Adaptive asymptotic tracking using barrier functions

This paper studies the global output tracking problem for a class of unknown time-varying nonlinear systems in strict-feedback form. By utilizing the barrier functions, a universal adaptive state-feedback control strategy is proposed that achieves asymptotic tracking performance. Unlike the existing results in the literature, the proposed control scheme utilizes the barrier functions to ensure the unknown system nonlinearities to be the bounded “disturbance-like” terms, which are adaptively compensated at each step, this enables any approximation structures are not needed. Furthermore, the “explosion of complexity” issue in backstepping-like approaches is avoided without using additional filtering. Simulation results are presented to demonstrate the effectiveness of the proposed methodology.