A System Identification Model for Adaptive Nonlinear Control

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 here as an artificial neural network, is considered in detail. The state estimator provides the necessary data so that continuous training of the neural network is possible during normal operation. The results of an application of the identification techniques to a nonlinear transport aircraft model are presented.