Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025/r/gumushane/19525
  Advanced adaptive control strategies application for speed regulation of a 12V small dc geared motor

  Cetin, Gokhan

  This paper investigates the implementation and performance of adaptive control techniques for a 12V small geared DC motor characterized by modeling errors and input disturbances. This paper discusses the following two primary approaches: Adaptive Radial Basis Function Neural Network (ARBFNN) Controllers and Model Reference Adaptive Control (MRAC). In model uncertainty, MRAC and ARBFNN outperformed the simple Proportional-Integral (PI) controller. The study is further expanded to involve Robust MRAC and Adaptive Sliding Mode Radial Basis Function Neural Network (ASRBFNN) Controllers to counter the compounded effects of model uncertainty and input disturbances. The versions of the robust controllers performed better than the conventional PI controller in cases involving both uncertainties and disturbances. Implementations were done on a 12V geared DC motor testbed with an Arduino microcontroller and MATLAB's System Identification Toolbox. The results from simulations and experimental applications highlight the greater flexibility and disturbance rejection capability of the developed advanced adaptive control schemes, making them perform better than standard PI controllers under challenging conditions.

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• MakaleAçık Erişim2019/r/gumushane/18125
  A fuzzy logic reasoning based real time energy price regulation approach for smart grids

  Cakmak, Recep; Çakanel, Ahmet

  Intelligent technologies have become pioneer force to provide flexible, dynamic and efficient energy generation andmanagement. Thus, smart algorithms such as fuzzy logic, artificial neural network, machine learning, soft computingtechniques are sole remedy against growing diverse and numerous distributed generations that make more complicatedpower systems. Real time closed loop controlling requires energy price as a featured variable to procure supply demandequilibrium point for a stable and reliable power system operation, where several dynamic models and estimationsoftware are introduced in the literature. In this study, a fuzzy logic reasoning-based price regulator (FLR-PR) is designedand simulated on MATLAB/Simulink environment using 2018 hourly data of a summer day taken from annual energyreport of Turkey. The proposed model has been compared to Proportional Integral Derivative (PID) price controller basedon performance indexes in the constituted simulation cases. FLR-PR tracks instant reference demand signal changes withminimum steady state error and fast transient response with respect to PID controller.

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  AtıfOpenAlex1

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