Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/59504
Title: A comprehensive analysis of model predictive control for lane keeping assist system
Keywords: Advanced driver assistance systems
Lane Keeping assist system
Model predictive control
MPC parameterization
Issue Date: Jan-2023
Citation: GARCIA, J.; TEIXEIRA, E.; MURILO, A.; RODRIGUES, R. A comprehensive analysis of model predictive control for lane keeping assist system. IEEE , v.11, 2023.
Abstract: Lane Keeping Assist System (LKAS) enhances comfort and safety while driving. It plays a significant role in the Advanced Driver Assistance System (ADAS) and future Automated Driving (AD). The LKAS solution aims to help the driver keep the vehicle within the road lines, preventing unintentional lane departure. Despite LKAS being an important solution for comfortable driving, robust LKAS steering control is still lacking, requiring constant driver intervention or premature LKAS deactivation. LKAS require optimal control solutions with real-time constraints. This paper comprehensively analyzes Model Predictive Control (MPC) for real-time LKAS applications. Classical and parameterized MPC schemes with distinct Quadratic Programming (QP) solvers are combined to evaluate LKAS closed-loop control performance and realtime constraints. A sideslip and lateral speed bicycle modes were used to evaluate classical, trivial, and exponential MPC schemes. Experimental results highlight the three MPC and QP-appropriate solutions with satisfactory reference tracking without steering command and real-time constraints violation.
URI: https://ieeexplore.ieee.org/document/10354292
http://repositorio.ufla.br/jspui/handle/1/59504
Appears in Collections:DAT - Artigos publicados em periódicos

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