DocumentCode :
736574
Title :
Data-driven design and implementation of an alternately adaptive residual generator for Hammerstein systems
Author :
Yulei, Wang ; Bingzhao, Gao ; Hongyan, Guo ; Hong, Chen
Author_Institution :
The State Key Laboratory of Automotive Simulation and Control, Department of Control Science and Engineering, Jilin University, Changchun 130025, P.R. China
fYear :
2015
fDate :
28-30 July 2015
Firstpage :
6242
Lastpage :
6247
Abstract :
This paper addresses the data-driven design and implementation of an adaptive observer-based residual generator for Hammerstein systems. The basic idea behind this study is the application of a one-to-one mapping between a parity vector and the solution of Luenberger equations, the identification of the parity space and the Hammerstein nonlinearity via the over-parameterization and least squares support vector machine (LS-SVM). For the realization of adaptivity, the linear and nonlinear parameters are separated and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. Hence the procedure is termed the alternately adaptive algorithm. Furthermore, the stability condition of algorithm is investigated.
Keywords :
Adaptation models; Adaptive systems; Algorithm design and analysis; Generators; Lyapunov methods; Mathematical model; Stability analysis; Adaptive systems; Data-driven methods; Fault detection; Hammerstein systems; Residual generation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2015 34th Chinese
Conference_Location :
Hangzhou, China
Type :
conf
DOI :
10.1109/ChiCC.2015.7260619
Filename :
7260619
Link To Document :
بازگشت