Robust Model Predictive Control-Based Recurrent Neural Networks for Autonomous Vehicles in Avoidance Collisions

Robust Model Predictive Control-Based Recurrent Neural Networks for Autonomous Vehicles in Avoidance Collisions

Ensuring safe driving under real-time uncertainties remains a critical challenge in autonomous vehicle control. To address this issue for a collision avoidance task, this study proposes a robust model predictive control (RMPC) framework that handles parametric uncertainties using optimization-based...

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Bibliographic Details
Main Authors: Hung Duy Nguyen, Duc Thinh Le, Tung Lam Nguyen, Minh Nhat Vu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Robust model predictive control
deep neural networks
data-driven control
linear matrix inequality
autonomous vehicles
Online Access:https://ieeexplore.ieee.org/document/11031406/
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Summary:Ensuring safe driving under real-time uncertainties remains a critical challenge in autonomous vehicle control. To address this issue for a collision avoidance task, this study proposes a robust model predictive control (RMPC) framework that handles parametric uncertainties using optimization-based linear matrix inequality (LMI). By incorporating system parametric uncertainties, the RMPC enhances driving stability and safety through the use of input-state constraints. However, due to its computational complexity, we employ a data-driven approach by collecting measurements under different road adhesion conditions to train deep neural networks with a long short-term memory layer (DNN-LSTM). The proposed DNN-LSTM effectively captures temporal dependencies, outperforming existing DNNs when using the same hyperparameters in accuracy and generalization. All comparative simulations are conducted and verified using the high-fidelity CarSim/Simulink co-simulation platform. Therefore, the proposed DNN-LSTM approach approximates the RMPC policy with high training performance and significantly reduces computational complexity, which is more beneficial for real-time implementation. Using the DNN-LSTM is further emphasized to maintain the ability to drive stability of autonomous vehicles compared with online and offline RMPCs, which show a stable region violation at some fixed operation points.
ISSN:2169-3536

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