Adaptive neuro control for output feedback nonlinear systems

The paper is extending output feedback nonlinear control and backstepping approaches to a class of systems approximately diffeomorphic to output feedback systems. The uncertainties under consideration are of two types-parametric and non-parametric. The parametric ones are modeled by universal approximators such as neural networks. The nonparametric ones include not only approximation errors but also some terms unmodeled by the output feedback form. The non-parametric terms are assumed to be bounded by unknown constants. The backstepping procedure is applied to adapt with respect to both parametric uncertainties and the upper bound on the non-parametric uncertainties. The main technology used to compensate for non-parametric uncertainties is recursive application of the adaptive bounding design.