1,721,013 research outputs found
A safe learning model reference adaptive controller for uncertain aircrafts models
No full textIn this paper, it is presented an approach for the design of a feedback Model Reference Adaptive Controller (MRAC) for uncertain linear systems with guaranteed evolution of the closed-loop error trajectories within a computable robust invariant set. The estimation of the UUB region has been derived exploiting robust quadratic stability arguments assuming bounded uncertainty on the state and input matrix as long as amplitude constrained adaptive control. In the scheme, a linear output feedback controller is augmented with an amplitude bounded adaptive control to improve the performance in the presence of significant modeling uncertainties. The robust invariant set design was performed solving a constrained convex optimization problem. The method allows the analysis of the joint effect of the modeling uncertainties and the adaptive control amplitude on the size of the UUB region, thus allowing the design of a safe MRAC scheme. An additional benefit is that no specific parameter adaptation algorithm is required, as long as the adaptive control output is confined within the predefined limits. For enforcing this confinement, the mechanism of adaptive control redistribution is introduced. A detailed simulation study was performed using the short period longitudinal dynamics of an F16 model to show the design steps and to highlight the benefits of the methodology
GOLN: Graph Object-based Localization Network
No full textIn the last decades, robotic localization has been mainly addressed with Visual Odometry (VO) or Simultaneous Localization and Mapping (SLAM) approaches, which usually provide an accurate metric precision. Despite the impressive results, these approaches have some shortcomings such as the amount of memory they require and the lack of robustness in non-ideal environments. Inspired by the human capabilities, in this paper we present a novel framework, named Graph Object-based Localization Network (GOLN), to address the topological localization problem with a novel approach, characterized by low memory requirements and robustness with respect to appearance. GOLN is based on a topological map, i.e., a graph, which is fed to a Graph Network (GN) along with global visual features of the environment and returns the estimation of the position node where the robot is located. Experiments have been performed in Unreal Engine (UE4) environments with a simulated ground robot, equipped with a monocular camera
A comprehensive case study of data-driven methods for robust aircraft sensor fault isolation
No full textRecent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors
Quantification of tolerable parametric and dynamic uncertainty for robust mrac systems
No full textAdaptive controllers have been proposed in a number of applications characterized by large modelling uncertainties in the open loop system. Despite this attractive feature it is not always immediate to guarantee, by design, a predictable closed-loop transient. Indeed the characterization of the transient response of a closed loop adaptive control system is still a challenging problem from the verification and validation standpoint. This issue is particularly relevant in the presence of unmodelled dynamics, time delays, disturbances and unmatched uncertainties. In this paper it is proposed a practical mythology for the quantification of the tolerable parametric and dynamic uncertainty in systems controlled by a Model Reference Adaptive Controller (MRAC) in the case the parameters that characterize the parametric and the dynamic uncertain as long as the controller adaptation weights are assumed unknown but bounded within a defined box domain. Given these bounded uncertainties, the MRAC design is formalized as a H2 controller design to guarantee minimum H2 gain between the reference model states and the tracking error states. Exploiting a quadratic Lyapunov function a parameter dependent LMI condition is derived whose feasibility guarantees a specified input-output H2 gain. This LMI condition can be exploited to quantify the tolerable matched and dynamic uncertainty and to evaluate the corresponding input-output H2 gain. The proposed approach has been applied to the design of a MRAC controller and to evaluate the tolerable uncertainties of a benchmark cart-pole system
Experimental Analysis of Neural Approaches for Synthetic Angle-of-Attack Estimation
No full textSynthetic sensors enable flight data estimation without devoted physical sensors. Within modern digital avionics, synthetic sensors can be implemented and used for several purposes such as analytical redundancy or monitoring functions. The angle of attack, measured at air data system level, can be estimated using synthetic sensors exploiting several solutions, e.g., model-based, data-driven, and model-free state observers. In the class of data-driven observers, multilayer perceptron neural networks are widely used to approximate the input-output mapping angle-of-attack function. Dealing with experimental flight test data, the multilayer perceptron can provide reliable estimation even though some issues can arise from noisy, sparse, and unbalanced training domain. An alternative is offered by regularization networks, such as radial basis function, to cope with training domain based on real flight data. The present work's objective is to evaluate performances of a single-layer feed-forward generalized radial basis function network for AoA estimation trained with a sequential algorithm. The proposed analysis is performed comparing results obtained using a multilayer perceptron network adopting the same training and validation data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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