Abstract
In this paper we consider the problem of finding a filter that minimizes the worst case magnitude (l∞) of the estimation error in the case of linear time invariant systems subjected to unknown but magnitude bounded (l∞) inputs. These inputs consist of process and observation noise, as well as initial conditions; also, the optimization problem is considered over an infinite time horizon. Taking a model matching approach, suboptimal solutions are presented which stem from the resulting l∞-induced norm minimization problem.
| Original language | English (US) |
|---|---|
| Pages | 314-318 |
| Number of pages | 5 |
| DOIs | |
| State | Published - Jan 1 1993 |
| Event | 1st IEEE Regional Conference on Aerospace Control Systems, AEROCS 1993 - Westlake Village, United States Duration: May 25 1993 → May 27 1993 |
Conference
| Conference | 1st IEEE Regional Conference on Aerospace Control Systems, AEROCS 1993 |
|---|---|
| Country | United States |
| City | Westlake Village |
| Period | 5/25/93 → 5/27/93 |
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ASJC Scopus subject areas
- Aerospace Engineering
- Control and Systems Engineering
- Control and Optimization
Cite this
OptiImal l1 estimation. / Voulgaris, Petros G.
1993. 314-318 Paper presented at 1st IEEE Regional Conference on Aerospace Control Systems, AEROCS 1993, Westlake Village, United States.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - OptiImal l1 estimation
AU - Voulgaris, Petros G
PY - 1993/1/1
Y1 - 1993/1/1
N2 - In this paper we consider the problem of finding a filter that minimizes the worst case magnitude (l∞) of the estimation error in the case of linear time invariant systems subjected to unknown but magnitude bounded (l∞) inputs. These inputs consist of process and observation noise, as well as initial conditions; also, the optimization problem is considered over an infinite time horizon. Taking a model matching approach, suboptimal solutions are presented which stem from the resulting l∞-induced norm minimization problem.
AB - In this paper we consider the problem of finding a filter that minimizes the worst case magnitude (l∞) of the estimation error in the case of linear time invariant systems subjected to unknown but magnitude bounded (l∞) inputs. These inputs consist of process and observation noise, as well as initial conditions; also, the optimization problem is considered over an infinite time horizon. Taking a model matching approach, suboptimal solutions are presented which stem from the resulting l∞-induced norm minimization problem.
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UR - http://www.scopus.com/inward/citedby.url?scp=85065668037&partnerID=8YFLogxK
U2 - 10.1109/AEROCS.1993.720949
DO - 10.1109/AEROCS.1993.720949
M3 - Paper
AN - SCOPUS:85065668037
SP - 314
EP - 318
ER -