Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order adjust the nonlinear and time-varying specifications motor. team first proposes ANSYS Maxwell-2D dynamic model that contains a maximum per ampere (MTPA) PMASynRM drive. A lookup table (LUT) composed finite element analysis (FEA) results, which bring about current angle command within MTPA. Subsequently, designs (CTC) speed reference command. Creating working CTC for practical applications quite complex because detailed dynamics, includes unpredictability system, not available beforehand. Thus, study suggests (RLFNN) can act as close substitute resolve its existing complications. Furthermore, modifies adaptive compensator proactively potential calculated deviance RLFNN. Asymptotical stability assured using Lyapunov method, generates RLFNN’s online learning algorithms. This concludes certain experimental results verify effective robust qualities suggested ICTCRLFNN