Experimental Identification of a Self-Sensing Magnetorheological Damper Using Soft Computing

Abstract

This paper presents the development and application of a soft-computing technique in identification of forward and inverse dynamics of a self-sensing magnetorheological (MR) damper based on experimental measurements. This technique is developed by the synthesis of an NARX (nonlinear autoregressive with exogenous inputs) model structure and neural network within a Bayesian inference framework. The Bayesian inference procedures essentially eschew overfitting that could occur in network learning and improve generalization (prediction) capability by regularizing the complexity of learning. In applying the developed technique to the self-sensing MR damper, the present and past information of its input and output quantities, which contain the physical knowledge of the damper, is used to formulate its nonlinear dynamics. The NARX network architecture is then optimized to enhance modeling effectiveness, efficiency, and robustness. Experimental data of the damper subjected to both harmonic and random excitations are used for model identification and assessment. Assessment results show that the formulated Bayesian NARX network accurately emulates the nonlinear forward dynamics of the self-sensing MR damper. Improved generalization (prediction) capability of the NARX network model by the Bayesian regulation is observed by comparing the modeling results with and without considering regularization. An inverse dynamic model for the self-sensing MR damper is further formulated by the developed technique. The proposed soft-computing technique is viable to formulate dynamic models of the self-sensing MR damper for structural control applications.