TY  - JOUR
AU  - Ahmad, Iftikhar
AU  - Ge, Xiaohua
AU  - Han, Qing-Long
PY  - 2024
TI  - Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles
JO  - Journal of Automation and Intelligence
SN  - 2097-504X
SP  - 2
EP  - 18
VL  - 3
IS  - 1
AB  - 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.
UR  - https://doi.org/10.1016/j.jai.2023.12.002
DO  - 10.1016/j.jai.2023.12.002