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  • Open Access

    ARTICLE

    Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers

    İhsan Uluocak1,*, Hakan Yavuz2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 221-234, 2023, DOI:10.32604/csse.2023.025426

    Abstract The recent studies on Artificial Intelligence (AI) accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) technique is a widely used, safe and reliable control method based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive model predictive control that utilizes selected AI methods. The presented approach presents an updated the mathematical model of… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based PID Controller for an Eddy Current Dynamometer

    İhsan Uluocak1,*, Hakan Yavuz2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1229-1243, 2022, DOI:10.32604/iasc.2022.023835

    Abstract This paper presents a design and real-time application of an efficient Artificial Intelligence (AI) method assembled with PID controller of an eddy current dynamometer (ECD) for robustness due to highly nonlinear system by reason of some magnetism phenomena such as skin effect and dissipated heat of eddy currents. PID Control which is known as the most popular conventional control method in industry is inadequate for such nonlinear systems. On the other hand, Adaptive Neural Fuzzy Interference System (ANFIS), Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), and Radial Basis Neural Network (RBNN) are examples used as artificial… More >

  • Open Access

    ARTICLE

    An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

    Ruichao Zhang1,*, Liqiang Wang1, Dechun Chen2

    Energy Engineering, Vol.118, No.4, pp. 1069-1082, 2021, DOI:10.32604/EE.2021.014961

    Abstract In recent years, deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields. In the diagnosis of sucker-rod pump working conditions, due to the lack of a large-scale dynamometer card data set, the advantages of a deep convolutional neural network are not well reflected, and its application is limited. Therefore, this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning, which is used to solve the problem of too few samples in a dynamometer card data set. Based… More >

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