Magnetorheological Damper Force Prediction using Particle Swarm Optimization and Long Short-Term Memory Model

Abstract

Smart materials, like magnetorheological (MR) fluid, are gaining attention for their ability to rapidly change properties under magnetic influence, making them useful in vibration control systems for vehicles, medical devices, and civil engineering structures. Common parametric models, such as Bouc-Wen and Bingham, are traditionally employed to model MR damper dynamics behavior. However, the manual tuning of numerous parameters in these models increases complexity and hinders the identification of inverse models, potentially leading to unpredictable optimum target forces. In response to these challenges, this study suggested a non-parametric approach using Long Short-Term Memory (LSTM) models to predict the optimum target force of MR dampers. Unlike parametric models, LSTM models capture dynamic behavior without the need for extensive manual tuning. To optimize the LSTM model, Particle Swarm Optimization (PSO) is employed to fine-tune hyperparameter values, ensuring robust performance. The proposed non-parametric method, specifically the PSO-LSTM model, demonstrates faster processing times compared to traditional parametric approaches. The proposed model produced an accurate damping force prediction with a root mean square error of less than 5%, This novel approach simplifies the modeling process and offers an efficient and precise alternative to traditional parametric methods.