Receding horizon control : a suboptimality based approach Within the proposed work we consider analytical conceptional and implementational issues of so called receding horizon controllers in a sampled data setting. The principle of such a controller is simple: Given the current state of a system we compute an open loop control which is optimal for a given costfunctional over a fixed prediction horizon. Then the control is implemented on the first sampling interval and the basic open loop optimal control problem is shifted forward in time which allows for a repeated evaluation. The contribution of this thesis is threefold: First we prove estimates for the performance of a receding horizon control a concept which we call suboptimality degree. These estimate are online computable and can be applied for stabilizing as well as practically stabilizing receding horizon control laws. Moreover they not only allow for guaranteeing stability of the closed loop but also for quantifying the loss of performance of the receding horizon control law compared to the infinite horizon control law. Based on these estimates we introduce adaptation strategies to modify the underlying receding horizon controller in order to guarantee a certain lower bound on the suboptimality degree while reducing the computing cost time necessary to solve this problem. Within this analysis the length of the optimization horizon is the parameter we wish to adapt ...