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

• 作者: Pyone Ei Ei Shwe Shigeru Yamamoto
• 来源期刊: 智能控制与自动化(英文)
• 学科: 工学
• 关键词: DATA-DRIVEN Control STATE FEEDBACK POLE PLACEMENT Nonlinear Systems
• 年，卷（期）: 2017,（3）
• 页码范围: 139-153
• 页数: 15页
• 分类号: TP1

摘要

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.