Fernando Tadeo

Calibrating and validating dynamic first-principles models in industrial plants is challenging due to limited and imperfect data. This work presents the calibration and validation of a distributed-parameter model for an industrial flash dryer and related equipment using only historical production data. To compensate for the lack of active process excitation, a data-selection strategy is used to identify those operating periods with maximum natural variability. The study also highlights key practical challenges encountered in a real factory environment, including unmeasured process disturbances, synchronization of high-frequency sensor data with delayed laboratory measurements, and unavoidable measurement uncertainty. The model was calibrated and validated with independent datasets from the SONAE factory in Valladolid. Results show that, despite data-quality limitations and closed-loop operating conditions, carefully selected historical data can successfully estimate fundamental parameters and provide a reliable predictive tool for industrial optimization.

A stability analysis of linear discrete-time neutral systems with both discrete and distributed delays is examined. To address this problem with accuracy, Lyapunov–Krasovskii candidates (LKCs) are formulated by heterogeneously splitting the whole delay interval into various parts; then, each part is assigned functionals with different weighting matrices. Then, new stability criteria are established and expressed in the form of linear matrix inequalities (LMIs) by combining a delay decomposition approach with an auxiliary function-based summation inequality method. These criteria provide a computationally efficient framework. Finally, several numerical examples are presented to confirm the validity and expanded feasibility region of our results when compared to existing approaches.

A robust methodology for parametric fault diagnosis in photovoltaic systems is proposed, focusing on DC-DC boost converters. The methodology uses Adaptive Sliding Mode Observers (ASMO) combined with adaptive thresholding. Specifically, an observer-based scheme detects and isolates faults in passive components of the converter, achieving complete isolation in about 0.05 s, even under varying environmental conditions. In addition, a dynamic fault discrimination approach is introduced, based on adaptive thresholds derived from Exponentially Weighted Moving Average (EWMA). This minimizes false alarms caused by transient conditions. Stability and robustness are guaranteed through Lyapunov-based conditions. Simulation results under sequential and simultaneous fault scenarios confirm rapid and precise fault detection, highly specific isolation, and exceptional resilience against environmental disturbances.

Este artículo resume diversas iniciativas para implementar el control predictivo (MPC) en controladores lógicos programables (PLCs), a partir de la experiencia acumulada, algoritmos de MPC utilizados, métodos de optimización, sistemas de proceso considerados y los estándares de programación y marcas comerciales de dispositivos PLCs empleadas. Los estudios demuestran la viabilidad de implementar algoritmos de MPC clásicos junto con métodos de optimización en forma embebida. Destacan, en particular, los algoritmos de optimización como el método de Hildreth y qp OASES simplificado, que han demostrado ser eficientes y codificables según el estándar IEC 61131-3, ampliamente utilizado en estos dispositivos. Además, los estudios resaltan la necesidad de reducir los requerimientos de memoria y cálculos computacionales, y que esto permita escalar estos algoritmos desde simulaciones hardware-in-the-loop (HiL) y procesos a escala de laborarorio hacia plantas industriales. Las tendencias actuales se orientan hacia la simplificación del uso de recursos computacionales en los PLCs, la mejora de los algoritmos de MPC y optimización, y la integración de estos algoritmos en dispositivos modernos basados en internet de las cosas (PLCs-IoT)

This paper introduces a novel strategy for the diagnosis and fault-tolerant control (FTC) of inter-turn short-circuit (ITSC) faults in the stator windings of Doubly Fed Induction Generator (DFIG)-based wind turbines. ITSC faults are among the most common electrical issues in rotating machines: early detection is therefore essential to reduce maintenance costs and prevent severe damage to the wind turbine system. To address this, a Fault Detection and Diagnosis (FDD) approach is proposed to identify and assess the severity of ITSC faults in the stator windings. A state-space model of the DFIG under ITSC fault conditions is first developed in the (d,q) reference frame. Based on this model, an Unknown Input Observer (UIO) structured using Takagi–Sugeno (T-S) fuzzy models is designed to estimate the fault level. To mitigate the impact of the fault and ensure continued operation under degraded conditions, a T-S fuzzy fault-tolerant controller is synthesized. This controller enables natural decoupling and optimal power extraction across a wide range of rotor speed variations. Since the effectiveness of the FTC relies on accurate fault information, a Proportional-Integral Observer (PIO) is employed to estimate the ITSC fault level. The proposed diagnosis and compensation strategy is validated through simulations performed on a 3 kW wind turbine system, demonstrating its efficiency and robustness.

Ordinary differential equations (ODEs) are widely used to describe the time evolution of natural phenomena across various scientific fields. Estimating the parameters of these systems from data is a challenging task, particularly when dealing with nonlinear and high-dimensional models. In this paper, we propose novel methodologies for parameter estimation in systems of ODEs by using the Newton-Raphson (NR) method and Gradient Descent (GD) method. By leveraging the discrete derivative and Taylor expansion, the problem is formulated in a way that enables the application of both methods, allowing for flexible, efficient solutions. Additionally, we extend these approaches to stochastic versions - Stochastic Newton-Raphson (SNR) and Stochastic Gradient Descent (SGD) - to handle large-scale systems with reduced computational cost. The proposed methods are evaluated by using numerical examples, including both linear and nonlinear parameter models, and compare the results to the well-known Nonlinear Least Squares (NLS) method. While NR converges rapidly to the optimal solution, GD demonstrates robustness in handling chaotic systems, though it may occasionally lead to suboptimal results. Overall, the proposed methods provide improved accuracy in parameter estimation for ODE systems, outperforming NLS in terms of error metrics such as bias, mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination R2. These methods offer a valuable tool for fitting ODE models, particularly in scenarios involving big data and complex dynamics.

The ongoing development of gain-scheduled filters is motivated by the estimation of the sideslip angle of ground vehicles. The present work proposes an approach based on finite-frequency specifications and an energy-to-peak (E2P) index for designing linear parameter-varying (LPV) filters. After reporting a new theoretical result, analysis and synthesis conditions are proposed for gain-scheduled and robust filters. Their properties are tested and illustrated using a simulated ground vehicle system via a comparative study, highlighting the advantages of the proposed approach.

En este artículo se describe una propuesta de entorno virtual para aplicación en la evaluación de estrategias de control en controladores lógicos programables (PLCs). Para ello, se ha considerado el uso de un gestor de la simulación del proceso, el cual comanda el modelo digital, y de un gestor de comunicación de componentes que integra los elementos del entorno virtual (PLC virtual y modelo digital) a través del protocolo de comunicaci´ón industrial OPC UA. El entorno virtual se ha validado mediante un caso de estudio que considera como sistema de proceso una unidad de esterilización de alimentos envasados, el cual requiere de lógicas de control discretas y continuas para llevar a cabo su operación. Esto permite verificar las lógicas de control en el entorno virtual, en un ambiente de simulación, para su posterior escalabilidad al proceso real utilizando protocolos adecuados para dispositivos de campo.

A hybrid combination of solar and wind energies for a remote seaside hotel is analyzed. More specifically, a hotel in Hammamet, Tunisia, powered by photovoltaic panels and a wind turbine is evaluated, with water generated by desalination using reverse osmosis. This isolated accommodation establishment is off-grid, having to produce its own drinking water, energy for cooking, lighting, etc. The power balances in these PV-wind-desalination systems is strongly influenced by the sunshine, the wind, and the demand. Based on an analysis of needs, the system is then sized. The variable nature of renewable energy sources, combined with unpredictable changes in load, require the simultaneous implementation of high power and high energy density storage systems. Thus, an storage system by lead-acid batteries for power and an elevated storage tank is considered, developing an autonomous microgrid connected to a variable charge. The system was validated by simulations (performed by Matlab/Simulink), based on data from the location, allowing us to predict the dynamic behavior. The simulation results validate the performance of the system.