Unifying approach to observer-filter design

summary:The paper examines similarities between observer design as introduced in Automatic Control Theory and filter design as established in Signal Processing. It is shown in the paper that there are obvious connections between them in spite of different aims for their design. Therefore, it is prospective to make them be compatible from the structural point of view. Introduced error invariance and error convergence properties of both of them are unifying tools for their design. Lyapunov's stability theory, signal power, system energy and a power balance relation are other basic terms used in the paper

Manifold interpolation and model reduction

One approach to parametric and adaptive model reduction is via the interpolation of orthogonal bases, subspaces or positive definite system matrices. In all these cases, the sampled inputs stem from matrix sets that feature a geometric structure and thus form so-called matrix manifolds. This work will be featured as a chapter in the upcoming Handbook on Model Order Reduction (P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W.H.A. Schilders, L.M. Silveira, eds, to appear on DE GRUYTER) and reviews the numerical treatment of the most important matrix manifolds that arise in the context of model reduction. Moreover, the principal approaches to data interpolation and Taylor-like extrapolation on matrix manifolds are outlined and complemented by algorithms in pseudo-code.Comment: 37 pages, 4 figures, featured chapter of upcoming "Handbook on Model Order Reduction

Observer Design for Interconnected Systems and Implementation via Differential-Algebraic Equations

A new approach to the design of observers of nonlinear dynamical systems is presented. Generally, linear or nonlinear control systems are expressed as explicit systems of differential equations and solved either analytically or numerically. If numerically, they are implemented using standard ordinary differential equation (ODE) solvers. In this thesis, a system is decomposed and modeled as an interconnection between two observer subsystems, particularly, as canonical DAE observers. In general, control design engineers may be faced with a formidable problem of solving this system analytically or in obtaining closed-form solutions. To attest to the complexity and complications in treating a system of interconnected DAE observer systems, a scaled-down version of a publication on “Small-Gain Theorem” is included in the appendix for the reader’s perusal. (A brief introduction to “Small-Gain Theorem” can be found in Chapter 4). The premise of this thesis is to demonstrate that, where the design of an observer plays a major role involving output feedback, there may be advantages in formulating a control system as a differential-algebraic equation (DAE), especially in the case of interconnected subsystems. An implicit system of interconnected DAE observers is considered and shown implementable using an existing DAE solver, whose resolution allows one the capability of computing input and output bounds. This is based on fixed or variable timesteps within the operating interval of each subsystem to ensure input-output stability (IOS) and the observability property of the interconnected observer system. The observer design method is based on the extended linearization approach. The basic background is provided for the design process of an interconnected observer system using DAE. Note, the application of the new approach has not been considered previously for the case of an interconnected DAE observer system

Observability, Identifiability and Sensitivity of Vision-Aided Navigation

We analyze the observability of motion estimates from the fusion of visual and inertial sensors. Because the model contains unknown parameters, such as sensor biases, the problem is usually cast as a mixed identification/filtering, and the resulting observability analysis provides a necessary condition for any algorithm to converge to a unique point estimate. Unfortunately, most models treat sensor bias rates as noise, independent of other states including biases themselves, an assumption that is patently violated in practice. When this assumption is lifted, the resulting model is not observable, and therefore past analyses cannot be used to conclude that the set of states that are indistinguishable from the measurements is a singleton. In other words, the resulting model is not observable. We therefore re-cast the analysis as one of sensitivity: Rather than attempting to prove that the indistinguishable set is a singleton, which is not the case, we derive bounds on its volume, as a function of characteristics of the input and its sufficient excitation. This provides an explicit characterization of the indistinguishable set that can be used for analysis and validation purposes

Observability studies for spacecraft attitude determination based on temperature data

Die Schätzung und Steuerung der Fluglage ist elementar für jede Raumfahrzeugmission. Die erforderliche Genauigkeit hängt von der jeweiligen Mission und ihren Nutzlasten ab. Ein funktionierendes Lageregelungssystem ist jedoch immer unverzichtbar, um die Zielgenauigkeit und Stabilität der Nutzlasten zu gewährleisten, die für den Erfolg der Mission entscheidend sind. Daher ist es sinnvoll, redundante Methoden zur Schätzung und Regelung der aktuellen Fluglage einzusetzen. Diese Arbeit fokussiert sich primär auf die Lageschätzung. Hierbei wird untersucht ob und wie Temperaturmessungen für die Lagebestimmung genutzt werden können. Diese Untersuchung wird durchgeführt, indem die zugrundeliegenden mathematischen Beschreibungen der Fluglage sowie der Temperaturdynamik betrachtet werden. Auf deren Grundlage wird dann ein Beobachter zur Lageschätzung entwickelt, der sich hauptsächlich auf die Temperaturdaten von zwei verschiedenen Sensorkonfigurationen stützt. In der ersten Konfiguration wird nur ein einziger Temperatursensor verwendet, dessen Informationen mit Gyroskopmessungen fusioniert werden, um die Lage zu bestimmen. Dies wird durch eine Transformation in Normalform und eine neuartige Lagebeschreibung erreicht. Auftretende Mehrdeutigkeiten bei der Lagebestimmung sowie alternative Beobachterdesigns werden vorgestellt. Die Analyse zeigt, dass mit dem vorgeschlagenen Beobachter lokale Aussagen zur Lageschätzung getroffen werden können - vorausgesetzt, die verwendeten Modelle und Messungen sind ausreichend genau und es steht genügend Rechenleistung zur Verfügung. In der zweiten Konfiguration werden sechs Paare von Temperatursensoren betrachtet. Jedes Paar besteht aus zwei Sensoren mit unterschiedlichen physikalischen Eigenschaften und zeigt in Richtung einer anderen Raumfahrzeugachse. Diese Sensorsignale enthalten genügend Informationen, um die Fluglage zu rekonstruieren, ohne dass die Verwendung von Ableitungen höherer Ordnung erforderlich ist. Es wird ein Algorithmus vorgeschlagen, der die Position der Sonne und der Erde schätzt und diese zur Bestimmung der Lage verwendet. Die Beobachter für beide Konfigurationen verwenden eine Transformation in eine kanonische Form, um ihre Schätzungen zu erhalten. Die resultierenden Beobachter sind daher sowohl in den transformierten als auch in den ursprünglichen Koordinaten formuliert. Während diese Beobachter unter Annahmen die häufig in der Literatur verwendeten werden äquivalent sind, kann es, sobald diese Annahmen fallengelassen werden, zu einer Reihe interessanter Phänomene wie Mehrdeutigkeit der Lösungen und sogar Instabilität kommen. Diese Phänomene werden an unserem vorgestellten System veranschaulicht und es werden Methoden vorgeschlagen, um sie zu bewältigen. Die für die zweite Konfiguration entworfenen Beobachter werden auf die von der Raumsondenmission GRACE erhaltenen Daten angewandt. Dabei hat sich gezeigt, dass die vorgeschlagenen Modelle für die Temperaturschätzung mit einem R2-Wert zwischen 78,8 % und 99,9 % gut geeignet sind. Die vorgeschlagenen Algorithmen erlauben eine Genauigkeit mit einem mittleren Fehler über eine Umlaufbahn von weniger als fünf Grad und lassen sich nachweislich leicht durch zusätzliche Messungen ergänzen.Attitude estimation and control is fundamental for every spacecraft mission. Accuracy requirements are strongly dependant on mission level goals and the respective payloads and experiments. However, it is always essential for the mission success to have a functioning attitude control system to allow a high pointing accuracy and stability of the payloads. Therefore, it is useful to employ redundant means to estimate and control the current attitude. The estimation of the attitude is the main topic of this work in which the information contained in temperature measurements for attitude estimation is investigated. This investigation is carried out by providing the underlying mathematical descriptions of the attitude as well as temperature dynamics. Different observer designs are considered based on these models to estimate the attitude relying mostly on the temperature data obtained from two different sensor configurations. In the first configuration, only a single temperature sensor is employed and the information is fused with gyroscope measurements to determine the attitude. This is achieved based on a transformation into normal form and a novel attitude description. Arising ambiguities in the attitude estimation, as well as alternative observer designs are presented. The analysis shows that with the proposed observer, it is possible to estimate the attitude provided that the employed models and measurements are sufficiently accurate and that enough computational power is available. The second configuration considers six pairs of temperature sensors. Each pair consists of two sensors with different physical properties and every pair points into a different body axis. These sensor signals contain enough information to reconstruct the attitude without requiring the usage of higher-order derivatives. An algorithm is proposed that estimates the position of the Sun and Earth and uses these to estimate the attitude. The observers for both configurations use a transformation of the system dynamics into canonical form to obtain a formulation of the problem that allows for estimation. The resulting observers are therefore formulated in transformed and original coordinates. While these observers are equivalent under assumptions widely used in literature, the moment these assumptions are dropped, a number of interesting phenomena such as ambiguity of the solutions and even instability can occur. These phenomena are illustrated by the system of interest and methods are proposed to deal with them. The designed observers for the second configuration are applied to the data obtained from the spacecraft mission GRACE. The results indicate that the proposed models are well suited for the temperature estimation with a R2 value between 78.8% and 99.9%. The proposed algorithms admit an accuracy with a mean error over an orbit of less than five degrees and are shown to be easily augmented with additional measurements

LPV modeling of nonlinear systems: A multi‐path feedback linearization approach

This article introduces a systematic approach to synthesize linear parameter‐varying (LPV) representations of nonlinear (NL) systems which are described by input affine state‐space (SS) representations. The conversion approach results in LPV‐SS representations in the observable canonical form. Based on the relative degree concept, first the SS description of a given NL representation is transformed to a normal form. In the SISO case, all nonlinearities of the original system are embedded into one NL function, which is factorized, based on a proposed algorithm, to construct an LPV representation of the original NL system. The overall procedure yields an LPV model in which the scheduling variable depends on the inputs and outputs of the system and their derivatives, achieving a practically applicable transformation of the model in case of low order derivatives. In addition, if the states of the NL model can be measured or estimated, then a modified procedure is proposed to provide LPV models scheduled by these states. Examples are included to demonstrate both approaches

Cooperativity and its use in robust control and state estimation for uncertain dynamic systems with engineering applications

This work shows a general applicable approach to robustly control uncertain dynamic systems, where the uncertainty is given by bounded intervals. The presented robust control methods rely on a verified enclosure of the state intervals. Since state-of-the-art-methods to calculate this fail, the property of cooperativity is used. However, since not all systems are naturally cooperative, a transformation routine is established to widen the possible application of this method. Different application scenarios chosen from a variety of engineering fields are used to validate the theoretical findings.Diese Arbeit zeigt einen generell verwendbaren Ansatz, um ein unsicheres dynamisches System robust zu regeln. Der gezeigte Ansatz verwendet dabei verifizierte Intervalleinschlüsse, die sich aus der intervallbasierten Unsicherheit ergeben. Da moderne Rechenmethoden hierbei versagen, wird die Eigenschaft der Kooperativität ausgenutzt, um dies dennoch zu ermöglichen. Da nicht alle Systeme diese Eigenschaft direkt aufweisen, wird eine Transformationsroutine entwickelt, um den gezeigten Ansatz auf andere Einsatzszenarien zu erweitern. Dies wird durch verschiedene Anwendungen in der Arbeit bewiesen

Parameter identification for biological models

This thesis concerns the identification of dynamic models in systems biology. and is structured into two parts. Both parts concern building dynamic models from observed data, but are quite different in perspective, rationale and mathematics. The first part considers the development of novel identification techniques that are particularly tailored to (molecular) biology and considers two approaches. The first approach reformulates the parameter estimation problem as a feasibility problem. This reformulation allows the invalidation of models by analysing entire parameter regions. The second approach utilises nonlinear observers and a transformation of the model equations into parameter free coordinates. The parameter free coordinates allow the design of a globally convergent observer, which in turn estimates the parameter values, and further, allows to identify modelling errors or unknown inputs/influences. Both approaches are bottom up approaches that require a mechanistic understanding of the underlying processes (in terms of a biochemical reaction network) leading to complex nonlinear models. The second part is an example of what can be done with classical, well developed tools from systems identification when applied to hitherto unattended problems.In particular, part two of my thesis develops a modelling framework for rat movements in an experimental setup that it widely used to study learning and memory.The approach is a top down approach that is data driven resulting in simple linear models