Practical gust load alleviation and flutter suppression control laws based on a LQG methodology

1981 ◽  
Author(s):  
D. GANGSAAS ◽  
U. LY ◽  
D. NORMAN
Keyword(s):  
Control Laws ◽  
Flutter Suppression ◽  
Gust Load Alleviation

Active aeroelastic wing design for gust load alleviation and flutter suppression

1997 ◽  
Author(s):  
Changho Nam ◽  
Youdan Kim ◽  
Jeffrey Layton ◽  
Changho Nam ◽  
Youdan Kim ◽  
...  
Keyword(s):  
Wing Design ◽  
Flutter Suppression ◽  
Gust Load Alleviation

Design of a multivariable flutter suppression/gust load alleviation system

1988 ◽  
Vol 11 (3) ◽  
pp. 220-229 ◽  
Author(s):  
B. S. Liebst ◽  
W. L. Garrard ◽  
Jerome A. Farm
Keyword(s):  
Flutter Suppression ◽  
Gust Load Alleviation

scholarly journals Synthesis of an active flutter suppression system in the transonic domain using a computational model

2021 ◽  
pp. 1-19
Author(s):  
R. Vepa ◽  
J.R. Kwon
Keyword(s):  
Computational Models ◽  
Optimum Control ◽  
Zeroth Order ◽  
Lattice Method ◽  
Control Laws ◽  
Aerodynamic Loads ◽  
Flutter Suppression ◽  
Active Flutter Suppression ◽  
Doublet Lattice Method ◽  
Suppression System

ABSTRACT Control laws for implementing active flutter suppression are generally derived from linear aeroelastic models. In this paper, families of control laws for implementing an active flutter suppression system were initially designed using linearised aeroelastic models based on the doublet lattice method after ignoring the aerodynamic loads associated with relatively faster time scales. Using these preliminary sets of control laws and the nonlinear transonic small disturbance theory, near-optimum control laws were chosen in the transonic domain to maximally increase the flutter speed of a typical aircraft wing by at least 16% or more. Thus it is shown that it is feasible to systematically design near-optimal control laws for active flutter suppression using computational models in transonic flow. The doublet lattice method coupled with the zeroth-order matrix Padé approximant provided the fastest method for synthesising a large number of preliminary control laws. The methodology was successfully demonstrated by applying it to two benchmarking examples.

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