• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.11
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• pp.1291-1297
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• 2014

This paper proposes integrated chassis control (ICC) with electronic stability control (ESC) and active rear steering (ARS). Direct yaw moment control is used to generate a control yaw moment. A weighted pseudo-inverse-based control allocation (WPCA) method is adopted to distribute the control yaw moment into tire forces, generated by ESC and ARS. Simulation-based tuning of variables weights in the WPCA is used to enhance the yaw moment distribution performance. Simulations using the vehicle simulation software $CarSim^{(R)}$ show that the proposed ICC is effective in improving maneuverability and lateral stability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.2
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• pp.180-187
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• 2010

In this paper, a GNSS-based AGV system was designed, and steering tested on a golf cart using electric wires in order to confirm the control efficiency of the low speed vehicle which used only position information of GNSS. After analyzed the existing AGVs system, we developed controller and steering algorithm using GNSS based position information. To analyze the performance of the developed controller and steering algorithm, straight-type and circle-type trajectory test are executed. The results show that steering performance of GNSS-based AGV system is ${\pm}\;0.2m$ for a reference trajectory.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.367-374
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• 2017

The goal of this study was to develop control algorithms to improve the steering performance of a 120-ton all-terrain crane. To accomplish this, a hydraulic steering system for the crane was modeled using AMESim software, and a PID steering control algorithm was designed in the MATLAB/Simulink environment. The performance of the designed controller was verified through multiphysics co-simulations based on a real-time simulator.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.12
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• pp.997-1003
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• 2016

This paper presents an application of adaptive algorithms for yaw moment distribution with electronic stability control (ESC) and active rear steering (ARS) in integrated chassis control (ICC). Integrated chassis control consists of upper- and lower-level controllers. In the upper-level controller, the control yaw moment is computed with sliding mode control required to stabilize a vehicle. In the lower-level controller, adaptive algorithms are applied to determine the required brake pressure of ESC and the necessary steering angle of ARS, in order to generate the control yaw moment. Simulation is performed using the vehicle simulation package CarSim to validate the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.6
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• pp.731-738
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• 2013

This paper describes a fault-tolerant driving control strategy for an independent steer-by-wire system in sixwheel-drive/six-wheel-steering vehicles. An algorithm has been designed to realize vehicle maneuverability that is as close as possible to that of non-faulty vehicles by inducing high slip ratio of the wheel through a faulty steer-by-wire system in order to reduce the lateral tire force, which is resistant to the yaw motion. Considering the transition of the longitudinal tire force of a wheel with a faulty steer-by-wire component, the longitudinal tire forces are optimally distributed to the other wheels. Fault-tolerant driving performance has been investigated via computer simulations. Simulation studies show that the proposed algorithm can significantly improve the maneuverability of a vehicle with a faulty steer-by-wire system as compared to the optimal traction distribution method.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1591-1592
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• 2007

본 논문에서는 상태궤환을 이용한 철도차량의 능동조향을 위한 제어기법에 대해 다루었다. 능동조향은 곡선부 주행시 발생되는 승차감 저하 및 차륜/레일의 마모, 소음을 줄이고 고속주행을 위한 조향성능 및 주행안정성을 확보하기 위한 기술이다. 논문에서 사용된 제어 방법은 반대차(Half Bogie) 차량모델을 기초로 측정된 휠-레일의 횡변위(Lateral Displacement) 와 요각(Yaw Ang;e)정보를 토대로 휠에 요모멘트를 제어하였으며 시뮬레이션을 통해 제안한 방법에 대한 성능을 검증하였다.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.1
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• pp.58-63
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• 1999

In this paper, We present a direction indicator algorithm for a mobile robot which uses VFF and neural networks. The structure of this neural network navigation system is composed of sensor system, backpropagation learning controllers for adjusting weight and the motion control system for real-time execution. The experimental results show that the direction indicator system operates properlv and strongly at any circumstance

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.551-557
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• 2014

This paper presents a robust lateral controller for autonomous guidance of a farm tractor in field operations. Although mechanical steering actuators have recently been used for passenger vehicles, the steering actuator of the farm tractor is based on a hydraulic system, resulting in limited bandwidth and a larger time delay. Based on a kinematic tractor model with steering actuator dynamics, a nonlinear control technique called dynamic surface control is applied to design a robust lateral controller that compensates for uncertainty owing to steering actuator and road geometry. Finally, tracking performance and robustness of the proposed controller are validated via commercial tractor simulations, with respect to the time delay of the steering actuator and road geometry (e.g., up and down hills), on a given field with a constant friction coefficient.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.699-708
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• 2021

In this paper, a steering control and speed control algorithm was presented for autonomous driving based on two vision sensors and road speed sign board. A car speed control algorithm was developed to recognize the speed sign by using TensorFlow, a deep learning program provided by Google to the road speed sign image provided from vision sensor B, and then let the car follows the recognized speed. At the same time, a steering angle control algorithm that detects lanes by analyzing road images transmitted from vision sensor A in real time, calculates steering angles, controls the front axle through PWM control, and allows the vehicle to track the lane. To verify the effectiveness of the proposed algorithm's steering and speed control algorithms, a car's prototype based on the Python language, Raspberry Pi and OpenCV was made. In addition, accuracy could be confirmed by verifying various scenarios related to steering and speed control on the test produced track.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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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.