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Modified fuzzy adaptive controller applied to nonlinear systems modeled under quasi-ARX neural network

机译:改进的模糊自适应控制器应用于准ARX神经网络建模的非线性系统

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摘要

In this article, a fuzzy adaptive controller approach is presented for nonlinear systems. The proposed quasi-ARX neural network based on Lyapunov learning algorithm is used to update its weight for prediction model as well as to modify fuzzy adaptive controller. The improving performances of the Lyapunov learning algorithm are stable in the learning process of the controller and able to increase the accuracy of the controller as well as fast convergence of error. The simulations are intended to show the effectiveness of the proposed method.
机译:在本文中,提出了一种针对非线性系统的模糊自适应控制器方法。提出的基于Lyapunov学习算法的准ARX神经网络用于更新其权重以用于预测模型以及修改模糊自适应控制器。 Lyapunov学习算法的改进性能在控制器的学习过程中是稳定的,能够提高控制器的精度以及误差的快速收敛。仿真旨在显示所提出方法的有效性。

著录项

  • 来源
    《Artificial life and robotics》 |2014年第1期|22-26|共5页
  • 作者单位

    Graduate School of Information, Production and Systems, Waseda University, Kitakyushu-shi, Fukuoka 808-0135, Japan,Politeknik Perkapalan Negeri Surabaya (PPNS), Surabaya, Indonesia;

    Graduate School of Information, Production and Systems, Waseda University, Kitakyushu-shi, Fukuoka 808-0135, Japan,Politeknik Perkapalan Negeri Surabaya (PPNS), Surabaya, Indonesia;

    Graduate School of Information, Production and Systems, Waseda University, Kitakyushu-shi, Fukuoka 808-0135, Japan;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Fuzzy adaptive controller; Lyapunov learning algorithm; Quasi-ARX neural network;

    机译:模糊自适应控制器Lyapunov学习算法;准ARX神经网络;

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