Inversion-Based Approach for Detection and Isolation of Faults in Switched Linear Systems

This paper addresses the problem of the left inversion of switched linear systems from a diagnostics perspective. The problem of left inversion is to reconstruct the input of a system with the knowledge of its output, whose differentiation is usually required. In the case of this work, the objective is to reconstruct the system’s unknown inputs, based on the knowledge of its outputs, switching sequence and known inputs. With the inverse model of the switched linear system, a real-time Fault Detection and Isolation (FDI) algorithm with an integrated Fuzzy Logic System (FLS) that is capable of detecting and isolating abrupt faults occurring in the system is developed. In order to attenuate the effects of unknown disturbances and noise at the output of the inverse model, a smoothing strategy is also used. The results are illustrated with an example. The performance of the method is validated experimentally in a dc-dc boost converter, using a low-cost microcontroller, without any additional components.This work was funded by FCT—Fundação para a Ciência e a Tecnologia, within the project SAICTPAC/0004/2015—POCI-01-0145-FEDER-016434.info:eu-repo/semantics/publishedVersio

Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast

Ultrasound Tomography has seen a revival of interest in the past decade, especially for breast imaging, due to improvements in both ultrasound and computing hardware. In particular, three-dimensional ultrasound tomography, a fully tomographic method in which the medium to be imaged is surrounded by ultrasound transducers, has become feasible. In this paper, a comprehensive derivation and study of a robust framework for large-scale bent-ray ultrasound tomography in 3D for a hemispherical detector array is presented. Two ray-tracing approaches are derived and compared. More significantly, the problem of linking the rays between emitters and receivers, which is challenging in 3D due to the high number of degrees of freedom for the trajectory of rays, is analysed both as a minimisation and as a root-finding problem. The ray-linking problem is parameterised for a convex detection surface and three robust, accurate, and efficient ray-linking algorithms are formulated and demonstrated. To stabilise these methods, novel adaptive-smoothing approaches are proposed that control the conditioning of the update matrices to ensure accurate linking. The nonlinear UST problem of estimating the sound speed was recast as a series of linearised subproblems, each solved using the above algorithms and within a steepest descent scheme. The whole imaging algorithm was demonstrated to be robust and accurate on realistic data simulated using a full-wave acoustic model and an anatomical breast phantom, and incorporating the errors due to time-of-flight picking that would be present with measured data. This method can used to provide a low-artefact, quantitatively accurate, 3D sound speed maps. In addition to being useful in their own right, such 3D sound speed maps can be used to initialise full-wave inversion methods, or as an input to photoacoustic tomography reconstructions

Cooperative lateral vehicle guidance control for automated vehicles with Steer-by-Wire systems

With the global trend towards automated driving, fault-tolerant onboard power supply systems are introduced into modern vehicles and the level of driving automation is continuously increasing. These advancements contribute to the applicability of Steer-by-Wire systems and the development of automated lateral vehicle guidance control functions. For the market acceptance of automated driving, the lateral vehicle guidance control function must hereby be cooperative, that is it must accept driver interventions. Existing approaches for automated lateral vehicle guidance commonly do not consider driver interventions. If unconsidered in the control loop, the driver intervention is interpreted as an external disturbance that is actively compensated by feedback. This thesis addresses the development of a cooperative lateral vehicle guidance control concept, which enables a true coexistence between manual steering control by the driver and automated steering control. To this end, the subordinate controls of the Steer-by-Wire system for the manual and automated driving mode are initially presented. These include the steering feel generation and steering torque control of the Steer-by-Wire Handwheel Actuator for the manual driving mode, which is structurally extended to a cascade steering position control for the automated driving mode. Subsequently, a superposition control is introduced, which fuses steering torque and position control. The resulting cooperative Handwheel Actuator control achieves precise tracking of the reference steering position in automated driving mode but accepts driver interventions. Thus, the driver can override the active control and experiences a natural steering feel. The transitions hereby are seamless as no blending, gain scheduling or controller output saturation is required. Subsequently, the superimposed lateral vehicle guidance controller for the automated driving mode is described, which computes the reference steering position for the respective Steer-by-Wire controls. In contrast to existing approaches, the plant model equations are rearranged to isolate the vehicle speed dependent dynamics. Thereafter, the concept of inverse nonlinearity control is employed, using a virtual control loop and feedback linearization for an online inversion of the nonlinear plant dynamics. The remaining plant is fully linear and independent of vehicle speed. Consequently, one controller can be synthesized that is valid for all vehicle speeds. The closed and open loop system thereby have the same dynamics independent of vehicle speed, which significantly simplifies control synthesis, analysis, and performance tuning in the vehicle. For considering the future reference path information and constraints on the maximum steering position within the control law, a linear Model Predictive Controller synthesis is selected. The combination of inverse nonlinearity control and linear Model Predictive Controller thus results in a Nonlinear Adaptive Model Predictive Control concept, which makes commonly applied gain scheduling fully obsolete. The controller is structurally extended by a cooperative dynamic feedforward control for considering driver interventions within the control loop. Consequently, the driver can override the active control and seamlessly modify the lateral vehicle motion. A variety of nonlinear simulation analyses and real vehicle tests demonstrate the effectiveness of the proposed control concept

Observability/Identifiability of Rigid Motion under Perspective Projection

The "visual motion" problem consists of estimating the motion of an object viewed under projection. In this paper we address the feasibility of such a problem. We will show that the model which defines the visual motion problem for feature points in the euclidean 3D space lacks of both linear and local (weak) observability. The locally observable manifold is covered with three levels of lie differentiations. Indeed, by imposing metric constraints on the state-space, it is possible to reduce the set of indistinguishable states. We will then analyze a model for visual motion estimation in terms of identification of an Exterior Differential System, with the parameters living on a topological manifold, called the "essential manifold", which includes explicitly in its definition the forementioned metric constraints. We will show that rigid motion is globally observable/identifiable under perspective projection with zero level of lie differentiation under some general position conditions. Such conditions hold when the viewer does not move on a quadric surface containing all the visible points

Recursive model-based virtual in-cylinder pressure sensing for internal combustion engines

Das Drucksignal im Zylinder ist ein sehr nützlicher Indikator für moderne Hochleistungs-Verbrennungsmotoren. Allerdings sind direkte Messungen des Zylinderdrucks unpraktisch, da die Bedingungen in den Zylindern von Verbrennungsmotoren ungünstig sind sowie die Installation von Zylinderdrucksensoren schwierig ist. Zahlreiche Methoden (z. B. virtuelle Messmethoden) wurden untersucht, um den Druck im Zylinder aus extern gemessenen Signalen zu rekonstruieren, z. B. aus dem Schwingungssignal des Motorblocks und der Winkelgeschwindigkeit der Kurbelwelle. Viele der vorgeschlagenen Methoden haben vielversprechende Ergebnisse erbracht. Allerdings gibt es immer noch einige Nachteile wie z.B. eine schlecht konditionierte Inversion oder die Notwendigkeit einer großen Datenmenge, um ein inverses Modell durch künstliche neuronale Netze abzuleiten. In dieser Arbeit werden unter Berücksichtigung der aktuellen Zylinderdruck-Rekonstruktionsprobleme lineare modellbasierte, nichtlineare modellbasierte und inverse modellbasierte ZylinderdruckRekonstruktionsmethoden vorgeschlagen, die eine Alternative zu den bestehenden ZylinderdruckRekonstruktionsmethoden darstellen. Alle vorgeschlagenen Methoden basieren auf der rekursiven Zustandsrekonstruktion unter Verwendung des Kalman-Filters oder eines Beobachters, so dass eine direkte Inversion vermieden werden kann. Darüber hinaus werden alle vorgeschlagenen Methoden rekursiv im Zeitbereich durchgeführt, so dass sie für Echtzeit-Implementierungen geeignet sind und auch keine Probleme im Frequenzbereich, wie z. B. Leckeffekte, aufweisen. Darüber hinaus handelt es sich bei allen vorgeschlagenen Methoden um modellbasierte Methoden, und die Modelle werden mit Hilfe von Systemidentifikationstechniken unter Ausschluss künstlicher neuronaler Netze identifiziert, so dass keine großen Datenmengen erforderlich sind. Für die Systemidentifikation und die Validierung der vorgeschlagenen Methoden wurden Datensätze eines Vierzylinder-Dieselmotors unter verschiedenen Motorbetriebsbedingungen erfasst. Die erfassten Daten reichen von der Betriebsbedingung 1200 U/min, 60 Nm bis zur Betriebsbedingung 3000 U/min, 180 Nm. Die rekonstruierten Zylinderdruckkurven und die beiden Verbrennungsmetriken Zylinderdruckspitze und Spitzenort wurden zur Validierung der vorgeschlagenen Zylinderdruckrekonstruktionsmethoden verwendet. Die Ergebnisse der Rekonstruktion des Zylinderdrucks, die mit den in dieser Arbeit vorgeschlagenen Methoden erzielt wurden, zeigen, dass alle vorgeschlagenen Methoden sowohl unter stationären als auch unter nicht-stationären Betriebsbedingungen verwendet werden können und dass die Ergebnisse der Rekonstruktion des Zylinderdrucks mit den Ergebnissen der bestehenden Methoden zur Rekonstruktion des Zylinderdrucks vergleichbar sind. Darüber hinaus kann festgestellt werden, dass es mehrere Faktoren gibt, die die Genauigkeit der Druckrekonstruktion beeinflussen, wie z.B. die Qualität der identifizierten Modelle, des Verzögerungsblocks und der momentanen Motordrehzahl.The in-cylinder pressure signal is a very useful indicator for modern high-performance internal combustion engines. Unfortunately, direct measurements of the in-cylinder pressure are impractical because installing cylinder pressure sensors is difficult and conditions in internal combustion engine cylinders are adverse. Numerous methods (such as virtual sensing methods) have been investigated to reconstruct the incylinder pressure from externally measured signals, such as the engine block structural vibration signal and the engine crank angular speed. Many of the proposed methodologies have shown promising results. However, there still exist some drawbacks, such as ill-conditioned inversion and the need of large number of data to derive an inverse model by artificial neural networks. In this thesis, considering current in-cylinder pressure reconstruction problems, linear model-based, nonlinear model-based, and inverse model-based in-cylinder pressure reconstruction methods, which are alternative to existing cylinder pressure reconstruction methods, are proposed. All the proposed methods are based on the recursive state reconstruction by using the Kalman filter or observer such that a direct inversion can be avoided. Moreover, all the proposed methods are recursively conducted in time domain, so they are suitable for real-time implementations and they also do not have frequency-domain problems such as spectral leakage. Additionally, all the proposed methods are model-based methods, and the models are identified by using system identification techniques excluding artificial neural networks, so the need of a large number of data is not necessary. For system identification and the validation of the proposed methods, the datasets under different engine operating conditions were acquired from a four-cylinder diesel engine. Data acquired is from the operating condition 1200 rpm, 60 Nm to the operating condition 3000 rpm, 180 Nm. The reconstructed cylinder pressure curves and two combustion metrics cylinder pressure peak and peak location were used for validating the proposed cylinder pressure reconstruction methods. According to the cylinder pressure reconstruction results obtained based on using the proposed methods in this thesis, it can be found that all the proposed methods can be used under both stationary and non-stationary operating conditions, and the reconstructed cylinder pressure results are comparable among existing cylinder pressure reconstruction methods. Furthermore, it can also be found that there exist several factors affecting the pressure reconstruction accuracy, such as the quality of the identified models, delay block and instantaneous engine cycle frequency