Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

José Raimundo Dantas Neto, José Soares Batista Lopes, Diego Antonio De Moura Fonsêca, Antonio Ronaldo Gomes Garcia, Jossana Maria de Souza Ferreira, Elmer Rolando Llanos Villarreal () and Andrés Ortiz Salazar
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José Raimundo Dantas Neto: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
José Soares Batista Lopes: Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Natal 59015-300, Brazil
Diego Antonio De Moura Fonsêca: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
Antonio Ronaldo Gomes Garcia: Department of Natural Sciences, Mathematics, and Statistics, Federal Rural University of Semi-Arid (DCME-UFERSA), Mossoró 59625-900, Brazil
Jossana Maria de Souza Ferreira: School of Science & Technology, Federal University of Rio Grande do Norte (ECT-UFRN), Natal 59072-970, Brazil
Elmer Rolando Llanos Villarreal: Department of Natural Sciences, Mathematics, and Statistics, Federal Rural University of Semi-Arid (DCME-UFERSA), Mossoró 59625-900, Brazil
Andrés Ortiz Salazar: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil

Energies, 2024, vol. 17, issue 5, 1-28

Abstract: This article describes the study and digital implementation of a system onboard a TMS 3208F28335 ® DSP for vector control of the bearing motor speed with four poles split winding with 250 W of power. Smart techniques: ANFIS and Neural Networks were investigated and computationally implemented to evaluate the bearing motor performance under the following conditions: operating as an estimator of uncertain parameters and as a speed controller. Therefore, the MATLAB program and its toolbox were used for the simulations and the parameter adjustments involving the structure ANFIS (Adaptive-Network-Based Fuzzy Inference System) and simulations with the Neural Network. The simulated results showed a good performance for the two techniques applied differently: the estimator and a speed controller using both a model of the induction motor operating as a bearing motor. The experimental part for velocity vector control uses three control loops: current, radial position, and speed, where the configurations of the peripherals, that is, the interfaces or drivers for driving the bearing motor.

Keywords: bearingless; DSP; induction motor; radial position control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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