A novel inverse dynamic model for a magnetorheological damper based on network inversion

Abstract

Semi-active suspensions based on magnetorheological (MR) dampers are receiving significant attention, especially for control of vibration isolation systems. The nonlinear hysteretic behavior of MR dampers can cause serious problems in controlled systems, such as instability and loss of robustness. Most of the developed controllers determine the desired damping forces which should be produced by the MR damper. Nevertheless, the MR damper behavior can only be controlled in terms of the applied current (or voltage). In addition to this, it is necessary to develop an adequate inverse dynamic model in order to calculate the command current (or voltage) for the MR damper to generate the desired forces as close as possible to the optimal ones. Due to MR dampers being highly nonlinear devices, the inverse dynamics model is difficult to obtain. In this paper, a novel inverse MR damper model based on a network inversion is presented to estimate the necessary current (or voltage) such that the desired force is exerted by the MR damper. The proposed inverse model is validated by carrying out experimental tests. In addition, a comparison of simulated tests with other damper controllers is also presented. Results show the effectiveness of the network inversion for inverse modeling of an MR damper. Thus, the proposed inverse model can act as a damper controller to generate the command current (or voltage) to track the desired damping force.

Keywords

Magnetorheological damper semi-active damper inverse model neural networks network inversion

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