Name
Title | ||
|---|---|---|
An Uncertainty-Based Control Lyapunov Approach for Control-Affine Systems Modeled by Gaussian Process. |
Abstract | ||
|---|---|---|
Data-driven approaches in control allow for identification of highly complex dynamical systems with minimal prior knowledge. However, properly incorporating model uncertainty in the design of a stabilizing control law remains challenging. Therefore, this letter proposes a control Lyapunov function framework which semiglobally asymptotically stabilizes a partially unknown fully actuated control aff... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LCSYS.2018.2841961 | IEEE Control Systems Letters |
Keywords | Field | DocType |
Kernel,Data models,Unified modeling language,Training data,Gaussian processes,Lyapunov methods,Control systems | Affine transformation,Dynamic programming,Data modeling,Nonlinear system,Control-Lyapunov function,Control theory,Computer science,Dynamical systems theory,Gaussian process,Control system | Journal |
Volume | Issue | ISSN |
2 | 3 | 2475-1456 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonas Umlauft | 1 | 4 | 5.14 |
| Lukas Pohler | 2 | 0 | 0.68 |
| Sandra Hirche | 3 | 961 | 106.36 |