Optimal neural network sliding mode control for a variable speed wind turbine based on APSO algorithm

In this paper, artificial neural network sliding mode (ANNSM) controller is designed for a variable speed wind turbine in order to optimize the energy captured from the wind. Sliding mode control (SMC) approach can be used for a variable speed wind turbine. However, in the presence of large uncertainties, the SMC produces chattering phenomenon due to the higher needed switching gain. In order to reduce this gain, artificial neural network (ANN) with one hidden layer is used to estimate the uncertain parts of the system plant with on-line training using backpropagation (BP) algorithm. The learning rate is one of the parameters of BP algorithm which have a significant influence on results; Adaptive particle swarm optimization (APSO) algorithm with global search capabilities is used in this study in order to improve the network performance in terms of the speed of convergence. The stability is shown by the Lyapunov theory and the control action used did not exhibit any chattering behavior. The performance of the proposed approach is investigated in simulations by the comparison with traditional sliding mode control.