Robust self-triggered model predictive control for discrete-time linear systems based on homothetic tubes

Abstract

In this diploma thesis a robust self-triggered model predictive control (MPC) scheme for discrete-time linear time-invariant systems subject to input and state constraints and additive disturbances is presented. The goal of the proposed control scheme is to reduce the communication between the control computer and the sensors and actuators, respectively, while still providing robust stability. This is achieved by combining the ideas of self-triggered control, where the time between two samplings is maximized, and (Homothetic) Tube MPC, which is a robust optimization based control method. Tube MPC uses the so called tubes around the nominal state and input trajectories, based on the bounds of the disturbances, to ensure the satisfaction of the constraints. Homothetic Tube MPC is an enhancement with additional degrees of freedom. It is shown that a closed and bounded set including the origin in its interior is stabilized.