Implémentation des techniques d’intelligence artificielles sur FPGA en vue de contrôle des systèmes d’entrainements électriques
| dc.contributor.author | AIB, Abdelghani | |
| dc.date.accessioned | 2022-03-08T09:08:10Z | |
| dc.date.available | 2022-03-08T09:08:10Z | |
| dc.date.issued | 2022 | |
| dc.description | Artificial intelligence techniques, FPGA (Field Programmable GateArray), DTC control, Asynchronous Machine (MAS), Hardware Co-Simulation | en_US |
| dc.description.abstract | The main goal of this work is to improve the performance of the asynchronous machine control using several technological implementation and simulation tools namely: Matlab / Simulink, Xilinx System Generator, Xilinx ISE and Xilinx ISE Simulator and circuits reconfigurable FPGAs. However, the implementation on FPGA of conventional and intelligent controllers (fuzzy and neural), provides advantages, namely: the reprogramming of these circuits, a flexible software tool, good performance, and very high integration density. Moreover, the asynchronous machine is known by its complex and non-linear model. Artificial intelligence based controllers represent an effective solution to problems associated with DTC control of MAS such as torque ripples, flux ripples and switching frequency. To this end, the control algorithms proposed in this work have been reworked and restructured in order to have simple hardware architecture, well structured and with the minimum of resources on the FPGA card. The modeling and simulation of this diagram are done in Matlab / Simulink with Xilinx System Generator and Xilinx ISE Simulator. Finally, the validation of the proposed architectures was carried out by a Co-Simulation hardware process between the ML402 card equipped with a Virtex-4 FPGA circuit of Xilinx type and XSG under Matlab / Simulink. | en_US |
| dc.identifier.uri | http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/28211 | |
| dc.language.iso | fr | en_US |
| dc.publisher | university of M'sila | en_US |
| dc.relation.ispartofseries | 1285/2022; | |
| dc.title | Implémentation des techniques d’intelligence artificielles sur FPGA en vue de contrôle des systèmes d’entrainements électriques | en_US |
| dc.type | Thesis | en_US |