Yazar "Yeroglu, Celaleddin" seçeneğine göre listele

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    A theoretical demonstration for reinforcement learning of PI control dynamics for optimal speed control of DC motors by using Twin Delay Deep Deterministic Policy Gradient Algorithm
    (Pergamon-Elsevier Science Ltd, 2023) Tufenkci, Sevilay; Alagoz, Baris Baykant; Kavuran, Gurkan; Yeroglu, Celaleddin; Herencsar, Norbert; Mahata, Shibendu
    To benefit from the advantages of Reinforcement Learning (RL) in industrial control applications, RL methods can be used for optimal tuning of the classical controllers based on the simulation scenarios of operating con-ditions. In this study, the Twin Delay Deep Deterministic (TD3) policy gradient method, which is an effective actor-critic RL strategy, is implemented to learn optimal Proportional Integral (PI) controller dynamics from a Direct Current (DC) motor speed control simulation environment. For this purpose, the PI controller dynamics are introduced to the actor-network by using the PI-based observer states from the control simulation envi-ronment. A suitable Simulink simulation environment is adapted to perform the training process of the TD3 algorithm. The actor-network learns the optimal PI controller dynamics by using the reward mechanism that implements the minimization of the optimal control objective function. A setpoint filter is used to describe the desired setpoint response, and step disturbance signals with random amplitude are incorporated in the simu-lation environment to improve disturbance rejection control skills with the help of experience based learning in the designed control simulation environment. When the training task is completed, the optimal PI controller coefficients are obtained from the weight coefficients of the actor-network. The performance of the optimal PI dynamics, which were learned by using the TD3 algorithm and Deep Deterministic Policy Gradient algorithm, are compared. Moreover, control performance improvement of this RL based PI controller tuning method (RL-PI) is demonstrated relative to performances of both integer and fractional order PI controllers that were tuned by using several popular metaheuristic optimization algorithms such as Genetic Algorithm, Particle Swarm Opti-mization, Grey Wolf Optimization and Differential Evolution.
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    An Approach for DC Motor Speed Control with Off-Policy Reinforcement Learning Method
    (2023) Tufenkci, Sevilay; Kavuran, Gürkan; Yeroglu, Celaleddin
    Integration of self-learning mechanisms with control systems is frequently encountered in the literature due to the development of autonomous systems. This paper proposes a tuning method of PI controllers using a deep reinforcement learning algorithm, which is known as self-learning structure. The coefficients of the PI controller, which is used to control a DC motors, are determined. The proposed method aims to adjust the voltage value applied to the input of the DC motor to reach the desired speed with the tuned PI controller using the twin- delayed deep deterministic policy gradient (TD3) reinforcement learning algorithm. The Kp and Ki coefficients of the PI controller are taken as the absolute values of the neural network weights, which are driven by Gradient descent optimization to positive values with a fully connected layer. The proposed tuning method has been shown to provide a higher gain margin and a more optimal solution.
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    COVID-19 and human development: An approach for classification of HDI with deep CNN
    (Elsevier Sci Ltd, 2023) Kavuran, Gurkan; Gokhan, Seyma; Yeroglu, Celaleddin
    The measures taken during the pandemic have had lasting effects on people's lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people's behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Con-volutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).
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    Implementations of TD3 and DDPG Reinforcement Learning Techniques for Tuning PID Controller of TRMS System
    (Springer Heidelberg, 2025) Tufenkci, Sevilay; Alagoz, Baris Baykant; Kavuran, Gurkan; Yeroglu, Celaleddin; Herencsar, Norbert; Mahata, Shibendu
    Reinforcement Learning (RL) is a learning method that utilizes interactions between agents and their environments, providing a valuable tool for controller design through simulations. However, traditional industrial systems such as PID control loops have yet to fully embrace the advantages of RL algorithms for effectively tuning controllers. This study presents an experimental initiative demonstrating the implementation of an RL-driven method for optimal PID controller tuning to address challenges in rotor control, explicitly focusing on the Twin-Rotor Multi-Input Multi-Output System (TRMS). Rotor control presents a complex challenge involving aerodynamics and external disturbances. The research implements two RL algorithms, namely the Deep Deterministic Policy Gradient (DDPG) and the Twin Delay Deep Deterministic Policy Gradient (TD3), in a tailored simulation environment to train RL agents to achieve optimal PID control dynamics. Results of simulation and experimental studies indicate that RL algorithms can be implemented for PID controller tuning when the simulation environment for training the RL algorithms well-represent the dominating dynamics and control complications of real-world systems. In this case, both the simulation and experimental results are in good-agreement.