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Preview Control of Automotive Active Suspension Systems to Improve Ride Comfort Using V2V Communication |
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Jae-Hoon Jeong, Sun Young Kim, Baek-soon Kwon(Kunsan National University, Korea) |
| This paper presents preview control of automotive actives suspension systems to improve ride comfort using vehicle-to-vehicle (V2V) communication. In this work, a preview control algorithm for active suspension systems has been developed without information about the road elevation. The proposed controller is designed with the future disturbance information, vertical wheel acceleration transmitted from preceding vehicle via V2V communication. |
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Hybrid Control of Service Robot Vehicle using CNN based Behavior Cloning Algorithm for Enhanced Cornering Performance |
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Jinhee Myoung, Sunmyung Lee, Hongju Jo(Sookmyung Women's University, Korea), Hyun-geun Kim(The New Feature, Korea), Kang-moon Park(LG CNS, Korea), Donghoon Eddie Shin(Sookmyung Women's University, Korea) |
| This paper presents a hybrid control of service robot vehicle using behavior cloning algorithm with PID control for enhanced cornering performance. The behavior cloning algorithm based on convolutional neural network (CNN) has been implemented to the autonomous driving algorithm of robot vehicle using supervised learning approach. The algorithm reflects human driving behavior for autonomous driving by obtaining and learning manual(human) control data. In order to realize the algorithm on the real system, low-cost camera sensors and controllers with actuators are implemented so that it achieves robust cornering control performance. With a successfully developed and validated CNN based behavio |
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Sliding Mode Approach for Partitioned Cost Function-based Fault-Tolerant Control of Automated Driving |
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Sechan Oh(Hankyong National University, Korea), Hakjoo Kim(Hankyong national university, Korea), Mun Jung Jang(Hankyong National University, Korea), jongmin lee(Seoul National University, Korea), Kwangseok Oh(Hankyong National University, Korea), kyongsu yi(seoul national university, Korea) |
| This paper presents sliding mode and partitioned cost function-based fault-tolerant controller of automated driving. The data-driven fault-tolerant control algorithm proposed in this study is based on the upper-level controller decoupled with the lower-level controller. The adaptive sliding mode observer (ASMO) using recursive least squares (RLS) for reconstruction of acceleration sensor fault signal has been designed with gradient descent method. The reconstructed fault signal has been used to compute the desired acceleration for fault-tolerant longitudinal control with the Lyapunov stability condition. |
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Development of a Human-Like Learning Frame for Data-Driven Adaptive Control Algorithm of Automated Driving |
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Kwangseok Oh, Sechan Oh(Hankyong National University, Korea), jongmin lee(Seoul National University, Korea), kyongsu yi(seoul national university, Korea) |
| This paper proposes a human-like learning frame for data-driven adaptive control algorithm of automated driving. Because there are unexpected uncertainties and changes in environment and system dynamic, derivation of relatively accurate mathematical model or dynamic parameters information is not easy in real world and it can have a negative impact on driving control performance. Therefore, this study proposes data-driven feedback control method for automated driving based on human-like learning frame in order to address the aforementioned limitation. |
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Path Tracking Control for Four-Wheel-Steering Autonomous Vehicles based on Adaptive Sliding Mode Control with Control Allocation |
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Yonghwan Jeong(Seoul National University of Science and Technology, Korea) |
| This paper presents a path tracking control algorithm for four-wheel steering autonomous vehicles. The desired yaw rate has been determined based on the geometric relationship between a reference path and vehicle position. A required yaw-moment to tracking the desired yaw-rate has been decided by using an adaptive sliding mode control approach. The optimization-based control allocation has been introduced to determine the front and rear-wheel steering inputs, considering a control effort, actuator limit, ride comfort, and body slip. The simulation study showed that the path tracking performance has been improved in a driving situation with a high curvature and a steep road slope. |
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