%0 Journal Article
%T An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification
%A Pyone Ei Ei Shwe
%A Shigeru Yamamoto
%J Intelligent Control and Automation
%P 139-153
%@ 2153-0661
%D 2017
%I Scientific Research Publishing
%R 10.4236/ica.2017.83011
%X 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.
%K Data-Driven Control
%K State Feedback
%K Pole Placement
%K Nonlinear Systems
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=77696