Experimental calibration of forward and inverse neural networks for rotary type magnetorheological damper

Subrata Bhowmik, Felix Weber, Jan Becker Høgsberg

    Research output: Contribution to journalJournal articleResearchpeer-review


    This paper presents a systematic design and training procedure for the feed-forward backpropagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where the observed modeling errors for the inverse model can be related to knocking effects in the measured force due to the bearing plays between hydraulic piston and MR damper rod. Finally, the validated models are used to emulate pure viscous damping. Comparison of numerical and experimental results demonstrates good agreement in the post-yield region of the MR damper, while the main error of the inverse NN occurs in the pre-yield region where the inverse NN overestimates the current to track the desired viscous force. Copyright © 2013 Techno-Press, Ltd.
    Original languageEnglish
    JournalStructural Engineering and Mechanics
    Issue number5
    Pages (from-to)673-693
    Publication statusPublished - 2013


    • Automobile seats
    • Neural networks
    • Damping
    • Experimental validation
    • Inverse MR damper model
    • Neural network
    • Rotary MR damper
    • Back propagation neural networks
    • Experimental calibration
    • Experimental validations
    • Harmonic displacement
    • Inverse neural network
    • Magneto-rheological dampers
    • MR dampers
    • Optimization procedures


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