An Experimental Assessment of Transverse Adaptive Fir Filters as Applied to Vibrating Structures Identification

Abstract

The present work is aimed at assessing the performance of adaptive Finite Impulse Response (FIR) filters on the identification of vibrating structures. Four adaptive algorithms were used: Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS), Transform-Domain Least Mean Squares (TD – LMS) and Set-Membership Binormalized Data-Reusing LMS Algorithm (SM – BNDRLMS). The capability of these filters to perform the identification of vibrating structures is shown on real experiments. The first experiment consists of an aluminum cantilever beam containing piezoelectric sensors and actuators and the second one is a steel pinned-pinned beam instrumented with accelerometers and an electromechanical shaker.