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Robust Model Predictive Control-Based Autonomous Steering System for Collision Avoidance |
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Nam Ngoc Nguyen, Hung Duy Nguyen, Kyoungseok Han(Kyungpook National University, Korea) |
| Harsh road conditions and sudden obstacles often arise when autonomous vehicles (AVs) operate on real roads. Therefore, designing control for autonomous steering systems under these conditions is challenging. To overcome this challenge, we introduce a robust model predictive control-based (RMPC) autonomous steering system. To deal with a sudden obstacle that appears on real roads, an artificial potential field (APF) approach is introduced. Additionally, an RMPC is proposed to ensure the AV system operates well in slippery road conditions. Furthermore, the input and output constraints are considered to guarantee the AV system works safely in complex and dynamic environments. |
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Model Predictive Control-based Path Tracking with Four-Wheel Independent Steering, Driving, and Braking Autonomous Vehicles on Low Friction Road |
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Yonghwan Jeong(SeoulTech, Korea), Dong Hyun Kim, Min Hyeok Youn, Sunyeap Park, Xian Jun Xian Jun Li, TaeYeon Lee(Seoul National University of Science and Technology, Korea) |
| This paper presents a path-tracking controller for four-wheel independent steering, driving, and braking autonomous vehicles. The planar model-based MPC and the point mass model-based MPC were designed to optimize the control input under low-friction road conditions. The simulation study showed that the MPC-based path-tracking algorithm prevented the divergence from the path and minimized the lateral and heading errors. |
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Game theory-based Overtaking-Preventing Controller for Competitive Racing Scenarios |
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Kyoungtae Ji, Kyoungseok Han(Kyungpook National University, Korea) |
| This paper introduces a novel overtaking-preventing controller for competitive racing scenarios, employing a game-theoretical approach. The proposed methodology generates fifth-order trajectories for each agent and utilizes level-k game theory to select the most effective trajectories. Online estimation is conducted to gauge the level of an opponent's mobility, subsequently, the ego mobility chooses the trajectory that can obstruct the opponent's trajectory. The mobilities follow the trajectories through a model-predictive-control approach based on a nonlinear differential drive model. Simulations conducted in a Matlab environment demonstrate the computational efficiency of our approach, wit |
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Development of a Human-Like Adaptation Rule-based Proportional-Integral Control Algorithm for Universal Steering Control of Autonomous Mobility with RLS |
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Hanbyeol La, Jiung Lee, Kwangseok Oh(Hankyong National University, Korea) |
| In this study, adaptation rules that represent human adaptation characteristics has been designed by relation functions designed using time derivatives of control error and input. The sensitivity in the function was estimated using the recursive least squares method. The integral input was derived using the Lyapunov direct method and the gradient descent method. The proportional input was derived based on the gradient descent method using the sensitivity of the time derivative of control error with respect to the time derivative of control input. |
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Development of a Human-Like Model Predictive Path Tracking Control Algorithm for Autonomous Vehicles with Self-Tuning of Control Period |
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Jiung Lee, Sehwan Kim, Kwangseok Oh(Hankyong National University, Korea) |
| This study proposes a model predictive path tracking control algorithm for autonomous vehicles based on self-tuning of control period. A model predictive control (MPC) algorithm was designed to compute optimal steering angle using bicycle model-based error dynamics. The path tracking errors such as lateral preview error and yaw angle error were computed using waypoints and current position of vehicle. The control period function was newly designed to represent human control characteristic and it was used to determine activation time of the MPC. The evaluation results showed that the proposed control algorithm can represent human control characteristics in terms of control errors. |
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Development of a Backstepping Control-based Path Tracking Algorithm for Autonomous Mobility with Control Delay Compensation |
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Munjung Jang(Hankyong National University, Korea), Sehwan Kim, Kwangseok Oh(Hankyng National University, Korea) |
| This paper describes a backstepping steering control algorithm to compensate the control delay in autonomous mobility for path tracking. Two particle filters were designed to track target RGB from reference path and path errors were derived using two clustered points of particles. In the proposed path tracking algorithm, the backstepping controller was designed to calculate desired steering angle using path errors considering the control delay in steering system with Lyapunov stability condition and the control delay was derived from actual platform test. |
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