  This paper considers model predictive control of Hammerstein systems, where
the linear dynamics are a priori unknown and the input nonlinearity is known.
Predictive cost adaptive control (PCAC) is applied to this system using
recursive least squares for online, closed-loop system identification with
optimization over a receding horizon performed by quadratic programming (QP).
In order to account for the input nonlinearity, the input matrix is defined to
be control dependent, and the optimization is performed iteratively. This
technique is applied to output stabilization of a chain of integrators with
unknown dynamics under control saturation and deadzone input nonlinearity.
