Scalable robust output feedback MPC of linear sampled-data systems

Cyber-physical control systems typically consist of two components: a clocked digital controller and a physical plant evolving in continuous time. Clearly, the state and input constraints must be satisfied not only at, but also between sampling times of the controller. We address this issue by proposing a robust output feedback model predictive control approach for sampled-data systems, which are affected by additive disturbances and measurement noise. To guarantee robust constraint satisfaction for an infinite time horizon, we present a scalable approach to compute safe terminal sets. Based on these sets and using scalable reachability analysis and convex optimization algorithms, we construct real-time controllers that explicitly consider all online computation times. We demonstrate the usefulness of our robust control approach using a vehicle platooning benchmark from the literature.