Robust Model Predictive Control for Linear Discrete-Time System With Saturated Inputs and Randomly Occurring Uncertainties

This paper investigates the robust model predictive control (RMPC) problem for a class of linear discrete-time systems subject to saturated inputs and randomly occurring uncertainties (ROUs). Due to limited bandwidth of the network channels, the networked transmission would inevitably lead to incomplete measurements and subsequently unavoidable network-induced phenomenon that include saturated inputs as a special case. The saturated inputs are assumed to be sector-bounded in the underlying system. In addition, the ROUs are taken into account to reflect the difficulties in precise system modelling, where the norm-bounded uncertainties are governed by certain uncorrelated Bernoulli-distributed white noise sequences with known conditional probabilities. Based on the invariant set theory, a sufficient condition is derived to guarantee the robust stability in the mean-square sense of the closed-loop system. By employing the convex optimization technique, the controller gain is obtained by solving an optimization problem with some inequality constraints. Finally, a simulation example is employed to demonstrate the effectiveness of the proposed RMPC scheme.