Bulanık model referans öğrenmeli denetim yöntemlerinde kullanılan kazançların bir genetik algoritma ile belirlenmesi

Bulanık Model Referans Öğrenmeli Denetim (BMRÖD), bulanık denetleyicilerin tasarımı için sistematik tasarım prosedürü sağlayan bir yöntemdir. Çoğu klasik bulanık denetim sisteminde, üyelik fonksiyonlarının bazı parametreleri deneme-yanılma yöntemi ile belirlenir. Buna karşın, BMRÖD metodunda, bu parametreler bir öğrenme mekanizması ile belirlenir. Bu makalede, BMRÖD metodu incelenerek kargo gemisi dümen denetimine uygulanmış ve benzetim sonuçları sunulmuştur. İncelemeler sırasında, BMRÖD sisteminde kullanılan giriş ve çıkış kazançlarının denetim performansı üzerinde çok etkili olduğu ve bu kazançların belirlenmesi için literatürde sistematik bir yolun olmadığı tespit edilmiştir. Bu nedenle, bu çalışmada, iyi bir denetim performansı sağlayacak kazançların bulunması için Genetik Algoritma (GA) kullanılmıştır. Sunulan denetim yapısının etkinliğini gösteren benzetim sonuçları makalede verilmiştir.

Determination of the gains used in model reference learning control method with a genetic algorithm

Fuzzy Model Reference Learning Control (FMRLC) is a method which provides a systematic design procedure for the design of fuzzy controllers. In most classical fuzzy control systems, some parameters of the membership functions are determined by trial and error method. However, in FMRLC method, these parameters are determined by a learning mechanism. In this paper, the FMRLC method is investigated and applied to the control for the cargo ship steering and, simulation results are presented. During the investigations, it is found that the input and output gains used in the FMRLC system are very effective on the control performance and there is no systematic way in the literature to determine the gains. Therefore, in this study, a Genetic Algorithms (GA) is used to find the input gains which will provide a good control performance. Simulation results showing the effectiveness of the proposed control structure are given in the paper.

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