ORCID Profile
0000-0001-7104-3233
Current Organisation
Universidad Técnica Federico Santa María
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Applied Mathematics | Systems Theory And Control | Signal Processing | Control Engineering | Control Systems, Robotics and Automation | Electrical Engineering | Electrical and Electronic Engineering | Communications Technologies Not Elsewhere Classified | Wireless Communications |
ANZSRC Socio-Economic Objective (SEO)
Application packages | Mobile Data Networks and Services | Industrial machinery and equipment | Communication equipment not elsewhere classified | Mathematical sciences
Related Links
Publications
The Control Handbook: Control System Fundamentals
Publisher: CRC Press
Date: 19-12-2017
DOI: 10.1201/B10383
On Identification of Networked Systems with Time-invariant Topology
Publisher: Elsevier BV
Date: 2015
A Two-Filter Approach for State Estimation Utilizing Quantized Output Data
Publisher: MDPI AG
Date: 18-11-2021
DOI: 10.3390/S21227675
Abstract: 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 noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss–Legendre quadrature hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.
EM-Based Maximum-Likelihood Channel Estimation in Multicarrier Systems With Phase Distortion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Scalability of bidirectional vehicle strings with static and dynamic measurement errors
Publisher: Elsevier BV
Date: 12-2015
Quadratic Model Predictive Control Including Input Cardinality Constraints
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Low Complexity Wiener Filtering in CDMA Systems Using a Class of Pseudo-Noise Spreading Codes
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2012
Fundamental Limitations on the Variance of Estimated Parametric Models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2009
Energy-based control of bidirectional vehicle strings
Publisher: IEEE
Date: 11-2013
On the equivalence of time and frequency domain maximum likelihood estimation
Publisher: Elsevier BV
Date: 02-2010
Sparse logistic regression utilizing cardinality constraints and information criteria
Publisher: IEEE
Date: 09-2016
Two-degree-of-freedom anti-aliasing technique for wide-band networked control
Publisher: Elsevier BV
Date: 2011
Passivity-based control for multi-vehicle systems subject to string constraints
Publisher: Elsevier BV
Date: 12-2014
On the Optimality of Binary-Open-Loop Experiments to Identify Hammerstein Systems
Publisher: IEEE
Date: 07-2007
Dual time–frequency domain system identification
Publisher: Elsevier BV
Date: 12-2012
On the equivalence of least costly and traditional experiment design for control
Publisher: Elsevier BV
Date: 11-2008
On Maximum Likelihood Estimation of Continuous-Time Oscillators Modelled as Continuous-Time Autoregressive System
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
An algorithm for estimating time-varying commodity price models
Publisher: IEEE
Date: 12-2009
A model-based feedback control strategy for heap bioleaching processes
Publisher: IEEE
Date: 2007
On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data
Publisher: MDPI AG
Date: 09-03-2023
DOI: 10.3390/MATH11061327
Abstract: 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 consensus in the control and estimation community on (1) which methods are more suitable for a particular application and why, and (2) how these methods compare in terms of accuracy, computational cost, and user friendliness. In this paper, we provide a comprehensive overview of the state-of-the-art algorithms to deal with state estimation subject to quantized measurements, and an exhaustive comparison among them. The comparison analysis is performed in terms of the accuracy of the state estimation, dimensionality issues, hyperparameter selection, user friendliness, and computational cost. We consider 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 extended Kalman filter/smoother, the quantized Kalman filter/smoother, the unscented Kalman filter/smoother, and the sequential Monte Carlo s ling method, also called particle filter/smoother, with its most relevant variants. We also consider a new approach based on the Gaussian sum filter/smoother. Extensive numerical simulations—including a practical application—are presented in order to analyze the accuracy of the state estimation and the computational cost.
On the dimension of the Krylov subspace in low complexity wireless communications linear receivers
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Identification of sparse FIR systems using a general quantisation scheme
Publisher: Informa UK Limited
Date: 18-12-2013
Low-order control design using a novel rank-constrained optimization approach
Publisher: IEEE
Date: 11-2016
Minimum variance control for mitigation of vibrations in adaptive optics systems
Publisher: The Optical Society
Date: 23-06-2017
DOI: 10.1364/AO.56.005388
ROBUST IDENTIFICATION OF PROCESS MODELS FROM PLANT DATA
Publisher: Elsevier BV
Date: 2007
Maximum Likelihood estimation for non-minimum-phase noise transfer function with Gaussian mixture noise distribution
Publisher: Elsevier BV
Date: 2022
An errors-in-variables method for non-stationary data with application to mineral exploration
Publisher: Elsevier BV
Date: 12-2009
A data augmentation approach for a class of statistical inference problems
Publisher: Public Library of Science (PLoS)
Date: 10-12-2018
Robustness in Experiment Design
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2012
A novel input design approach for systems with quantized output data
Publisher: IEEE
Date: 06-2014
Robust identification of process models from plant data
Publisher: Elsevier BV
Date: 10-2008
A novel approach to model error modelling using the expectation-maximization algorithm
Publisher: IEEE
Date: 12-2012
Evaluation of Measures to Detect the Synchrony between Interacting Pendulum Oscillators
Publisher: IEEE
Date: 05-2011
Disturbance Modelling for Minimum Variance Control in Adaptive Optics Systems Using Wavefront Sensor Sampled-Data
Publisher: MDPI AG
Date: 27-04-2021
DOI: 10.3390/S21093054
Abstract: 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 instrumentation systems, such as fans and cooling pumps. The mitigation of wavefront distortions is typically attained via a control law that is based on an adequate and accurate model. In this paper, we develop a modelling technique based on continuous-time d ed-oscillators and on the Whittle’s likelihood method to estimate the parameters of disturbance models from wavefront sensor time-domain s led-data. On the other hand, when the model is not accurate, the performance of the minimum variance controller is affected. We show that our modelling and identification techniques not only allow for more accurate estimates, but also for better minimum variance control performance. We illustrate the benefits of our proposal via numerical simulations.
On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models
Publisher: MDPI AG
Date: 06-2021
DOI: 10.3390/S21113837
Abstract: 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 systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.
Variance or spectral density in sampled data filtering?
Publisher: Springer Science and Business Media LLC
Date: 10-02-2011
A novel representation of rank constraints for real matrices
Publisher: Elsevier BV
Date: 05-2016
An identification method for Errors-in-Variables systems using incomplete data
Publisher: Elsevier BV
Date: 07-2012
Identification of continuous-time state-space models from non-uniform fast-sampled data
Publisher: Institution of Engineering and Technology (IET)
Date: 05-05-2011
Application of Rank-Constrained Optimisation to Nonlinear System Identification∗∗J. C. Agüero was partially supported by the Chilean Research Council (CONICYT) through Basal Project FB0008, and FONDE-
Publisher: Elsevier BV
Date: 2015
Sampling and Sampled-Data Models: The Interface Between the Continuous World and Digital Algorithms
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
An EM-based identification algorithm for a class of hybrid systems with application to power electronics
Publisher: Informa UK Limited
Date: 06-03-2014
Related Organisations
Related Funding Activities
Linkage Projects - Grant ID: LP120200321
Start Date: 09-2012
End Date: 12-2015
Amount: $360,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP1095123
Start Date: 2010
End Date: 07-2013
Amount: $220,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP0989757
Start Date: 2009
End Date: 12-2012
Amount: $370,000.00
Funder: Australian Research Council
View Funded Activity