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首页> 外文期刊>Magnetics, IEEE Transactions on >Recurrent Functional-Link-Based Fuzzy Neural Network Controller With Improved Particle Swarm Optimization for a Linear Synchronous Motor Drive
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Recurrent Functional-Link-Based Fuzzy Neural Network Controller With Improved Particle Swarm Optimization for a Linear Synchronous Motor Drive

机译:具有改进粒子群算法的线性同步电动机驱动器基于功能链接的递归模糊神经网络控制器

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摘要

A recurrent functional link (FL)-based fuzzy neural network (FNN) controller is proposed in this study to control the mover of a permanent-magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, a recurrent FL-based FNN controller is proposed in this study to control the PMLSM. Moreover, the online learning algorithms of the connective weights, means, and standard deviations of the recurrent FL-based FNN are derived using the back-propagation (BP) method. However, divergence or degenerated responses will result from the inappropriate selection of large or small learning rates. Therefore, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the recurrent FL-based FNN online. Finally, the control performance of the proposed recurrent FL-based FNN controller with IPSO is verified by some simulated and experimental results.
机译:提出了一种基于递归功能链接(FL)的模糊神经网络(FNN)控制器,以控制永磁直线同步电动机(PMLSM)伺服驱动器的动子来跟踪周期性参考轨迹。首先,推导了PMLSM驱动系统的动态模型。接下来,本研究提出了一种基于FL的递归FNN控制器来控制PMLSM。此外,使用反向传播(BP)方法得出基于FL的递归神经网络的连接权重,均值和标准差的在线学习算法。但是,对学习率的大小选择不当会导致发散或反应退化。因此,采用了改进的粒子群优化算法(IPSO)来适应在线基于FL的递归FNN的学习率。最后,通过仿真和实验结果验证了所提出的基于IPSO的基于FL的递归FNN控制器的控制性能。

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