Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO

Abstract

The structural parameters of the magnetorheological (MR) damper significantly affect the output damping force and dynamic range. This paper presents a design optimization method to improve the damping performance of a novel MR damper with a bended magnetic circuit and folded flow gap. The multiobjective optimization of the structural parameters of this MR damper was carried out based on the optimal Latin hypercube design (Opt LHD), ellipsoidal basis function neural network (EBFNN), and multiobjective particle swarm optimization (MOPSO). By using the Opt LHD and EBFNN, determination of the optimization variables on the structural parameters was conducted, and a prediction model was proposed for further optimization. Then, the MOPSO algorithm was adopted to obtain the optimal structure of the MR damper. The simulation and experimental results demonstrate that the damping performance indicators of the optimal MR damper were greatly improved. The simulation results show that the damping force increased from 4585 to 6917 N, and the gain was optimized by 50.8%. The dynamic range increased from 12.4 to 13.2, which was optimized by 6.4%. The experimental results show that the damping force and dynamic range of the optimal MR damper were increased to 7247 N and 13.8, respectively.