CDM controller order and disturbance rejection ability

The coefficient diagram method is primarily an algebraic control design method whose objective is to easily obtain a good controller with minimum user effort. As a matter of fact, if a system model, in the form of linear differential equations, is known, the user only need to define a time-constant and the controller order. The later can be established regarding the expected disturbance type via a lookup table first published by Koksal and Hamamci in 2004. However an inaccuracy in this table was detected and pointed-out in the present work. Moreover the above mentioned table was expanded in order to enclose any k order type disturbanc

Frequency Estimation for Periodical Signal with Noise in Finite Time

International audienceThe frequency estimation technique with guaranteed finite time of convergence to a given accuracy of identification is presented. The approach for a high frequency noise rejection is proposed. The possibility of switching algorithm introduction for estimation quality improvement is discussed. The proposed solution has order three, that is smaller than in other existent solutions. Efficiency of the approach is demonstrated on examples of computer simulation

A Fast Algebraic Estimator for System Parameter Estimation and Online Controller Tuning : A Nanopositioning Application

Spanish Agencia Estatal de Investigacion; 10.13039/501100004895-European Social FundPeer reviewedPostprin

Real-time detection of auditory : steady-state brainstem potentials evoked by auditory stimuli

The auditory steady-state response (ASSR) is advantageous against other hearing techniques because of its capability in providing objective and frequency specific information. The objectives are to reduce the lengthy test duration, and improve the signal detection rate and the robustness of the detection against the background noise and unwanted artefacts.Two prominent state estimation techniques of Luenberger observer and Kalman filter have been used in the development of the autonomous ASSR detection scheme. Both techniques are real-time implementable, while the challenges faced in the application of the observer and Kalman filter techniques are the very poor SNR (could be as low as −30dB) of ASSRs and unknown statistics of the noise. Dual-channel architecture is proposed, one is for the estimate of sinusoid and the other for the estimate of the background noise. Simulation and experimental studies were also conducted to evaluate the performances of the developed ASSR detection scheme, and to compare the new method with other conventional techniques. In general, both the state estimation techniques within the detection scheme produced comparable results as compared to the conventional techniques, but achieved significant measurement time reduction in some cases. A guide is given for the determination of the observer gains, while an adaptive algorithm has been used for adjustment of the gains in the Kalman filters.In order to enhance the robustness of the ASSR detection scheme with adaptive Kalman filters against possible artefacts (outliers), a multisensory data fusion approach is used to combine both standard mean operation and median operation in the ASSR detection algorithm. In addition, a self-tuned statistical-based thresholding using the regression technique is applied in the autonomous ASSR detection scheme. The scheme with adaptive Kalman filters is capable of estimating the variances of system and background noise to improve the ASSR detection rate

Signal processing with Fourier analysis, novel algorithms and applications

Fourier analysis is the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions, also analogously known as sinusoidal modeling. The original idea of Fourier had a profound impact on mathematical analysis, physics and engineering because it diagonalizes time-invariant convolution operators. In the past signal processing was a topic that stayed almost exclusively in electrical engineering, where only the experts could cancel noise, compress and reconstruct signals. Nowadays it is almost ubiquitous, as everyone now deals with modern digital signals. Medical imaging, wireless communications and power systems of the future will experience more data processing conditions and wider range of applications requirements than the systems of today. Such systems will require more powerful, efficient and flexible signal processing algorithms that are well designed to handle such needs. No matter how advanced our hardware technology becomes we will still need intelligent and efficient algorithms to address the growing demands in signal processing. In this thesis, we investigate novel techniques to solve a suite of four fundamental problems in signal processing that have a wide range of applications. The relevant equations, literature of signal processing applications, analysis and final numerical algorithms/methods to solve them using Fourier analysis are discussed for different applications in the electrical engineering/computer science. The first four chapters cover the following topics of central importance in the field of signal processing: • Fast Phasor Estimation using Adaptive Signal Processing (Chapter 2) • Frequency Estimation from Nonuniform Samples (Chapter 3) • 2D Polar and 3D Spherical Polar Nonuniform Discrete Fourier Transform (Chapter 4) • Robust 3D registration using Spherical Polar Discrete Fourier Transform and Spherical Harmonics (Chapter 5) Even though each of these four methods discussed may seem completely disparate, the underlying motivation for more efficient processing by exploiting the Fourier domain signal structure remains the same. The main contribution of this thesis is the innovation in the analysis, synthesis, discretization of certain well known problems like phasor estimation, frequency estimation, computations of a particular non-uniform Fourier transform and signal registration on the transformed domain. We conduct propositions and evaluations of certain applications relevant algorithms such as, frequency estimation algorithm using non-uniform sampling, polar and spherical polar Fourier transform. The techniques proposed are also useful in the field of computer vision and medical imaging. From a practical perspective, the proposed algorithms are shown to improve the existing solutions in the respective fields where they are applied/evaluated. The formulation and final proposition is shown to have a variety of benefits. Future work with potentials in medical imaging, directional wavelets, volume rendering, video/3D object classifications, high dimensional registration are also discussed in the final chapter. Finally, in the spirit of reproducible research we release the implementation of these algorithms to the public using Github

DISCRETE-TIME ADAPTIVE CONTROL ALGORITHMS FOR REJECTION OF SINUSOIDAL DISTURBANCES

We present new adaptive control algorithms that address the problem of rejecting sinusoids with known frequencies that act on an unknown asymptotically stable linear time-invariant system. To achieve asymptotic disturbance rejection, adaptive control algorithms of this dissertation rely on limited or no system model information. These algorithms are developed in discrete time, meaning that the control computations use sampled-data measurements. We demonstrate the effectiveness of algorithms via analysis, numerical simulations, and experimental testings. We also present extensions to these algorithms that address systems with decentralized control architecture and systems subject to disturbances with unknown frequencies

Transient And Distributed Algorithms To Improve Islanding Detection Capability Of Inverter Based Distributed Generation

Recently, a lot of research work has been dedicated toward enhancing performance, reliability and integrity of distributed energy resources that are integrated into distribution networks. The problem of islanding detection and islanding prevention (i.e. anti-islanding) has stimulated a lot of research due to its role in severely compromising the safety of working personnel and resulting in equipment damages. Various Islanding Detection Methods (IDMs) have been developed within the last ten years in anticipation of the tremendous increase in the penetration of Distributed Generation (DG) in distribution system. This work proposes new IDMs that rely on transient and distributed behaviors to improve integrity and performance of DGs while maintaining multi-DG islanding detection capability. In this thesis, the following questions have been addressed: How to utilize the transient behavior arising from an islanding condition to improve detectability and robust performance of IDMs in a distributive manner? How to reduce the negative stability impact of the well-known Sandia Frequency Shift (SFS) IDM while maintaining its islanding detection capability? How to incorporate the perturbations provided by each of DGs in such a way that the negative interference of different IDMs is minimized without the need of any type of communication among the different DGs? It is shown that the proposed techniques are local, scalable and robust against different loading conditions and topology changes. Also, the proposed techniques can successfully distinguish an islanding condition from other disturbances that may occur in power system networks. This work improves the efficiency, reliability and safety of integrated DGs, which presents a necessary advance toward making electric power grids a smart grid

Técnicas de identificación algebraicas y espectrales de señales armónicas. Aplicaciones en mecatrónica y economía

La identificación de señales armónicas abarca un amplio rango de aplicaciones procedentes de disciplinas como la mecatrónica o la economía. En esta tesis se trata el problema de la identificación de señales armónicas utilizando técnicas de identificación de sistemas y análisis de series temporales. En referencia a las aplicaciones mecatrónicas se han utilizado técnicas derivativas algebraicas para diseñar algoritmos capaces de estimar en línea los parámetros de una o varias ondas sinusoidales con y sin amortiguamiento en un tiempo inferior al periodo de dicha señal. Con el fin de validar estos estimadores se han aplicado a la monitorización de vibraciones procedentes de brazos flexibles experimentales, comparando los resultados obtenidos con otros estimadores de frecuencia recientemente publicados como son los filtros adaptativos de ranura. Además se han combinado los estimadores con controles en lazo cerrado y en lazo abierto para realizar controles adaptativos. Estos controles adaptativos han mostrado ser robustos frente al problema de cambios de masa en el extremo de brazos manipuladores flexibles. Se ha aprovechado el conocimiento adquirido en el análisis de vibraciones de estructuras flexibles para abordar señales armónicas procedentes de aplicaciones económicas. Concretamente se ha tratado el problema de la predicción a corto plazo de la demanda y precios de energía eléctrica en el mercado liberalizado. Se han elegido estas series temporales ya que poseen un fuerte componente periódico, es decir tienen una estacionalidad diaria, semanal y un ciclo anual. Se han utilizado técnicas de identificación en el dominio de la frecuencia junto con modelos en espacio de los estados (EE) para la predicción de estas series temporales. La representación en EE permite extraer componentes no observables de la serie temporal como son la tendencia, la estacionalidad o el término irregular por otro lado, la estimación en el dominio la frecuencia permite realizar predicciones automáticas sin necesidad de cambiar los modelos cada cierto tiempo. Estos resultados obtenidos mejoran a otras metodologías típicas del análisis de series temporales. El mismo modelo desarrollado en EE se adapta para realizar predicciones de la demanda a medio y largo plazo. Por último, es interesante el punto de vista que esta tesis aporta sobre el análisis del ciclo económico, donde se utilizan técnicas de identificación algebraica y filtros adaptativos de ranura para poder estudiar la evolución del ciclo de un indicador económico típico

Analyse und Entwurf von Beobachtern mit unbekannten Signalen und Parametern

Die Arbeit sondiert Erweiterungsmöglichkeiten von Beobachterverfahren und zeigt Lösungen für die Fälle auf, in denen die üblichen Voraussetzungen wie Beobachtbarkeit, bekannte Parameter und vollständige Messbarkeit von Ein- und Ausgangssignalen verletzt sind

Amplitude and frequency estimator of a sinusoid

A dynamic estimator with global convergence property is proposed to simultaneously reconstruct the unknown values of the amplitude, frequency, and offset of a sinusoidal signal which is measured. Several versions of the estimator along with illustrative simulations are presented