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Application of artificial neural network state feedback controller to torque ripple minimization of PMSM

机译:人工神经网络状态反馈控制器在永磁同步电机转矩脉动最小化中的应用

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This paper deals with the problem of torque ripple minimization of permanent magnet synchronous motor. The novelty of the presented approach lays in precisely maintain the level of the voltage source inverter DC voltage demanded for proper operation of the motor. An additional voltage matching circuit with state feedback controller is introduced in order to control of the inverter DC voltage. In the proposed solution model of a plant (i.e. permanent magnet synchronous motor fed by voltage source inverter with additional voltage matching circuit) is non-linear and non-stationary. An adaptive state feedback controller is developed by using an artificial neural network, which approximates non-linear control gain surfaces. A simple adaptation algorithm based on 2 low-order low-pass filters is used. Simulation results illustrate the proposed approach in comparison to typical drive with voltage source inverter and stationary state feedback controller.
机译:本文探讨了永磁同步电动机转矩脉动最小化的问题。所提出的方法的新颖之处在于精确地维持了电动机正常工作所需的电压源逆变器DC电压的水平。为了控制逆变器直流电压,引入了带有状态反馈控制器的附加电压匹配电路。在拟议的工厂解决方案模型中(即由带有附加电压匹配电路的电压源逆变器供电的永磁同步电动机)是非线性且不平稳的。通过使用人工神经网络来开发自适应状态反馈控制器,该人工神经网络可以近似非线性控制增益面。使用基于2个低阶低通滤波器的简单自适应算法。仿真结果与带电压源逆变器和稳态反馈控制器的典型驱动器相比,说明了所提出的方法。

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