Author(s)

Pyone Ei Ei Shwe¹, Shigeru Yamamoto²

¹Division of Electrical Engineering and Computer Science, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan.
²Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan.

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.

Data-Driven Control, State Feedback, Pole Placement, Nonlinear Systems

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.