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首页> 外文学位 >Adaptive robust neural fuzzy control of uncertain systems: A Lyapunov theory approach.
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Adaptive robust neural fuzzy control of uncertain systems: A Lyapunov theory approach.

机译：不确定系统的自适应鲁棒神经模糊控制：一种Lyapunov理论方法。

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摘要

The objective of this research was to develop effective control strategies for uncertain nonlinear dynamical systems. In the first stage of the research, neural fuzzy controllers were proposed. Genetic algorithms were employed to design and fine-tune the proposed neural fuzzy controllers, which then were tested on an anti-lock brake system model and a ground vehicle.; Training or fine-tuning of the above described controllers was performed off-line and found to be time consuming. To overcome this problem, an adaptive control algorithm was developed that learns and compensates for the unmodeled dynamics of the plant online. In addition, a robustifying component was proposed whose role is to suppress modeling errors and uncertainties. Integrating the adaptive and the robust approaches resulted in a guaranteed transient tracking performance and a guaranteed final tracking error accuracy in the presence of modeling errors and disturbances. The closed-loop system driven by the proposed controllers was shown, using the Lyapunov method, to be stable with all the adaptation parameters being bounded.; To further enhance the performance of the proposed control strategies, a self-organizing raised-cosine radial basis functions (RCRBFs) component was included in the control architecture. The proposed self-organizing RCRBF network can adjust its size by growing or shrinking the number of basis functions used according to the design specification. Performance comparison of the proposed controllers with the one in the literature was conducted. The proposed controller outperformed the controllers found in the literature.
机译：这项研究的目的是为不确定的非线性动力学系统开发有效的控制策略。在研究的第一阶段，提出了神经模糊控制器。遗传算法被用来设计和微调所提出的神经模糊控制器，然后在防抱死制动系统模型和地面车辆上进行测试。离线执行上述控制器的训练或微调，发现这很耗时。为了克服这个问题，开发了一种自适应控制算法，该算法可以在线学习和补偿未建模的工厂动态。另外，提出了一种稳定组件，其作用是抑制建模误差和不确定性。在存在建模误差和干扰的情况下，将自适应方法和鲁棒性方法集成在一起，可以保证有保证的瞬态跟踪性能和有保证的最终跟踪误差精度。用李雅普诺夫方法证明了由所提出的控制器驱动的闭环系统是稳定的，并且所有自适应参数都是有界的。为了进一步提高所提出的控制策略的性能，控制体系结构中包含了一个自组织的余弦径向基函数（RCRBF）组件。所提出的自组织RCRBF网络可以根据设计规范通过增加或缩小所使用的基础函数的数量来调整其大小。所提出的控制器与文献中的控制器进行了性能比较。提出的控制器的性能优于文献中的控制器。

著录项

  • 作者

    Lee, Yonggon.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 179 p.
  • 总页数 179
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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