Adaptive tracking of nonlinear dynamic plants is currently an important area of research. The main difficulty being felt by the research community is the lack of a general modelling framework that can facilitate synthesis of a simplistic control law, while being capable of providing accurate approximation of nonlinear systems. The aim of this study is to alleviate that problem by introducing a novel technique based on the control-oriented U- Model for the adaptive tracking of a wide range of stable nonlinear dynamic plants using only input- output data. The overall scheme is based on the robust internal model control (IMC) structure wherein different internal models, using nonlinear adaptive filtering and higher-order neural networks, are used. In each case, the U-Model equivalence of the internal model is developed and a simplistic control law based on polynomial root-solving is synthesized. The effectiveness of the proposed adaptive schemes is demonstrated through simulations and real-time applications to a variety of nonlinear plants.