title: Adaptive recurrent neural control for nonlinear system tracking.
abstract: We present a new indirect adaptive control law based on recurrent neural networks, which are linear on the input. For the identifier, we adapt a recently published algorithm to fit the neural network type used for identification; this algorithm ensures exponential stability for the identification error. The proposed controller is based on sliding mode techniques. Our main result, stated as a theorem, concerns tracking error asymptotic stability. Applicability of the proposed scheme is tested via simulations.
authors.affiliation: CINVESTAV-IPN, Mexico City, Mexico.
authors.forename: E N
authors.initials: EN
authors.lastname: Sanchez
authors.forename: M A
authors.initials: MA
authors.lastname: Bernal