• 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 the Korean Society for Precision Engineering
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• v.17 no.4
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• pp.129-134
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• 2000

This paper presents the development of ultra-precision machining process of large aspheric aluminum mirrors with a maximum diameter of 620 mm. An ultra-precision machine, "Nanoturn60", developed by Daewoo Heavy Industries Ltd. is used for machining and motion errors of the machine are compensated by using the FTS developed by IAE(Institue for Advanced Engineering) during the machining process. To check the form accuracy of machined aspheric surfaces, on-machine form measurement system is developed. This measurement system consists of air bearing touch probe, straight edge, and laser sensor. With in-process error compensation by FTS(Fast Tool Servo), aspheric mirrors with the from accuracy of submicron order are obtained. obtained.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.4
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• pp.339-344
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• 2015

Machine tools are one of the energy-intensive equipment used in the manufacturing industry. The importance of energy has increased and the machine tools are required to be energy-efficient. The servo systems of the machine tool consume electrical power to rotate a spindle and to feed a tool during machining. Servo system consumes a lot of energy when the machine tool is operated. The energy consumption pattern of each axis needs to be investigated in order to optimize the machining process with regard to energy cost. In this paper, an energy monitoring system is developed considering various measuring points of servo system in order to grasp the energy consumption pattern of each axis.

• Journal of KSNVE
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• v.11 no.1
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• pp.49-56
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• 2001

In this paper, two plant models, of which one is newly developed and the other one is the conventional one, of the focus servo system of DVD drive are presented and a two-degree-of freedom controller consisted of Inverse dynamics feedforward and LQG/LTR feedback controller is designed. The newly developed plant model is used to design the feedforward controller and the conventional model is used for the design of feedback controller. The output of newly developed model is the displacement of objective lens and the output of conventional model is the focus error of the DVD focus servo system. The displacement of the objective lens is estimated by the dynamics model of the DVD focus servo system. The disturbance rejection performance of the two-degree-of freedom controller is compared with that of an LQG/LTR one.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.2023-2032
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• 1993

This study suggests a new type shaft generating system driven by hydrostatic transmission suitable for small size vessels. Since the shaft generating system is affected ceaselessly by disturbances such as speed variation in pump driving speed and variation in external load, a robust servo control must be implemented to obtain stable electric power with constant frequency. Thus, in this study, a digital robust servo control algorithm is applied to the controller design. By the experiment and the numerical computation, the frequency variation characteristics of the generating system under various disturbances are investigated. Conclusively, it is said that the shaft generating system proposed in this study shows excellent control performances.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.2
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• pp.151-160
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• 2000

An experimental and theoretical analysis for the improvement of dynamic characteristics and design of electro-hydraulic flow control servo valve are performed. The theoretical results are compared with the experimental step responses, and the important design parameters of an electro-hydraulic flow control servo valve are derived by using the simulation program. Simulation parameters of nozzle jet coefficient and orifice and spool valve discharge coefficient are given through experiment. The theoretical and experimental step response curves show that the valve gain depends on the fixed orifice and nozzle $ratio(R_on)$ and is maximum at $R_on=1.$ And drain orifice in the flapper - nozzle return line creates a small back pressure, which improves the performance fur the valve.

• 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 Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.435-441
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• 2003

This paper addresses that an approximation and tracking control of realtime recurrent neural networks(RTRN) using two-dimensional iterative teaming algorithm for an electro-hydraulic servo system. Two dimensional learning rule is driven in the discrete system which consists of nonlinear output fuction and linear input. In order to control the trajectory of position, two RTRN with the same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two identical RTRN was very effective to trajectory tracking of the electro-hydraulic servo system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1577-1585
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• 2011

This paper proposes a posture stabilization control algorithm for a wheeled mobile robot using hybrid visual servo control method with a position based and an image based visual servoing (PBVS and IBVS). To overcome chattering phenomena which were shown in the previous researches using a simple switching function based on a threshold, the proposed hybrid visual servo control law introduces the fusion function based on a blending function. Then, the chattering problem and rapid motion of the mobile robot can be eliminated. Also, we consider the nonlinearity of the wheeled mobile robot unlike the previous visual servo control laws using linear control methods to improve the performances of the visual servo control law. The proposed posture stabilization control law using hybrid visual servoing is verified by a theoretical analysis and simulation and experimental results.

• 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.