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...

Full description

Bibliographic Details
Main Authors: Ahmed Hassan, Sara Ruiz-Moreno, Jose Ramon D. Frejo, Jose M. Maestre, Eduardo Fernandez Camacho
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10419329/
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.
ISSN:2169-3536