Rodrigo Carvajal
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View article: On Modelling and State Estimation of DC Motors
On Modelling and State Estimation of DC Motors Open
Direct current motors are widely used in a plethora of applications, ranging from industrial to modern electric (and intelligent) vehicle applications. Most recent operation methods of these motors involve drives that are designed based on…
View article: Model Error Modeling for a Class of Multivariable Systems Utilizing Stochastic Embedding Approach with Gaussian Mixture Models
Model Error Modeling for a Class of Multivariable Systems Utilizing Stochastic Embedding Approach with Gaussian Mixture Models Open
Many real-world multivariable systems need to be modeled to capture the interconnected behavior of their physical variables and to understand how uncertainty in actuators and sensors affects the system dynamics. In system identification, s…
View article: A Complex-Valued Stationary Kalman Filter for Positive and Negative Sequence Estimation in DER Systems
A Complex-Valued Stationary Kalman Filter for Positive and Negative Sequence Estimation in DER Systems Open
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 …
View article: On Accurate Discrete-Time Dynamic Models of an Induction Machine
On Accurate Discrete-Time Dynamic Models of an Induction Machine Open
Induction machines have become the standard for highly demanding industrial applications. This has led to the utilization of modern discrete-time control techniques (such as model predictive control) that require the estimation of internal…
View article: Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements
Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements Open
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…
View article: An Optimal Integral Controller for Adaptive Optics Systems
An Optimal Integral Controller for Adaptive Optics Systems Open
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 t…
View article: On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data
On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data Open
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. …
View article: Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models
Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models Open
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-Maximizat…
View article: A Bayesian Filtering Method for Wiener State-Space Systems Utilizing a Piece-wise Linear Approximation
A Bayesian Filtering Method for Wiener State-Space Systems Utilizing a Piece-wise Linear Approximation Open
In this paper, we develop a filtering algorithm for Wiener systems written in state-space form which considers correlated noise sources. The output non-linearity is approximated by using a piece-wise linear function. The probability functi…
View article: An Identification Method for Stochastic Continuous-time Disturbances in Adaptive Optics Systems*
An Identification Method for Stochastic Continuous-time Disturbances in Adaptive Optics Systems* Open
This paper presents a novel identification method for stochastic continuous-time systems applied to Adaptive Optics. We consider a discrete-time sampled-data model of a linear combination of continuous-time second-order systems for modelli…
View article: LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile
LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile Open
Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential ro…
View article: On Recursive State Estimation for Linear State-Space Models Having Quantized Output Data
On Recursive State Estimation for Linear State-Space Models Having Quantized Output Data Open
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 a…
View article: A Two-Filter Approach for State Estimation Utilizing Quantized Output Data
A Two-Filter Approach for State Estimation Utilizing Quantized Output Data Open
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.…
View article: On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models
On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models Open
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 p…
View article: Disturbance Modelling for Minimum Variance Control in Adaptive Optics Systems Using Wavefront Sensor Sampled-Data
Disturbance Modelling for Minimum Variance Control in Adaptive Optics Systems Using Wavefront Sensor Sampled-Data Open
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 wav…
View article: On the Uncertainty Modelling for Linear Continuous-Time Systems Utilising Sampled Data and Gaussian Mixture Models
On the Uncertainty Modelling for Linear Continuous-Time Systems Utilising Sampled Data and Gaussian Mixture Models Open
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 c…
View article: On Filtering Methods for State-Space Systems having Binary Output Measurements
On Filtering Methods for State-Space Systems having Binary Output Measurements Open
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 approxim…
View article: Identification of continuous-time systems utilising Kautz basis functions from sampled-data
Identification of continuous-time systems utilising Kautz basis functions from sampled-data Open
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 mode…
View article: Model Error Modelling using a Stochastic Embedding approach with Gaussian Mixture Models for FIR systems
Model Error Modelling using a Stochastic Embedding approach with Gaussian Mixture Models for FIR systems Open
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 ba…
View article: EM-based identification of static errors-in-variables systems utilizing Gaussian Mixture models
EM-based identification of static errors-in-variables systems utilizing Gaussian Mixture models Open
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…
View article: A method to deconvolve stellar rotational velocities III. The probability distribution function via Maximum Likelihood utilizing Finite Distribution Mixtures
A method to deconvolve stellar rotational velocities III. The probability distribution function via Maximum Likelihood utilizing Finite Distribution Mixtures Open
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…
View article: A method to deconvolve stellar rotational velocities
A method to deconvolve stellar rotational velocities Open
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 …
View article: A data augmentation approach for a class of statistical inference problems
A data augmentation approach for a class of statistical inference problems Open
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 motivat…
View article: Maximum Likelihood Identification of a Continuous-Time Oscillator Utilizing Sampled Data
Maximum Likelihood Identification of a Continuous-Time Oscillator Utilizing Sampled Data Open
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…
View article: Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures
Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures Open
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 approximate…
View article: EM-based identification of ARX systems having quantized output data
EM-based identification of ARX systems having quantized output data Open
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.
View article: On the dimension of the Krylov subspace in low complexity wireless communications linear receivers
On the dimension of the Krylov subspace in low complexity wireless communications linear receivers Open
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 si…
View article: A systematic optimization approach for a class of statistical inference problems utilizing data augmentation
A systematic optimization approach for a class of statistical inference problems utilizing data augmentation Open
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 motivat…
View article: An optimization-oriented framework for a class of system identification problems utilizing data augmentation
An optimization-oriented framework for a class of system identification problems utilizing data augmentation Open
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 motivat…
View article: A MAP approach for $\ell_q$-norm regularized sparse parameter estimation using the EM algorithm
A MAP approach for $\ell_q$-norm regularized sparse parameter estimation using the EM algorithm Open
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 t…