TY - GEN
T1 - A system identification model for adaptive nonlinear control
AU - Linse, Dennis J.
AU - Stengel, Robert F.
PY - 1991
Y1 - 1991
N2 - A system identification model that combines generalized-spline function approximation with a nonlinear control system is described. The complete control system contains three main elements: a nonlinear-inverse-dynamic control law that depends on a comprehensive model of the plant, a state estimator whose outputs drive the control law, and a function approximation scheme that models the system dynamics. The system-identification task, which combines an extended Kalman filter with a function approximator modeled as an artificial neural network, is considered. The results of an application of the identification techniques to a nonlinear transport aircraft model are presented.
AB - A system identification model that combines generalized-spline function approximation with a nonlinear control system is described. The complete control system contains three main elements: a nonlinear-inverse-dynamic control law that depends on a comprehensive model of the plant, a state estimator whose outputs drive the control law, and a function approximation scheme that models the system dynamics. The system-identification task, which combines an extended Kalman filter with a function approximator modeled as an artificial neural network, is considered. The results of an application of the identification techniques to a nonlinear transport aircraft model are presented.
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U2 - 10.23919/acc.1991.4791685
DO - 10.23919/acc.1991.4791685
M3 - Conference contribution
AN - SCOPUS:0026368923
SN - 0879425652
SN - 9780879425654
T3 - Proceedings of the American Control Conference
SP - 1752
EP - 1757
BT - Proceedings of the American Control Conference
PB - Publ by American Automatic Control Council
T2 - Proceedings of the 1991 American Control Conference
Y2 - 26 June 1991 through 28 June 1991
ER -