• 대한임베디드공학회논문지
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• 제12권5호
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• pp.311-318
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• 2017

A reduced order observer is developed for depth control of a hybrid underwater glider which combines the good aspects of a conventional autonomous underwater vehicle and a underwater glider. State variables include the center of gravity of the robot and the weight of the buoyancy bag, which can not be directly measured. By using the mathematical model and available information such as directional velocities, accelerations, and attitudes, we developed a Luenberger's reduced order observer to estimate the center of gravity and the buoyancy weight. By simulations using Matlab/Simulink, the efficiency of the proposed observer is shown, where a LQR controller using full state variables is adopted as a depth controller.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.71-76
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• 2008

This paper is concerned with the active vibration control of elevator by means of the active roller guide. To this end, a dynamic model for the horizontal vibration of the elevator consisting of a supporting frame, cage and active roller guides was derived using the energy method. Free vibration analysis was then carried out based on the equations of motion. Active vibration controller was designed based on the equations of motion using the LQR theory and applied to the numerical model. Rail irregularity and wind pressure variation were considered as external disturbance in the numerical simulations. The numerical results show that the active vibration control of elevator is possible.

• 조명전기설비학회논문지
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• 제17권5호
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• pp.111-117
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• 2003

제안한 Taylor모델 개념은 단지 입출력 데이터만을 이용하여 제어기를 설계하기 위해 사용되는데, Taylor모델의 매개변수는 입출력 데이터들을 사용하여 추정되고 제어기는 Taylor모델을 통하여 얻어진다. Taylor모델 근사화의 정확성은 관측창의 크기와Taylor모델의 차수가 커짐에 따라 좋아진다. Taylor모델을 이용한 전력계통의 안정화를 위해 LQR 제어기가 제안되고 컴퓨터 시뮬레이션을 통해 기존의 방법과의 성능을 비교한다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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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.

• 한국자동차공학회논문집
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• 제8권5호
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• pp.165-172
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• 2000

The VDC(Vehicle Dynamic Control) is a control system whose target is to improve stability of a vehicle under lateral motion. A lateral vehicle motion, especially on a slippery road, can lead to a hazardous situation, and the situation can even worsen by the driver`s inappropriate response. In this paper, two VDC systems, a fuzzy-based controller and an LQR-based controller have been developed. The controllers take as input the yaw rate and the sideslip angle of either body or rear wheel, and they yield the direct yaw moment signal by which the vehicle can gain stability during cornering. Simulations have been conducted to evaluate the performance of the control system. The results indicated that the controllers can successfully improve vehicle stability under potentially dangerous driving conditions.

• 대한전기학회논문지
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• 제40권2호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.183-186
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• 2007

This Paper presents a solution to control the Brushless DC Motor(BLDCM) in Hard Disk Drive(HDD) using the Linear Quadratic Regulator(LQR). Generally, The speed of BLDCM in HDD 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 simulation and experimental results.

• 한국산업융합학회 논문집
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• 제2권1호
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1014-1019
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• 2005

In this paper, a neural network technique for automatic steering control of a four wheel drive autonomous highway snowplow vehicle is presented. Controllers are designed by the LQR method based on the vehicle model. Then, neural network is used as an auxiliary controller to minimize lateral tracking error under the presence of load. Simulation studies of LQR control and neural network control are conducted for the vehicle model under a virtual snowplowing situation. Tracking performances are also compared for two and four wheeled steering vehicles.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1201-1207
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• 2010

This paper shows to stabilize a balancing robot. We derive the dynamics of a balancing robot and design its controller using LQR method. For stabilizing balancing robot, we introduce a method to detect an angle using inertial sensors. In this study, we use a complementary filter to fuse signals by frequency response of gyroscope and accelerometer in order to measure the inclined angle of balancing robot. The filter coefficients are obtained by least square to minimize error in angle-detecting filter design. And then, after we derive a dynamics of balancing robot using Lagrange method, we linearize that dynamics for using LQR method.