Research on semi-active suspension variable weight model predictive control under car-following scenario

Junjun Yan; Chunyuan Yuan; Datian Fu

doi:10.1117/12.3025444

28 February 2024 Research on semi-active suspension variable weight model predictive control under car-following scenario

Junjun Yan, Chunyuan Yuan, Datian Fu

Author Affiliations +

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130711L (2024) https://doi.org/10.1117/12.3025444
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

In view of the automotive comfort under high-speed following conditions, a semi-active suspension control method based on model predictive control is proposed. The performance function of the controller consists of the predicted output of the vehicle, the output force of the actuator, and their respective weights. Usually, the selection of the weights requires sufficient engineering experience and a large number of experiments to determine. There are mainly three working conditions for constant speed, acceleration and braking conditions, and different weight groups are proposed for different driving conditions. Therefore, the Particle swarm algorithm (PSO) is used to optimize the parameters of the MPC controller, and in order to obtain three groups of weights under different driving conditions. The simulation results show that compared with the passive suspension system, the MPC controller based on the particle swarm algorithm reduces the RMS value of the vertical acceleration of the vehicle under the three working conditions, so the suspension control strategy effectively improves the ride comfort of passengers.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junjun Yan, Chunyuan Yuan, and Datian Fu "Research on semi-active suspension variable weight model predictive control under car-following scenario", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130711L (28 February 2024); https://doi.org/10.1117/12.3025444

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KEYWORDS

Control systems

Particle swarm optimization

Device simulation

Roads

Systems modeling

Actuators

Autonomous driving

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