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AK, AYÇA

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AK

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AYÇA

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End date: 2019 × Has files: false ×

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    Fiber optik uygulamaları
    (Proje Kitabı, 2013-12-01) AK, AYÇA; Ak A.
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    Fiber optic training program with intensive experiments using both real laboratory and simulation environments
    (WILEY, 2019) SARIKAŞ, ALİ; Aydin, Serkan; Sarikas, Ali; Ak, Ayca; Yayla, Ayse; Kesen, Ugur; Oral, Bekir
    This paper highlights a fiber optic training program developed according to the occupational competencies using real and simulation platforms to train young people aged between 15 and 24. The most important objective is to overcome the shortages of fiber optic employees by providing training qualifications accredited by the Fiber Optic Association. This training program was designed on three levels, and participants were tested at the end of each level. Successful participants continued with a higher level of training. Theoretical knowledge was given to the participants at the first two levels and extensive practical applications were done. At the third level, computer networks trainings were provided to identify the much more fiber optic network modules by using simulation software tool. The training program includes installation of DVB-X (Digital Video Broadcast - satellite, cable) and the FFT-X (fiber-to-the - home, building, curb) devices that have a fiber optic cable infrastructure, and point-to-point line measurements. This training program differs from similar programs due to the inclusion of effective real laboratory, simulation platform, and field practices. It is significantly found that this training program supported by more extensive real experiments and simulations besides of theoretical education increases the technical qualifications and satisfaction ratio of the participants.
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    ROBOT TRAJECTORY TRACKING WITH ADAPTIVE RBFNN-BASED FUZZY SLIDING MODE CONTROL
    (KAUNAS UNIV TECHNOLOGY, 2011) AK, AYÇA; Ak, Ayca Gokhan; Cansever, Galip; Delibasi, Akin
    Due to computational burden and dynamic uncertainty, the classical model-based control approaches are hard to be implemented in the multivariable robotic systems. In this paper, a model-free fuzzy sliding mode control based on neural network is proposed. In classical sliding mode controllers, system dynamics and system parameters are required to compute the equivalent control. In Radial Basis Function Neural Network (RBFNN) based fuzzy sliding mode control, a RBFNN is developed to mimic the equivalent control law in the Sliding Mode Control (SMC). The weights of the RBFNN are changed for the system state to hit the sliding surface and slide along it with an adaptive algorithm. The initial weights of the RBFNN are set to zero and then tuned online, no supervised learning procedures are needed. In the proposed method, by introducing the fuzzy concept to the sliding mode and fuzzifying the sliding surface, the chattering can be alleviated. The proposed method is implemented on industrial robot (Manutec-r15) and compared with a PID controller. Experimental studies carried out have shown that this approach is a good candidate for trajectory tracking applications of industrial robot.