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Intelligent neural network and fuzzy logic control of industrial and power systems.

机译：工业和电力系统的智能神经网络和模糊逻辑控制。

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摘要

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given.; The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described.; Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods.; It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given.; Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given.; Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control.; The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.
机译：如今，神经网络和模糊逻辑智能控制算法的主要作用是识别和补偿未知的非线性系统动力学。开发了许多方法，但是通常没有提供神经网络和模糊控制系统的稳定性分析。这项工作将为几种算法解决这些问题。一些更复杂的控制算法包括backstepping和自适应评论器将被设计。还分析了具有非自适应模糊控制器的非线性模糊控制。给出了确定SISO模糊控制器描述功能的实验方法。分析了自主水下航行器的自适应神经网络跟踪控制器。提供了新颖的稳定性证明。描述了用于耦合电动机驱动器的反步神经网络控制器的实现。工作中还提供了对自适应评论者神经网络控制的分析和综合。给出了具有动作生成神经网络和自适应模糊评论器的系统的新型调节律。所有这些控制方法都获得了稳定性证明。显示了如何在实际工程控制中使用这些控制算法和方法。给出了稳定性证明。分析了自适应模糊逻辑控制。通过仿真研究来分析自适应模糊系统在不同环境变化下的行为。给出了自适应模糊逻辑系统的一种新的稳定性证明。而且，描述和分析了自适应弹性模糊逻辑控制体系结构。一种新颖的隶属函数用于弹性模糊逻辑系统。提供稳定性证明。将自适应弹性模糊逻辑控制与自适应非弹性模糊逻辑控制进行比较。本文所描述的工作是对特定的代表性工业系统进行分析的基础。同样，它为分析自适应和神经网络控制系统的学习能力以及分析不同算法（例如弹性模糊系统）提供了一个良好的起点。

著录项

  • 作者

    Kuljaca, Ognjen.;

  • 作者单位

    The University of Texas at Arlington.;

  • 授予单位 The University of Texas at Arlington.;
  • 学科 Engineering Electronics and Electrical.; Energy.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 186 p.
  • 总页数 186
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;能源与动力工程;
  • 关键词

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