Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system

被引：3
作者：
Moon, BY [1 ]
机构：
[1] Pusan Natl Univ, ILIC, Pusan 659735, South Korea
来源：
ICMIT 2005: CONTROL SYSTEMS AND ROBOTICS, PTS 1 AND 2 | 2005年 / 6042卷
关键词：
electro-hydraulic servo system; neural-genetic algorithm; hybrid multi-model; parameter estimation algorithm Design of servo system; signal processing;
D O I：
10.1117/12.664659
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The hybrid neural-genetic multi-model parameter estimation algorithm was demonstrated. This method can be applied to structured system identification of electro-hydraulic servo system. This algorithms consist of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. To evaluate the proposed method, electro-hydraulic servo system was designed and manufactured. The experiment was carried out to figure out the hybrid neural-genetic multi-model parameter estimation algorithm. As a result, the dynamic characteristics were obtained such as the parameters(mass, damping coefficient, bulk modulus, spring coefficient), which minimize total square error. The result of this study can be applied to hydraulic systems in industrial fields.
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页数：6
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