method for nonlinear optimal control

Published

Conference Paper

© 2017 IEEE. Nonlinear optimal control problems are challenging to solve due to the prevalence of local minima that prevent convergence and/or optimality. This paper describes nearest-neighbors optimal control (NNOC), a data-driven framework for nonlinear optimal control using indirect methods. It determines initial guesses for new problems with the help of precomputed solutions to similar problems, retrieved using k-nearest neighbors. A sensitivity analysis technique is introduced to linearly approximate the variation of solutions between new and precomputed problems based on their variation of parameters. Experiments show that NNOC can obtain the global optimal solution orders of magnitude faster than standard random restart methods, and sensitivity analysis can further reduce the solving time almost by half. Examples are shown on two optimal control problems in vehicle control.

Full Text

Duke Authors

Cited Authors

  • Tang, G; Hauser, K

Published Date

  • December 13, 2017

Published In

Volume / Issue

  • 2017-September /

Start / End Page

  • 4854 - 4861

Electronic International Standard Serial Number (EISSN)

  • 2153-0866

International Standard Serial Number (ISSN)

  • 2153-0858

International Standard Book Number 13 (ISBN-13)

  • 9781538626825

Digital Object Identifier (DOI)

  • 10.1109/IROS.2017.8206362

Citation Source

  • Scopus