神经模糊控制器及其实时应用

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI:10.1109/CICN.2016.141

Saikat Mondal, Tanmoy Sahoo, P. Chattopadhyay

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引用次数: 1

摘要

本文的核心是将模糊逻辑和神经网络相结合，得到一种适合于实时应用的鲁棒、硬件友好的神经模糊控制器。本文在Xilinx SPARTAN 3AN FPGA开发板上实现了一个简单的规则库，以增强神经模糊系统(NFS)相对于模糊系统(FS)的可行性和优越性。最后，利用电枢电压控制拓扑结构，实现了神经模糊控制器对单独励磁直流电动机速度控制等经典参考跟踪控制系统问题的控制。仿真结果和仿真测试共同验证了该混合控制器在实时应用中的有效性和优越性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Neuro–Fuzzy Controller and Its Real Time Application

Essence of the paper is to combine fuzzy logic and neural networks together, obtaining a robust, hardware friendly neuro-fuzzy controller suitable for real time application. Here, a simple rule base has been implemented on the Xilinx SPARTAN 3AN FPGA development board to bolster the feasibility and superiority of the neuro-fuzzy system (NFS) over fuzzy system (FS). Finally, the neuro-fuzzy controller has been realized to govern a classic control system problem of reference tracking like speed control of a separately excited dc motor using armature voltage control topology. Simulation results along with emulation testing have together concreted the effectiveness and superiority of such hybrid controller in real time applications.

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2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)

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