Robust output-feedback control of strict-feedback systems with unknown nonlinearities

We study the problem of output tracking for uncertain strict-feedback systems with output and derivative information. The unknown nonlinear terms in the system description are not linearly parametrized, but it is assumed that the optimal parameters that characterize a neural net based approximation to the nonlinearities lie within a known compact set. The proposed controllers, with output and derivative information, guarantee the boundedness of all signals in the closed-loop system, and, at the expense of increased control effort, the output tracking error can be driven to an arbitrarily small neighborhood of the origin arbitrarily fast. Also, the closed loop signals satisfy a relevant disturbance attenuation inequality, which, under certain conditions, implies asymptotic tracking.