Abstract
| Language | English |
|---|---|
| DOIs | |
| Publication status | Published - Dec 2007 |
| Event | 46th IEEE Conference on Decision and Control - New Orleans, USA Duration: 12 Dec 2007 → 14 Dec 2007 |
Conference
| Conference | 46th IEEE Conference on Decision and Control |
|---|---|
| City | New Orleans, USA |
| Period | 12/12/07 → 14/12/07 |
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Keywords
- control nonlinearities
- discrete time systems
- multivariable systems
- nonlinear control systems
- optimal control
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NMV optimal estimation for nonlinear discrete-time multi-channel systems. / Grimble, M.J.
2007. Paper presented at 46th IEEE Conference on Decision and Control, New Orleans, USA, .Research output: Contribution to conference › Paper
TY - CONF
T1 - NMV optimal estimation for nonlinear discrete-time multi-channel systems
AU - Grimble, M.J.
PY - 2007/12
Y1 - 2007/12
N2 - A nonlinear operator approach to estimation in discrete-time multivariable systems is described. It involves inferential estimation of a signal which enters a communications channel involving both nonlinearities and transport delays. The measurements are assumed to be corrupted by a colored noise signal which is correlated with the signal to be estimated. The system model also includes a communications channel involving hard or dynamic nonlinearities. The signal and noise channels are represented in a very general nonlinear operator form. The algorithm is relatively simple to derive and to implement. The optimal nonlinear estimator is derived in terms of the nonlinear operators that describe the system. The results are the dual of the nonlinear generalized minimum variance control problem that has been very successful in providing a practical simple algorithm for nonlinear processes.
AB - A nonlinear operator approach to estimation in discrete-time multivariable systems is described. It involves inferential estimation of a signal which enters a communications channel involving both nonlinearities and transport delays. The measurements are assumed to be corrupted by a colored noise signal which is correlated with the signal to be estimated. The system model also includes a communications channel involving hard or dynamic nonlinearities. The signal and noise channels are represented in a very general nonlinear operator form. The algorithm is relatively simple to derive and to implement. The optimal nonlinear estimator is derived in terms of the nonlinear operators that describe the system. The results are the dual of the nonlinear generalized minimum variance control problem that has been very successful in providing a practical simple algorithm for nonlinear processes.
KW - control nonlinearities
KW - discrete time systems
KW - multivariable systems
KW - nonlinear control systems
KW - optimal control
UR - http://dx.doi.org/10.1109/CDC.2007.4434006
U2 - 10.1109/CDC.2007.4434006
DO - 10.1109/CDC.2007.4434006
M3 - Paper
ER -