Application of inverse data-driven parametric models in the reconstruction of passenger vehicle wheel vertical movement under ride conditions

Abstract

This paper considers a method for reconstructing the vertical displacement of wheels resulting from the road profile, using an inverse parametric data-driven model. Wheel movement is required in variable damping semi-active suspension systems, which use an onboard control system that improves ride quality and vehicle handling in typical maneuvers. This paper presents a feasibility study of such an approach which was performed in laboratory conditions. Experimental validation tests were conducted on a setup consisting of a servo-hydraulic test rig equipped with displacement, force and acceleration transducers and a data-acquisition system. The fidelity and adequacy of various parametric single-input single-output model structures were evaluated in the time and frequency domain based on correlation coefficient, Akaike information criterion, and final prediction error criteria. The experimental test results showed that inverse models provide high-level accuracy of inversion in the time domain, ranging from more than 70% for the ARX model structure to over 90% for the OE model structure.