2017
DOI: 10.4236/ica.2017.83011
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An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

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 t… Show more

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