Self-triggered robust output feedback model predictive control of constrained linear systems

This paper presents a self-triggered model predictive control (MPC) algorithm for constrained linear discrete-time systems in the presence of bounded state and output disturbances, when only the system output can be measured at triggering instants. The proposed algorithm mainly relies on a state estimator whose estimation error is bounded by an invariant set, and on the self-triggered robust model predictive control of a nominal system, in which the cost function penalizes the nominal system state and control effort as well as the triggering interval. The proposed algorithm is proved to drive the system to an invariant set with respect to the disturbances. Numerical examples are also provided to validate the proposed algorithm and to further illustrate better performance compared with the periodic MPC.