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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Jordan, Mouné Cédric
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Control & Systems Engineering Electrical & Electronics Engineering

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Artificial intelligence-enhanced DTC command methods used for a four-wheel-drive system Max, Ndoumbé Matéké; Eric, Njock Batake Emmanuel; Maurice, Nyobe Yomé Jean; Jordan, Mouné Cédric; Moise, Manyol; Georges, Olong; Biboum, Alain
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i4.pp1983-1994

Abstract

This paper presents an artificial intelligence direct torque control (DTC) method for an electric vehicle (EV) drive system. The architecture of the proposed electric vehicle is that of four wheels each with an induction motor (IM). A comparative study of the different torque and speed controllers proposed in this paper is made. An electronic differential is used to control the speed of each wheel as well as a variable master-slave control (VMSC) for the management of the magnetic quantities because the motors on the same side are fed by the same converter. This study allows highlights the performance of the propulsion system in terms of dynamics and safety of the vehicle and better stability. The different controllers are implemented by the MATLAB/Simulink software and the simulation results obtained show better flexibility in the control of the vehicle. It is worth noting that direct torque control with fuzzy logic (DTFC) performs better than DTC associated with neural networks in terms of a time reduction increase of 1.47%, an overshoot of less than 5.33, and a static steady-state error close to zero.
Performance improvement of the RSM970S radar speed controller of ASECNA, Douala International Airport Angon, Moussa Bessike; Florence, Offolé; Séverin, Nguiya; Jordan, Moune Cédric; Max, Ndoumbé Matéké
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2080-2088

Abstract

This article addresses the direct torque control associated with artificial intelligence for the control of the RSM970S radar of The Agency for the Safety of Air Navigation in Africa and Madagascar (ASECNA), Douala International Airport. The radar is driven by two identical induction motors powered by a single inverter. Direct torque control is associated with fuzzy logic and artificial neural networks. The test results performed in MATLAB/Simulink showed an improvement in the dynamic performance of the drive system compared to a conventional direct torque control. There is an overshoot in speed during the steady state of less than 0.955% of the neural technique over the fuzzy technique. But also, an accuracy of 99.58% of the two techniques proposed in this article. These results ensure good precision and stability of the radar in the detection of airplanes in the Cameroonian airspace.
Co-Authors Angon, Moussa Bessike Biboum, Alain Eric, Njock Batake Emmanuel Florence, Offolé Georges, Olong Maurice, Nyobe Yomé Jean Max, Ndoumbé Matéké Moise, Manyol Séverin, Nguiya