@article{Ahmad2024, 
author = {Iftikhar Ahmad and Xiaohua Ge and Qing-Long Han},
title = {Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles},
year = {2024},
journal = {Journal of Automation and Intelligence},
volume = {3},
number = {1},
pages = {2-18},
keywords = {Active suspension system, Electric vehicles, In-wheel motor, Stochastic sampling, Dynamic dampers, Sampled-data control, Multi-objective control},
url = {https://www.sciopen.com/article/10.1016/j.jai.2023.12.002},
doi = {10.1016/j.jai.2023.12.002},
abstract = {This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables. The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets. For this purpose, a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities. Then, an asynchronous fuzzy sampled-data controller, featuring distinct premise variables from the active suspension system, is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership. Furthermore, novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous H2 and H∞ performance requirements. Finally, the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.}
}