Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

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