Scaled Conjugate Gradient Based Adaptive ANN Control for SVM-DTC Induction Motor Drive

Abstract : In this work, an Artificial Neural Network (ANN) is developed to improve the performance of Space Vector Modulation (SVM) based Direct Torque Controlled (DTC) Induction Motor (IM) drive. The ANN control algorithm based on Scaled Conjugate Gradient (SCG) method is developed. The algorithm is tested on MATLAB Simulink platform. Results show smooth steady state operation as well as fast and dynamic transient performance. This is due to the SCG training algorithm of ANN which has the benchmarked performance against the standard Back-propagation (BP) algorithm. BP uses gradient descent optimization theory which has user selected parameters; learning rate and momentum constant. The network is trained offline and has fixed parameters. This leads to extra control effort and demands for online tuning of the parameters. SCG algorithm tunes these parameters with the use of second order approximation. Additionally, it takes less learning iterations and hence results in faster learning. Robustness to parameter variations and disturbances is the basic advantage of ANN, thus effectively controlling inherently non linear IM.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.384-395, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_33〉
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Lochan Babani, Sadhana Jadhav, Bhalchandra Chaudhari. Scaled Conjugate Gradient Based Adaptive ANN Control for SVM-DTC Induction Motor Drive. Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.384-395, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_33〉. 〈hal-01557626〉

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