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Fuzzy discrete event systems (FDESs) have been introduced in recent years to model systems whose discrete states or discrete state transitions can be uncertain and are, hence, determined by a possibility degree. This paper develops an FDES framework for the control of sampled data systems that have to fulfill multiple objectives. The choice of a fuzzy system representation is justified by the assumption of a controller realization that depends on various potentially imprecise sensor measurements. The proposed framework consists of three basic steps that are performed at each sampling instant. First, the current fuzzy state of the system is determined by a sensor evaluation. Second, the fuzzy state in the future sampling instant is predicted for all possible control actions of the system. Finally, an original multiobjective weighting strategy is proposed to determine the control action to be applied in the current sampling instant. The features of the proposed approach are demonstrated by a detailed mobile robot example, which includes a simulation study.