Affiliations: School of Mechanical Engineering, Federal University
of Uberlândia, Av. João Naves de Ávila 2121, Campus Santa
Mônica, 38400-902, Uberlândia, MG, Brazil | Embraer – Empresa Brasileira de Aeronautica S.A., Av.
Brigadeiro Faria Lima 2170, 12227-901, São José dos Campos, SP,
Brazil | Department of Mechanical-Aeronautical Engineering,
Instituto Tecnológico de Aeronáutica, Praça Marechal Eduardo Gomes
50, 12228-900, Sao José dos Campos, SP, Brazil
Abstract: This work deals with the application of a nature-inspired
optimization technique to solve an inverse problem represented by the
identification of an aircraft landing gear model. The model is described in
terms of the landing gear geometry, internal volumes and areas, shock absorber
travel, tire type, and gas and oil characteristics of the shock absorber. The
solution to this inverse problem can be obtained by using classical
gradient-based optimization methods. However, this is a difficult task due to
the existence of local minima in the design space and the requirement of an
initial guess. These aspects have motivated the authors to explore a
nature-inspired approach using a method known as LifeCycle Model. In the
present formulation two nature-based methods, namely the Genetic Algorithms and
the Particle Swarm Optimization were used. An optimization problem is
formulated in which the objective function represents the difference between
the measured characteristics of the system and its model counterpart. The
polytropic coefficient of the gas and the damping parameter of the shock
absorber are assumed as being unknown: they are considered as design variables.
As an illustration, experimental drop test data, obtained under zero horizontal
speed, were used in the non-linear landing gear model updating of a small
aircraft.
Keywords: Inverse problems in dynamics, model updating of a landing gear, nature-inspired optimization techniques, particle swarm optimization, genetic algorithms
