• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.957-961
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• 1991

The parameters of electro-hydraulic servo system are closely dependent on the variation of system characteristics. Especially the parameter sensitivity is incleased in the servo system with heavy load and wide operating range. This paper shows the effect of parameter variation and the experimental parameter values of high power servo system.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.10
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• pp.125-131
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• 2000

By using the test of signal error, model structure of an electrohydraulic servo system is determined. For determining parameter of the electrohydraulic servo system, using time discrete model of parametric method, parameters in time discrete model are searched by the least square method. By bilinear transform, we have found the model of electrohydraulic servo system in s domain. Afterwards, we have compared experimental data with simulation data by MATLAB having the identified parameter. As the result, experimental data is agreed with simulation data very well.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are 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. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.442-447
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• 2003

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.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the 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 the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.5 s.236
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• pp.770-776
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• 2005

The gains fur NC controller parameter are fixed when the controller is combined with a machine. However, the characteristics of controller could be changed as it has being used by the machine or other environmental conditions. Those result in that the tool positioning accuracy is influenced. The loading torque in servo motor influences on the tool positioning accuracy and it is controlled by the parameter gains. It is required to analyze the torque variation with angular positioning accuracy of the servo motor. This study focus on a measuring method and device for verifying angular positioning accuracy of NC servo motor. We used a high resolution An converter for acquiring analogue signal of rotary encoder in servo motor. The positional accuracy for a nominal tool path, which is generated by the combination of axial movements (X,Y,Z), is analyzed with the servo motor torque. The current variation signal is acquired at the power line using a hall sensor and converted to the loading torque of servo motor. The method of measurement and analysis proposed in this study will be used for determining the gains of parameter in NC controller. This gain tuning is also necessary when the controller is set up at a machine.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.418-421
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• 1988

A disturbance and parameter variations cause a steady and/or transient error in the conventional de servo motor drive system. In this paper robust control system for dc servo motor drive taking disturbance and parameter variations into account is proposed. The proposed control system compensates rapidly the state error caused by disturbance and parameter variations. Simulation results show that the proposed method is robust for the steady and transient response in the presence of both disturance and parameter variations.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.738-741
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• 2004

The overall performance of AC servo system is greatly affected by the uncertainties of unpredictable mechanical parameter variations and external load disturbances. Therefore, to compensate this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an online identification method of mechanical parameters/load disturbances for AC servo system using Support Vector Regression (SVR). The proposed methodology advocates analytic parameter regression directly from the training data, rather than adaptive controller and observer approaches commonly used in motion control applications. The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with large measurement noise.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.28-31
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• 1988

This paper proposes a new linear adaptive position controller of DC servo motor. The proposed method can improve the drive performance and rapidly reject the state error caused by both parameter variations and force disturbance. The structure of this adaptive control method is based multiloop feedback control and model reference control. Simulation results are presented to verify the improved response when parameter variations and load disturbance give relatively significant effects to the servo system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1510-1514
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• 2011

In this paper, linear discrete model of the electro-hydraulic servo system are made for parameters estimation. The parameters of electro-hydraulic servo system are estimated using the recursive least square method. Persistent excitation conditions are studied in order to estimate parameters of electro-hydraulic servo system to real values and parameters estimation affections are studied due to the forgetting factors variation. As the results, An parameter estimation method has been synthesized for minimizing the error between reference and error.