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Conference Object Citation - WoS: 1Citation - Scopus: 2Hardware-In Assessment of a Fault Tolerant Fuzzy Control Scheme for an Offshore Wind Farm Simulator(Elsevier, 2022) Simani, Silvio; Farsoni, Saverio; Turhan, CihanTo enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data-driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input-output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data-driven approach, thus representing the key point if real-time solutions have to implement the proposed fault tolerant control strategy. Finally, a high-fidelity simulator relying on a hardware-in-the-loop tool is exploited to verify and validate the reliability and robustness characteristics of the developed methodology also for on-line and more realistic implementations. Copyright (C) 2022 The Authors.Article Citation - WoS: 51Citation - Scopus: 63Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method(Sage Publications Ltd, 2014) Arvin, Farshad; Turgut, Ali Emre; Bazyari, Farhad; Arikan, Kutluk Bilge; Bellotto, Nicola; Yue, ShigangAggregation in swarm robotics is referred to as the gathering of spatially distributed robots into a single aggregate. Aggregation can be classified as cue-based or self-organized. In cue-based aggregation, there is a cue in the environment that points to the aggregation area, whereas in self-organized aggregation no cue is present. In this paper, we proposed a novel fuzzy-based method for cue-based aggregation based on the state-of-the-art BEECLUST algorithm. In particular, we proposed three different methods: naive, that uses a deterministic decision-making mechanism; vector-averaging, using a vectorial summation of all perceived inputs; and fuzzy, that uses a fuzzy logic controller. We used different experiment settings: one-source and two-source environments with static and dynamic conditions to compare all the methods. We observed that the fuzzy method outperformed all the other methods and it is the most robust method against noise.
