TY - JOUR
KW - Fuzzy
KW - Neural Network
KW - Particle Swarm Optimization
KW - Adaptive Fuzzy Neural Network Sliding Mode
KW - Sliding Mode Control
KW - Variable Speed Wind Turbine.
AU - Nabil Farhane
AU - Ismail Boumhidi
AU - Jaouad Boumhidi
AB - In this paper, a robust adaptive fuzzy neural network sliding mode (AFNNSM) control design is proposed to maximize the captured energy for a variable speed wind turbine and to minimize the efforts of the drive shaft. Fuzzy neural network (FNN) is used to improve the mathematical system model, by the prediction of model unknown function, which is used by the Sliding mode control approach (SMC) and enables a lower switching gain to be used despite the presence of large uncertainties. As a result, the used robust control action did not exhibit any chattering behavior. This FNN is trained on-line using the backpropagation algorithm (BP). The particle swarm optimization (PSO) algorithm is used in this study to optimize the learning rate of BP algorithm in order to improve the network performance in term of the speed of convergence. The stability is shown by the Lyapunov theory and the trajectory tracking errors converge to zero without any oscillatory behavior. Simulations illustrate the effectiveness of the designed method.
IS - Regular Issue
M1 - 6
N2 - In this paper, a robust adaptive fuzzy neural network sliding mode (AFNNSM) control design is proposed to maximize the captured energy for a variable speed wind turbine and to minimize the efforts of the drive shaft. Fuzzy neural network (FNN) is used to improve the mathematical system model, by the prediction of model unknown function, which is used by the Sliding mode control approach (SMC) and enables a lower switching gain to be used despite the presence of large uncertainties. As a result, the used robust control action did not exhibit any chattering behavior. This FNN is trained on-line using the backpropagation algorithm (BP). The particle swarm optimization (PSO) algorithm is used in this study to optimize the learning rate of BP algorithm in order to improve the network performance in term of the speed of convergence. The stability is shown by the Lyapunov theory and the trajectory tracking errors converge to zero without any oscillatory behavior. Simulations illustrate the effectiveness of the designed method.
PY - 2017
SP - 88
EP - 95
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Smart Algorithms to Control a Variable Speed Wind Turbine
UR - http://www.ijimai.org/journal/sites/default/files/files/2017/08/ijimai20174_6_12_pdf_81478.pdf
VL - 4
SN - 1989-1660
ER -