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Browsing by Author "Boutalis, Y."

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    Citation - Scopus: 1
    Fuzzy Hybrid Systems Modeling With Application in Decision Making and Control
    (2012) Boutalis, Y.; Moor, T.; Schmidt, K.; 06.08. Mekatronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
    Hybrid Systems are systems containing both discrete event and continuous variable components. Many recent contributions address crisp situations, where ambiguity or subjectivity in the measured data is absent. In this paper, we propose Fuzzy Hybrid Systems to account for inaccurate measurements and uncertain dynamics. We present a strategy to determine the most appropriate control actions in a sampled data setting. The proposed approach is based on three basic steps that are performed in each sampling period. First, the current discrete fuzzy state of the system is determined by a sensor evaluation. Next, the future discrete fuzzy state is predicted for the possible control actions and the best action, in respect to desired continuous states, is selected. Finally, the decision is cross-evaluated by a limited horizon prediction of the continuous system variables. The proposed method is explained and demonstrated for a variation of the a well-known two-tank scenario. © 2012 IEEE.
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    Citation - Scopus: 8
    Multi-Objective Decision Making Using Fuzzy Discrete Event Systems: a Mobile Robot Example
    (2010) Boutalis, Y.; Schmidt, K.; 06.08. Mekatronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
    In this paper, we propose an approach for the multi-objective control of sampled data systems that can be modeled as fuzzy discrete event systems (FDES). In our work, the choice of a "fuzzy" system representation is justified by the assumption of a controller realization that depends on various potentially imprecise sensor measurements. Our approach consists of three basic steps that are performed in each sampling instant. First, the current fuzzy state of the system is determined by a sensor evaluation. Second, the future fuzzy state is predicted for the possible control actions, and finally, a particular multi-objective weighting strategy allows to determine the control action to be applied. We demonstrate the features of our method by a mobile robot example. © 2010 IEEE.