Mishra, Prabhat K.Diwale, Sanket S.Jones, Colin N.Chatterjee, Debasish2021-04-242021-04-242021-04-242021-05-0110.1016/j.automatica.2021.109512https://infoscience.epfl.ch/handle/20.500.14299/177599WOS:000634882100016A 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 those 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. (C) 2021 Elsevier Ltd. All rights reserved.Automation & Control SystemsEngineering, Electrical & ElectronicAutomation & Control SystemsEngineeringtrackingstochastic mpcbounded controlspacket dropoutsnetworked systemoutput-feedbacklinear-systemsrobuststabilitytext::journal::journal article::research article