TY - JOUR
T1 - Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor
AU - Lin, Faa Jeng
AU - Huang, Ming Shi
AU - Hung, Chung Yu
AU - Chien, Yu Chen
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-varying control specifications of the motor. The team first proposes an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) control PMASynRM drive. A lookup table (LUT) is composed of the finite element analysis (FEA) results, which bring about the current angle of command within the MTPA. Subsequently, the team designs a computed torque control (CTC) system to control the speed reference command. Creating a working CTC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the PMASynRM drive system, is not available beforehand. Thus, this study suggests that a recurrent Legendre fuzzy neural network (RLFNN) can act as a close substitute for the CTC to resolve its existing complications. Furthermore, the team modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RLFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RLFNN's online learning algorithms. This study concludes that certain experimental results verify the effective and robust qualities of the suggested ICTCRLFNN controlled PMASynRM drive.
AB - The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-varying control specifications of the motor. The team first proposes an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) control PMASynRM drive. A lookup table (LUT) is composed of the finite element analysis (FEA) results, which bring about the current angle of command within the MTPA. Subsequently, the team designs a computed torque control (CTC) system to control the speed reference command. Creating a working CTC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the PMASynRM drive system, is not available beforehand. Thus, this study suggests that a recurrent Legendre fuzzy neural network (RLFNN) can act as a close substitute for the CTC to resolve its existing complications. Furthermore, the team modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RLFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RLFNN's online learning algorithms. This study concludes that certain experimental results verify the effective and robust qualities of the suggested ICTCRLFNN controlled PMASynRM drive.
KW - Permanent-magnet assisted synchronous reluctance motor (PMASynRM)
KW - computed torque control (CTC)
KW - intelligent computed torque control using recurrent Legendre fuzzy neural network (ICTCRLFNN)
KW - maximum torque per ampere (MTPA)
UR - http://www.scopus.com/inward/record.url?scp=85161067082&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3279275
DO - 10.1109/ACCESS.2023.3279275
M3 - 期刊論文
AN - SCOPUS:85161067082
SN - 2169-3536
VL - 11
SP - 54017
EP - 54028
JO - IEEE Access
JF - IEEE Access
ER -