Name
Abstract | ||
|---|---|---|
In this paper, a novel framework to address the problem of parametric estimation for continuous-time linear time-invariant dynamic systems is dealt with. The proposed methodology entails the design of suitable kernels of non-anticipative linear integral operators thus obtaining estimators showing, in the ideal case, non-asymptotic (i.e., finite-time) convergence. The analysis of the properties of the kernels guaranteeing such a convergence behaviour is addressed and a novel class of admissible kernel functions is introduced. The operators induced by the proposed kernels admit implementable (i.e., finite-dimensional and internally stable) state-space realizations. Extensive numerical results are reported to show the effectiveness of the proposed methodology. Comparisons with some existing continuous-time estimators are addressed as well and insights on the possible bias affecting the estimates are provided. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TAC.2015.2434075 | IEEE Trans. Automat. Contr. |
Keywords | Field | DocType |
Kernel,Convergence,Stability analysis,Time-varying systems,Estimation,Linear systems,Data models | Kernel (linear algebra),Convergence (routing),Mathematical optimization,Linear system,Control theory,Computer science,Operator (computer programming),Estimation theory,Dynamical system,Estimator,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
61 | 2 | 0018-9286 |
Citations | PageRank | References |
2 | 0.52 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gilberto Pin | 1 | 136 | 17.21 |
| Andrea Assalone | 2 | 4 | 1.72 |
| Marco Lovera | 3 | 283 | 44.29 |
| T Parisini | 4 | 935 | 113.17 |