5 research outputs found

    Generalized H∞ observers design for systems with unknown inputs

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    Abstract-A generalized H ∞ observers design is proposed for linear systems with unknown inputs. It generalizes the existing results concerning the proportional observer (PO) design and the proportional integral observer (PIO) design. The approach is based on the solutions of the algebraic constraints obtained from the unbiasedness conditions of the estimation error. The observer design is obtained from the solutions of linear matrix inequalities (LMIs). A numerical example is given to illustrate our approach

    Lie Group Observer Design for Robotic Systems: Extensions, Synthesis, and Higher-Order Filtering

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    The kinematics and dynamics of many robotic systems evolve on differential manifolds, rather than strictly in Euclidean space. Lie groups, a class of differential manifold with a group structure, arise naturally in the study of rigid-body kinematics. This dissertation studies the design of state observers for systems whose state evolves on a Lie group. State observers, or state estimators, are a crucial part of the guidance, navigation, and control algorithms necessary for autonomous operation of many ground, air, and marine vehicles. The design of state observers on Lie groups is therefore a highly practical exercise. One such nonlinear observer, the gradient-based observer, has generated significant interest in the literature due to its computational simplicity and stability guarantees. The first part of this dissertation explores several applications of the gradient-based observer, including both the attitude estimation problem and the simultaneous localization and mapping (SLAM) problem. By modifying the cost function associated with the observer, several novel attitude estimators are introduced that provide faster convergence when the initial attitude error is large. Further, a SLAM algorithm with guaranteed convergence is introduced and tested in both simulation and experiment. In the second part of this dissertation, the state of the art in Lie group observer design is extended by the development of a higher-order filter on a Lie group. By analogy to the classical linear complementary filter, the proposed method can be interpreted as a nonlinear complementary filter on a Lie group. A disturbance observer that accounts for constant and harmonic disturbances in the group velocity measurements is also considered. Local asymptotic stability about the desired equilibrium point is demonstrated. In addition, an H2-optimal filter synthesis method is derived and disturbance rejection via the internal model principle is considered. A numerical example that demonstrates the desirable properties of the higher-order nonlinear complementary filter, as well as the synthesis techniques, is presented in the context of rigid-body attitude estimation.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147655/1/dzlotnik_1.pd

    Entwurf robuster modellbasierter Fehlerisolationsfilter

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    Die vorliegende Arbeit behandelt den Entwurf robuster modellbasierter Fehlerisolationsfilter für lineare zeitinvariante Systeme. Die Verfahren erlauben eine zuverlässige Lokalisierung von Fehlern auf Basis eines quantitativen Systemmodells. Zunächst wird die Dualität des Fehlerisolationsproblems zu Methoden des Entkopplungsreglerentwurfes herausgearbeitet. Auf dieser Grundlage werden Methoden zum Entwurf von Fehlerisolationsbeobachtern präsentiert. Die Robustheit der Beobachter sowohl hinsichtlich exogener Störungen als auch bezüglich unsicherer Systemparameter wird dabei unter Rückgriff auf lineare Matrixungleichungen optimiert. Schließlich werden Fehlerisolationsfilter allgemeinerer Struktur betrachtet, die gegenüber den beobachterbasierten Methoden einen vereinheitlichenden Entwurf liefern. Dieser erfolgt optimierungsbasiert und erlaubt eine einfache Auslegung, bei der lediglich intuitive Parameter einzustellen sind

    Entwurf robuster modellbasierter Fehlerisolationsfilter

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