TY - GEN
T1 - Adaptive parameter estimation for MISO system using decomposition principle
AU - Li, Linwei
AU - Ren, Xuemei
AU - Zhang, Lufeng
AU - Lv, Yongfeng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In the paper, we discuss the identification of the multiple-input and single-output (MISO) system. Based on the internal relation of the linear subsystem and nonlinear element, the estimation model of the system considered is recasted as the entirety estimation equation. For the equation, here the single parameter item and bilinear parameter item coexist. Because the bilinear item includes compound parameters, the calculative burden of the identification method will be high. Then, to cut down the amount of calculation, the matrix conversion technology is utilized to reconstruct two estimation models. In parameter estimation process, for each model, an adaptive parameter estimation scheme is submitted to interactively identify the estimated parameters by virtue of hierarchical identification idea. Via the stochastic theory and martingale theorem, the convergence of parameter estimation is provided. The numerical simulation verifies the usefulness of the presented estimator.
AB - In the paper, we discuss the identification of the multiple-input and single-output (MISO) system. Based on the internal relation of the linear subsystem and nonlinear element, the estimation model of the system considered is recasted as the entirety estimation equation. For the equation, here the single parameter item and bilinear parameter item coexist. Because the bilinear item includes compound parameters, the calculative burden of the identification method will be high. Then, to cut down the amount of calculation, the matrix conversion technology is utilized to reconstruct two estimation models. In parameter estimation process, for each model, an adaptive parameter estimation scheme is submitted to interactively identify the estimated parameters by virtue of hierarchical identification idea. Via the stochastic theory and martingale theorem, the convergence of parameter estimation is provided. The numerical simulation verifies the usefulness of the presented estimator.
KW - Hierarchical identification
KW - Matrix conversion technology
KW - Miso system
KW - Parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=85076388434&partnerID=8YFLogxK
U2 - 10.1109/DDCLS.2019.8908861
DO - 10.1109/DDCLS.2019.8908861
M3 - Conference contribution
AN - SCOPUS:85076388434
T3 - Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019
SP - 310
EP - 315
BT - Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019
Y2 - 24 May 2019 through 27 May 2019
ER -