Amadeo Hernandez

Este trabajo presenta el diseño, la construcción y la validación experimental de un péndulo invertido de bajo costo sobre un carro, como plataforma didáctica de hardware abierta para la enseñanza del control. La dinámica no lineal, subactuada e indirectamente inestable se derivó mediante la formulación de Euler-Lagrange y se linealizó alrededor del equilibrio inestable para obtener un modelo de tiempo continuo, posteriormente discretizado en una ley de control Proporcional-Integral-Derivativo (PID) digital implementada en un microcontrolador Arduino de 8 bits. La retroalimentación se obtuvo de dos codificadores de cuadratura, mientras que la actuación se proporcionó mediante un motor de CC accionado por puente H. Se evaluaron dos conjuntos de ganancia ajustados empíricamente: una configuración agresiva logró la estabilización en lazo cerrado en 33 segundos a pesar del aumento de la vibración mecánica, mientras que una configuración conservadora resultó insuficiente, produciendo un ciclo límite oscilatorio sostenido. Estos resultados confirman que el control PID clásico, ajustado experimentalmente, puede compensar fenómenos no modelados como la fricción, las zonas muertas del actuador y el juego mecánico. Con un precio inferior a 112 dólares estadounidenses, el prototipo ofrece una alternativa reproducible y accesible a los equipos de laboratorio de ingeniería de control comercial. Spanish-language metadata / Metadatos en españolTítulo en español:Control PID discreto en tiempo real de un péndulo invertido de bajo costo sobre un carro: diseño, construcción y validación experimental Resumen:Este trabajo presenta el diseño, la construcción y la validación experimental de un péndulo invertido de bajo costo sobre un carro como plataforma didáctica de hardware abierto para la enseñanza del control. La dinámica no lineal, subactuada e inherentemente inestable se derivó mediante la formulación de Euler–Lagrange y se linealizó en torno al equilibrio inestable para obtener un modelo en tiempo continuo. Posteriormente, se discretizó una ley de control proporcional-integral-derivativo (PID), que se implementó digitalmente en un microcontrolador Arduino de 8 bits. La retroalimentación se obtuvo mediante dos codificadores en cuadratura, mientras que la actuación se realizó mediante un motor de corriente continua accionado por un puente H. Se evaluaron dos conjuntos de ganancias ajustados empíricamente: una configuración agresiva logró la estabilización en lazo cerrado en 33 segundos, aunque con un aumento de la vibración mecánica, mientras que una configuración conservadora resultó insuficiente y produjo un ciclo límite oscilatorio sostenido. Estos resultados confirman que el control PID clásico, ajustado experimentalmente, puede compensar fenómenos no modelados, como la fricción, las zonas muertas del actuador y el juego mecánico. Con un costo inferior a 112 dólares estadounidenses, el prototipo ofrece una alternativa reproducible y accesible frente a los equipos comerciales de laboratorio para la enseñanza de la ingeniería de control. Palabras Claves:Péndulo invertido sobre un carro, sistemas subactuados, control PID en tiempo discreto, plataforma embebida de hardware abierto, ajuste experimental. Smart citations: SciteAI.Dimensions.Open Alex.

This paper presents the design, mathematical modeling, control, and virtual validation of a fifteen-degree-of-freedom anthropomorphic robotic hand within a Digital Twin-oriented framework. The mechanical structure was designed in SolidWorks and integrated into MATLAB/Simscape Multibody to create a high-fidelity virtual prototype. Forward and inverse kinematic models were derived using the Denavit–Hartenberg methodology, while the dynamic behavior was obtained through the Euler–Lagrange formulation. A proportional-derivative controller with gravity compensation was implemented to evaluate trajectory tracking and motion performance. Stability of the closed-loop system was demonstrated using Lyapunov theory and LaSalle’s Invariance Principle. Simulation results confirmed accurate position tracking, low torque requirements, and stable convergence for both individual and simultaneous joint movements. The proposed Digital Twin framework enables efficient design validation, performance assessment, and future integration with real-time sensing and intelligent robotic manipulation systems. Keywords: Robotic hand; Digital Twin; Simscape Multibody; PD control with gravity compensation; Virtual prototyping. Spanish-language metadata / Metadatos en españolTítulo en español:Marco orientado a Gemelo Digital para el modelado, control y validación virtual de una mano robótica antropomórfica Resumen:Este artículo presenta el diseño, el modelado matemático, el control y la validación virtual de una mano robótica antropomórfica de quince grados de libertad dentro de un marco orientado a Gemelo Digital. La estructura mecánica se diseñó en SolidWorks y se integró en MATLAB/Simscape Multibody para crear un prototipo virtual de alta fidelidad. Los modelos cinemáticos directo e inverso se derivaron mediante la metodología de Denavit–Hartenberg, mientras que el comportamiento dinámico se obtuvo mediante la formulación de Euler–Lagrange. Se implementó un controlador proporcional-derivativo con compensación de gravedad para evaluar el seguimiento de trayectorias y el desempeño del movimiento. La estabilidad del sistema en lazo cerrado se demostró mediante la teoría de Lyapunov y el Principio de Invariancia de LaSalle. Los resultados de la simulación confirmaron un seguimiento preciso de la posición, bajos requerimientos de par y una convergencia estable tanto para movimientos articulares individuales como simultáneos. El marco de Gemelo Digital propuesto permite una validación eficiente del diseño, la evaluación del desempeño y la futura integración con sistemas de sensado en tiempo real y de manipulación robótica inteligente. Palabras Claves:Mano robótica, Gemelo Digital, Simscape Multibody, prototipado virtual Smart citations: SciteAI. Dimensions.Open Alex.

A hybrid model (RF and PSO) is proposed to predict power generation in combined-cycle power plants. The study was conducted using 9,568 operational records collected over six years, with ambient temperature, atmospheric pressure, relative humidity, and exhaust vacuum as input conditions. Upon examining the dataset for physical consistency and identifying ambient temperature as the primary predictive variable in terms of power output, a correlation of -0.9485 was also obtained. During the training process, the baseline RF with grid search optimization achieved an RMSE of 3.334528 MW. Adaptive-PSO was evaluated by applying linear, exponential, and oscillatory update schemes. The results show that the oscillatory variant had the best cross-validation RMSE of 3.329416 MW, while the linear and exponential variants exhibited the lowest test RMSE of 3.330879 MW. A statistically significant difference was found between the oscillatory PSO variant and the reference model according to the Wilcoxon signed-rank test. Spanish-language metadata / Metadatos en españolTítulo en español:Random Forest optimizado mediante PSO adaptativo para la predicción de la potencia de salida en centrales eléctricas de ciclo combinado Resumen:Se propone un modelo híbrido (RF y PSO) para predecir la generación de energía en centrales eléctricas de ciclo combinado. El estudio se llevó a cabo utilizando 9.568 registros operativos recopilados durante seis años, con la temperatura ambiente, la presión atmosférica, la humedad relativa y el vacío de escape como condiciones de entrada. Tras examinar la consistencia física del conjunto de datos e identificar la temperatura ambiente como la principal variable predictora de la potencia de salida, también se obtuvo una correlación de −0,9485. Durante el proceso de entrenamiento, el RF de referencia con optimización mediante búsqueda en cuadrícula alcanzó un RMSE de 3,334528 MW. El PSO adaptativo se evaluó mediante la aplicación de esquemas de actualización lineal, exponencial y oscilatorio. Los resultados muestran que la variante oscilatoria obtuvo el mejor RMSE de validación cruzada, con un valor de 3,329416 MW, mientras que las variantes lineal y exponencial presentaron el menor RMSE de prueba, de 3,330879 MW. De acuerdo con la prueba de rangos con signo de Wilcoxon, se encontró una diferencia estadísticamente significativa entre la variante oscilatoria de PSO y el modelo de referencia. Palabras Claves:Central eléctrica de ciclo combinado, predicción de la potencia de salida, Random Forest, optimización adaptativa por enjambre de partículas, optimización de hiperparámetros Smart citations: SciteAI. Dimensions.Open Alex.

Recent global disruptions have exposed the fragility of tightly coordinated supply chains, particularly under transportation and information delays, motivating the need for analytical tools to assess their stability limits. This study analyzes a two-echelon supply chain system to determine how delays affect stability and performance, with an emphasis on the role of feedback coordination. A continuous-time delay-differential modeling framework was developed to examine both uncoupled and coupled configurations. Stability is analyzed through characteristic equations, and explicit closed-form expressions for the critical delay threshold are derived as functions of the coupling gain and shipment rate. The uncoupled system is shown to exhibit delay-independent marginal stability but lacks the ability to regulate downstream inventory. In contrast, the coupled system achieves inventory regulation but introduces delay-dependent stability with a critical delay, beyond which oscillations grow unbounded. A key result revealed an inverse relationship between coupling strength and delay tolerance, highlighting a trade-off between responsiveness and robustness. An optimal control formulation further demonstrates that the stability constraints limit the achievable performance. These findings provide a theoretical explanation for the vulnerability of just-in-time systems and offer practical guidelines for resilient logistics design, enabling supply chain practitioners to quantify stability margins and balance coordination efficiency with robustness to transportation delays.

The optimal placement of robotic manipulators within industrial workstations is a critical problem that directly affects task feasibility, accessibility, and operational efficiency. Improper base positioning can lead to joint saturation, reduced manipulability, and limited workspace utilization. This work presents an optimization framework for determining the optimal base placement of robotic manipulators by maximizing a joint-centering performance index based on the κ-index, which quantifies the proximity of joint variables to their allowable limits. The proposed methodology integrates geometric accessibility constraints with a constrained optimization formulation to ensure feasible robot configurations within the workspace. Three optimization strategies—constrained nonlinear programming, gradient projection methods, and genetic algorithms—are evaluated and compared in terms of solution quality and computational performance. Numerical simulations are conducted using a planar 2-DOF manipulator to illustrate the proposed framework and to analyze the influence of workspace geometry on optimal base placement. Additionally, an industrial case study involving the ABB IRB 120 robotic manipulator is presented to assess the practical applicability of the proposed approach. The results demonstrate that the optimization framework improves joint distribution within the allowable limits and enhances robot accessibility across the task workspace. The proposed method provides a practical tool for intelligent workstation design and robotic cell layout optimization in modern industrial environments.

This study introduces a predictive air quality monitoring system based on Random Forest machine learning models and low-cost embedded sensors. The system was designed and implemented in Guadalupe, Nuevo León, Mexico, to monitor carbon monoxide (CO), car-bon dioxide (CO2), and particulate matter (PM). Real-time data was collected using a Particle Photon 2 microcontroller with four different sensors. The data was processed using Python scripts, and the Random Forest model was trained to predict future pollutant values. Results demonstrated strong model performance, validated through statistical evaluation metrics and graphical comparisons. The proposed system shows promise for deployment in smart urban environments. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1234Dimensions.Open Alex.

This chapter addresses smart cities not simply as a technological deployment, but as a space where different branches of engineering complement each other. It analyzes some of the contributions of mechanical, mechatronic, electrical, electronic, computational, and chemical engineering, highlighting how each of them, from its perspective, integrates innovative and preferred factors to make a city more efficient, safe, and, above all, tailored to its inhabitants. It also presents some specific examples of the implementation and management of smart cities and governments, such as Barcelona, Singapore, New York, and Amsterdam. The text details some of the actions these places have taken to become smart. It analyzes how the knowledge contributed by different branches of engineering articulates with the Internet of Things, electrical grids, and the mechatronic design of smart sensor networks to give rise to innovative systems.

This research communication aims to present three mathematical methods for analyzing inventory management in dynamic supply chains, starting from the basic definition in differential equations for inventory levels, which relates with production and demand rates. Initially, the study adopts a systemic perspective to examine the role of energy within a production–inventory system. Subsequently, the analysis focuses on inventory dynamics under parameters expressed in complex variables, with the aim of quantifying fluctuations in a generic production system and demonstrating the influence of inventory variation rates on system behavior. Finally, the investigation addresses the impact of variable capacity on production system inventories, drawing on analogies with corresponding physical systems to support the analysis.

An automatic temperature control system is an important application used in almost all modern gadgets, smart homes and industries; however, there are almost no available prototypes to practise with, and the ones that do exist cost more than an average student in Mexico can afford. This project is based on the implementation of a digital PID controller, realised through widely studied mathematical concepts, applied to a fan that blows air into an enclosed area housing an incandescent bulb, thereby controlling the internal temperature. A Kalman filter was used to reduce the thermocouple noise. The performance of the controller was tested by observing its ability to maintain the desired temperature setpoint steadily. This test highlighted the PID controller’s ability to reach and maintain the desired temperature, validating its effectiveness as both an educational tool and a method of control for industrial applications.

Agri-food supply chains have experienced notable changes in recent decades, with tomatoes (Solanum lycopersicum) maintaining their status as a key global crop in terms of both production and consumption. These supply chains comprise a complex network of stakeholders—including producers, processors, distributors, and retailers—who collectively ensure the delivery of tomatoes from farms to consumers. This study develops mathematical models of agri-food tomato supply chains (AFTSCs) and examines their behavior through stability analysis and dynamic simulations based on a compartmental approach. Furthermore, the environmental impact is evaluated using a sustainability index, to which the waste diversion rate is introduced. This metric is defined as the proportion of diverted waste (i.e., materials recycled, reused, or composted) relative to the total waste generated, thereby enabling the quantification of sustainability performance within the system. Finally, a sensitivity analysis is conducted on the proposed dynamical models to validate and reinforce the findings.