• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.1
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• pp.103-111
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• 2016

This paper presents an adaptive variable weights tuning system for an integrated chassis control with electronic stability control (ESC) and active front steering (AFS) for lateral stability enhancement. After calculating the control yaw moment needed to stabilize a vehicle with a controller design method, it is distributed into the tire forces generated by ESC and AFS using weighted pseudo-inverse-based control allocation (WPCA). On a low friction road, lateral stability can deteriorate due to high vehicle speed. To cope with the problem, adaptive tuning rules on variable weights of the WPCA are proposed. To check the effectiveness of the proposed method, a simulation was performed on the vehicle simulation package, CarSim.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.5
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• pp.527-534
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• 2015

This paper presents an integrated chassis control with electronic stability control (ESC) and active front steering (AFS) under lateral force constraint on AFS. The control yaw moment is calculated using a sliding mode control. The tire forces generated by ESC and AFS are determined using weighted pseudo-inverse based control allocation (WPCA) in order to generate the control yaw moment. On a low friction road, AFS is not effective when the lateral tire forces of front wheels are easily saturated. To solve problem, the lateral force of AFS is limited to its maximum and the braking of ESC is applied with WPCA. To evaluate the effectiveness of the proposed method, a simulation was performed on the vehicle simulation package, $CarSim^{(R)}$. From the simulation, it was verified that the proposed method could enhance the maneuverability and lateral stability if the lateral force of AFS exceeds its maximum.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.1-8
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• 2021

This study developed low-cost and high-efficiency pavement condition monitoring technology to produce the key information required for pavement management. A noise and artificial intelligence-based monitoring system was devised to compensate for the shortcomings of existing high-end equipment that relies on visual information and high-end sensors. From idea establishment to system development, functional definition, information flow, architecture design, and finally, on-site field evaluations were carried out. As a result, confidence in the high level of artificial intelligence evaluation was secured. In addition, hardware and software elements and well-organized guidelines on system utilization were developed. The on-site evaluation process confirmed that non-experts could easily and quickly investigate and visualized the data. The evaluation results could support the management works of road managers. Furthermore, it could improve the completeness of the technologies, such as prior discriminating techniques for external conditions that are not considered in AI learning, system simplification, and variable speed response techniques. This paper presents a new paradigm for pavement monitoring technology that has lasted since the 1960s.