TY - GEN
T1 - System identification and state estimation under lebesgue sampling
T2 - 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017
AU - Kawaguchi, Takahiro
AU - Inoue, Masaki
AU - Maruta, Ichiro
AU - Adachi, Shuichi
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/10/6
Y1 - 2017/10/6
N2 - In conventional system identification and state estimation problems, it is commonly assumed that the output signal of a dynamical system is sampled at every regular time interval. This paper addresses the identification and estimation problems under the Lebesgue sampling, which is a type of event-triggered sampling such that the output signal is sampled only when it crosses a specific threshold. In this paper, it is assumed that the output signal is sampled under the Lebesgue sampling rule. Then, the time interval between two samples possesses information such that the signal crosses none of the thresholds during the interval. The inter-sample information plays a key role to improve the accuracy of modeling and estimation. The problems utilizing the information are formulated. We propose likelihood-based methods of both system identification and state estimation to solve the problems. The effectiveness of the methods are illustrated in numerical examples.
AB - In conventional system identification and state estimation problems, it is commonly assumed that the output signal of a dynamical system is sampled at every regular time interval. This paper addresses the identification and estimation problems under the Lebesgue sampling, which is a type of event-triggered sampling such that the output signal is sampled only when it crosses a specific threshold. In this paper, it is assumed that the output signal is sampled under the Lebesgue sampling rule. Then, the time interval between two samples possesses information such that the signal crosses none of the thresholds during the interval. The inter-sample information plays a key role to improve the accuracy of modeling and estimation. The problems utilizing the information are formulated. We propose likelihood-based methods of both system identification and state estimation to solve the problems. The effectiveness of the methods are illustrated in numerical examples.
UR - http://www.scopus.com/inward/record.url?scp=85047736199&partnerID=8YFLogxK
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U2 - 10.1109/CCTA.2017.8062656
DO - 10.1109/CCTA.2017.8062656
M3 - Conference contribution
AN - SCOPUS:85047736199
T3 - 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017
SP - 1408
EP - 1413
BT - 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 August 2017 through 30 August 2017
ER -