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Variable-structure control for linear synchronous motor using recurrent fuzzy neural network

机译:用复制模糊神经网络的线性同步电动机的可变结构控制

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A newly designed variable-structure controller using recurrent fuzzy neural network (RFNN) to control the mover position of a permant magnet linear synchronous motor (PMLSM) servo drive is developed in this study. First, a variable-structure adaptive (VSA) controller is adopted to control the mover position of the PMLSM where a simple adaptive algorithm is utilized to estimate the uncertainty bounds. Then, to further improve the rate of convergence of the estimation, a variable-structure controller using RFNN is investigated, in which the RFNN is utilized to estimate the lumped uncertainty real-time. Simulated and experimental results show that the proposed variable-structure controller using RFNN provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external disturbance. Furthermore, comparing with the VSA controller, smaller control effort is resulted and the chattering phenomenon is reduced by the proposed variable-structure controller using RFNN.
机译:在本研究中开发了一种使用反复模糊神经网络(RFNN)来控制磁体线性同步电动机(PMLSM)伺服驱动器的动机位置的新设计的可变结构控制器。首先,采用可变结构自适应(VSA)控制器来控制PMLSM的移动位置,其中利用简单的自适应算法来估计不确定性界限。然后,为了进一步提高估计的收敛速率,研究了使用RFNN的可变结构控制器,其中利用RFNN来估计大量的不确定性实时。模拟和实验结果表明,所提出的使用RFNN的可变结构控制器提供高性能动态特性,并且对于工厂参数变化和外部干扰是强大的。此外,与VSA控制器相比,导致较小的控制工作,并且使用RFNN的所提出的可变结构控制器减少了抖动现象。

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