Comparison of real-time flight loads estimation methods

Abstract

Nowadays flight load exceedance monitoring is an important task to both: the aircraft manufacturer as well as the operator. The estimation of flight loads is required in several phases during development and operation of an aircraft. The requirements are usually different, for e.g. calculation of design loads for certification and operational loads monitoring of stress and fatigue. The ability to determine aircraft operational loads (more) precisely may reduce the time in maintenance. Being able to detect critical load exceedance events during flight or in a post-process is also an enabler for e.g. loads/fatigue monitoring at operator level. In this paper, a novel system identification method named local model networks is applied to the field of flight loads estimation and compared to approaches based on artificial neural networks as found in the literature. The presented approach tries to overcome some limitations with respect to model creation, robustness, interand extrapolation.

A predictive envelope protection system using linear, parameter-varying models

“…However, the use of neural networks within safety critical systems implies strong restrictions on the design process and the behavior of the network. Only a few network types, e.g., local model networks [5], feature a deterministic design process and a deterministic behavior and, therefore, remain eligible for certification requirements. In this respect, linear, parameter-varying (LPV) models appear to be an attractive alternative to previous approaches directly linking the nonlinear aircraft behavior, described by Eq.…”