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This research analyses the dynamic response of a single-cylinder magnetic reciprocating engine (MRE) and the mathematical model formulation with experimental validation. In efforts to reduce the pollution generated by internal combustion engines (ICE), the MREs have emerged as an alternative to this problem. An MRE uses the ICE structure and replaces fossil fuels with magnetic fields to work with the principle of magnetic repulsion between an electromagnet and a permanent magnet. The work focuses on determining the correct angles of when to energize the electromagnet based on the angular position of the crankshaft concerning the top dead center (TDC) of the engine to generate the magnetic repulsion and obtain the highest possible speed in the engine as in the ICE. The experimental results indicate that the energization angle should be between 45° and 55° before the TDC for generating the highest engine velocity. Using the energization angle of 55° before the TDC allows the engine’s velocities to be between 836 RPM and 840 RPM, and the mathematical model shows a goodness of fit (FIT) of 81.21% throughout its dynamics with the experimental comparison. The present work demonstrates the mathematical model’s ability to represent the MRE’s experimental dynamics and how the magnetic force is formulated and integrated with the mechanical analysis of the engine.

In this paper, the main objective is to estimate the percentage of glycosylated hemoglobin through an easily accessible computational platform to estimate the risk of generating type 2 diabetes mellitus in the Mexican population. The estimation of the computational tool is developed through an artificial neural network model, which was trained and validated according to a population sample of 1120 Mexican people between 18 and 59 years old. The model inputs were gender, age, body mass index, waist circumference, weekly food consumption, family history, and whether the person suffers from any chronic degenerative disease other than T2DM. We used the percentage of glycosylated hemoglobin as output, estimated according to a dynamic glucose model. The estimation results present a coefficient of determination of 99 %, demonstrating an acceptable performance of the neural network model. The developed platform is an aid tool for health personnel, which seeks to generate a first approximation to the glycemic status of those communities with a high marginalization index for generating disease prevention strategies.

In this paper, we present the design of a fractional-order sliding mode observer (FO-SMO) for actuator fault estimation in a quadrotor unmanned aerial vehicle (QUAV) system. Actuator faults can significantly compromise the stability and performance of QUAV systems; therefore, early detection and compensation are crucial. Sliding mode observers (SMOs) have recently demonstrated their accuracy in estimating faults in QUAV systems under matched uncertainties. However, existing SMOs encounter difficulties associated with chattering and sensitivity to initial conditions and noise. These challenges significantly impact the precision of fault estimation and may even render fault estimation impossible depending on the magnitude of the fault. To address these challenges, we propose a new fractional-order SMO structure based on the Caputo derivative definition. To demonstrate the effectiveness of the proposed FO-SMO in overcoming the limitations associated with classical SMOs, we assess the robustness of the FO-SMO under three distinct scenarios. First, we examined its performance in estimating actuator faults under varying initial conditions. Second, we evaluated its ability to handle significant chattering phenomena during fault estimation. Finally, we analyzed its performance in fault estimation under noisy conditions. For comparison purposes, we assess the performance of both observers using the Normalized Root-Mean-Square Error (NRMSE) criterion. The results demonstrate that our approach enables more accurate actuator fault estimation, particularly in scenarios involving chattering phenomena and noise. In contrast, the performance of classical (non-fractional) SMO suffers significantly under these conditions. We concluded that our FO-SMO is more robust to initial conditions, chattering phenomena, and noise than the classical SMO.

In the present manuscript, we design a fractional multi-order high-gain observer to estimate temperature in a double pipe heat exchange process. For comparison purposes and since we want to prove that when using our novel technique, the estimation is more robust than the classical approach, we design a non-fractional high-gain observer, and then we compare the performance of both observers. We consider three scenarios: The first one considers the estimation of the system states by measuring only one output with no noise added on it and under ideal conditions. Second, we add noise to the measured output and then reconstruct the system states, and, third, in addition to the noise, we increase the gain parameter in both observers (non-fractional and fractional) due to the fact that we want to prove that the robustness changes in this parameter. The results showed that, using our approach, the estimated states can be recovered under noise circumstances in the measured output and under parameter change in the observer, contrary to using classical (non-fractional) observers where the states cannot be recovered. In all our tests, we used the normalized root-mean-square, integral square error, and integral absolute error indices, resulting in a better performance for our approach than that obtained using the classical approach. We concluded that our fractional multi-order high-gain observer is more robust to input noise than the classical high-gain observer.

This work focused on a new developed and experimentally tested non-linear control approach to control a double tube heat exchanger's output cold water temperature. The control approach uses an inverse artificial neural network (ANNi) and an integral control law. The ANNi objective is to obtain the cold water flow considering as a reference the desidered output cold water temperature. The ANNi was built from a feedforward ANN with four inputs (input cold water temperature, input hot water temperature, cold water flow, hot water flow), a hidden layer with a neuron, and two outputs (output cold water and output hot water temperature). The integral control law is implemented to reduce the error between the setpoint and the ANNi estimate. As a result, the output cold water temperature regulation is achieved through a simple controller that does not need a complex ANN model and does not depend on the process parameters' exact value. The proposed control scheme (Inverse Artificial Neural Network with Integral Control (ICANNi)) showed good reference tracking, obtaining a root mean square error (RMSE) of 0.2025, standard deviation (SD) of 0.0410, establishment time of 23 s, and overshoots less than 2.7 °C. In addition, the ICANNi control has a settling time that is 1.91 times faster than the PID control and 1.5 times faster than the ANNi control.

This work presents an analysis of the effect on glycemic variation caused by modifying the macronutrient intake sequence in a person without a diagnosis of diabetes. In this work, three types of nutritional studies were developed: (1) glucose variation under conditions of daily intake (food mixture); (2) glucose variation under conditions of daily intake modifying the macronutrient intake sequence; (3) glucose variation after a modification in the diet and macronutrient intake sequence. The focus of this research is to obtain preliminary results on the effectiveness of a nutritional intervention based on the modification of the sequence of macronutrient intake in a healthy person during 14-day periods. The results obtained corroborate the positive effect on the glucose of consuming vegetables, fiber, or proteins before carbohydrates, decreasing the peaks in the postprandial glucose curves (vegetables: 113–117 mg/dL; proteins: 107–112 mg/dL; carbohydrates: 115–125 mg/dL) and reducing the average levels of blood glucose concentrations (vegetables: 87–95 mg/dL; proteins: 82–99 mg/dL; carbohydrates: 90–98 mg/dL). The present work demonstrates the preliminary potential of the sequence in the macronutrient intake for the generation of alternatives of prevention and solution of chronic degenerative diseases, improving the management of glucose in the organism and permeating in the reduction of weight and the state of health of the individuals.

In this paper, a robust leader-following consensus protocol for multi-agent systems (MASs) subject to faults in the information exchange and disturbance is presented. The problem under consideration is to guarantee the convergence of the agent trajectories to a leader agent when all the agent followers are under faults in the information exchange as smooth time-varying delays and disturbances. The main contribution in this paper is the design of a robust leader-following control through the Lyapunov approach and an optimal $\mathscr {H}_{\infty }$ criterion such that all agents follow a virtual leader agent despite faults in the information exchange and disturbances. Linear matrix inequalities (LMIs)-based conditions are obtained whose solution allows computing the robust controller gain. In order to show the effectiveness of the proposed approach, numerical examples are carried out comparing a state-of-the-art approach and the proposed strategy in a fleet of unmanned aerial vehicles (UAVs) subject to wind turbulence which are shown to achieve the formation control.

Parameterizing high-complexity thermal models is a common challenge in bioreactor engineering. This paper rapports the mathematical modeling of thermal interactions in a photobioreactor. Energy balances, physical measurements, and mathematical relations from the literature were used. Two experiments (Tests 1 and 2) were done for model parameterization and to validate their mathematical estimation. The difficulty or impossibility of generating thermophysical properties data led to use the Kalman filter for parameter estimation. A mean absolute percentage error from 0.024 to 0.136% was obtained for parametrized models regarding estimates vs. measured values. A significative correlation was obtained for all paired measured vs. estimated values in Test 1 (r = 0.931, p

This paper presents an experimental platform for regulating the DC motor angular speed powered by photovoltaic cells. The experimental platform comprises an Eco Green Energy EGE-260P-60 solar panel, DC/DC SEPIC converter, DC bus, DC/DC buck converter, DC motor and Nexys 4 board with an Artix-7 100T FPGA. The DC/DC SEPIC converter is used for harvesting the maximum amount of energy from the PV cells using the perturb and observe algorithm to track the maximum power point. The DC/DC buck converter is used as the motor drive using the active disturbance rejection control to regulate the angular speed of the DC motor. In addition, the FPGA architecture design is presented using a hierarchical top-down methodology with the VHDL hardware description language and Xilinx System Generator tool. The software takes advantage of the FPGA’s concurrency to simultaneously evaluate the different processes, which is the main reason for choosing this digital device. Several tests were performed on the platform such as irradiance changes, DC bus variations, DC motor connection and load torque variations applied in the motor shaft. The results indicate that the maximum power is obtained from the photovoltaic cells, establishing the minimum operating conditions. In addition, the control approach estimates and cancels the effects of disturbances caused by variations in the environmental conditions, photovoltaic system, DC bus, and load changes in order to regulate DC motor speed.

In this paper, we present the design of a fractional-order sliding mode observer (FO-SMO) for actuator fault estimation in a quadrotor unmanned aerial vehicle (QUAV) system. Actuator faults can significantly compromise the stability and performance of QUAV systems; therefore, early detection and compensation are crucial. Sliding mode observers (SMOs) have recently demonstrated their accuracy in estimating faults in QUAV systems under matched uncertainties. However, existing SMOs encounter difficulties associated with chattering and sensitivity to initial conditions and noise. These challenges significantly impact the precision of fault estimation and may even render fault estimation impossible depending on the magnitude of the fault. To address these challenges, we propose a new fractional-order SMO structure based on the Caputo derivative definition. To demonstrate the effectiveness of the proposed FO-SMO in overcoming the limitations associated with classical SMOs, we assess the robustness of the FO-SMO under three distinct scenarios. First, we examined its performance in estimating actuator faults under varying initial conditions. Second, we evaluated its ability to handle significant chattering phenomena during fault estimation. Finally, we analyzed its performance in fault estimation under noisy conditions. For comparison purposes, we assess the performance of both observers using the Normalized Root-Mean-Square Error (NRMSE) criterion. The results demonstrate that our approach enables more accurate actuator fault estimation, particularly in scenarios involving chattering phenomena and noise. In contrast, the performance of classical (non-fractional) SMO suffers significantly under these conditions. We concluded that our FO-SMO is more robust to initial conditions, chattering phenomena, and noise than the classical SMO. [ABSTRACT FROM AUTHOR]

In this paper, a portable exoskeleton for the rehabilitation of upper extremities of three degrees of freedom (DOF) is proposed. With these degrees of freedom, the exoskeleton provides the movements of flexion–extension and abduction–adduction of the arm and flexion–extension of the forearm. A robust generalized proportional integral (GPI) controller for trajectory tracking to provide smooth movements for rehabilitation with the exoskeleton is proposed. This controller only requires output measurements and is robust against different types of disturbances. Simulation results are presented in the MSC Adams® software environment in co-simulation with Matlab-Simulink® to show the controller’s performance against different types of disturbances. The results of a PID type controller are also contrasted with the results of the GPI controller.

This work presents the simulation results of an active fault tolerant control system on a distillation column. The aim of the control system is to maintain the binary distillation process (ethanol-water) in continuous operation even if both sensors or actuator fault occurs. To develop the active fault tolerant control system, it was designed a fault detection and diagnosis system by using a full-order high-gain observer to estimate the temperatures and concentrations on the distillation column; the estimation of the temperatures and concentrations was made by using only one measured temperature. Also, there were employed three adaptive observers, one is used to estimate the vapor pressure, the other two observers are used to estimate a parameter in each actuator, in that way is estimated the size, instant and magnitude of the fault. The active fault tolerant system is based on the compensation of the fault based on the information given by the fault detection and diagnosis system. The fault tolerant in sensor is based in the reconfiguration of the faulty sensor. The main contribution of this work is that proposed scheme is able to detect and diagnosis, multiple and simultaneous failures in sensors and at least in one actuator. The simulation test shows that the control law allows that the liquid molar concentrations required in distillates, follow the reference properly, even in presence of faults.

Continuous production processes and an adequate supply of raw materials are necessary to satisfy the growing demand for biodiesel. The use of different feed oils could be necessary to ensure sufficient supply for biodiesel production in certain circumstances; however, changing feed oil during the operation of a continuous reactor causes process disturbances. The present study analyses the effect of feed oil changes on the continuous operation of a industrial continuous stirred-tank reactor (CSTR) using a model which takes the dynamics of oil changes into account. The models previously reported only consider the operation of reactor with only one vegetable oil. The model in this work was developed by mass and energy balances. A methodology to model oil changes is presented. Glycerides and esters were characterized using adequate approaches. Moreover, accurate methods for predicting essential properties in the biodiesel manufacturing were used for the estimation of their thermo-physical properties. The kinetic parameters of the transesterifications were calculated from consistent studies selected from an exhaustive literature revision. The results show that temperature is practically not affected after oil changes; however, the ester concentration varies considerably. The ester mass fraction varying by up to 22.07% after an disturbance.

Resumen: En este artículo se presenta el desarrollo de un control predictivo distribuido óptimo (DOMPC) el cual está basado en el control predictivo óptimo centralizado (OMPC) y el control predictivo en modo dual. Esta adaptación engloba la partición del sistema a controlar en s subsistemas y la optimización de manera distribuida de las señales de control. Se considera que los controladores se comunican mediante una red de área local (LAN), la cual introduce un retardo de un instante de muestreo en la transmisión de los datos para la optimización. El esquema DOMPC propuesto se aplica a un sistema de 4 tanques y se realiza una comparación con el esquema OMPC centralizado. Abstract: This paper presents the development of an distributed optimal predictive control (DOMPC), this controller is based on the centralized optimal predictive control (OMPC) and the dual-mode predictive control. This adaptation encompasses the partition of the system in s subsystems and the distributed optimization of the control signals. It is assumed that the controllers are connected by a local area network, which introduces a communication delay of one sampling instant. The proposed scheme is applied to a 4 tanks benchmark system and it is compared with the centralized OMPC scheme. Palabras clave: Control distribuido, Control predictivo., Keywords: Predictive control, Distributed control.

This paper presents the development of an distributed optimal predictive control (DOMPC), this controller is based on the centralized optimal predictive control (OMPC) and the dual-mode predictive control. This adaptation encompasses the partition of the system in s subsystems and the distributed optimization of the control signals. It is assumed that the controllers are connected by a local area network, which introduces a communication delay of one sampling instant. The proposed scheme is applied to a 4 tanks benchmark system and it is compared with the centralized OMPC scheme.

In this article, a toolbox based on a monitoring and control interface (MCI) is presented and applied in a heat exchanger. The MCI was programed in order to realize sensor fault detection and isolation and fault tolerance using virtual sensors. The virtual sensors were designed from model-based high-gain observers. To develop the control task, different kinds of control laws were included in the monitoring and control interface. These control laws are PID, MPC and a non-linear model-based control law. The MCI helps to maintain the heat exchanger under operation, even if a temperature outlet sensor fault occurs; in the case of outlet temperature sensor failure, the MCI will display an alarm. The monitoring and control interface is used as a practical tool to support electronic engineering students with heat transfer and control concepts to be applied in a double-pipe heat exchanger pilot plant. The method aims to teach the students through the observation and manipulation of the main variables of the process and by the interaction with the monitoring and control interface (MCI) developed in LabVIEW©. The MCI provides the electronic engineering students with the knowledge of heat exchanger behavior, since the interface is provided with a thermodynamic model that approximates the temperatures and the physical properties of the fluid (density and heat capacity). An advantage of the interface is the easy manipulation of the actuator for an automatic or manual operation. Another advantage of the monitoring and control interface is that all algorithms can be manipulated and modified by the users.

This article proposes a virtual sensor for piecewise linear systems based on observability analysis that is in function of a commutation law related with the system’s outpu. This virtual sensor is also known as a state estimator. Besides, it presents a detector of active mode when the commutation sequences of each linear subsystem are arbitrary and unknown. For the previous, this article proposes a set of virtual estimators that discern the commutation paths of the system and allow estimating their output. In this work a methodology in order to test the observability for piecewise linear systems with discrete time is proposed. An academic example is presented to show the obtained results.

El objetivo de este trabajo es describir paso a paso la implementación de un sensor basado en observador para estimar las variables de un proceso para el cual no existe la disponibilidad de un sensor físico. El procedimiento de diseño e implementación se ilustra mediante su aplicación a un intercambiador de calor de doble tubo con fl ujos a contracorriente. El enfoque empleado para diseñar el observador no lineal se basa en un modelo matemático simplifi cado del proceso. Se desarrollaron simulaciones numéricas y posteriormente pruebas experimentales en una planta piloto (intercambiador de calor) con el fi n de validar el esquema propuesto.

There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors.

En este artículo se presenta el desarrollo de un método de diagnóstico de fallas para sistemas no lineales representado por el enfoque de múltiples modelos. La principal contribución consiste en el diseño de un esquema de diagnóstico de fallas (FDD) a través de un filtro adaptativo desacoplado de fallas. El comportamiento del sistema está dado mediante múltiples modelos lineales alrededor de diferentes puntos de operación. Los residuos se establecen de tal manera que permiten estimar funciones de activación robustas a fallas. El filtro adaptivo diseñado permite la detección, localización y estimación de fallas múltiples o simultáneas. Las condiciones de estabilidad del filtro son estudiadas a través de LMI (Linear Matrix Inequality). El desempeño del esquema propuesto se valida en un sistema de tres tanques.

En este trabajo se presenta el diseño e implementación de un sistema de detección y localización de fallas para un proceso de copolimerización en emulsión. El enfoque propuesto para el diseño de los generadores de residuos está basado en técnicas de desacoplo usando geometría diferencial y el uso de observadores no lineales de alta ganancia, en el cual cada uno de los residuos se encuentra dedicado a la detección y a la localización de una falla en particular. Las fallas consideradas en este trabajo son de tipo aditivo, presentes en los actuadores del proceso, que representan un mal funcionamiento de las bombas de alimentación de flujo de monómeros hacia el interior del reactor. Una de las principales ventajas del método propuesto son los observadores utilizados como generadores de residuos, los cuales son fáciles de implementar y de sencilla calibración, lo que favorece su aplicación en línea. La eficacia del método se valida en simulación para el caso de un proceso de copolimerización en emulsión entre el acrilato de butilo y el estireno.

En este artículo se desarrolla un método para la representación de sistemas no lineales, así como un esquema de diagnóstico de fallas; en donde ambos utilizan un enfoque multi-modelos. El comportamiento dinámico del sistema se describe mediante el uso de diferentes puntos de operación por cada modelo lineal invariante en el tiempo. Por cada modelo un filtro de Kalman desacoplado de fallas es utilizado. Esto permite la generación de un banco de residuos con la finalidad de estimar funciones de ponderación robusta a fallas. Estas últimas se enlazan directamente al esquema de detección, localización y estimación de fallascon objeto de mejorar su eficacia. Finalmente, se realiza un ejemplo de simulación a fin de probar el desempeño del método desarrollado.