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Vehicle suspension is considered a vital system of modern automotive and necessary to offer an adequate level of ride comfort and roadholding. In the present paper, a fuzzy-based sliding surface (FBSS) controller is designed, as a system controller for the first time, for a semi-active vehicle suspension using a magnetorheological (MR) damper in order to minimize the transmitted unwanted vibrations to the passengers. Therefore, an ideal reference skyhook model is employed to construct the sliding surface, which is the input of fuzzy logic. MR damper is a semi-active device and is controlled indirectly using an external voltage source. So a neural-based damper controller is used to compute the applied voltage to the magnet coil of the MR damper in series with the FBSS system controller. The proposed semi-active controlled quarter-vehicle suspension using an MR damper is solved numerically by Matlab.

Adaptive neuro-fuzzy inference system (ANFIS) technique has been developed and applied by numerous researchers as a very useful predictor for nonlinear systems. In this paper, non-parametric models have been investigated to predict the direct and inverse nonlinear dynamic behavior of magnetorheological (MR) fluid dampers using ANFIS technique to demonstrate more accurate and efficient models. The direct ANFIS model can be used to predict the damping force of the MR fluid damper and the inverse dynamic ANFIS model can be used to offer a suitable command voltage applied to the damper coil. The architectures and the learning details of the direct and inverse ANFIS models for MR fluid dampers are introduced and simulation results are discussed. The suggested ANFIS models are used to predict the damping force of the MR fluid damper accurately and precisely. Moreover, validation results for the ANFIS models are proposed and used to evaluate their performance.