Estimation-based synthesis of H∞-optimal adaptive FIR filtersfor filtered-LMS problems

This paper presents a systematic synthesis procedure for H∞-optimal adaptive FIR filters in the context of an active noise cancellation (ANC) problem. An estimation interpretation of the adaptive control problem is introduced first. Based on this interpretation, an H∞ estimation problem is formulated, and its finite horizon prediction (filtering) solution is discussed. The solution minimizes the maximum energy gain from the disturbances to the predicted (filtered) estimation error and serves as the adaptation criterion for the weight vector in the adaptive FIR filter. We refer to this adaptation scheme as estimation-based adaptive filtering (EBAF). We show that the steady-state gain vector in the EBAF algorithm approaches that of the classical (normalized) filtered-X LMS algorithm. The error terms, however, are shown to be different. Thus, these classical algorithms can be considered to be approximations of our algorithm. We examine the performance of the proposed EBAF algorithm (both experimentally and in simulation) in an active noise cancellation problem of a one-dimensional (1-D) acoustic duct for both narrowband and broadband cases. Comparisons to the results from a conventional filtered-LMS (FxLMS) algorithm show faster convergence without compromising steady-state performance and/or robustness of the algorithm to feedback contamination of the reference signal

The GstLAL Search Analysis Methods for Compact Binary Mergers in Advanced LIGO's Second and Advanced Virgo's First Observing Runs

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter to reduce the over-all latency of the pipeline by up to 32 seconds, incorporation of the Virgo data stream in the analysis, introduction of a single-detector search to analyze data from the periods when only one of the detectors is running, addition of new parameters to the likelihood ratio ranking statistic, increase in the parameter space of the search, and introduction of a template mass-dependent glitch-excision thresholding method.Comment: 12 pages, 7 figures, to be submitted to Phys. Rev. D, comments welcom

XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge

Bio-signals exhibit high redundancy, and the algorithms for their processing are inherently error resilient. This property can be leveraged to improve the energy-efficiency of IoT-Edge (wearables) through the emerging trend of approximate computing. This paper presents XBioSiP, a novel methodology for approximate bio-signal processing that employs two quality evaluation stages, during the pre-processing and bio-signal processing stages, to determine the approximation parameters. It thereby achieves high energy savings while satisfying the user-determined quality constraint. Our methodology achieves, up to 19x and 22x reduction in the energy consumption of a QRS peak detection algorithm for 0% and <1% loss in peak detection accuracy, respectively.Comment: Accepted for publication at the Design Automation Conference 2019 (DAC'19), Las Vegas, Nevada, US

Robust Adaptive Control in H(infinity).

This dissertation addresses the problem of unifying identification and control in the paradigm of {\cal H}\sb\infty to achieve robust adaptive control. To achieve robust adaptive control, we employ the same approach used for identification in {\cal H}\sb\infty and robust control in {\cal H}\sb\infty. In the modeling part, we aim not only to identify the nominal plant, but also to quantify the modeling error in {\cal H}\sb\infty norm. The linear algorithm based on least-squares is used, and the upper bounds for the corresponding modeling error are derived. In the control part, we aim to achieve the performance specification in frequency domain using innovative model reference control. New algorithms are derived that minimize an {\cal H}\sb\infty index function associated with the deviation between the performance of the feedback system to be designed, and that of the reference model. The results for the modeling and control part are then combined and applied to adaptive control. It is shown that with mild assumption on persistent excitation, the least squares algorithm in frequency domain is equivalent to the recursive least squares algorithm in time domain. Moreover, finite horizon {\cal H}\sb\infty is employed to design feedback controller recursively using the identified model that is time varying in nature. The robust stability of the adaptive feedback system is then established

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