• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1102-1103
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• 2007

This Paper presents a solution to control the Brushless DC Motor(BLDCM) in Digital Lightening Processor(DLP) using the Linear Quadratic Regulator(LQR). Generally, The speed of BLDCM in DLP is controlled by the lead angle control or the input voltage control using PAM(Pulse Amplitude Modulation) etc. These control methods have speed overshoot in speed control and need the long time until BLDCM reaches at the steady state. In order to improve the performance, this paper present the PI speed controller using the LQR based on vector control and the rotor position detection methods at the space vector PWM inverter. The proposed methods are proved by the experimental results

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.217-223
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• 1991

Most common applications of neural networks to control problems are the automatic motor controls using the artificial perceptual function. These control mechanisms are similar to those of the intelligent and pattern recognition control of an adaptive method frequently performed by the animate nature. In this paper, the pole-balancing problem is selected as the control object and an actual cart-pole controller is implemented by a computer interfacing and demonstrated as motor control using the reinforcement learning rule. In the experiment, given a change of the main parameters of cart-pole dynamics, a comparison is made between the LQR scheme and neural network method. The neural network method exhibits a more effecftive control action in a real situation having a large uncertainty than the LQR scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.225-228
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• 1996

This paper concerns a SCARA type robot with the second arm flexible. Its equations of motion are derived by the Lagrangian mechanics. For controller design, the perturbation approach is taken to separate the original equations of motion into linear equations describing small perturbed motions and nonlinear equations describing purely rigid motion of the robot. To effect the desired payload motion, open loop control inputs are first determined based on the inverse dynamics of the latter. Next, in order to reduce the positional error during maneuver, an active vibration suppression is done. To this end, a feedback control is designed for robustness against disturbance on the basis of the linear equations and the LQR theory modified with a prescribed degree of stability. The numerical simulations results show the satisfactory control performance.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.876-879
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• 1996

The vibration of an attractive magnetic levitation(Maglev) vehicle transportation system is caused by the irregularity of the guideway track and the performance of the suspensions of the Maglev system. It is essential for us to give attention to the secondary suspension of the vehicle system as it determines the ride quality. In order to improve the ride quality and running stability, active secondary suspensions have been developed and applied to the vibration problems. This paper analyzes the performance of the active secondary suspension which is applied to an attractive magnetic levitation vehicle system running on a rough track. The dynamics of the suspension system and the optimal control problems are studied. According to the transient and frequency response analyses to the track disturbance, the ride quality of an attractive Maglev vehicle has been improved by applying the designed LQR active controller, and it has been confirmed that this improvement was also influenced by the configuration of the system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.311-316
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• 1994

In this study, a governing equation for 4-axis active magnetic bearing system composed of a rigid rotor and two radial magnetic bearings is derived. We find out that there are two kind of coupling between control axes in the system. And digital contralized controller is designed based on state-space approach and linear quadratic regulator(LQR) theory. By numerical simulation, it is shown what the designed controller can stabilize the system and control the coupling effectively using limited control input.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.1
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• pp.56-62
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• 2001

In this paper, skyhook control of a semi-active ER(Electro-Rheological) damper is investigated. The strength of the ER damper is controlled by a high voltage power supply. This paper deals with a two-degree-of-freedom suspension using the damper with ERF for a quarter vehicle system. The control law for semi-active suspensions modeled in this study is developed using skyhook and Linear Quadratic Regulator(LQR) optimal control method. Computer simulation and experimental results show that the semi-active suspension with ERF damper has good performances of ride quality.

• Coupled systems mechanics
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• v.13 no.1
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• pp.73-93
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• 2024

In this work, we propose combining an advanced optimal control algorithm with a geometrically exact beam model. For simplicity, the 2D Reissner beam model is chosen to represent large displacements and rotations. The difficulty pertains to the nonlinear nature of beam kinematics affecting the tangent stiffness matrix, making it non-constant, which compromises direct use of optimal control methods for linear problems. Thus, we seek to accommodate a time varying control using linear-quadratic regulator (LQR) algorithm with the proposed geometrically nonlinear beam model. We provide a detailed theoretical formulation and its numerical implementation in a variational format form. Several illustrative numerical examples are provided to confirm an excellent performance of the proposed methodology.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.1
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• pp.1-9
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• 2014

Structural loading on a wind turbine is due to cyclic loads acting on the blades under turbulence and periodic wind field. The structural loading generates fatigue damage and fatigue failure of the wind turbine. The individual pitch control(IPC) is an efficient control method for reducing structural loading. In this paper, we present an IPC design method using Decentralized LQR(DLQR) and Disturbance accommodating control(DAC). DLQR is used for regulating rotor speed and DAC is used for canceling out disturbances. The performance of the proposed IPC is compared with CPC, which was designed with a gain-scheduled PI controller. We confirm the effect of fatigue load reduction with the use of damage equivalent load(DEL).

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.540-547
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• 2009

In the near future, military robots are likely to be substituted for military personnel in the field of battle. The power system of a legged robot is considerably more complex than the one used for a land vehicle because of the coordination and stability issues due to the large number of degree of freedom. In this paper, a servovalve-piston combination system for a straight-line motion of robot leg is modeled as three degree of freedom based on double inputs and single output transfer function. The output is the displacement of piston from neutral. The inputs are valve displacement from neutral and arbitrary load force in this system. LQR(Linear Quadratic Regulator) technique is applied in order to achieve robust stability and fast responses of the system. The Kalman filter loop, rejection of disturbance and noise, riccati equation, filter gain matrix, and frequency domain equality are analyzed and designed.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.62-65
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• 2004

An optimal robust controller design method for gun driving system is discussed in this paper. The parameters of the gun driving controller are tuned by using the LQR characteristics for the performance and robustness. Tuning method that optimize velocity error gives a significant improvement over the existing PID tuning methods. It is shown that the tuning result of real gun driving system which is regarded as rigidness model or stiffness model satisfy performance and robustness.