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Adaptive Neural Network Control Of Chaos In Permanent Magnet Synchronous Motor

机译:永磁同步电动机混沌的自适应神经网络控制

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摘要

Permanent magnet synchronous motors have been used as variable-speed drives, especially for speed control and position, but it exhibits chaotic performance under certain parameter's changes. This work presents the use of a B-Spline neural network scheme to stabilize chaos and to adjust the rotor speed of synchronous motors. The B-spline neural network is an efficient tool to implement the adaptive speed control, with the possibility of carrying out this task on-line, taking into account the systems nonlinearities. One of the main tasks is the adjustment of the proportional-integral parameters for rotor speed controller. In this work, a neural network algorithm is used to solve this problem. A nonlinear observer is designed for estimation of the rotor speed and load torque. The results of numerical simulations demonstrate that the permanent magnet synchronous motors with the B-Spline control scheme has a good dynamic performance and steady state accuracy.
机译:永磁同步电动机已被用作变速驱动器,特别是用于速度控制和位置,但是在某些参数变化的情况下却表现出混沌性能。这项工作介绍了使用B样条神经网络方案来稳定混沌并调整同步电动机的转子速度。 B样条神经网络是一种有效的工具,可以实现自适应速度控制,并考虑到系统的非线性,可以在线执行此任务。主要任务之一是调整转子速度控制器的比例积分参数。在这项工作中,使用神经网络算法来解决此问题。设计了一个非线性观测器,用于估算转子速度和负载转矩。数值模拟结果表明,采用B样条控制方案的永磁同步电动机具有良好的动态性能和稳态精度。

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