A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. With the fitted surface, sensitivity information can be updated at each iteration with less computational effort than that required by either a finite-difference method or integration of the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, and thus provides flexibility to use model equations in any convenient format. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. The degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels and to predict the degree of agreement between CR bounds and search estimates

Economic scheduling in electric power systems: a mathematical model for the U.A.E

Economic scheduling in electric power systems: a mathematical model for the U.A.

Measurements, Models, Systems and Design

531 s.

Joint University Program for Air Transportation Research, 1989-1990

Research conducted during the academic year 1989-90 under the NASA/FAA sponsored Joint University Program for Air Transportation research is discussed. Completed works, status reports and annotated bibliographies are presented for research topics, which include navigation, guidance and control theory and practice, aircraft performance, human factors, and expert systems concepts applied to airport operations. An overview of the year's activities for each university is also presented

Combined state and parameter estimation for on-line applications,

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1972.Bibliography: leaves 354-361.by Peter S. Maybeck.Ph.D

An integrated study of earth resources in the State of California using remote sensing techniques

The author has identified the following significant results. The supply, demand, and impact relationships of California's water resources as exemplified by the Feather River project and other aspects of the California Water Plan are discussed

Techniques de détection de défauts à base d’estimation d’état ensembliste pour systèmes incertains

This thesis proposes a new Fault Detection approach for linear systems with interval uncertainties, bounded perturbations and bounded measurement noises. In this context, the Fault Detection is based on a set-membership state estimation of the system. The main contributions of this thesis are divided into three parts:- The first part proposes an improved method which combines the good accuracy of the zonotopic set-membership state estimation and the reduced complexity of the ellipsoidal set-membership estimation.- In the second part, a new ellipsoidal state estimation approach based on the minimization of the ellipsoidal radius is developed, leading to Linear Matrix Inequality optimization problems. In this context, both multivariable linear time-invariant systems and linear time-variant systems are considered. An extension of these approaches to systems with interval uncertainties is also proposed. - In the continuity of the previous approaches, two Fault Detection techniques have been proposed in the third part based on these set-membership estimation techniques. The first technique allows to detect sensor faults by checking the consistency between the model and the measurements. The second technique is based on Multiple Models. It deals with actuator/component/sensor faults in the same time. A Min-Max Model Predictive Control is developed in order to find the optimal control and the best model to use for the system in spite of the presence of these faults.Cette thèse propose une nouvelle approche de détection de défauts pour des systèmes linéaires soumis à des incertitudes par intervalles, des perturbations et des bruits de mesures bornés. Dans ce contexte, la détection de défauts est fondée sur une estimation ensembliste de l'état du système. Les contributions de cette thèse concernent trois directions principales :- La première partie propose une méthode d'estimation d'état ensembliste améliorée combinant l'estimation à base des zonotopes (qui offre une bonne précision) et l'estimation à base d'ellipsoïdes (qui offre une complexité réduite).- Dans la deuxième partie, une nouvelle approche d'estimation d'état ellipsoïdale fondée sur la minimisation du rayon de l'ellipsoïde est développée. Dans ce cadre, des systèmes multivariables linéaires invariants dans le temps, ainsi que des systèmes linéaires variants dans le temps ont été considérés. Ces approches, résolues à l'aide de problèmes d'optimisation sous la forme d'Inégalités Matricielles Linéaires, ont été étendues au cas des systèmes soumis à des incertitudes par intervalles.- Dans la continuité des approches précédentes, deux techniques de détection de défauts ont été proposées dans la troisième partie utilisant les méthodes d'estimation ensemblistes. La première technique permet de détecter des défauts capteur en testant la cohérence entre le modèle et les mesures. La deuxième technique fondée sur les modèles multiples permet de traiter simultanément les défauts actionneur/composant/capteur. Une commande prédictive Min-Max a été développée afin de déterminer la commande optimale et le meilleur modèle à utiliser pour le système, malgré la présence des différents défauts

Post-stack inversion of seismic reflection data from the belvoir coalfield

Post-Stack inversion of reflection data in seismic exploration can be used to obtain detailed information about lithology variations in the zone of interest. Generalized Linear Inversion (GLI) has previously been applied as a useful tool to achieve this. The purpose of my investigation is to apply GLI to data from the Coal Measures. It is known that in the Coal Measures the most strongly reflecting horizons are the coal seams, which are the exploration targets. In the seismic bandwidth they are thin beds, which causes particular problems associated with vertical resolution for the inversion. The method is applied to a seismic line from the Belvoir Coalfield supplied by British Coal. In order to get better relative amplitudes and to keep the same bandwidth down the whole section, the data were carefully reprocessed using the ProMAX software. Wireline log data from two boreholes intersected by the seismic line were edited to generate acoustic impedance logs as functions of time. Software was developed to implement GLI, and tested on synthetic data before applying it to the reprocessed data. The initial guesses for earth and wavelet models at the boreholes were obtained after systematic studies to determine the best strategy. The construction of the initial guess for the boundary locations elsewhere on the section is very critical for the success of the search for the global minimum. A combination of structural interpretation and the inversion results obtained from the previous trace was found to do the best job. I have tried to invert separately for the boundary locations, acoustic impedances and the wavelet, with the wavelet parameterized in the frequency domain. I found that, provided that the wavelet extracted at a borehole is a good estimate with low error energy, the most successful strategy is just to invert for the boundary locations, keeping the acoustic impedances and the extracted wavelet fixed. If the extracted wavelet is not a good estimate, then parameterizing the wavelet in the frequency domain and optimizing those parameters at the borehole is a useful approach. None of the implemented inversion strategies produced a perfect result. Discrepancies were due to the difficulty in obtaining true relative amplitude values on the processed section. The inversion results and systematic studies on the field dataset indicate that the assumptions of the convolutional model are not satisfied by the processed section

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications