Optimizing Vibration Attenuation Performance of a Magnetorheological Damper-Based Semi-active Seat Suspension Using Artificial Intelligence

Abstract

This paper aims to improve the control performance for a magnetorheological damper (MRD) based semi-active seat suspension system. The vibration of the suspension is isolated by controlling the stiffness of the MRD using a proportion integration differentiation (PID) controller. A new intelligent method is proposed to optimize the PID parameters in this work. This new method appropriately incorporates the particle swarm optimization (PSO) into the PID-parameter searching processing of an improved fruit fly optimization algorithm (IFOA). Thus the PSO-IFOA method possesses better optimization ability than IFOA and is able to find a globally optimal PID-parameter set. The PID controller optimized by the proposed PSO-IFOA was evaluated for attenuating the vibration of the MRD-suspension using a numerical model and an experimental platform, respectively. Both the simulation and experimental analysis results demonstrate that the proposed PSO-IFOA is able to optimize the PID parameters in controlling the MRD semi-active seat suspension. The control performance of the PSO-IFOA based PID is superior to that of individual PSO, FOA or IFOA based methods.