  By optimizing the predicted performance over a receding horizon, model
predictive control (MPC) provides the ability to enforce state and control
constraints. The present paper considers an extension of MPC for nonlinear
systems that can be written in pseudo-linear form with state- and
control-dependent coefficients. The main innovation is to apply quadratic
programming iteratively over the horizon, where the predicted state trajectory
is updated based on the updated control sequence. Output-feedback control is
facilitated by using the block-observable canonical form for linear,
time-varying dynamics. This control technique is illustrated on various
numerical examples, including the Kapitza pendulum with slider-crank actuation,
the nonholonomic integrator, the electromagnetically controlled oscillator, and
the triple integrator with control-magnitude saturation.
