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 constrains 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.weiterlesen