TY - GEN
T1 - Recursive Parsimonious System Identification Algorithm for Dynamical Systems
AU - Bekiroglu, Korkut
AU - Srinivasan, Seshadhri
AU - Su, Rong
AU - Lagoa, Constantino
AU - Poolla, Kameshwar
N1 - Funding Information:
ACKNOWLEDGMENT This research is partially funded by the Building and Construction Authority through the NRF GBIC Program with the project reference NRF2015ENC-GBICRD001-057; the Republic of Singapore National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (Sin-BerBEST) Program, and National Science Foundation (NSF) Grants CNS-1329422.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Recursive system identification is the heart of many industrial applications that include tracking systems, time-varying behaviors, and fault-detection systems. Two significant challenges in the recursive system identification are a prior specification of the model order and computation complexity of recursive system identification. Further, when controller design ensues the model identification, it is desirable to compute the lowest order model that explains the input-output data with reasonable computations. Therefore this paper presents a Recursive Parsimonious System Identification (RPSI) algorithm for recursively identifying the lowest order model from measurements. To simplify the computations the method uses the recently developed concept of an atomic norm. The main advantage of the method is that, it provides a way to recursively estimate a lowest order model without the use of Riccati recursions on covariance matrices and other such computations. Also, it does not require any assumptions on the model order as with other methods in the literature. The proposed method is illustrated on two examples-first a simulation and second an industrial example from the cement industry.
AB - Recursive system identification is the heart of many industrial applications that include tracking systems, time-varying behaviors, and fault-detection systems. Two significant challenges in the recursive system identification are a prior specification of the model order and computation complexity of recursive system identification. Further, when controller design ensues the model identification, it is desirable to compute the lowest order model that explains the input-output data with reasonable computations. Therefore this paper presents a Recursive Parsimonious System Identification (RPSI) algorithm for recursively identifying the lowest order model from measurements. To simplify the computations the method uses the recently developed concept of an atomic norm. The main advantage of the method is that, it provides a way to recursively estimate a lowest order model without the use of Riccati recursions on covariance matrices and other such computations. Also, it does not require any assumptions on the model order as with other methods in the literature. The proposed method is illustrated on two examples-first a simulation and second an industrial example from the cement industry.
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U2 - 10.1109/CCTA.2018.8511460
DO - 10.1109/CCTA.2018.8511460
M3 - Conference contribution
AN - SCOPUS:85056846621
T3 - 2018 IEEE Conference on Control Technology and Applications, CCTA 2018
SP - 1520
EP - 1525
BT - 2018 IEEE Conference on Control Technology and Applications, CCTA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Conference on Control Technology and Applications, CCTA 2018
Y2 - 21 August 2018 through 24 August 2018
ER -