University of Washington

Steven Brunton

Xing Fu

Jose Nathan Kutz

Jacob R Grosek

Seth Pendergrass

Systems and methods for tuning multi-input complex dynamic systems in order to automatically obtain optimal performance are provided. Training is performed by measuring performance of the complex system using an objective function for a sparse sampling of input values over a variety of dynamic regimes. A sparse representation of the performance for each dynamic regime is stored in a library. At run-time, performance is measured and matched to a sparse representation in the library, and the complex system is configured with the optimal input values associated with the matching sparse representation from the library. Performance may then be optimized using an extremum-seeking controller. In some embodiments, the disclosed techniques are applied to a self-tuning mode-locked laser. In some embodiments, the disclosed techniques are applied to other complex systems such as phased array antennas and neurostimulation systems.

Tuning multi-input complex dynamic systems using sparse representations of performance and extremum-seeking control

The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.

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Seattle, WA, United States of America

Steven Brunton