  A stochastic model predictive control framework over unreliable Bernoulli
communication channels, in the presence of unbounded process noise and under
bounded control inputs, is presented for tracking a reference signal. The data
losses in the control channel are compensated by a carefully designed
transmission protocol, and that of the sensor channel by a dropout compensator.
A class of saturated, disturbance feedback policies is proposed for control in
the presence of noisy dropout compensation. A reference governor is employed to
generate trackable reference trajectories and stability constraints are
employed to ensure mean-square boundedness of the reference tracking error. The
overall approach yields a computationally tractable quadratic program, which
can be iteratively solved online.
