## About

166

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Introduction

Additional affiliations

January 2015 - present

April 2005 - December 2018

## Publications

Publications (166)

In medium- and low-voltage three-phase distribution networks, the load imbalance among the phases may compromise the network voltage symmetry. Inverter-interfaced distributed energy resources (DERs) can contribute to compensating for such imbalances by sharing the required negative sequence current while providing active power synchronized with the...

In this paper, we have proposed a new paradigm for modeling of SAG mills. Typically, important parameters found in the modeling of such processes are described as state-space system model rather than unknown parameters. Here, we propose to estimate the system model using the maximum likelihood approach. Additionally, we propose using a new measurem...

Integral controllers are commonly employed in astronomical adaptive optics. This work presents a novel tuning procedure for integral controllers in adaptive optics systems which relies on information about the measured disturbances. This tuning procedure consists of two main steps. First, it models and identifies measured disturbances as continuous...

This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue sampling consists of taking measurements of a continuous-time signal whenever it crosses fixed and regularly partitioned thresholds. The knowledge of the intersample behavi...

In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-in-Variables systems is developed. We consider that the noise-free input signal is Gaussian-mixture distributed. We propose an Expectation-Maximization-based algorithm to estimate the system model parameters, the input and output noise variances, and the Gaus...

The problem of state estimation of a linear, dynamical state-space system where the output is subject to quantization is challenging and important in different areas of research, such as control systems, communications, and power systems. There are a number of methods and algorithms to deal with this state estimation problem. However, there is no c...

This paper proposes a model-based predictive control strategy based on mixed-integer linear programming for a photovoltaic power plant with battery energy storage. The control objective is to maximize the revenues from energy delivered from both photovoltaic panels and batteries to the grid in a deregulated electricity market. For each control inte...

The state estimation of nonlinear dynamical systems has gained significant attention due to the growing number of applications that involve nonlinearities such as (i) quantization introduced by low-cost and low-resolution sensors, (ii) saturation due to limited actuator's capacity, and inherent physical or mechanical constraints, or (iii) common no...

In this paper, we study the problem of estimating the state of a dynamic state-space system where the output is subject to quantization. We compare some classical approaches and a new development in the literature to obtain the filtering and smoothing distributions of the state conditioned to quantized data. The classical approaches include the Ext...

Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available nois...

In this paper a Maximum Likelihood estimation algorithm for a linear dynamic system driven by an exogenous input signal, with non-minimum-phase noise transfer function and a Gaussian mixture noise is developed. We propose a flexible identification technique to estimate the system model parameters and the Gaussian mixture parameters based on the Exp...

In this paper a Maximum Likelihood estimation algorithm for model error modelling in a continuous-time system is developed utilising sampled data and a Stochastic Embedding approach. Orthonormal basis functions are used to model both the continuous-time nominal model and the error-model. The stochastic properties of the error-model distribution are...

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real syst...

Modern large telescopes are built based on the effectiveness of adaptive optics systems in mitigating the detrimental effects of wavefront distortions on astronomical images. In astronomical adaptive optics systems, the main sources of wavefront distortions are atmospheric turbulence and mechanical vibrations that are induced by the wind or the ins...

In this paper a Maximum Likelihood estimation algorithm for error-model modelling using a stochastic embedding approach is developed. The error-model distribution is approximated by a finite Gaussian mixture. An Expectation-Maximization based algorithm is proposed to estimate the nominal model and the distribution of the parameters of the error-mod...

In this paper we address the problem of identifying a static errors-in-variables system. Our proposal is based on the Expectation-Maximization algorithm, in which we consider that the distribution of the noise-free input is approximated by a finite Gaussian mixture. This approach allows us to estimate the static system parameters, the input and out...

In this paper we address the problem of identifying a continuous-time deterministic system utilising sampled-data with instantaneous sampling. We develop an identification algorithm based on Maximum Likelihood. The exact discrete-time model is obtained for two cases: i) known continuous-time model structure and ii) using Kautz basis functions to ap...

In this paper we develop two filtering algorithms for state-space systems with binary outputs. We approximate the conditional probability mass function of the output signal given the state by using a Gaussian quadrature rule. This approximation naturally leads to a Gaussian Sum structure for the a posteriori density function. Our first algorithm is...

The dynamic performance of a microgrid is governed by the decentralized primary control strategy that is embedded in each of its hosted Distributed Energy Resource (DER) units. The primary control computes the voltage synthesized by the interfacing voltage-sourced converter such that the DER unit contributes active and reactive powers in support of...

In this paper we address the problem of identifying a static errors-in-variables system. Our proposal is based on the Expectation-Maximization algorithm, in which we consider that the distribution of the noise-free input is approximated by a finite Gaussian mixture. This approach allows us to estimate the static system parameters, the input and out...

In this paper we propose a sliding mode control strategy for cascade systems. Models of first and second order are utilized to design the internal and external control loop respectively. The dead time is considered in the control tunning, which is performed by optimization of the integral square error subject to the existence of sliding mode condit...

In this paper, we address the problem of identifying a continuous-time oscillator. We use a continuous-time autore-gressive model to represent the oscillators. We asume that only discrete-time measurements are available, from which we obtain the oscillator equivalent discrete-time model in terms of the continuous-time model parameters. We identify...

This paper considers the identification of a linear dynamic system driven by a non-Gaussian noise distribution. The noise is approximated by a finite Gaussian mixture, whilst the parameters of the system and the parameters that approximate the noise distribution are simultaneously estimated using the principle of Maximum Likelihood. To this end, a...

In this paper we address the identification problem of a continuous-time deterministic system. We consider that the continuous-time system can be approximated by using the orthonormal continuous basis function of Laguerre. We assume that only discrete-time measurements are available and we obtain the exact discrete-time model in terms of the contin...

In this paper we develop an identification algorithm to obtain an estimation of the prior distribution in the classical problem of Bayesian inference. We consider the Empirical Bayes approach to obtain the prior distribution approximation by a finite Gaussian mixture. An Expectation-Maximization based algorithm is used to obtain an estimate of the...

The control law of electronically-interfaced distributed energy resources (DERs) must be able to maintain the stability and voltage regulation of the host microgrid in the two modes of operation. Ideally, this should be achieved by a decentralized primary control strategy that is independent of any communication infrastructure in order to increase...

The control of a grid-forming distributed energy resource (DER) unit requires the control of the voltage at its output filter. This is a fourth order system that can be controlled by state feedback. In this control scheme the rejection of disturbances, such the load connections, disconnections and modeling errors, can be improved by disturbance est...

Single particle tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal both trajectories of individual particles, with a resolution well below the diffraction limit of light, and the parameters defining the motion model, such as diffusion coefficients and confinement length...

Single particle tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal both trajectories of individual particles, with a resolution well below the diffraction limit of light, and the parameters defining the motion model, such as diffusion coefficients and confinement length...

Aims . The study of accurate methods to estimate the distribution of stellar rotational velocities is important for understanding many aspects of stellar evolution. From such observations we obtain the projected rotational speed ( v sin i ) in order to recover the true distribution of the rotational velocity. To that end, we need to solve a difficu...

We present an algorithm for a class of statistical inference problems. The main idea is to reformulate the inference problem as an optimization procedure, based on the generation of surrogate (auxiliary) functions. This approach is motivated by the MM algorithm, combined with the systematic and iterative structure of the Expectation-Maximization al...

Monte Carlo simulations for Examples 1 and 2.
(ZIP)

Vibration effects acting in the science light path reduce the performance of the adaptive optics systems (AO). In order to mitigate the vibration effects and to improve the performance of the AO systems, an adequate model for the vibration in necessary. Traditionally, those vibrations are modelled as oscillators (with or without damping) driven by...

In this paper we analyze the likelihood function corresponding to a continuous-time oscillator utilizing regular sampling. We analyze the equivalent sampled-data model for two cases i) instantaneous sampling and ii) integrated sampling. We illustrate the behavior of the log-likelihood function via numerical examples showing that it presents several...

In this paper we develop a Maximum Likelihood estimation algorithm for the estimation of infinite mixture distributions. We assume a known conditional distribution, whilst the weighting distribution is assumed unknown and it is approximated by a finite Gaussian mixture. Our approach allows for the correct estimation of the Gaussian mixture paramete...

The performance of an adaptive optics system depends on multiple factors, including the quality of the laser beam before being projected to the mesosphere. In general, cumbersome procedures are required to optimize the laser beam in terms of amplitude and phase. However, aberrations produced by the optics of the laser beam system are still detected...

In this paper we develop a novel algorithm to identify an auto-regressive with exogenous signal system utilizing quantized output data. We use the Expectation-Maximization algorithm to obtain the Maximum Likelihood estimate.

In this paper, we address the design of a minimum variance controller (MVC) for the mitigation of vibrations in modern telescope adaptive optics (AO) systems. It is widely accepted that a main source of non-turbulent perturbations is the mechanical resonance induced by the wind or the instrumentation systems, such as fans and cooling pumps. To adeq...

Adaptive Optics (AO) is a technique used to mitigate the effect of the atmosphere in the resolution of scientific images. The performance of an adaptive optics system strongly depends on the quality of the laser beam projected to the sky in terms of amplitude and phase. Currently, cumbersome procedures are carried out to optimize the laser beam. Th...

In this paper we present a novel algorithm for identifying continuous-time autoregressive moving-average models utilizing irregularly sampled data. The proposed algorithm is based on the expectation–maximization algorithm and obtains maximum-likelihood estimates. The proposed algorithm shows a fast convergence rate, good robustness to initial value...

This note addresses the problem of feedback control with a constrained number of active inputs. This problem is known as sparse control. Specifically, we describe a novel quadratic model predictive control strategy that guarantees sparsity by bounding directly the l0 -norm of the control input vector at each control horizon instant. Besides this sp...

In this paper, we consider an optimization approach for model selection using Akaike's Information Criterion (AIC) by incorporating the l 0 -(pseudo)norm as a penalty function to the log-likelihood function. In order to reduce the numerical complexity of the optimization problem, we propose to approximate the l 0 -(pseudo)norm by an exponential ter...

A recent equivalent representation of rank constraints is used to design a low-order controller with prescribed degree of stability. We solve an optimization problem involving linear matrix inequalities and rank constraints. We illustrate the potential of the proposed approach by comparing with similar approaches available in the literature.

This study investigates the estimation of continuous-time Box-Jenkins model parameters from irregularly sampled data. The Box-Jenkins structure has been successful in describing systems subject to coloured noise, since it contains two submodels that feature the characteristics of both plant and noise systems. Based on plant-noise model decompositio...

In this paper we address the problem of estimating a sparse parameter vector that defines a logistic regression. The problem is then solved using two approaches: i) inequality constrained Maximum Likelihood estimation and ii) penalized Maximum Likelihood which is closely related to Information Criteria such as AIC. For the promotion of sparsity, we...

We present a novel representation of rank constraints for non-square real
matrices. We establish relationships with some existing results, which are
particular cases of our representation. One of these particular cases, is a
representation of the $\ell_0$ pseudo-norm, which is used in sparse
representation problems. Finally, we describe how our rep...

In this paper, we analyse the dimension of the Krylov subspace obtained in Krylov solvers applied to signal detection in low complexity communication receivers. These receivers are based on the Wiener filter as a pre-processing step for signal detection, requiring the computation of a matrix inverse, which is computationally demanding for large sys...

We present an algorithm for a class of statistical inference problems. The
main idea is to reformulate the inference problem as an optimization procedure,
based on the generation of surrogate (auxiliary) functions. This approach is
motivated by the MM algorithm, combined with the systematic and iterative
structure of the EM algorithm. The resulting...

To estimate a model of useful complexity for control design, at the same time as having a good insight into its reliability is a central issue in system identification, in particular for identification for control. Basically one can think of a (simpler) design model and a (more complex and exible) error model. These concepts are discussed in terms...

This paper studies the problem of identification for networked systems. We consider both heterogeneous and homogeneous networks. It is assumed that the interconnection topology is time-invariant and known. We demonstrate how the parameter matrices of each individual subsystem can be identified from the input-output information obtained from the who...

Poor scalability arises in many vehicle platoon problems. Bidirectional strings appear to show some promise for mitigating these problems. In some cases these solutions have the undesirable side effect of non-scalable response to measurement errors. In this paper, we examine this problem and show how information exchange between vehicles may elimin...

In this paper, Bayesian parameter estimation through the consideration of the Maximum A Posteriori (MAP) criterion is revisited under the prism of the Expectation-Maximization (EM) algorithm. By incorporating a sparsity-promoting penalty term in the cost function of the estimation problem through the use of an appropriate prior distribution, we sho...

It is well known that appropriately biasing an estimator can potentially lead to a lower mean square error (MSE) than the achievable MSE within the class of unbiased estimators. Nevertheless, the choice of an appropriate bias is generally unclear and only recently there have been attempts to systematize such a selection. These systematic approaches...

In this paper, Bayesian parameter estimation through the consideration of the
Maximum A Posteriori (MAP) criterion is revisited under the prism of the
Expectation-Maximization (EM) algorithm. By incorporating a sparsity-promoting
penalty term in the cost function of the estimation problem through the use of
an appropriate prior distribution, we sho...

In this paper, the problem of experiment design in single input single output (SISO) single carrier (SC) systems under a deterministic channel assumption is investigated and several connections with optimal preamble designs in multicarrier (MC) systems are established. In the context of SC systems, we derive optimal input sequences for the least-sq...

The solution to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise affine (PWA) state feedback law defined on polyhedral regions of the state space. Even though real-time optimization is avoided, implementation of the PWA state-feedback law may still require a significant amoun...

There is a very extensive literature on various aspects of the central Bias-Variance trade-off in linear system identification. In the 80's and 90's the focus was on bias characterization, model error models and Stochastic Embedding. Recently, there has been a new interest in Bayesian or kernel methods. This paper puts part of this literature into...

In this paper we address the joint estimation of the channel impulse response in orthogonal frequency division multiplexing systems with phase distortion, namely phase noise and carrier frequency offset, phase noise bandwidth and the additive noise variance. The estimation algorithm is based on an implementation of the Extended Kalman Filter within...

In this paper, we show how heterogeneous bidirectional vehicle strings can be modelled as port-Hamiltonian systems. Analysis of stability and string stability within this framework is straightforward and leads to a better understanding of the underlying problem. Nonlinear local control and additional integral action is introduced to design a suitab...