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Learning identification and control for repetitive linear time-varying systems.

机译：学习识别和控制重复线性时变系统。

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摘要

There are many manufacturing systems that can be described as Linear Time-Varying (LTV) systems that have large parameter variations. When using a feedback controller for these systems, the lag in transient tracking response is always inevitable. The high-level precision tracking requirements of these systems provide new challenges in algorithm development.;In this research, we consider identification and precise motion control for repetitive LTV systems. In particular, we focus on the iterative learning concept, which capitalizes on the repetition of task to update and improve identification and control with each trial. This concept is originally developed from Iterative Learning Control (ILC), which reduces the tracking error of the current iteration by incorporating information learnt from previous executions. In this research, we explore the extension of the ILC concept to both identification and control.;This dissertation develops two contributions to the identification and control of repetitive LTV systems. First, an Iterative Learning Identification (ILI) algorithm is developed for identifying the parameters of repetitive LTV systems. The proposed ILI scheme takes advantage of the repetitive nature of the system, and non-causal data is used to minimize the estimation transient. The design, analysis, simulation and experimental results for ILI on LTV systems are presented in the thesis.;Second, we integrate the identification with norm-optimal ILC design approach. These techniques are used to improve the convergence speed of norm-optimal ILC when the LTV model of the system is not initially available. The integrated ILI and ILC is applied to a pick and place robot with a time-varying mass and yields an improved convergence speed over an ILC controller developed from a recursive model.
机译：有许多制造系统可以描述为具有较大参数变化的线性时变（LTV）系统。在这些系统上使用反馈控制器时，瞬态跟踪响应的滞后总是不可避免的。这些系统的高级精确跟踪要求在算法开发中提出了新的挑战。在本研究中，我们考虑了重复LTV系统的识别和精确运动控制。特别是，我们专注于迭代学习概念，该概念利用重复任务来更新和改进每次试验的识别和控制。该概念最初是从迭代学习控制（ILC）中开发的，该迭代学习控制通过合并从以前的执行中学到的信息来减少当前迭代的跟踪误差。在本研究中，我们探索了ILC概念在识别和控制方面的扩展。本文对重复LTV系统的识别和控制做出了两个贡献。首先，开发了一种迭代学习识别（ILI）算法，用于识别重复LTV系统的参数。所提出的ILI方案利用了系统的重复性，并且使用非因果数据来最小化估计瞬变。本文介绍了在LTV系统上ILI的设计，分析，仿真和实验结果。其次，我们将识别与规范最优ILC设计方法相结合。当系统的LTV模型最初不可用时，可以使用这些技术来提高标准最优ILC的收敛速度。集成的ILI和ILC应用于具有随时间变化的质量的拾取和放置机器人，与从递归模型开发的ILC控制器相比，可以提高收敛速度。

著录项

  • 作者

    Liu, Nanjun.;

  • 作者单位

    University of Illinois at Urbana-Champaign.;

  • 授予单位 University of Illinois at Urbana-Champaign.;
  • 学科 Mechanical engineering.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 123 p.
  • 总页数 123
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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