Stochastic Subspace identification Applied to the Weave Mode of Motorcycles.

This paper presents a safe and practical method for the identification of the weave mode of motorcycles without the need for the test rider to provide a deliberate lateral input to excite a large perceptible weave response. The solution utilizes stochastic subspace iden-tification (SSI) and relies on the smooth surface of the road under normal steady-state running conditions to randomly excite the steering system. Three SSI variants: covari-ance (COV), unweighted principal component (UPC), and the canonical variate analysis (CVA) are outlined and pole selection via stabilization diagrams is discussed. Then a motorcycle test protocol necessary to collect quality data for identification analysis is described. Strong correlation between stochastic identifications and traditional impulse-based weave testing of several straight running motorcycles under multiple trim states is shown. Because of the ability to use data collected under normal steady-state running conditions, the proposed stochastic technique has the potential for allowing the identifi-cation of weave modal properties under trim state conditions that are not possible with traditional weave testing, like hands-on the handlebars in straight running or when the motorcycle is cornering. Results from identifications under these hands-on trim states are presented, demonstrating the potential for deeper understanding of these conditions. [ABSTRACT FROM AUTHOR]