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 language | English (US) |
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Pages (from-to) | 3854-3858 |
Number of pages | 5 |
Journal | Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics |
Volume | 56 |
Issue number | 4 |
DOIs | |
State | Published - 1997 |
All Science Journal Classification (ASJC) codes
- Statistical and Nonlinear Physics
- Statistics and Probability
- Condensed Matter Physics