基于遗传算法的无刷双馈电机模糊神经网络滑模控制.pdfVIP

201O International Conference on Computer and Communication Technologies in Agriculture Engineering Genetic Algorithms-based Fuzzy Neural Network Sliding Mode Control for Brushless Doubly Fed Machine Zongkai Shao School of Hydropower Information Engineering Huazhong University of Science and Technology Wuhan, China shaozongkai@126.com Abstract-In this paper, because a genetic algorithms-based control (DTC), the fuzzy logic control, the model reference fuzzy neural network control is incorporated into the sliding adaptive control, and so on. mode control (SMC) to adaptively regulate the adaptive law of SMC, a genetic algorithm fuzzy neural network sliding mode Sarasola et al [3] presented a direct torque control strategy controller (GAFNSMC) for brushless doubly fed machine for the BDFM by obtaining the voltage vector table, which has (BDFM) adjustable speed system is presented. The proposed been developed analyzing the flux and torque derivatives for controller for BDFM eliminates the average chattering each voltage vector as a function of control winding flux angle. encountered by most SMC schemes, and employs the robustness Huang et al [4] proposed a fuzzy PID control strategy for and excellent static and dynamic performances of SMC. power factor based on the model of BDFM and its simulation, Simulation results show that the proposed control strategy is of which results show that the power factor of BDFM can be