Learning control algorithm for nonlinear maps

Jair Botina, Herschel Rabitz

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A feedback optimal control algorithm is developed for [Formula Presented]-dimensional maps, which uses learning-based feedback optimal control techniques. The algorithm has two steps: (1) Learn the control of a reference map containing a stochastic term. (2) Apply the learned control to the laboratory system employing real time feedback. The stochastic component of the learning step is important to provide a close knit family of controls to handle laboratory uncertainty and noise. As an example, the formalism is applied to simulated two- and three-dimensional nonlinear laboratory maps in the presence of noise.

Original languageEnglish (US)
Pages (from-to)3854-3858
Number of pages5
JournalPhysical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Volume56
Issue number4
DOIs
StatePublished - Jan 1 1997

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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