DocumentCode :
3573912
Title :
Parameters identification of passive force control system based on backstepping genetic algorithm
Author :
Zhang Biao ; Dong Yanliang
Author_Institution :
Sch. of Mechatron. Eng., Harbin Inst. of Technol., Harbin, China
fYear :
2014
Firstpage :
5846
Lastpage :
5851
Abstract :
To improve the identification accuracy of complex system, a genetic algorithm parameters identification method based on backstepping theory is proposed. First disassemble the passive force control system into input subsystem and output subsystem based on backstepping theory. Then identify parameter values of each subsystem using genetic algorithm method. And find the best mathematical model to approximate the real system with the goal that under the input of system the output of identification model approximates the output of real system. At last use another group of experiments data to test the efficiency of the identification parameters, and compare the result with the result of normal genetic algorithm parameters identification method. The result shows that the model which identified by proposed method can approximate the real system better, it not only meet the matching between input and output data sets, and to meet the matching between internal variables and output. This identification method can be used in the parameters identification of complex dynamic system.
Keywords :
control nonlinearities; force control; genetic algorithms; large-scale systems; parameter estimation; backstepping genetic algorithm; backstepping theory; complex system; identification accuracy; mathematical model; parameters identification; passive force control system; Backstepping; Equations; Force control; Genetic algorithms; Loading; Mathematical model; Parameter estimation; Backstepping Genetic Algorithm; Parameters Identification; Passive Force Control System;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
Type :
conf
DOI :
10.1109/WCICA.2014.7053719
Filename :
7053719
Link To Document :
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