A model reference adaptive control scheme for pure-feedback nonlinear systems

We present a model reference adaptive control scheme for nonlinear systems in a pure-feedback canonical form with unknown parameters. The presence of parameter uncertainty in the system causes imperfect linearization, i.e., it introduces nonlinear additive terms in the transformed coordinates. Provided that these nonlinear terms are dominated by the norm of the transformed state, we establish global convergence of the output error for all initial estimates of the parameter vector lying in an open neighborhood of the true parameters in the parameter space.