Name
Abstract | ||
|---|---|---|
This paper presents a data-driven controller-tuning algorithm that includes a sufficient condition for closed-loop stability. This stability condition is defined by a set of convex constraints on the Fourier transform of specific auto- and cross-correlation functions. The constraints are included in a correlation-based controller-tuning method that solves a model-reference problem. This entirely data-driven method requires a single experiment and can also be applied to nonminimum-phase and unstable systems. The resulting controller is guaranteed to stabilize the plant as the data length tends to infinity. The performance with finite data length is illustrated through a simulation example. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CDC.2008.4739326 | CDC |
Keywords | Field | DocType |
fourier transform,fourier transforms,tuning,noise,transfer functions,cross correlation function,stability analysis,stability | Mathematical optimization,Control theory,Data-driven,Computer science,Control theory,Infinity,Regular polygon,Fourier transform,Transfer function | Conference |
ISSN | ISBN | Citations |
0191-2216 E-ISBN : 978-1-4244-3124-3 | 978-1-4244-3124-3 | 6 |
PageRank | References | Authors |
0.56 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Klaske van Heusden | 1 | 65 | 11.65 |
| A. Karimi | 2 | 289 | 40.41 |
| Dominique Bonvin | 3 | 133 | 23.58 |