Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Parameter Identification of an Electro-Hydraulic Servo System Using a Modified Hybrid Neural-Genetic Algorithm

전기.유압 서보시스템의 수정된 신경망-유전자 알고리즘에 의한 파라미터 식별

  • 곽동훈 (부산대학교 대학원 지능기계공학과) ;
  • 이춘태 (부산대학교 대학원 지능기계공학과) ;
  • 정봉호 (부산대학교 대학원 지능기계공학과) ;
  • 이진걸 (부산대학교 기계공학부)
  • Published : 2003.06.01
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Abstract

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists 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. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

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References

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