Extended neural network-based scheme for real-time force tracking with magnetorheological dampers : EXTENDED NEURAL NETWORK-BASED SCHEME

This paper validates numerically and experimentally a new neural network-based real-time force tracking scheme for magnetorheological (MR) dampers on a five-storey shear frame with MR damper. The inverse model is trained with absolute values of measured velocity and force because the targeted current is a positive quantity.

The validation shows accurate results except of small current spikes when the desired force is in the vicinity of the residual MR damper force. In the closed-loop, higher frequency components in the current are triggered by the transition of the actual MR damper force from the pre-yield to the post-yield region.

A control-oriented approach is presented to compensate for these drawbacks. The resulting control force tracking scheme is validated for the emulation of viscous damping, clipped viscous damping with negative stiffness, and friction damping with negative stiffness. The tests indicate that the proposed tracking scheme works better when the frequency content of the estimated current is close to that of the training data.

Copyright © 2013 John Wiley & Sons, Ltd.