Adaptive Vibration Control of In-wheel Motor Drive Vehicles with Preview Information

In-wheel motor drive (IWMD) presents a pivotal solution to surmount obstacles in the electrification of transportation technology. However, the increased unsprung mass due to IWMD deteriorates ride comfort. Various control strategies have been proposed to address this issue. Road irregularities limit control systems, with few adapting to changing conditions. Most rely on vision-based monitoring, offering limited road profile data without past or future predictions to improve performance. This study presents an adaptive fractional-order proportional integral derivative (FOPID) controller for a semi-active suspension system of a vehicle using IWMD. The proposed control strategy leverages cloud computing, storing and processing road preview data in a cloud database. Adaptive particle swarm optimization (APSO) optimizes control parameters for enhanced performance, with real-time optimization enabling adaptive acquisition of optimal parameters. Body acceleration (BA), suspension working space (SWS), and dynamic tire load (DTL) are used to assess suspension performance. Simulation results confirm the proposed method's effectiveness in improving dynamic performance.