A Neuroadaptive Position-Sensorless Robust Control for Permanent Magnet Synchronous Motor Drive System with Uncertain Disturbance

Omar Aguilar-Mejia, Antonio Valderrabano-Gonzalez (), Norberto Hernández-Romero, Juan Carlos Seck-Tuoh-Mora, Julio Cesar Hernandez-Ochoa and Hertwin Minor-Popocatl
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Omar Aguilar-Mejia: Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo, Área Académica de Ingeniería y Arquitectura, Pachuca 42184, Hidalgo, Mexico
Antonio Valderrabano-Gonzalez: Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Jalisco, Mexico
Norberto Hernández-Romero: Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo, Área Académica de Ingeniería y Arquitectura, Pachuca 42184, Hidalgo, Mexico
Juan Carlos Seck-Tuoh-Mora: Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo, Área Académica de Ingeniería y Arquitectura, Pachuca 42184, Hidalgo, Mexico
Julio Cesar Hernandez-Ochoa: Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Jalisco, Mexico
Hertwin Minor-Popocatl: School of Engineering, UPAEP University, 21 Sur 1103, Puebla 72410, Puebla, Mexico

Energies, 2024, vol. 17, issue 21, 1-17

Abstract: The Permanent Magnet Synchronous Motor (PMSM) drive system is extensively utilized in high-precision positioning applications that demand superior dynamic performance across various operating conditions. Given the non-linear characteristics of the PMSM, a neuroadaptive sensorless controller based on B-spline neural networks is proposed to determine the control signals necessary for achieving the desired performance. The proposed control technique considers the system’s non-linearities and can be adapted to varying operating conditions, all while maintaining a low computational cost suitable for real-time operation. The introduced neuroadaptive controller is evaluated under conditions of uncertainty, and its performance is compared to that of a conventional PI controller optimized using the Whale Optimization Algorithm (WOA). The results demonstrate the viability of the proposed approach.

Keywords: adaptive B-spline controller; whale optimization; low computational cost (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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