TY - GEN
T1 - Data-driven analysis methods for controllability and observability of a class of discrete LTI systems with delays
AU - Zhou, Binquan
AU - Wang, Zhuo
AU - Zhai, Yueyang
AU - Yuan, Heng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/30
Y1 - 2018/10/30
N2 - We propose a couple of data-driven analysis methods for the state controllability and state observability of a class of discrete linear time-invariant (LTI) systems with delays, which have unknown parameter matrices. To analyze the state controlla-bility and the state observability, these data-driven methods first transform the system model into an augmented state-space model, and then use the state/output data that were previously measured, to directly build the controllability/observability matrices of this augmented model. Our methods have two main advantages over the traditional model-based characteristics analysis approaches. First, the unknown parameter matrices are not necessary to be identified for verifying the state controllability/observability of the system, but these characteristics can be directly verified according to the measured data, thus our methods have less workload. Second, their computational complexity is lower for the construction of the state controllability/observability matrices.
AB - We propose a couple of data-driven analysis methods for the state controllability and state observability of a class of discrete linear time-invariant (LTI) systems with delays, which have unknown parameter matrices. To analyze the state controlla-bility and the state observability, these data-driven methods first transform the system model into an augmented state-space model, and then use the state/output data that were previously measured, to directly build the controllability/observability matrices of this augmented model. Our methods have two main advantages over the traditional model-based characteristics analysis approaches. First, the unknown parameter matrices are not necessary to be identified for verifying the state controllability/observability of the system, but these characteristics can be directly verified according to the measured data, thus our methods have less workload. Second, their computational complexity is lower for the construction of the state controllability/observability matrices.
KW - Augmented State-Space Model
KW - Data-Driven Analysis Methods
KW - Discrete LTI Systems with Delays
KW - Measured Data
KW - Unknown Parameter Matrices
UR - https://www.scopus.com/pages/publications/85057038782
U2 - 10.1109/DDCLS.2018.8515909
DO - 10.1109/DDCLS.2018.8515909
M3 - 会议稿件
AN - SCOPUS:85057038782
T3 - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
SP - 380
EP - 384
BT - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018
Y2 - 25 May 2018 through 27 May 2018
ER -