Robust Tube-Based Model Predictive Control for Autonomous Vehicle Path Tracking

Hu, Kangle; Cheng, Kai

doi:10.1109/access.2022.3231443

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Wischnewski et al [11] implemented TMPC to consider model uncertainty for path planning in AV racing. Hu and Cheng [12] adopted a dynamically adjustable TMPC scheme to overcome model mismatch errors in AV path tracking. Despite its advantages, the TMPC method may be overly cautious for stochastic disturbances.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Jo,

Kwon,

2024

IEEE Access

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This study proposes a novel motion planning strategy to address localization uncertainty in autonomous buses. Conventional motion planning algorithms utilize information from high-definition (HD) maps to overcome the limited detection range of on-board sensors. However, this information contains uncertainty due to the utilization of estimated localization results during reference frame transformation. The wider dimensions of autonomous buses, compared to regular vehicles, amplify the potential dangers associated with localization uncertainty. Therefore, this research focuses on enhancing motion planning for autonomous buses by effectively addressing localization uncertainty. The investigation of manual driving data from autonomous buses highlights the need to handle three issues: heading bias, lateral position error, and longitudinal position error. Firstly, the heading bias was dealt with by implementing an Offset-free Model Predictive Control (OF-MPC) with a Moving Horizon Estimation (MHE) scheme for lateral motion planning. Secondly, the lateral position error was handled by incorporating a drivable corridor to determine the desired path. Lastly, the longitudinal position error was resolved by implementing a chance-constrained MPC for longitudinal motion planning. The proposed approach showed noticeable enhancement in path tracking performance while still securing ride comfort in lateral motion, collision safety, and prevention of stop-line violations. We evaluated the feasibility of the proposed approach through vehicle tests on a test track, and its applicability was further confirmed through fully autonomous driving tests on actual urban bus-only lanes.INDEX TERMS Autonomous bus, autonomous driving, chance-constrained model predictive control, localization uncertainty, moving horizon estimation, offset-free model predictive control.

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Section: Introductionmentioning

confidence: 99%

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Jo,

Kwon,

2024

IEEE Access

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Energy Efficiency Oriented Robust Model Predictive Stability Control for Autonomous Electric Vehicles

Tian,

Yu,

2024

Lecture Notes in Mechanical Engineering

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The four-wheel independent steering and drive autonomous vehicle is a typical over-actuated system. The complexity of controlling it is increasing with the number of actuators. Since the model-based approach can solve the constrained multiple output problem, it is mostly utilized in the existing works. However, they usually investigate a single objective optimization, while employing simplified prediction models to relieve computational burdens. In this case, the robustness of the controller will inevitably suffer from model mismatch, which makes it hard to fulfill the various demands of autonomous driving. This work proposes a multi-objective control framework, which optimizes stability and energy efficiency simultaneously. Furthermore, robust model predictive control is introduced to address the model mismatch. Compared with the state-of-the-art, the effectiveness of the proposed approach has been validated by hardware-in-the-loop tests. Under the double lane change Maneuver, the longitudinal speed is maintained 1.7% higher. The vehicle stability is enhanced, while the motor energy loss and tire slip energy are reduced by 23.3% and 8.3%, respectively.

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Robust MPC-based trajectory tracking of autonomous underwater vehicles with model uncertainty

Yan,

Cai

et al. 2023

Ocean Engineering

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Robust Tube-Based Model Predictive Control for Autonomous Vehicle Path Tracking

Cited by 3 publications

References 38 publications

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Energy Efficiency Oriented Robust Model Predictive Stability Control for Autonomous Electric Vehicles

Robust MPC-based trajectory tracking of autonomous underwater vehicles with model uncertainty

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