• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.634-640
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• 2011

When a servo position controller of a robot or a driving units is composed of a PID controller, servomechanism which is modelled is composed of current, velocity and position control loops. After this model is simulated, the technique operating gain of each controller is suggested. The model consists of current, velocity and position controllers from the inside to the outside gradually. Also, to combine velocity and position controllers with 2 order system, simulation is performed after current controllers are composed, which are able for current loop to work ideally. If a current controller is treated with constant, it is possible for velocity and position controller to consist of controller into 2 order system. The technique is verified by applying T-company servo motor which is much more applied to current, velocity and position controller robots.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.270-273
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• 1996

A scheme of observer-based MFAC(Model Following Acceleration Control) system is proposed for the robustness control of DC servo position control systems. The proposed system is composed of LMFC, variable structure feedback controller, and reduced-order state observer. As the servo motor is controlled by the acceleration command, the total servo system becomes the acceleration control system. Simulation results show that the proposed system have robust properties against parameter variations and external disturbances.

• 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.

• 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.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.5
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• pp.468-480
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• 2005

The overall performance of AC servo system is greatly affected the uncertainties of unpredictable mechanical parameter variations and external load disturbances. To overcome this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an on-line identification method of mechanical parameters/load disturbances for AC servo system using support vector regression(SVR). The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with time-varying/nonlinear parameters.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.207-213
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• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2076-2080
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• 2005

Study in this paper concerns the optimal $H_{2}$ integral servo problems for linear crane model systems via the constraints of the derivatives of state variables added to the standard constraints. It is shown in the paper that the derivative state constrained optimal $H_{2}$ integral servo problems can be reduced to the standard optimal $H_{2}$ control problem. The main subject of the paper is to apply the results of derivative state constrained $H_{2}$ integral servo theorem in crane system. The effect of our proposed controller with respect to mitigate an under damping for crane model system is also verified.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1960-1965
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• 2005

This paper presents theory for stability analysis and design of a servo system for a MIMO Wiener system with nonlinear uncertainty. The Wiener system consists of a linear time-invariant system(LTI) in cascade with a static nonlinear part ${\psi}$(y) at the output. We assume that the uncertain static nonlinear part is sector bounded and decoupled. In this research, we treat the static nonlinear part as multiplicative uncertainty by dividing the nonlinear part ${\psi}$(y) into ${\phi}$(y) := ${\psi}$(y)-y and y, and then we reduce this stabilizing problem to a Lur'e problem. As a result, we show that the servo system with no steady state error for step references can be constructed for the Wiener system.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.443-449
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• 1997

LQ-servo design procedure introduced by Athans is a method using a partial states feedback and an output feedback in order to improve the poor performance robustness of the LQR as well as to maintain its stability robustness. Although the method guarantees good stability robustness, it is not effective in performance robustness as it does not match the singular value at low or high frequencies of the transfer matrix obtained by breaking at the plant output. This paper intends propose of a new method, using the limited behaviour of the control gain introduced by Kwakernaak and Sivan, in order to improve it does it refer to controlga introduced by kwakernaak or the new metho Anblguouls.