Oil 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.
Face detection is one of the challenging problems in the image processing, as a main part of automatic face recognition. Employing the color and image segmentation procedures, a simple and effective algorithm is presented to detect human faces on the input image. To evaluate the performance, the results of the proposed methodology is compared with ViolaJones face detection method.
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