• 한국자동차공학회논문집
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• 제20권2호
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• pp.15-20
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• 2012

This study is to propose and develop an integrated dynamics control system to improve and enhance the lateral stability and handling performance. To achieve this target, we integrate an AFS and a 4WS systems with a fuzzy logic controller. The IDCS determines active additional steering angle of front wheel and controls the steering angle of rear wheel. The results show that the IDCS improves the lateral stability and controllability on dry asphalt and snow paved road when double lane change and step steering inputs are applied. Yaw rate of the IDCS vehicle tracks reference yaw rate very well and body slip angle is reduced about by 50%. Response time of the IDCS vehicle is also decreased.

• 드라이브 ㆍ 컨트롤
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• 제14권2호
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• 한국자동차공학회논문집
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• 제10권3호
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• pp.245-253
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• 2002

A driver model with nervous neuromuscular system was developed to steer a vehicle along the prescribed path during handling simulations. A 3-dimensional vehicle model with 10 DOF and 3 DOF steering handle are used to perform a computer simulation. PID and fuzzy controller are used to perform single and double lane change, and their tracking abilities were compared. The effects of time delay and preview distance are also investigated, and it is demonstrated that the driver model developed can be an aid far objective evaluation of vehicle handling simulation.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.1126-1131
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• 2007

This paper presents unified chassis control (UCC) to improve the vehicle lateral stability. The unified chassis control implies combined control of active front steering (AFS), electronic stability control (ESC) and continuous damping control (CDC). A direct yaw moment controller based on a 2-D bicycle model is designed by using sliding mode control law. A direct roll moment controller based on a 2-D roll model is designed. The computed direct yaw moment and the direct roll moment are generated by AFS, ESP and CDC control modules respectively. A control authority of the AFS and the ESC is determined by tire slip angle. Computer simulation is conducted to evaluate the proposed integrated chassis controller by using the Matlab, simulink and the validated vehicle simulator. From the simulation results, it is shown that the proposed unified chassis control can provide with improved performance over the modular chassis control.

• 전자공학회논문지
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• 제51권2호
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• pp.190-196
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• 2014

본 논문에서는 밸런싱 로봇의 동역학적 모델의 해석으로부터 기울기와 조향이 독립되어 있어 서로 영향을 받지 않는 것을 증명하고, 다변수 시스템에 적합한 제어기로써 두 개의 최적 LQR 제어기 구조를 갖는 제어시스템을 제안하였다. 또한 제안한 제어시스템의 성능을 입증하기 위하여 밸런싱 로봇의 자세제어에 적용하여 모의실험과 실험을 수행하였고, PID 제어기와의 비교평가를 통하여 그 우수성을 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.972-975
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• 1999

Until now, all of the port goods are transported by container transporter driven manually but recently there are a lot of researches about unmanned vehicle driven automatically. In this paper, we present a design of the TDOF PID controller using a hybrid schematic algorithm to control steering system. We used the ES and SA algorithms to construct hybrid tuning algorithm. Then the computer simulation shows that our proposed controller has better Performances than the other one.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.120.3-120
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• 2002

1. Introduction $\textbullet$ Immune system is an evolutionary biological system to protect innumerable foreign materials such as virus, germ cell, and etc. Immune algorithm is the modeling of this system's response that has adaptation and reliableness when disturbance occur. $\textbullet$ In this paper, Immune algorithm is applied to the Steering Controller of AGV in container yard. $\textbullet$ And then the computer simulation result from the viewpoint of yaw rate and lateral displacement is analyzed and compared with result of conventional PID controller. 2. Dynamic Modeling of AGV $\textbullet$ Dynamic modeling has high degree of freedom. But, basic assumptions of this model are that the center of gravity(CG)...

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.688-693
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• 2001

It is almost impossible to have a straight web for processing in the continuous process systems. The cambered web usually causes the strip walking and damage during process. It is necessary to identify the lateral dynamics of the cambered web for the precise control of lateral behavior. In this paper, a dynamic model of the lateral behavior for a cambered web is developed by introducing the concept of steering angle equivalent to moment caused by the camber. This model can be extended to include terms associated with moment, induced by roller's tilting, web slippage, and shear force, etc. Using this model, a new feed-forward controller is proposed to enable the on-line camber estimation, which is difficult to be measured directly, and the prediction of lateral deflection caused by camber. Computer simulation study shows that the proposed controller successfully eliminates the effect of camber and has better control performance than that of the existing PID controller.

• 한국해양공학회지
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• 제19권2호
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• pp.67-73
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• 2005

This paper describes depth, heading and speed control of an underwater vehicle that has four stern thrusters of which forces are coupled in the diving and, steering motion, as well as the speed of the vehicle. The optimal linear quadratic controller is designed based on a linearized- state space model, developed by combining the dynamic equations of speed, steering and diving motion. The designed controller gives provides an optimal thrust distribution, minimizing the given performance index to control speed, depth and heading simultaneously. To validate the performance of the controller, a simulation and tank-test are carried out with DUSAUV (Dual Use Semi-Autonomous Underwater Vehicle), developed by KORDI as a test-bed for testing new underwater technologies. Optimal gains of the controller are tuned, using a computer simulation environment with a nonlinear 6-DOF numerical DUSAUV model, developed by PMM (Planner Motion Mechanism) test. To verify the performance of the presented controller in experiment, a tank-test with DUSAUV is carried out in the ocean engineering basin in KORDI. The experimental results are also compared with the simulation results to investigate the accordance of the numerical and the real mode.

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.