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SSMFs fuzzy logic control and neural network based alpha compensation of phase controlled rectifier fed DC drives

机译:基于SSMF的模糊逻辑控制和基于神经网络的相控整流器直流驱动的α补偿

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In this paper, fuzzy logic and neural network concepts are applied to DC drive system. A systematic approach to construct membership functions of FLC using shrinking span membership functions (SSMFs), is adopted here. This paper proposes a SSMFs fuzzy logic controller for current and speed control loops of DC drive systems. Neural network is employed to linearize the rectifier transfer characteristics in discontinuous conduction mode. The digital simulation study of model system is carried out with proposed and PI controllers using MATLAB-SIMULINK software. Simulation result shows the superiority of proposed controller over fixed parameter PI controller and best possible FLC can be designed without expert knowledge and extensive tuning of parameters. The proposed method reduces the design time of fuzzy controller from down to earth.
机译:本文将模糊逻辑和神经网络概念应用于直流驱动系统。这里采用了一种系统的方法来使用收缩跨度隶属函数(SSMF)构造FLC的隶属函数。本文提出了一种用于直流驱动系统电流和速度控制回路的SSMFs模糊逻辑控制器。神经网络用于在不连续导通模式下线性化整流器的传输特性。利用MATLAB-SIMULINK软件,利用所提出的PI控制器对模型系统进行了数字仿真研究。仿真结果表明,所提出的控制器优于固定参数PI控制器,并且无需专业知识和广泛的参数调整就可以设计出最佳的FLC。所提出的方法减少了模糊控制器自下而上的设计时间。

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