Ride comfort and handling characteristics are two important aspects of vehicle dynamics that generally require contrasting suspension settings. Different damper settings of the suspension system are required in order to meet these conflicting requirements. A magneto-rheological (MR) damper allows variable suspension settings to achieve enhanced ride comfort as well as handling characteristics by providing adaptable damping. Implementation of semi-active control requires an accurate MR damper model and online identification of model parameters. However, modeling a MR damper for a wide range of input conditions is challenging, especially when there are constraints on necessary measurements that are required for modeling. Although the available literature proposes various parametric models, many of these models are computationally expensive and are not viable for online identification. This paper presents a non-parametric model as well as a recursive model to predict the damping force of a MR damper in order to implement a semi-active control algorithm on an off-road vehicle. The results of the two models are compared to a conventional parametric model. The recursive model is used to demonstrate the significance of including the measured damping force in the model. Whereas the availability of the measured damping force yields a reasonably accurate model, the lack of measured damping force significantly impairs the recursive model.

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