An Extended Kalman Filter-Based Technique for On-Line Identification of Unmanned Aerial System Parameters

• Autor(a) principal: Grillo,Caterina
• Data de Publicação: 2015
• Outros Autores: Montano,Fernando
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal of Aerospace Technology and Management (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462015000300323
• topic: Aircraft dynamic derivatives; Extended Kalman Filter; On-line identification; Unmanned Aerial System

ABSTRACT: The present article deals with the identification, at the same time, of aircraft stability and control parameters taking into account dynamic damping derivatives. Such derivatives, due to the rate of change of the angle of attack, are usually neglected. So the damping characteristics of aircraft dynamics are attributed only on pitch rate derivatives. To cope with the dynamic effects of these derivatives, authors developed devoted procedures to estimate them. In the present paper, a complete model of aerodynamic coefficients has been tuned-up to identify simultaneously the whole set of derivatives. Besides, in spite of the employed reduced order model and/or decoupled dynamics, a six degrees of freedom model has been postulated without decoupling longitudinal and lateral dynamics. A recursive non-linear filtering approach via Extended Kalman Filter is proposed, and the filter tuning is performed by inserting the effects of dynamic derivatives into the mentioned mathematical model of the studied aircraft. The tuned-up procedure allows determining with noticeable precision the stability and control derivatives. In fact, either by activating maneuvers generated by all the control surfaces or by inserting noticeable measurement noise, the identified derivatives show very small values of standard deviation. The present study shows the possibility to identify simultaneously the aircraft derivatives without using devoted procedures and decoupled dynamics. The proposed technique is particularly suited for on-line para-metrical identification of Unmanned Aerial Systems. In fact, to estimate both state and aircraft parameters, low power and time are required even using measurement noises typical of low-cost sensors.