Yazar "Herencsar, Norbert" 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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    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.