A parallel prefiltering approach for the identification of a biased sinusoidal signal: theory and experiments

The problem of estimating the amplitude, frequency, and phase of an unknown sinusoidal signal from a noisy-biased measurement is addressed in this paper by a family of parallel prefiltering schemes. The proposed methodology consists in using a pair of linear filters of specified order to generate a suitable number of auxiliary signals that are used to estimate\u2014in an adaptive way\u2014the frequency, the amplitude, and the phase of the sinusoid. Increasing the order of the prefilters improves the noise immunity of the estimator, at the cost of an increase of the computational complexity. Among the whole family of estimators realizable by varying the order of the filters, the simple parallel prefilters of orders 2 C 2 and 3 C 3 are discussed in detail, being the most attractive from the implementability point of view. The behavior of the two algorithms with respect to bounded external disturbances is characterized by input-to-state stability arguments. Finally, the effectiveness of the proposed technique is shown both by comparative numerical simulations and by a real experiment addressing the estimation of the frequency of the electrical mains from a noisy voltage measurement

Detection and cancellation of sinusoidal fading power variation in wireless communication systems

Fading channel estimation in wireless communication systems depends on an expected model for fading and any assumptions made about the channel itself. The bit error rate (BER) performance of the communication system is affected by how closely these assumptions made in designing the estimation technique match the deployment environment. Any unforeseen disturbances or hindrances in the environment deteriorate the BER performance of the system when the estimation system is not designed to combat such disturbances. To deal with such unforeseen obstacles, additional mathematical models can be proposed to model such disturbances and then the estimation techniques can either be reinforced with modular systems which work with the proposed models, or be redesigned as a whole with the help of actual observed data of the disturbances. The current thesis deals with such a scenario where sinusoidal variation is expected in the received power in addition to fading. A mathematical model of such power variation is assumed and a modular scheme is proposed to detect and combat the sinusoidal variation. The proposed scheme is tested by employing it in a simulated Multiple Input Multiple Output (MIMO) wireless communication system which adopts Space Time Block Coding (STBC) techniques --Abstract, page iii

Asymptotic rejection of sinusoidal disturbances based voltage balance control in back-to-back power converters

This paper addresses the imbalance problem of the dc-link capacitor voltages in the three-level diode-clamped back-to-back power converter. In order to cope with it, a mathematical analysis of the capacitor voltage difference dynamics, based on a continuous model of the converter, is first carried out. It leads to an approximated model which contains explicitly several sinusoidal functions of time. In view of this result, the voltage imbalance phenomenon can be addressed as an output regulation problem, considering the sinusoidal functions as exogenous disturbances. Thus, a novel approach to deal with the mentioned problem in the back- to-back converter is presented. Then, the particular features of the disturbances are used to design several controllers. They all follow an asymptotic disturbance rejection approach. In this way, the estimations of the disturbances are used to apply a control law that cancels them while regulating the capacitor voltage balance as well. Finally, the performance of the proposed control laws is evaluated, presenting the simulation results obtained when the different controllers are implemented.MICINN-FEDER DPI2009-0966

Signal Identification In Discrete-Time Based On Internal-Model-Principle

This work presents an implementation of a signal identification algorithm which is based on the internal model principle. By using several internal models in feedback with a tuning function, this algorithm can decompose a signal into narrow-band signals and identify the frequencies, amplitudes and relative phases. A desired band-pass filter response can be achieved by selecting appropriate coefficients of the controllers and tuning functions, which can reject the noise and improve the performance. To achieve a result with fast transient characteristics, this system is then modified by adding a low-pass filter. This work is based on the previous work in continuous time. However, a discrete implementation should be much more practical. The simulation result shows a good tracking of the original signal with minimal response to measurement noise

RealTime Implementation Of An Internal-Model-Principle Signal Identifier

This thesis presents a new means approach of tuning an adaptive internal model principle based signal identification algorithm whose computational costs are low enough to allow a realtime implementation. The algorithm allows an instantaneous Fourier decomposition of nonstationary signals that have a strongly predictable component. The algorithm is implemented as a feedback loop resulting in a closed loop system with a frequency response of a bandpass filter with notches at the frequencies of the Fourier decomposition. This is achieved through real time selection of the coefficients of the transfer functions in the feedback loop. Previous work showed how the dynamics of the algorithm could be chosen to be represented by a bandpass filter with notches. However this involved solving a large set of coupled linear equations. This thesis shows how the equations can be decoupled into pairs of linear equations which have explicit solutions. In other word, rules for explicitly solving for these parameters are given that only involve evaluating frequency responses at the frequencies of the instantaneous Fourier decomposition. Last but not the least, alternative approach for choosing suitable coefficients to eliminate the DC component of the signal under consideration has been proposed as well by replacing a frequency response of a bandpass filter with lowpass filter and adding a model of the constant signal to the feedback loop

A Generalized Predictive Controlled T-type power inverter with a deterministic dc-link capacitor voltage balancing approach

The thesis consists of implementing a Generalized Predictive Control (GPC) strategy for controlling the output voltage of the T-type converter with output LC filter, whose control signals are modulated by a fast three-dimensional Space Vector Modulation (SVM). The GPC strategy used for the T-type converter involves developing a system of dynamic equations from the output LC filter and load, which is transformed to a Controlled Auto-Regressive and Moving-Average (CARIMA) model in order to obtain a sequence of control signals, so that a cost function is optimized and the reference is tracked. The core of the thesis addresses the main problem of dc-link capacitor balancing. This is done by modeling the converter and deploying a mathematical analysis of the capacitor voltage difference dynamics, by singular perturbation approach. This analysis results in an explicit sinusoidal disturbance. Now, classical control theory is applied by using a Luenberger Observer (LO) in order to estimate the disturbance and encounter it, thereby keeping the dc-link capacitor voltage balanced in the due flow of the modulation and output voltage control. By this method, the output voltage across the filter capacitor is controlled, the dc-link capacitor voltage is balanced and the lowfrequency voltage ripples present in the dc-link of the T-type converter are reduced to an acceptable level.Máster en Electrónica, Tratamiento de Señal y Comunicacione

Nonlinear adaptive estimation with application to sinusoidal identification

Parameter estimation of a sinusoidal signal in real-time is encountered in applications in numerous areas of engineering. Parameters of interest are usually amplitude, frequency and phase wherein frequency tracking is the fundamental task in sinusoidal estimation. This thesis deals with the problem of identifying a signal that comprises n (n ≥ 1) harmonics from a measurement possibly affected by structured and unstructured disturbances. The structured perturbations are modeled as a time-polynomial so as to represent, for example, bias and drift phenomena typically present in applications, whereas the unstructured disturbances are characterized as bounded perturbation. Several approaches upon different theoretical tools are presented in this thesis, and classified into two main categories: asymptotic and non-asymptotic methodologies, depending on the qualitative characteristics of the convergence behavior over time. The first part of the thesis is devoted to the asymptotic estimators, which typically consist in a pre-filtering module for generating a number of auxiliary signals, independent of the structured perturbations. These auxiliary signals can be used either directly or indirectly to estimate—in an adaptive way—the frequency, the amplitude and the phase of the sinusoidal signals. More specifically, the direct approach is based on a simple gradient method, which ensures Input-to-State Stability of the estimation error with respect to the bounded-unstructured disturbances. The indirect method exploits a specific adaptive observer scheme equipped with a switching criterion allowing to properly address in a stable way the poor excitation scenarios. It is shown that the adaptive observer method can be applied for estimating multi-frequencies through an augmented but unified framework, which is a crucial advantage with respect to direct approaches. The estimators’ stability properties are also analyzed by Input-to-State-Stability (ISS) arguments. In the second part we present a non-asymptotic estimation methodology characterized by a distinctive feature that permits finite-time convergence of the estimates. Resorting to the Volterra integral operators with suitably designed kernels, the measured signal is processed, yielding a set of auxiliary signals, in which the influence of the unknown initial conditions is annihilated. A sliding mode-based adaptation law, fed by the aforementioned auxiliary signals, is proposed for deadbeat estimation of the frequency and amplitude, which are dealt with in a step-by-step manner. The worst case behavior of the proposed algorithm in the presence of bounded perturbation is studied by ISS tools. The practical characteristics of all estimation techniques are evaluated and compared with other existing techniques by extensive simulations and experimental trials.Open Acces

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