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Article Citation - WoS: 7Citation - Scopus: 8Robust and Adaptive Control Design of a Drilling Rig During the Operating Modes(Sage Publications Ltd, 2019) Sadeghi, Amir Nobahar; Arikan, Kutluk Bilge; Ozbek, Mehmet Efe; Baranoglu, BesimOil well drilling towers have different operating modes during a real operation, like drilling, tripping, and reaming. Each mode involves certain external disturbances and uncertainties. In this study, using the nonlinear model for the modes of the operation, robust and/or adaptive control systems are designed based on the models. These control strategies include five types of controllers: cascaded proportional-integral-derivative, active disturbance rejection controller, loop shaping, feedback error learning, and sliding mode controller. The study presents the design process of these controllers and evaluates the performances of the proposed control systems to track the reference signal and reject the uncertain forces including the parametric uncertainties and the external disturbances. This comparison is based on the mathematical performance measures and energy consumption. In addition, three architectures are presented to control the weight on bit during drilling process, and also to maintain a preset constant weight on bit, two control approaches are designed and presented.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.
