Name
Abstract | ||
|---|---|---|
In this paper, we consider the problem of set-membership identification of multiple-input multiple-output (MIMO) linear models when both input and output measurements are affected by bounded additive noise. Firstly, we propose a general formulation that allows the user to take into account possible a-priori information on the structure of the MIMO model to be identified. Then, we formulate the problem in terms of a suitable polynomial optimization problem that is solved by means of a convex relaxation approach. To show the effectiveness of the proposed approach, we test the original MIMO identification algorithm on a simulation example, as well as on a set of input–output experimental data, collected on a multiple-input multiple-output electronic process simulator. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.automatica.2017.12.042 | Automatica |
Keywords | Field | DocType |
Set-membership identification,MIMO,Bounded error | Errors-in-variables models,Polynomial optimization,Mathematical optimization,Experimental data,Linear system,Linear model,MIMO,Input/output,Mathematics,Bounded function | Journal |
Volume | Issue | ISSN |
90 | 1 | 0005-1098 |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vito Cerone | 1 | 100 | 17.07 |
| Valentino Razza | 2 | 6 | 3.54 |
| Diego Regruto | 3 | 174 | 22.43 |