Observer-based Sensor Fault Tolerant Control with Prescribed Tracking Performance for a Class of Nonlinear Systems

peer reviewedIn this note, a robust output feedback Fault-Tolerant Control (FTC) for a high-performance tracking problem of a Lipschitz nonlinear system under simultaneous sensor fault and disturbance is developed. The proposed scheme includes the design of an adaptive sliding mode observer which recovers the separation principle. A tangent-type barrier Lyapunov function is incorporated in the backstepping framework to maintain the system states in a prescribed performance bound. Moreover, the unknown estimation error is taken into account. Furthermore, the bounded initial condition assumption is relaxed by defining a time variable bound. The effectiveness of the proposed solution is numerically examined on a DC motor model

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

A New Approach to Automatic Saliency Identification in Images Based on Irregularity of Regions

This research introduces an image retrieval system which is, in different ways, inspired by the human vision system. The main problems with existing machine vision systems and image understanding are studied and identified, in order to design a system that relies on human image understanding. The main improvement of the developed system is that it uses the human attention principles in the process of image contents identification. Human attention shall be represented by saliency extraction algorithms, which extract the salient regions or in other words, the regions of interest. This work presents a new approach for the saliency identification which relies on the irregularity of the region. Irregularity is clearly defined and measuring tools developed. These measures are derived from the formality and variation of the region with respect to the surrounding regions. Both local and global saliency have been studied and appropriate algorithms were developed based on the local and global irregularity defined in this work. The need for suitable automatic clustering techniques motivate us to study the available clustering techniques and to development of a technique that is suitable for salient points clustering. Based on the fact that humans usually look at the surrounding region of the gaze point, an agglomerative clustering technique is developed utilising the principles of blobs extraction and intersection. Automatic thresholding was needed in different stages of the system development. Therefore, a Fuzzy thresholding technique was developed. Evaluation methods of saliency region extraction have been studied and analysed; subsequently we have developed evaluation techniques based on the extracted regions (or points) and compared them with the ground truth data. The proposed algorithms were tested against standard datasets and compared with the existing state-of-the-art algorithms. Both quantitative and qualitative benchmarking are presented in this thesis and a detailed discussion for the results has been included. The benchmarking showed promising results in different algorithms. The developed algorithms have been utilised in designing an integrated saliency-based image retrieval system which uses the salient regions to give a description for the scene. The system auto-labels the objects in the image by identifying the salient objects and gives labels based on the knowledge database contents. In addition, the system identifies the unimportant part of the image (background) to give a full description for the scene

Guaranteed Verification of Dynamic Systems

Diese Arbeit beschreibt einen neuen Spezifikations- und Verifikationsansatz für dynamische Systeme. Der neue Ansatz ermöglicht dabei Ergebnisse, die per Definition frei von Fehlern 2. Art sind. Dies bedeutet, dass das Ergebnis der Verifikation keine versteckten Fehler enthalten kann. Somit können zuverlässige Ergebnisse für die Analyse von sicherheitskritischen Systemen generiert werden. Dazu wird ein neues Verständnis von mengenbasierter Konsistenz dynamischer Systeme mit einer gegebenen Spezifikation eingeführt. Dieses basiert auf der Verwendung von Kaucher Intervall Arithmetik zur Einschließung von Messdaten. Konsistenz wird anhand der vereinigten Lösungsmenge der Kaucher Arithmetik definiert. Dies führt zu mathematisch garantierten Ergebnissen. Die resultierende Methode kann das spezifizierte Verhalten eines dynamischen System auch im Falle von Rauschen und Sensorungenauigkeiten anhand von Messdaten verifizieren. Die mathematische Beweisbarkeit der Konsistenz wird für eine große Klasse von Systemen gezeigt. Diese beinhalten zeitinvariante, intervallartige und hybride Systeme, wobei letztere auch zur Beschreibung von Nichtlinearitäten verwendet werden können. Darüber hinaus werden zahlreiche Erweiterungen dargestellt. Diese führen bis hin zu einem neuartigen iterativen Identifikations- und Segmentierungsverfahren für hybride Systeme. Dieses ermöglicht die Verfikation hybrider Systeme auch ohne Wissen über Schaltzeitpunkte. Die entwickelten Verfahren können darüber hinaus zur Diagnose von dynamischen Systemen verwendet werden, falls eine ausreichend schnelle Berechnung der Ergebnisse möglich ist. Die Verfahren werden erfolgreich auf eine beispielhafte Variation verschiedener Tanksysteme angewendet. Die neuen Theorien, Methoden und Algortihmen dieser Arbeit bilden die Grundlage für eine zuverlässige Analyse von hochautomatisierten sicherheitskritischen Systemen