A system identification model for adaptive nonlinear controlA 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.
Document ID
19920046624
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Linse, Dennis J. (NASA Headquarters Washington, DC United States)
Stengel, Robert F. (Princeton University NJ, United States)