Analysing dynamic deep stall recovery using a nonlinear frequency approach

Abstract Based on bifurcation theory, nonlinear frequency response analysis is a recent development in the field of flight dynamics studies. Here, we consider how this method can be used to inform us on how to devise the control input such that the system transitions from an undesirable equilibrium solution—an aircraft deep stall solution in our case—to a desirable solution. We show that it is still possible to induce a large-amplitude oscillation via harmonic forcing of the pitch control device and escape the otherwise unrecoverable deep stall, despite very little control power available in such a high angle-of-attack flight condition. The forcing frequencies that excite these resonances are reflected as asymptotically unstable solutions using bifurcation analysis and Floquet theory. Due to the softening behaviour observed in the frequency response, these unstable (divergent) solutions have slightly lower frequencies than the value predicted using linear analysis. Subharmonic resonances are also detected, which are reflected in the time-domain unforced responses. These nonlinear phenomena show strong dependency on the forcing/perturbation amplitude and result in complex dynamics that can impede recovery if the existing procedures are followed. The proposed method is shown to be a useful tool for nonlinear flight dynamics analysis as well as to complement the rather thin literature on deep stall analysis—a topic of relevance for recent research on unconventional landing techniques in unmanned aerial vehicles. A full description of the aircraft model used, the unstable F-16 fighter jet, is provided in the appendix..

Medienart:	Artikel

Erscheinungsjahr:	2022
Erschienen:	2022

Enthalten in:	Zur Gesamtaufnahme - volume:108
Enthalten in:	Nonlinear dynamics - 108(2022), 2 vom: 21. Feb., Seite 1179-1196

Sprache:	Englisch

Beteiligte Personen:	Nguyen, Duc H. [VerfasserIn] Lowenberg, Mark H. [VerfasserIn] Neild, Simon A. [VerfasserIn]

Links:	Volltext [lizenzpflichtig]

Anmerkungen:	© The Author(s) 2022

doi:	10.1007/s11071-022-07283-z

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	OLC2078425435