A Class of High Order Tuners for Adaptive Systems
| Autor: | Michael A. Bolender, Eugene Lavretsky, Travis E. Gibson, Joseph E. Gaudio, Anuradha M. Annaswamy |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Lyapunov stability
Novel technique Normalization (statistics) Mathematical optimization Control and Optimization Differential equation Computer science 010501 environmental sciences 01 natural sciences Parameter estimation algorithm 03 medical and health sciences 0302 clinical medicine Control and Systems Engineering Adaptive system High order 030217 neurology & neurosurgery 0105 earth and related environmental sciences Symplectic geometry |
| Zdroj: | IEEE Control Systems Letters. 5:391-396 |
| ISSN: | 2475-1456 |
| DOI: | 10.1109/lcsys.2020.3002513 |
| Popis: | Parameter estimation algorithms using higher order gradient-based methods are increasingly sought after in machine learning. Such methods however, may become unstable when regressors are time-varying. Inspired by techniques employed in adaptive systems, this letter proposes a new variational perspective to derive four higher order tuners with provable stability guarantees. This perspective includes concepts based on higher order tuners and normalization and allows stability to be established for problems with time-varying regressors. The stability analysis builds on a novel technique which stems from symplectic mechanics, that links Lagrangians and Hamiltonians to the underlying Lyapunov stability analysis, and is provided for common linear-in-parameter models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|