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

Authors: Rachid Alika; El Mehdi Mellouli; El Houssaine Tissir

Addresses: Faculty of Sciences Dhar El Mehraz, Department of Physics, LISAC Laboratory, University Sidi Mohammed Ben Abdellah, Fez, 30000, Morocco ' National School of Applied Sciences, LISA Laboratory, University Sidi Mohammed Ben Abdellah, Fez, 30000, Morocco ' Faculty of Sciences Dhar El Mehraz, Department of Physics, LISAC Laboratory, University Sidi Mohammed Ben Abdellah, Fez, 30000, Morocco

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 are the longitudinal velocity, the yaw angle, the longitudinal and lateral displacement. The system is multi-input and multi-output (MIMO) and has non-linear dynamics. Simulation results show some improvements over the literature.

Keywords: autonomous vehicles; MPC; model predictive control; MPC constraints; LPV system; PSO; particle swarm optimisation; MIMO system; nonlinear dynamic; path planning; fuzzy logic.

DOI: 10.1504/IJAAC.2025.142998

International Journal of Automation and Control, 2025 Vol.19 No.1, pp.59 - 100

Received: 30 Dec 2022
Accepted: 19 Feb 2024
Published online: 02 Dec 2024 *

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