• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Dynamic parameter identification of parallel kinematic machines using the unscented Kalman filter.
【24h】

Dynamic parameter identification of parallel kinematic machines using the unscented Kalman filter.

机译：使用无味卡尔曼滤波器的并联运动机动态参数识别。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Due to little research on the dynamics of parallel kinematic mechanisms/machines (PKMs) or lack thereof has created an increasing awareness of the dynamic needs. Therefore, the thrust of the work presented in this dissertation is to establish a dynamic parameter identification approach for parallel kinematic machines. This approach is to acquire parameter values necessary for modeling the dynamics of parallel kinematic machines, e.g., mass, inertia, and friction.; To determine the best approach, several identification methods were compared and evaluated in terms of accuracy, convergence, ability to handle noisy data, and ease of implementation. The main comparison studies were conducted between the least squares (LS) method and the unscented Kalman filter (UKF) method. To illustrate the superiority of the unscented Kalman filter (method of preference), both the LS and UKF methods were applied to the inverted double pendulum case. Using results found in the literature, the extended Kalman filter capabilities were compared to these two methods, further substantiating the superiority of UKF method for nonlinear systems. In addition, force-based and energy-based modeling methods are compared to determine if there is any benefit other than reduced modeling effort in deriving the theoretical model. A significant improvement using energy-based method were not generally realized. To experimentally validate the capability and the implementation of the UKF method for dynamic parameter identification, the University of Florida Space, Automation and Manufacturing Mechanisms Laboratory's PKM dynamic parameters were identified.; The simulated and experimentally validated results show that the unscented Kalman filter performs well in identifying the system parameters. This merit corresponds to the significant reduction in modeling effort required to generate the basic algorithm used for the system identification. The overall impact to technology is provision of a parameter identification method that yields improved rigid body dynamics models of parallel kinematic mechanisms. This will lead to the ability to introduce advanced controllers that exploit the model dynamics to improve system performance. In addition, the presented method sets the foundation for formulating an online tuning algorithm.
机译：由于对并行运动机构/机器（PKM）的动力学的研究很少，或者缺乏并行运动学机制/机器（PKM）的动力学，已经使人们对动态需求有了越来越多的认识。因此，本文的工作重点是建立并联运动机的动态参数辨识方法。这种方法是获取建模并联运动机器动力学所必需的参数值，例如质量，惯性和摩擦。为了确定最佳方法，比较了几种识别方法，并在准确性，收敛性，处理嘈杂数据的能力以及易于实现方面进行了评估。主要的比较研究是在最小二乘法（LS）和无味卡尔曼滤波（UKF）方法之间进行的。为了说明无味卡尔曼滤波器（首选方法）的优越性，将LS和UKF方法都应用于倒立双摆情况。使用文献中的结果，将扩展的卡尔曼滤波器功能与这两种方法进行了比较，进一步证实了UKF方法在非线性系统中的优越性。此外，比较了基于力和基于能量的建模方法，以确定在推导理论模型中除了减少建模工作之外是否还有其他好处。使用基于能量的方法的重大改进通常没有实现。为了通过实验验证UKF方法用于动态参数识别的能力和实施，确定了佛罗里达大学空间，自动化和制造机制实验室的PKM动态参数。仿真和实验验证的结果表明，无味卡尔曼滤波器在识别系统参数方面表现良好。该优点对应于显着减少了用于生成用于系统识别的基本算法所需的建模工作。对技术的总体影响是提供了一种参数识别方法，该方法可以改进并联运动机构的刚体动力学模型。这将导致能够引入利用模型动力学来改善系统性能的高级控制器。此外，本文提出的方法为制定在线调整算法奠定了基础。

著录项

  • 作者

    Oh, Young Hoon.;

  • 作者单位

    University of Florida.;

  • 授予单位 University of Florida.;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 183 p.
  • 总页数 183
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号