dc.creator | Hassan, Ahmed | es |
dc.creator | Ruiz-Moreno, Sara | es |
dc.creator | Domínguez Frejo, José Ramón | es |
dc.creator | Maestre Torreblanca, José María | es |
dc.creator | Fernández Camacho, Eduardo | es |
dc.date.accessioned | 2024-02-20T16:21:24Z | |
dc.date.available | 2024-02-20T16:21:24Z | |
dc.date.issued | 2024-02 | |
dc.identifier.citation | Hassan, A., Ruiz-Moreno, S., Domínguez Frejo, J.R., Maestre Torreblanca, J.M. y Fernández Camacho, E. (2024). Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles. IEEE Access, 12, 23265-23278. https://doi.org/10.1109/ACCESS.2024.3362236. | |
dc.identifier.issn | 2169-3536 | es |
dc.identifier.uri | https://hdl.handle.net/11441/155389 | |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 14 p. | es |
dc.language.iso | eng | es |
dc.publisher | Institute of Electrical and Electronics Engineers | es |
dc.relation.ispartof | IEEE Access, 12, 23265-23278. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Artificial intelligence (AI) | es |
dc.subject | Nonlinear model predictive control (NMPC) | es |
dc.subject | Model predictive control (MPC) | es |
dc.subject | Machine learning (ML) | es |
dc.subject | Nonlinear prediction-nonlinear optimization (NMPC-NO) | es |
dc.subject | Nonlinear prediction-linearization (MPC-NPL) | es |
dc.title | Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | PID2020-119476RB-I00 | es |
dc.relation.projectID | FPU20/01958 | es |
dc.relation.projectID | PID2022-142069OB-I00 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/10419329 | es |
dc.identifier.doi | 10.1109/ACCESS.2024.3362236 | es |
dc.contributor.group | Universidad de Sevilla. TEP116: Automática y Robótica Industrial | es |
dc.journaltitle | IEEE Access | es |
dc.publication.volumen | 12 | es |
dc.publication.initialPage | 23265 | es |
dc.publication.endPage | 23278 | es |
dc.contributor.funder | Spanish MCIN/AEI C3PO-R2D2 Project under Grant PID2020-119476RB-I00 | es |
dc.contributor.funder | Egyptian Government, the Spanish Ministry of Science, Innovation, and Universities under Grant FPU20/01958 | es |
dc.contributor.funder | AEI/10.13039/501100011033/FEDER, UE Grant PID2022-142069OB-I00 | es |