Name
Abstract | ||
|---|---|---|
The polynomial chaos of Wiener provides a framework for the statistical analysis of dynamical systems, with computational cost far superior to Monte Carlo simulations. It is a useful tool for control systems analysis because it allows probabilistic description of the effects of uncertainty, especially in systems having nonlinearities and where other techniques, such as Lyapunov's method, may fail. We show that stability of a system can be inferred from the evolution of modal amplitudes, covering nearly the full support of the uncertain parameters with a finite series. By casting uncertain parameters as unknown gains, we show that the separation of stochastic from deterministic elements in the response points to fast iterative design methods for nonlinear control. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.automatica.2006.01.010 | Automatica |
Keywords | Field | DocType |
Polynomial chaos,Control applications | Lyapunov function,Mathematical optimization,Monte Carlo method,Nonlinear control,Control theory,Polynomial chaos,Dynamical systems theory,Iterative design,Control system,Probabilistic logic,Mathematics | Journal |
Volume | Issue | ISSN |
42 | 5 | Automatica |
Citations | PageRank | References |
24 | 1.59 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franz S. Hover | 1 | 255 | 18.09 |
| Michael S. Triantafyllou | 2 | 25 | 2.97 |