Tufenkci, SevilayKavuran, GürkanYeroglu, Celaleddin2025-10-242025-10-2420232147-284Xhttps://doi.org/10.17694/bajece.1114868https://search.trdizin.gov.tr/tr/yayin/detay/1263141https://hdl.handle.net/20.500.12899/2268Integration 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.eninfo:eu-repo/semantics/openAccessPI controllerDeep reinforcement learningDC motorTwin-delayed deep deterministic policy gradientArticle10.17694/bajece.11148681121841891263141