感应电机定子磁链神经观测器的在线训练算法

IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468) Pub Date : 2003-11-02 DOI:10.1109/IECON.2003.1279967

A. Nied, I. S. Seleme, G. G. Parma, B.R. de Menezes

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引用次数: 3

摘要

提出了一种基于神经网络的定子磁链观测器。虽然网络拓扑是一个标准的多层感知器网络，但训练算法是新的。提出了两种基于变结构系统(VSS)理论和滑模控制(SMC)的在线训练算法。所得到的观测器对两种训练算法均表现出良好的收敛速度和对异步电机参数的鲁棒性。

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On-line training algorithms for an induction motor stator flux neural observer

This work presents a neural network based stator flux observer. Although the network topology is a standard multilayer perceptron network, the training algorithms are new. This paper presents two on-line training algorithms, which are based on Variable Structure Systems (VSS) theory and Sliding Mode Control (SMC). The resulting observer shows good convergence velocity and robustness with respect to the induction motor parameters for both training algorithms tested.

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IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468)

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