Enhanced generalization of nonparametric model for magnetorheological dampers

All right reserved. The dynamic modeling for magnetorheological (MR) dampers to describe their highly nonlinear dynamic characteristics is essential for the design and implementation of a smart MR control system. One critical concern in constructing a nonparametric MR damper model by employing the artificial neural network technique is its generalization capability, which is also significant to guarantee the stability and reliability of the MR control system. The paper presents the modeling of MR dampers with the employment of the NARX (nonlinear autoregressive with exogenous inputs) network technique within a Bayesian inference framework, and addresses the enhancement of model prediction accuracy and generalization capability in terms of the network architecture optimization and regularized network learning algorithm. The Bayesian regularized NARX network model for the MR damper is demonstrated to outperform the non-regularized network model with the superior prediction and generalization performance in the scenarios of harmonic and random excitations. Therefore, the proposed model with enhanced generalization is beneficial to realize the real-time and robust smart control of MR systems.