Robust and Stochastic Model Predictive Control of Linear Systems with Predictable Additive Disturbance

In this dissertation, novel robust model predictive control (MPC) and stochastic MPC concepts are presented which can
efficiently handle a general class of additive disturbances perturbing discrete-time linear time-invariant systems. In particular, methods to use knowledge about the disturbance for improving the
performance while ensuring stability and feasibility are proposed. The robust MPC scheme is applied
to realize a robust adaptive cruise control (ACC) with which multiple objectives including driving
comfort, fuel economy, and recursive satisfaction of constraints can be achieved. Herein the speed of
the leading vehicle is considered as an additive disturbance for which a novel prediction procedure is
developed. Furthermore, an efficient solver is introduced to ensure the real-time capability of the
robust ACC.