Application of an Inverse Data-Driven Model for Reconstructing Wheel Movement Signals

Abstract

This paper considers a method for indirect measuring the vertical displacement of wheels resulting from the road profile, using an inverse parametric data-driven model. Wheel movement is required in variable damping suspension systems, which use an onboard electronic control system that improves ride quality and vehicle handling in typical maneuvres. 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 SISO model structures were evaluated in the time domain based on correlation coefficient, FPE and AIC criteria. The experimental test results showed that inverse models provide accuracy of inversion, ranging from more than 70% for the ARX model structure to over 90% for the OE model structure.

Understanding and controlling vorticity transport in unsteady, separated flows

“…Therefore, H −1 (s) takes the form of an improper transfer function -that is, the polynomial in the denominator is of a lower degree than that in numerator (N > M). As a result, any attempt to transform the results of Equation 4.10 into the time domain using the inverse Laplace transform will yield a solution that does not converge [31]. This is the fundamental invertibility problem that is often encountered with inverse identification models, for which two solutions are developed and presented here.…”