Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles

Distributed electric drive vehicles offer maneuver-ability but face stability challenges under different driving conditions. Model Predictive Control (MPC) algorithms can improve lateral stability, but their high computational demands hinder real-time implementation. To address this, the proposed st...

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Bibliographic Details
Authors: Hassan, Ahmed, Ruiz-Moreno, Sara, Domínguez Frejo, José Ramón, Maestre Torreblanca, José María, Camacho, Eduardo F.
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/155389
Online Access:https://hdl.handle.net/11441/155389
https://doi.org/10.1109/ACCESS.2024.3362236
Access Level:Open access
Keyword:Artificial intelligence (AI)
Nonlinear model predictive control (NMPC)
Model predictive control (MPC)
Machine learning (ML)
Nonlinear prediction-nonlinear optimization (NMPC-NO)
Nonlinear prediction-linearization (MPC-NPL)
Description
Summary:Distributed electric drive vehicles offer maneuver-ability but face stability challenges under different driving conditions. Model Predictive Control (MPC) algorithms can improve lateral stability, but their high computational demands hinder real-time implementation. To address this, the proposed strategy combines Nonlinear Autoregressive Exogenous (NARX) neural networks with MPC in two ways, namely, Nonlinear Prediction-Nonlinear Optimization (NMPC-NO) and Nonlinear Prediction-Linearization (MPC-NPL). While NMPC-NO involves online nonlinear optimization, MPC-NPL uses local linearization, reducing both the computational load significantly to about 40% of the computation time of MPC and 0.05% of that of nonlinear model predictive control (NMPC). The neural networks are trained and validated on 20 different datasets, with alternative training methods investigated. MATLAB/Simulink simulations under various standardized tests demonstrate the effectiveness of the proposed techniques, highlighting improved handling performance, reduced computation time, and real-time deployment capabilities.

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