01764nas a2200253   4500000000100000000000100001008004100002260001200043653001000055653001900065653003200084653004700116653002500163653003300188100001800221700002000239700002000259245006200279856009900341300001000440490000600450520104000456022001401496       2017                            d  c12/201710aFuzzy10aNeural Network10aParticle Swarm Optimization10aAdaptive Fuzzy Neural Network Sliding Mode10aSliding Mode Control10aVariable Speed Wind Turbine.1 aNabil Farhane1 aIsmail Boumhidi1 aJaouad Boumhidi00aSmart Algorithms to Control a Variable Speed Wind Turbine  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/08/ijimai20174_6_12_pdf_81478.pdf  a88-950 v43 aIn 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.  a1989-1660