Neuro-fuzzy Sliding Mode Controller Based on a Brushless Doubly Fed Induction Generator

Abstract

The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

Improved Backstepping Control of a DFIG based Wind Energy Conversion System using Ant Lion Optimizer Algorithm

“…In [16], the authors use the MRAS speed estimator based on Type-2 Fuzzy logic control for sensorless speed direct torque and flux control of an induction motor drive. In addition, the literature has proposed fuzzy-neural strategies as a robust control for electrical drives designed to optimize the performance of the overall processes under consideration [17,18]. Neuro-fuzzy systems directly equate to the advantages of ANNs and AFL schemes.…”