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

Byung-Young Moon

doi:10.1117/12.664659

2 May 2006 Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system

Byung-Young Moon

Proceedings Volume 6042, ICMIT 2005: Control Systems and Robotics; 60422G (2006) https://doi.org/10.1117/12.664659
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

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.

Citation Download Citation

Byung-Young Moon "Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system", Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60422G (2 May 2006); https://doi.org/10.1117/12.664659

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