TITLE:
An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification
AUTHORS:
Pyone Ei Ei Shwe, Shigeru Yamamoto
KEYWORDS:
Data-Driven Control, State Feedback, Pole Placement, Nonlinear Systems
JOURNAL NAME:
Intelligent Control and Automation,
Vol.8 No.3,
July
18,
2017
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.