An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

Pyone Ei Ei Shwe, S. Yamamoto
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引用次数: 1

Abstract

The recently proposed data-driven pole placement method is able to make use of measurement data to simultaneously identify a state space model and derive pole placement state feedback gain. It can achieve this precisely for systems that are linear time-invariant and for which noiseless measurement datasets are available. However, for nonlinear systems, and/or when the only noisy measurement datasets available contain noise, this approach is unable to yield satisfactory results. In this study, we investigated the effect on data-driven pole placement performance of introducing a prefilter to reduce the noise present in datasets. Using numerical simulations of a self-balancing robot, we demonstrated the important role that prefiltering can play in reducing the interference caused by noise.
用于状态反馈控制和模型辨识的数据驱动极点配置的改进
最近提出的数据驱动极点放置方法能够利用测量数据同时识别状态空间模型并获得极点放置状态反馈增益。它可以精确地实现线性时不变和无噪声测量数据集可用的系统。然而,对于非线性系统,和/或当唯一可用的噪声测量数据集包含噪声时,这种方法无法产生令人满意的结果。在本研究中,我们研究了引入预滤波器以减少数据集中存在的噪声对数据驱动的极点放置性能的影响。通过对自平衡机器人的数值模拟,我们证明了预滤波在减少噪声干扰方面的重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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