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Design of Adaptive Control and Fuzzy Neural Network Control for Single-Stage Boost Inverter

机译:单级升压逆变器的自适应控制和模糊神经网络控制设计

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摘要

This study mainly focuses on the development of two newly designed control strategies including an adaptive control scheme and a fuzzy neural network (FNN) control system for a single-stage boost inverter. First, the dynamic model of a single-stage boost inverter is analyzed and built for the later control manipulation. Then, a model-based adaptive control scheme and a model-free FNN control system with varied learning rates are designed sequentially. The effectiveness of the proposed adaptive control scheme and the proposed FNN control system is verified by experimental results of a 1-kW single-stage boost inverter prototype, and their merits are indicated in comparison with a traditional double-loop proportional-integral (PI) control framework. Experimental results show that the superior FNN control system has significant improvements of 45.2% total harmonic distortion and 37.1% normalized mean square error compared to the conventional double-loop PI control framework under nonlinear loads.
机译:这项研究主要侧重于两种新设计的控制策略的开发,包括用于单级升压逆变器的自适应控制方案和模糊神经网络(FNN)控制系统。首先,分析并建立了单级升压逆变器的动态模型,以用于以后的控制操作。然后,依次设计了基于模型的自适应控制方案和学习率变化的无模型FNN控制系统。提出的自适应控制方案和提出的FNN控制系统的有效性通过1 kW单级升压逆变器原型的实验结果进行了验证,并且与传统的双环比例积分(PI)相比,它们的优点得到了说明。控制框架。实验结果表明,与传统的双环PI控制框架在非线性负载下相比,卓越的FNN控制系统具有45.2%的总谐波失真和37.1%的均方误差的显着改善。

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