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

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