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An Evolutionary Model-Free Controller and its Application to the Swing-Up of a Double Inverted Pendulum

机译:无进化模型的控制器及其在双倒立摆中的应用

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摘要

Advancements in the field of machine learning has made a model-free approach for nonlinear control of dynamical systems more viable. Traditionally, the controller design is based on the analysis of the system model. In practice, however, it might not be possible to estimate a system model that truly reflects the complex behavior of the real system. A model-free controller self-learns the required control decisions by applying machine learning techniques, avoiding the need for estimation and analytical design. In this thesis, a parameterized dynamical system known as Dynamic Movement Primitive (DMP) is used as a feedforward model-free controller. An advanced, nature-inspired, evolutionary machine learning algorithm called Covariance Matrix Adaption Evolution Strategy (CMA-ES) was used to self-learn the control decisions. It was demonstrated through computer simulated experiments that such an evolutionary model-free controller could successfully learn to accomplish the difficult task of swinging up a double inverted pendulum, motivating further research.
机译:机器学习领域的进步使动态系统的非线性控制的无模型方法变得更加可行。传统上,控制器设计基于系统模型的分析。但是,在实践中,可能无法估计能够真实反映实际系统复杂行为的系统模型。无模型控制器通过应用机器学习技术自学习所需的控制决策,从而避免了估计和分析设计的需要。本文将一种称为动态运动原语(Dynamic Movement Primitive,DMP)的参数化动力学系统用作前馈无模型控制器。一种先进的,受自然启发的进化机器学习算法,称为协方差矩阵适应进化策略(CMA-ES),用于自学习控制决策。通过计算机仿真实验证明,这种无进化模型的控制器可以成功地学习完成摆动双倒立摆的艰巨任务,从而推动了进一步的研究。

著录项

  • 作者单位

    California State University, Long Beach.;

  • 授予单位 California State University, Long Beach.;
  • 学科 Robotics.
  • 学位 M.S.
  • 年度 2017
  • 页码 73 p.
  • 总页数 73
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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