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An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification
Division of Electrical Engineering and Computer Science, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- 1 Division of Electrical Engineering and Computer Science, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 2 Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Intelligent Control and Automation·Volume 08 (2017)·Pages 139–153·Published 18 July 2017·DOI10.4236/ica.2017.83011
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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.
KeywordsData-Driven ControlState FeedbackPole PlacementNonlinear Systems
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