Model predictive control with constraints based on PSO and fuzzy logic applied to the control of coupled longitudinal-lateral dynamics of the autonomous vehicle

In this paper, a strategy for controlling the longitudinal and lateral dynamics of an autonomous vehicle is developed.This strategy is based on the model predictive control (MPC) with constraints combined with the LPV form.The three degrees of freedom (3DOF) model of the autonomous vehicle is used.The cornering stiffness is approximated by a fuzzy logic type, Takagi-Sugeno, with the aim of finally approximating the nonlinear lateral forces.In order to improve the systems performance, constraints for controller inputs and also for system outputs are defined.The MPC weights are determined using the particle swarm optimisation (PSO).The objective of this strategy is to follow the reference trajectory of the autonomous vehicle while reducing the lateral and longitudinal displacement error.The steering angle and the longitudinal acceleration are the control inputs, the outputs of this system

Authors

Publication Details

Journal
International Journal of Automation and Control
Published
2024-12-03
DOI
https://doi.org/10.1504/ijaac.2025.142998
Citations
2
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.66
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article

Model predictive control with constraints based on PSO and fuzzy logic applied to the control of coupled longitudinal-lateral dynamics of the autonomous vehicle

El Houssaine Tissir, Rachid Alika, El Mehdi Mellouli
2 citations
International Journal of Automation and Control
Advanced Control Systems Optimization
0.66
article

Model predictive control with constraints based on PSO and fuzzy logic applied to the control of coupled longitudinal-lateral dynamics of the autonomous vehicle

El Houssaine Tissir, Rachid Alika, El Mehdi Mellouli
article en
2 citations

Abstract

In this paper, a strategy for controlling the longitudinal and lateral dynamics of an autonomous vehicle is developed.This strategy is based on the model predictive control (MPC) with constraints combined with the LPV form.The three degrees of freedom (3DOF) model of the autonomous vehicle is used.The cornering stiffness is approximated by a fuzzy logic type, Takagi-Sugeno, with the aim of finally approximating the nonlinear lateral forces.In order to improve the systems performance, constraints for controller inputs and also for system outputs are defined.The MPC weights are determined using the particle swarm optimisation (PSO).The objective of this strategy is to follow the reference trajectory of the autonomous vehicle while reducing the lateral and longitudinal displacement error.The steering angle and the longitudinal acceleration are the control inputs, the outputs of this system

International Journal of Automation and ControlVol. 19(1)
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Openalex Percentile: Top 28%
Advanced Control Systems Optimization
0.66
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