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has been cited by the following article:

• 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.