| Title |
Data-driven Kalman Decomposition Considering Controllability of Linear Time Invariant System |
| Authors |
강동운(Dongwoon Kang) ; 이주원(Juwon Lee) ; 김범수(Bumsu Kim) ; 한민규(Minkyu Han) ; 김진성(Jinsung Kim) ; 방재성(Jaesung Bang) ; 백주훈(Juhoon Back) |
| DOI |
https://doi.org/10.5370/KIEE.2024.73.4.718 |
| Keywords |
Kalman decomposition; Data-driven system; LTI system |
| Abstract |
The model-based control technique requires an accurate system model identification process because the performance of the controller varies depending on the accuracy of the system model information. However, there is a limit to finding accurate model information of the system due to noise of measurement data or system disturbance. Recently, active research on data-based controllers has proposed a data-driven problem structure that can design a controller using only data without identifying a system model. In this paper, we propose a method for obtaining a coordinate transformation matrix that enables Kalman decomposition of a linear system within this data-driven problem structure. Using the pre-experimental data, we obtain the uncontrollable generalized left eigenvector and use it as a basis vector to span the uncontrollable subspace. Finally, the proposed algorithm was verified through an example with uncontrollable repeated eigenvalues. |