  We analyze the convergence properties of a robust adaptive model predictive
control algorithm used to control an unknown nonlinear system. We show that by
employing a standard quadratic stabilizing cost function, and by recursively
updating the nominal model through kinky inference, the resulting controller
ensures convergence of the true system to the origin, despite the presence of
model uncertainty. We illustrate our theoretical findings through a numerical
simulation.
