首页> 外文学位 >Iterative learning control for nonsmooth dynamical systems.
【24h】

Iterative learning control for nonsmooth dynamical systems.

机译:非光滑动力系统的迭代学习控制。

获取原文
获取原文并翻译 | 示例

摘要

Iterative learning control (ILC) is a control technique that improves the performance of dynamical systems working in a repetitive mode. ILC emulates the human capability of learning from practice. In the same way a tennis player improves the shots after practicing over and over, an iterative learning controller uses previous trial information to get better performance of a system with respect to some desired performance objective.; ILC has been applied to robots performing repetitive tasks, where it has proven to be effective in compensating nonlinear effects such as gravity, Coriolis, and centrifugal forces. ILC has also been used in many other applications, e.g., batch chemical processes, injection molding machines, power electronics, and aerospace, to mention a few. However, ILC for nonsmooth dynamical systems has not been extensively explored. There has been some applications and heuristic procedures but not an analytical study for those cases. Examples of nonsmooth dynamics can be found in mechanical systems with dry (Coulomb) friction, systems with intermittent contacts, e.g., in robot assembly and walking robots, switching controllers such as in gain scheduling control, systems with backlash, systems containing discontinuous actuators like relays and solenoid valves, system with hysteresis, and switchings in electronic circuits. They are also present in economics and biology.; In this thesis, we analyze the implementation of ILC to nonsmooth dynamical systems. Two approaches for the design of ILC update laws are considered. The first approach is based on the application of the passivity theory. Derivation of the convergence of proportional ILC algorithms for nonsmooth systems containing incremental sector bounded nonlinearities is presented for the first time. The second approach uses the optimal control theory. Optimal control based ILC algorithms for nonsmooth Lipschitz continuous dynamical systems are derived and a new ILC algorithm capable of generating piecewise-continuous inputs for nonsmooth dynamical systems containing discontinuities is developed. As application examples, these ILC algorithms are tested on mechanical systems with intermittent contacts and on mechanical systems with dry friction.
机译:迭代学习控制(ILC)是一种控制技术,可提高以重复模式工作的动态系统的性能。 ILC模仿人类从实践中学习的能力。以类似网球运动员反复练习后提高击球的方式,迭代学习控制器使用先前的试用信息来获得系统相对于某些所需性能目标的更好性能。 ILC已应用于执行重复性任务的机器人,在这种情况下,ILC已被证明可有效补偿重力,科里奥利和离心力等非线性影响。 ILC还用于许多其他应用程序,例如批处理化学过程,注塑机,电力电子设备和航空航天等。但是,尚未广泛探索非光滑动力系统的ILC。已经有一些应用程序和启发式程序,但没有针对这些情况的分析研究。非平稳动力学的示例可以在具有干(库仑)摩擦的机械系统,具有间歇接触的系统(例如,机器人组装和步行机器人),切换控制器(例如,增益调度控制),带隙的系统,包含不连续执行器(如继电器)的系统中找到电磁阀,具有磁滞的系统以及电子电路中的开关。它们还存在于经济学和生物学中。本文分析了非光滑动力系统中ILC的实现。考虑了两种设计ILC更新定律的方法。第一种方法是基于无源性理论的应用。首次提出了包含增量扇区有界非线性的非光滑系统比例ILC算法的收敛性。第二种方法使用最佳控制理论。推导了非光滑Lipschitz连续动力系统基于最优控制的ILC算法,并开发了一种新的ILC算法,该算法可为包含不连续性的非光滑动力系统生成分段连续输入。作为应用示例,这些ILC算法在具有间歇接触的机械系统和具有干摩擦的机械系统上进行了测试。

著录项

  • 作者单位

    Rensselaer Polytechnic Institute.;

  • 授予单位 Rensselaer Polytechnic Institute.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 111 p.
  • 总页数 111
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 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