Scalable Robust Model Predictive Control for Linear Sampled-Data Systems

We propose a robust reachable-set-based model predictive control method for constrained linear systems. The systems are described by sampled-data models, where a continuous-time physical plant is controlled by a discrete-time digital controller. Thus, the state measurement and the control input are only updated at discrete sampling times, while the constraint satisfaction must be guaranteed not only at, but also between two consecutive time steps. By considering the computation time and using scalable reachability analysis and convex optimization tools, we compute real-time controllers that ensure constraint satisfaction for an infinite time horizon. We demonstrate the applicability of our proposed method using a vehicle platooning benchmark.