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Generalization Backstepping Method Based Stabilization of Parameters Perturbation Lorenz Chaos Using Adaptive Neuro-Fuzzy Inference System

机译:自适应神经模糊推理系统基于广义Backstepping的参数摄动Lorenz混沌镇定

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This study deals with the control of chaos using Generalized Backstepping Method. This new method to control nonlinear systems was called generalized backstepping method because of its similarity to backstepping but its abilities to control systems more than it and could achieve better performance in respect of lower signal control,short settling time and overshoot,control ability of MIMO systems and non strict feedback systems.The generalized backstepping approach consists of parameters which accept positive values.The parameters are usually chosen optional.The system responded differently for each value. This paper introduces novel adaptive neuro fuzzy control method which trained by different error data and learns online to achieve optimal parameters.
机译:本研究使用广义Backstepping方法处理混沌的控制。这种新的控制非线性系统的方法被称为广义反推法,因为它与反推相似,但是它具有比反推法更多的控制系统的能力,并且在较低的信号控制,较短的建立时间和过冲,MIMO系统的控制能力方面可以实现更好的性能。广义backstepping方法由接受正值的参数组成,这些参数通常是可选的,系统对每个值的响应都不同。本文介绍了一种新的自适应神经模糊控制方法,该方法通过不同的误差数据进行训练并在线学习以获取最佳参数。

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