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On‐line identification of continuous time‐delay systems using the genetic algorithm
Author(s) -
Hachino Tomohiro,
Yang ZiJiang,
Tsuji Teruo
Publication year - 1996
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391160611
Subject(s) - convergence (economics) , algorithm , computer science , identification (biology) , genetic algorithm , nonlinear system , least squares function approximation , recursive least squares filter , line (geometry) , control theory (sociology) , system identification , binary number , mathematics , artificial intelligence , adaptive filter , data modeling , database , estimator , economic growth , arithmetic , biology , geometry , control (management) , quantum mechanics , machine learning , statistics , botany , physics , economics
This paper proposes a new kind of on‐line identification method of continuous time‐delay systems from sampled input‐output data. In order to track the time‐varying system parameters as well as time‐delay, the recursive linear least squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA) which has a high potential for global optimization. The time‐delay is coded into binary bit strings and searched by the GA, while the system parameters are updated by the RLS method. Furthermore, this method (GALS method) is hybridized with the sequential nonlinear least squares method to improve the speed of convergence. Simulation results show that both the GALS and hybrid methods are efficient in the case that the system changes abruptly, and among them the hybrid method has superior convergence property to the GALs method and yields excellent estimation results in the case that the system changes with time continuously.