Adaptive Control of Discrete Time-Varying LQGko
Mathematica Applicanda (1999)
- Volume: 27, Issue: 41
- ISSN: 1730-2668
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topAdam Czornik. "Adaptive Control of Discrete Time-Varying LQGko." Mathematica Applicanda 27.41 (1999): null. <http://eudml.org/doc/292650>.
@article{AdamCzornik1999,
abstract = {The adaptive version of the discrete time-varying linear quadratic control is considered under the assumption that the coefficients have limits as time tends to infinity sufficiently fast in certain sense and the limiting system is observable and stabilizable. It is proved that time invariant LS estimator can be used to estimate the limits of the coefficients and that it is strongly consistent under some conditions well known from the time invariant case. The estimator of the parameters is used to define an adaptive control law and it is shown that the control law is optimal.},
author = {Adam Czornik},
journal = {Mathematica Applicanda},
keywords = {Estimation and detection; Adaptive control; Discrete-time systems; Stochastic learning and adaptive control},
language = {eng},
number = {41},
pages = {null},
title = {Adaptive Control of Discrete Time-Varying LQGko},
url = {http://eudml.org/doc/292650},
volume = {27},
year = {1999},
}
TY - JOUR
AU - Adam Czornik
TI - Adaptive Control of Discrete Time-Varying LQGko
JO - Mathematica Applicanda
PY - 1999
VL - 27
IS - 41
SP - null
AB - The adaptive version of the discrete time-varying linear quadratic control is considered under the assumption that the coefficients have limits as time tends to infinity sufficiently fast in certain sense and the limiting system is observable and stabilizable. It is proved that time invariant LS estimator can be used to estimate the limits of the coefficients and that it is strongly consistent under some conditions well known from the time invariant case. The estimator of the parameters is used to define an adaptive control law and it is shown that the control law is optimal.
LA - eng
KW - Estimation and detection; Adaptive control; Discrete-time systems; Stochastic learning and adaptive control
UR - http://eudml.org/doc/292650
ER -
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