293 research outputs found
Limited Authority Adaptive Flight Control for Reusable Launch Vehicles
No full textPublished in the Journal of Guidance Control and Dynamics,
Vol. 26, No. 6, November–December 2003.Presented as Paper 2000-4157 at the AIAA Guidance, Navigation, and
Control Conference, Denver, CO, 14–17 August 2000; received 13 December
2001; revision received 23 May 2003; accepted for publication 9 June
2003. Copyright c° 2003 by Eric N. Johnson and Anthony J. Calise. Published
by the American Institute of Aeronautics and Astronautics, Inc., with
permission.In the application of adaptive flight control, significant issues arise due to limitations in the plant inputs, such as
actuator displacement limits, actuator rate limits, linear input dynamics, and time delay. A method is introduced
that allows an adaptive law to be designed for the system without these input characteristics and then to be applied
to the system with these characteristics, without affecting adaptation. This includes allowing correct adaptation
while the plant input is saturated and allows the adaptation law to function when not actually in control of the
plant. To apply the method, estimates of actuator positions must be found. However, the adaptation law can
correct for errors in these estimates. Proof of boundedness of system signals is provided for a single hidden-layer
perceptron neural network adaptive law. Simulation results utilizing the methods introduced for neural network
adaptive control of a reusable launch vehicle are presented for nominal flight and under failure cases that require
considerable adaptation
Neural Network Augmented Kalman Filtering in the Presence of Unknown System Inputs
No full textPresented at the AIAA Guidance, Navigation, and Control Conference and Exhibit
21 - 24 August 2006, Keystone, Colorado.Copyright © 2006 by Ramachandra Sattigeri and Anthony J. Calise. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.We present an approach for augmenting a linear, time-varying Kalman filter with an adaptive neural
network (NN) for the state estimation of systems with linear process models acted upon by unknown inputs.
The application is to the problem of tracking maneuvering targets. The unknown system inputs represent the
effect of unmodeled disturbances acting on the system and are assumed to be continuous and bounded. The
NN is trained online to estimate the unknown inputs. The training signal for the NN consists of two error
signals. The first error signal is the residual of the Kalman filter that is augmented with the NN output. The
second error signal is obtained after deriving a linear parameterization model of available system signals in
terms of the ideal, unknown NN weights that linearly parameterize the unknown system inputs. The
combination of two different sources of error signals to train the NN represents a composite adaptation type
approach to adaptive state estimation. The approach is applied in a vision-based formation flight simulation
of a leader and a follower unmanned aerial vehicle (UAV). The adaptive estimator onboard the follower UAV
estimates the range, azimuth angle, and elevation angle to the leader UAV, the derivatives of these LOS
variables, and the unknown leader aircraft acceleration along the axes of the Cartesian coordinate inertial
frame. Simulation results with the presented approach are greatly improved when compared to those
obtained with just a linear, time-varying Kalman filter and a particular adaptive state estimation method that
utilizes just one source of error signals to train the NN [17]
An Adaptive Vision-based Approach to Decentralized Formation Control
No full textPresented at the AIAA Guidance, Navigation, and Control Conference and Exhibit
16 - 19 August 2004, Providence, Rhode Island.Copyright © 2004 by Ramachandra Sattigeri, Anthony J. Calise. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.In considering the problem of formation control in the deployment of intelligent
munitions, it would be highly desirable, both from a mission and a cost perspective, to limit
the information that is transmitted between vehicles in formation. In a previous paper, we
proposed an adaptive output feedback approach to address this problem. Adaptive
formation controllers were designed that allow each vehicle in formation to maintain
separation and relative orientation with respect to neighboring vehicles, while avoiding
obstacles. In this paper, we consider a modification to the adaptive control law that enables
each vehicle in a leader-follower formation to track line-of-sight (LOS) range with respect to two or more neighboring vehicles with zero steady-state error. We also propose a
coordination scheme in which each vehicle tracks LOS range to up to two nearest vehicles
while simultaneously navigating towards a common set of waypoints. This coordination
scheme does not require a unique leader for the formation, increasing robustness of the
formation. As our results show, such leaderless formations can perform maneuvers like
splitting to go around obstacles, rejoining after negotiating the obstacles, and changing into
line-shaped formation in order to move through narrow corridors
Integration of Adaptive Estimation and Adaptive Control Design for Uncertain Nonlinear Systems
No full textPresented at the AIAA Guidance, Navigation and Control Conference and Exhibit,
20 - 23 August 2007, Hilton Head, South Carolina.Copyright © 2007 by Ramachandra Sattigeri, Anthony J. Calise and Byoung Soo Kim. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.This paper presents a method to integrate adaptive estimation and adaptive control
designs for a class of uncertain nonlinear systems having both parametric uncertainties and
unmodeled dynamics. The method is based on Lyapunov-like stability analysis of all the
errors in the closed-loop system. The adaptive estimator considered is a linear, time-varying
Kalman filter augmented by the output of an observer neural network. The observer neural
network compensates the nominal Kalman filter for modeling errors. The estimated states
are used in the construction of an adaptive control solution that is based on approximate
feedback linearization augmented with the outputs of an adaptive neural network controller.
The presented approach is then applied to a vision-based formation flight control problem.
The objective is for a follower aircraft to maintain range from a maneuvering leader aircraft
using a monocular fixed camera for passive sensing of the leader's relative motion. In the
implementation, the states of the adaptive estimator are estimates of line-of-sight variables
and the outputs of the observer neural network are estimates of the leader acceleration. The
adaptive control solution considered is an integrated guidance and control design that
includes online adaptation to unmodeled nonlinearities such as the unknown leader aircraft
acceleration and parametric uncertainties in the own-aircraft aerodynamic derivatives.
Simulation results using a nonlinear 6DOF simulation model of a fixed-wing UAV are
presented to illustrate the feasibility and efficacy of the approach
Correction to: Diffusion, outcomes and implementation of minimally invasive liver surgery: a snapshot from the I Go MILS (Italian Group of Minimally Invasive Liver Surgery) Registry
No full textA technical error led to incorrect rendering of the author group in this article. The correct authorship is as follows: Luca Aldrighetti, Francesca Ratti, Umberto Cillo, Alessandro Ferrero, Giuseppe Maria Ettorre, Alfredo Guglielmi, Felice Giuliante, Fulvio Calise on behalf of the Italian Group of Minimally Invasive Liver Surgery (I GO MILS) The collaborators are: Raffaele Dalla Valle, AOU Parma, Parma; Vincenzo Mazzaferro, Istituto Nazionale Tumori, Milano; Elio Jovine, Ospedale Maggiore, Bologna; Luciano Gregorio De Carlis, Ospedale Niguarda Ca’ Granda, Milano; Ugo Boggi, AOU Pisana, Pisa; Salvatore Gruttadauria, ISMETT, Palermo; Fabrizio Di Benedetto, AOU Policlinico di Modena, Modena; Paolo Reggiani, Ospedale Maggiore Policlinico, Milano; Stefano Berti, Ospedale Civile S.Andrea, La Spezia; Graziano Ceccarelli, Ospedale San Donato, Arezzo; Leonardo Vincenti, AOU Consorziale Policlinico, Bari; Giulio Belli, Ospedale SM Loreto Nuovo, Napoli; Guido Torzilli, Istituto Clinico Humanitas, Rozzano; Fausto Zamboni, Ospedale Brotzu, Cagliari; Andrea Coratti, AOU Careggi, Firenze; Pietro Mezzatesta, Casa di Cura La Maddalena, Palermo; Roberto Santambrogio, AO San Paolo, Milano; Giuseppe Navarra, AOU Policlinico G. Martino, Messina; Antonio Giuliani, AO R.N. Cardarelli, Napoli; Antonio Daniele Pinna, Policlinico Sant’Orsola Malpighi, Bologna; Amilcare Parisi, AO Santa Maria di Terni, Terni; Michele Colledan, AO Papa Giovanni XXIII, Bergamo; Abdallah Slim, AO Desio e Vimercate, Vimercate; Adelmo Antonucci, Policlinico di Monza, Monza; Gian Luca Grazi, Istituto Nazionale Tumori Regina Elena, Roma; Antonio Frena, Ospedale Centrale, Bolzano; Giovanni Sgroi, AO Treviglio-Caravaggio, Treviglio; Alberto Brolese, Ospedale S.Chiara, Trento; Luca Morelli, AOU Pisana, Pisa; Antonio Floridi, AO Ospedale Maggiore, Crema; Alberto Patriti, Ospedale San Matteo degli Infermi, Spoleto; Luigi Veneroni, Ospedale Infermi AUSL Romagna, Rimini; Giorgio Ercolani, Ospedale Morgagni Pierantoni, Forlì; Luigi Boni, AOU Fondazione Macchi, Varese; Pietro Maida, Ospedale Villa Betania, Napoli; Guido Griseri, Ospedale San Paolo, Savona; Andrea Percivale, Ospedale Santa Corona, Pietraligure; Marco Filauro, AO Galliera, Genova; Silvio Guerriero, Ospedale San Martino, Belluno; Giuseppe Tisone, Policlinico Tor Vergata, Roma; Raffaele Romito, AOU Maggiore della Carità, Novara; Umberto Tedeschi, AOU Integrata Verona, Verona; Giuseppe Zimmitti, Fondazione Poliambulanza, Brescia
Real-Time Vision-Based Relative Aircraft Navigation
No full textReceived 22 February 2006; revision received 11 September 2006; accepted for publication 11 September 2006. Copyright ©
2007 by Eric N. Johnson, Anthony J. Calise,YokoWatanabe, Jincheol Ha, and James C. Neidhoefer. Published by the American
Institute of Aeronautics and Astronautics, Inc., with permission.Published in Journal of Aerospace Computing, Information, and Communication, Vol. 4, Issue 4, January 2004.This paper describes two vision-based techniques for the navigation of an aircraft relative
to an airborne target using only information from a single camera fixed to the aircraft. These
techniques are motivated by problems such as "see and avoid", pursuit, formation flying,
and in-air refueling. By applying an Extended Kalman Filter for relative state estimation,
both the velocity and position of the aircraft relative to the target can be estimated. While
relative states such as bearing can be estimated fairly easily, estimating the range to the
target is more difficult because it requires achieving valid depth perception with a single
camera. The two techniques presented here offer distinct solutions to this problem. The
first technique, Center Only Relative State Estimation, uses optimal control to generate an
optimal (sinusoidal) trajectory to a desired location relative to the target that results in
accurate range-to-target estimates while making minimal demands on the image processing
system.The second technique, Subtended Angle Relative State Estimation, uses more rigorous
image processing to arrive at a valid range estimate without requiring the aircraft to follow
a prescribed path. Simulation results indicate that both methods yield range estimates of
comparable accuracy while placing different demands on the aircraft and its systems
Robust Control Of Hypersonic Vehicles Considering Propulsive And Aeroelastic Effects
No full textThe influence of propulsion system variations and elastic fuselage behavior on the flight control system of an airbreathing hypersonic vehicle is investigated. Thrust vector magnitude and direction changes due to angle of attack variations affect the pitching moment. Low structural vibration frequencies may occur close to the rigid body modes influencing the angle of attack and lead to possible cross coupling. These effects are modeled as uncertainties in the context of a robust control study of a hypersonic vehicle model accelerating through Mach 8 using H1 and ¯ synthesis techniques. Various levels of uncertainty are introduced into the system. Both individual and simultaneous appearance of uncertainty are considered. The results indicate that the chosen design technique is suitable for this kind of problem provided that a fairly good knowledge of the effects mentioned above is available. The order of the designed controller is reduced but robust performance is lost which shows the n..
Hypersonic Flight Control System Design Using Fixed Order Robust Controllers
No full textControl system design for hypersonic vehicles will be complicated by coupling effects between aerodynamics, propulsion, and structure. Using H1 and ¯-synthesis techniques, these features can be modeled as uncertainties and incorporated into the design procedure of a flight control system. However, modern control theory techniques generally lead to high order controllers. A technique to constrain controller dimension a priori in the design process is used to design fixed order ¯ controllers which are robust to mixed real/complex uncertainties. Typical hypersonic effects like propulsion system perturbations and aeroelastic fuselage bending are modeled as uncertainties in a robust control design framework. Former Graduate Research Assistant, currently with Bodenseewerk Geratetechnik GmbH (BGT), D-88641 Uberlingen, Germany. Member AIAA. Professor. Fellow AIAA. Phone (404) 894-7145, Fax (404) 894-2760, e-mail [email protected] Presented at the 1995 Sixth Internation..
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