Robust Model Predictive Control for Autonomous Lane-Changing
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Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Autonomous vehicles need to plan trajectories to a specific goal while avoiding collisions with surrounding vehicles. To this aim, it is essential to take into account the inherited uncertainties due to unmodeled dynamics, uncertain localization, and disturbances. This paper deals with the problem of robust trajectory planning for autonomous lane-changing in the presence of uncertainties. Considering trajectory planning as an online decision-making problem, we propose a robust model predictive control (rMPC), which minimizes deviations from a reference speed and a lateral target position while keeping a subject vehicle within road limits and avoiding collisions with an in-lane vehicle. Uncertainties are explicitly modeled as an additive disturbance in the formulation, wherein the optimal control decisions are obtained by solving a quadratic program (QP). The resulting rMPC guarantees robust state-input satisfaction under the additive disturbance even when the QP solver iterations are stopped prematurely. A set of simulation experiments is studied under different initial scenarios to validate the design, demonstrating the potential utility of the proposed control algorithm for reliable lane-changing. © 2021 IEEE.
Açıklama
3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2021 -- 11 June 2021 through 13 June 2021 -- Ankara -- 171163
Anahtar Kelimeler
autonomous lane-changing, model predictive control, quadratic programming, robust control, uncertainty
Kaynak
HORA 2021 - 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
