Adaptive backstepping controller design based on neural network for PMSM speed control

Abstract

<span lang="EN-US">The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.</span>

Non-parametric induction motor rotor flux estimator based on feed-forward neural network

“…With neural network (NN) system, rotor flux estimator (RFE) can be modeled without requiring detail knowledge of the IM mathematical model and all its drive systems [19]- [21]. The modeling process can be done using optimization algorithm performed by machine [22]- [24].…”