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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Abdelhaq, Laoufi
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Control & Systems Engineering Electrical & Electronics Engineering

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Improvement direct torque control of induction motor using robust intelligence artificial ANFIS speed controller Abdelhaq, Laoufi; Moulay-Idriss, Chergui; Chekroun, Soufiane
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i3.pp1552-1565

Abstract

This paper proposes a study aimed at improving the conventional direct torque control (DTC) technique applied to induction motors (IM). The primary aim is to reduce the harmonic distortions and fluctuations associated with the electrical current, flux variations, and generated torque, while ensuring accurate speed reference tracking and ensuring optimal dynamic performance of the drive, especially under variable speed conditions. To achieve this, we introduce an intelligent control system that utilizes a hybrid neuro-fuzzy inference model (ANFIS), through the application of the back propagation method. The DTC-ANFIS technique is compared with the traditional DTC-PI method and simulated using MATLAB/Simulink in different scenarios. The obtained results reveal a significant improvement in performance over DTC-PI, with superior results over a wide speed range.
Optimization of photovoltaic pumping system using neuro fuzzy inference system ANFIS control technique Abdelhaq, Laoufi; Moulay-Idriss, Chergui; Chekroun, Soufyane
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1270-1284

Abstract

In recent years, artificial intelligence has become increasingly used due to the development of microcontrollers. In this paper, we propose an intelligent technique that employs the adaptive neuro-fuzzy inference system (ANFIS). We use this approach to improve the conventional direct torque control (DTC), which relies on a PI controller for the induction machine, and to enhance the conventional MPPT control based on the Perturb and Observe algorithm. The overall goal is to improve the performance of the photovoltaic pumping system. In this work, we apply ANFIS control to maximum power point tracking (MPPT-ANFIS). Additionally, we simultaneously optimize the efficiency of the DTC by applying ANFIS control (DTC-ANFIS). We present the results by comparing the photovoltaic pumping system using ANFIS control with the conventional photovoltaic pumping system, using MATLAB/Simulink. The results show that ANFIS control significantly improves the photovoltaic system compared to the conventional control, offering excellent dynamic performance of the induction motor and better utilization of photovoltaic solar energy. However, the ANFIS has some drawbacks, such as high computational time consumption and challenges in implementing a database.
Co-Authors Chekroun, Soufiane Chekroun, Soufyane Moulay-Idriss, Chergui