Intelligent Control and Automation

Volume 8, Issue 3 (August 2017)

ISSN Print: 2153-0653   ISSN Online: 2153-0661

Google-based Impact Factor: 1.27  Citations  h5-index & Ranking

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

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DOI: 10.4236/ica.2017.83011    931 Downloads   1,695 Views  Citations

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.

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Shwe, P. and Yamamoto, S. (2017) An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification. Intelligent Control and Automation, 8, 139-153. doi: 10.4236/ica.2017.83011.

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