Modeling of linear fading memory systems

Motivated by questions of approximate modeling and identification, we consider various classes of linear time-varying bounded-input-bounded output (BIBO) stable fading memory systems and the characterizations are proved. These include fading memory systems in general, almost periodic systems, and asymptotically periodic systems. We also show that the norm and strong convergence coincide for BIBO stable causal fading memory system

Worst-case analysis of identification - BIBO robustness for closed loop data

This paper deals with the worst-case analysis of identification of linear shift-invariant (possibly) infinite-dimensional systems. A necessary and sufficient input richness condition for the existence of robustly convergent identification algorithms in l1 is given. A closed-loop identification setting is studied to cover both stable and unstable (but BIBO stabilizable) systems. Identification (or modeling) error is then measured by distance functions which lead to the weakest convergence notions for systems such that closed-loop stability, in the sense of BIBO stability, is a robust property. Worst-case modeling error bounds in several distance functions are include

Input-output stabilization of linear systems on Z

A formal framework is set up for the discussion of generalized autoregressive with external input models of the form Ay__Bu, where A and B are linear operators, with the main emphasis being on signal spaces consisting of bounded sequences parametrized by the integers. Different notions of stability are explored, and topological notions such as the idea of a closed system are linked with questions of stabilizability in this very general context. Various problems inherent in using Z as the time axis are analyzed in this operatorial framework

Robust identification of strongly stabilizable systems

For strongly stabilizable systems for which a strongly stabilizing controller is known approximately, the authors consider system identification in the graph, gap, and chordal metrics using robust H ∞ identification of the closed-loop transfer function in the framework proposed by A.J. Helmicki et al. (1990). Error bounds are derived showing that robust convergence is guaranteed and that the identification can be satisfactorily combined with a model reduction step. Two notions of robust identification of stable systems are compared, and an alternative robust identification technique based on smoothing, which can be used to yield polynomial models directly, is develope

A robust high-order mixed L2-linfty estimation for linear-in-the-parameters models

10.1007/s10915-008-9231-7Journal of Scientific Computing382185-206JSCO

Overview of Recent Flight Flutter Testing Research at NASA Dryden

In response to the concerns of the aeroelastic community, NASA Dryden Flight Research Center, Edwards, California, is conducting research into improving the flight flutter (including aeroservoelasticity) test process with more accurate and automated techniques for stability boundary prediction. The important elements of this effort so far include the following: 1) excitation mechanisms for enhanced vibration data to reduce uncertainty levels in stability estimates; 2) investigation of a variety of frequency, time, and wavelet analysis techniques for signal processing, stability estimation, and nonlinear identification; and 3) robust flutter boundary prediction to substantially reduce the test matrix for flutter clearance. These are critical research topics addressing the concerns of a recent AGARD Specialists' Meeting on Advanced Aeroservoelastic Testing and Data Analysis. This paper addresses these items using flight test data from the F/A-18 Systems Research Aircraft and the F/A-18 H..