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The global growth in electrical consumption faces two main problems: the depletion of non- renewable resources due to predominant fossil fuel generation and the high environmental impact of greenhouse gas emissions. The proposed solution is to transition towards decentra- lized systems with renewable sources, such as photovoltaic and wind energy, integrated into microgrids connected to the central system. However, the natural variability of these resources affects frequency and voltage stability, necessitating a four-level hierarchical control: maintai- ning internal setpoints, adjusting voltages based on power, compensating for deviations, and optimizing power flows. An approach developed in this work combines techniques like neural networks and universal controllers to minimize stochastic impacts, optimizing energy manage- ment and photovoltaic panel performance. The results demonstrate effective battery integration up to 59 % of the time, even in renewable resource scarcity, successfully mitigating voltage and frequency fluctuations within acceptable operational limits, even during faults, with variations of ±0.05Hz and ±7.16V respectively.

This paper analyzes three new control strategies that improve system response and its com- parison with classic PID control. The efficiency of the PID controller depends only on its gains Kp, Ki and Kd. Therefore, the strategies improve the control process by finding the optimal gains through the programed algorithm. The programmed gain control algorithms are based on artificial intelligence techniques such as fuzzy logic, the simulated annealing algorithm and the A* search method, resulting in the FGS-PID, SA-PID and A*-PID controllers. The control algorithms are tested in MATLAB and adapted on the STM32 core-144 embedded system, with the objective of controlling the water level of a multitank system. For this purpose, different performance tests are carried out in simulations and on the real system where the response of the plant to reference changes is evaluated. In addition, the settling time and percentage of overshoot are used as performance indexes. Also, the operation of the control system is tested by introducing possible failures in the plant, such as through leaks in the tanks caused by a hole in the bottom. Finally, the results show the effectiveness of the algorithms in reducing the settling time and overshoot in the response of the multitank system, furthermore, adding some tolerance to failures in the system. The aforementioned results allow the tracking of the reference in the presence of faults, which is not achieved with the classical PID control system.

Industry 4.0 makes possible the automation, modernization and digitization of indus- trial networks for the optimization of time and resources in a plant. In this environment, control systems play an important role, since they allow efficient management and control of field devices; facilitating the supervision of industrial processes, in addition to benefiting the scalability and interoperability between communication protocols of the system to adapt to the changing needs of the industrial environment. In this con- text, a complementary SCADA system is proposed for the monitoring and supervision of the hydrogen cell, power supply and programmable load in DC. The control network is characterized by being scalable and capable of operating in situations of connec- tion failures of one or more equipment. The communication management between the interface host and the field devices is done by a programmable automation controller (PAC) under the Modbus and TCP/IP protocols. The user interface (HMI) monitors, supervises and manages the different equipment. The work was carried out through the surveying, studying and analyzing of the industrial network in the Balzay micro- grid, which is reflected in the diagram of connections and protocols of the existing network and that proposed for the complementary system. The proposed and subjec- tively validated implementation is very useful for the microgrid, because it facilitates the incorporation of equipment into the management system in future research and projects to be carried out on site.

This work presents details about the design and implementation of two home automation systems, in order to compare the efficiency, reliability, security and scalability between a technology dedicated to home automation and another low cost and easy integration (DIY - Do It Yourself). In this context, Local Control Network (LCN) technology is compared, on the one hand, and devices that can be integrated with Amazon's voice assistant, Alexa, on the other. Each network is planned with the intention of automating the luminaires, as well as their intensity, the automatic disconnection of equipment that consumes energy unnecessarily, and a surveillance system. The two houses present similar consumption habits, which allows them to be compared and to determine to what extent the low-cost solutions have efficient performance compared to dedicated technologies. Additionally, to improve the performance of home automation systems, architectural modifications of each technology are proposed, such as the implementation of a visualization system through a free software platform, Home Assistant; likewise, the sectioning of the low-cost network. This implementation made it possible to define the main advantages of dedicated technologies that use bus lines, highlighting scalability, low latency and security over low-cost technologies; On the other hand, its cost is very high and it has many benefits for application in home automation.

This degree work develops and implements a device to aid in the diagnosis of speech therapy in post-COVID-19 patients by obtaining fundamental parameters of the voice through a sensor placed on the surface of the individual’s neck. The implementation of the device consists of 2 parts, hardware and software. The fundamental part of the hardware is the ESP32 module. It powers the circuit and controls the data handling. The second most important part of the circuit is the sensor used, which is an accelerometer BU-21771, which is robust to noise. For the sensor to work properly, a power supply, pre-amplification, amplification, filtering and data storage stages were designed. The software part, consists of a web server with several functions, one of them, allows to collect the data obtained through the sensor. These data are saved in the available storage, which allows downloading, deleting and visualizing the data through the server. The data obtained were processed by Praat software, in order to obtain the main parameters of the voice, and thus be able to perform a detailed acoustic analysis. The main parameters obtained are: fundamental frequency, intensity, phonation time, jitter, shimmer and HNR. To complement the analysis in Praat, there is the possibility of analyzing each of the parameters visually with acoustic images (oscillogram and spectrogram). The final device was tested on 21 post-COVID-19 individuals, 12 males and 9 females. Initial tests comparing the results of the device with a professional microphone showed that jitter, shimmer and HNR were the parameters that showed the most difference. Subsequent tests demonstrated the robustness of the accelerometer to noise with results similar to the first test. Finally, a comparison of the results of 10 of 21 individuals diagnosed by speech therapists with the data obtained by the device was performed. In conclusion, the prototype developed presents a certain margin of error with respect to a professional diagn

This document describes the development of a monitoring system for a manufacturing cell. The implementation of this system utilizes enabling technologies from the Industrial Internet of Things IIoT. The University of Cuenca’s Electrical Machines Laboratory owns a flexible manufacturing cell FMC that has been mostly left in disuse. This project is purposed to take advantage of this FMC. This cell consists primarily of actuators and sensors. These devices all interface with a PiXtend, a component of the FMC. Additionally, a Raspberry Pi acts as an Interface between the industrial components and a cloud computing platform. The communication between the PLC and the Raspberry Pi is by use of the OPC-UA protocol. A client software program on the Raspberry Pi receives data from an OPC-UA server. It interfaces with the cloud server by way of the MQTT protocol. This online server manages, processes and stores a database of the generated industrial data. This project considers both the hardware and software necessary for producing, compiling, transmitting, storing and processing the data stream. The process of compiling the data is carried out on the PiXtend, while the data storage is done locally as well as uploaded to the cloud server by way of the Raspberry Pi. The control and monitoring can be done with a low-cost local computer as well as remotely through other devices. The cloud server communicates through WebSockets. With the use of a webpage interface, it’s possible to monitor the various industrial processes. Various traffic tests are also carried out between the different protocols as well as a qualitative comparison of the FMC performance with the use of specific tools designed for the IIoT.

Wind energy is the fastest growing technology in market sharing and efficiency. Wind energy conversion systems (WECS) transform kinetic energy from wind to electric energy through a combination of wind turbines (WT) and electrical generators. There are several possible configurations of wind turbines and electric generators, which allow different control approaches to regulate the energy output of WECS that mainly are: stall control, and pitch angle control. For any case, several approaches based on classic control and modern control have been successfully applied to the solely objective of improving the power capture of WECS, while, in most cases, leaving unattended other important operating variables, such as the blades’ mechanical stress. This research work proposes a methodology of design and testing of a self-optimizing control (SOC) algorithm to maximize the power output of a WECS while reducing the mechanical stress of the blades of the WT. This improved operation of the WECS is achieved by minimizing a multi objective cost function that relates the output power and the stress factor. The cost function calculation is performed by using a combined model of the blade element momentum (BEM) and thin wall beam (TWB) approaches. Since the SOC deployment implies low computational cost due to an optimization space reduction via a matrix projection applied to a measurement vector, the design of this algorithm requires off-line calculation of this projection matrix, H. SOC optimizes operation of the WECS in the presence of uncertainty or disturbance, which is assumed to be the variation of the wind speed, by controlling a carefully chosen combination of measurements to a constant set point. The performance of the WECS when operating with the SOC is compared with a baseline classic controller (BCS), and a predictive control algorithm (NMPC). Simulation results show the feasibility of the controller for real-time operation, as well an improved operation in comparison wi

Today there is a heavy workload in health systems; the staff is not too aware of patients and their needs. Such is the case of bedridden patients with limited mobility who require different positioning during the day. Not doing this promotes the appearance of skin lesions known as pressure ulcers, potentially affecting the patient’s health and even their quality of life in the long term. With this in mind, this work proposes the design and implementation of a system that monitors the position of bedridden patients to alert medical personnel and thus prevent the appearance of pressure ulcers. The system comprises an adjustable artifact on the patient’s chest that contains an electronic device capable of capturing angular components according to the patient’s position. The data obtained is processed through the k-NN classification algorithm, obtaining positional information. This information is visible from a web application that allows real-time patient monitoring. The system is evaluated in healthy people and patients, with encouraging results regarding posture classification. The classifier’s performance is very high according to the statistical analysis of the experimental results. Since people are not prone to a large amount of movement and since the angular values that determine each position are sufficiently separated in the scatterplot, the position reported by the device in all cases is equal to the actual position of the person.

This paper towards obtaining an academic degree, develops and implements a methodology allowing the integration and interconnection of heterogeneous processes using an industrial internet of things (IIoT) communication approach in a simulation environment under the concept of Industry 4.0. The protocols used are ,mainly, MQQT and HTTP and operate under the client-server architecture, in addition to filtering information through ,respectively , Publisher / Subscriber and Request / Response events. An industrial-like process is designed through Factory I / O software . An orange juice production line, controlled by a programmable logic controller (PLC). An important aspect is the design of a PID controller to automate the process efficiently. The communication process is implemented with the aid of the OPC server, which refers to a communication protocol in the field of control and supervision of industrial processes. OPC interacts as middleware and allows the connection between each of the levels of the automation pyramid. In such a way, the option of sending information from the factory to the cloud is enabled, in order to monitor the current state of each of the variables involved in the production process. The Node-RED software was used to manage the plant information, in this way, it is possible to establish a link with the database of each of the IIoT development environments, likewise, storing the factory data in real time. In order to carry out a comparative análisis, based on the efficiency of information management, the Wireshark tool was used, thus obtaining different results based on the percentage of lost packets for each communication protocol. For the development of the project, an open source platform known as Thinger.io and a payment model AWS, was utilized. Similarly, a MySQL block is used to capture packets locally. For visualization purposes, an HMI system is implemented in LabVIEW and its respective digital-twin is created in the Node-RED environm

In the current environment of the production and manufacturing sector, industries face di↵erent challenges, such as technological improvement, the competition amongst manufacturers in the market and the Internet of Things (IoT) going worldwide. In order to deal with this, the industry evolves and reinvents itself, thus conceiving the 4.0 Industry and the Industrial Internet of Things (IIoT). It is key for IIoT to constantly work on the integration of heterogeneous technologies, since the industry hardly implements solutions using equipment from the same manufacturer or supplier. This will allow the industry to optimize its manufacturing processes, achieve greater flexibility and generate stronger value proposals for users or customers. In this context, this work proposes the implementation of an IIoT application that allows the integration of heterogeneous communication technologies, using laboratory equipment from the University of Cuenca for the automation of a multi-tank system. This application integrates a variety of hardware, from high-end equipment such as the Simatic Siemens S7-1500 programmable logic controller (PLC) to low-cost equipment such as the Controllino Maxi Automatization PLC, through the development of an Open Platform Communications with Unified Architecture industrial server (OPC-UA) and mostly free software. The OPC-UA server allows the integration of the IIoT application with the control and supervision systems of the plant. The proposed architecture is based on Computer Integrated Manufacturing (CIM), from a virtualization of the Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) layers by means of an IIoT solution. The IIoT solution enables total interaction with the plant, from monitoring systems implemented in dashboards, data storage in a local database and in the cloud, chat consultation systems and fault detection services using machine learning; all executed in parallel and in isolation. The purpose of this p

Robotic devices have reached an important role in modern society, where their presence is increasingly common in the industry and personal use. Specifically to the rehabilitation field, it is entirely beneficial to have robotic devices that help specific pathologies’ recovery processes. This project introduces the design and implementation of a quasi-optimal control architecture to manage distributed controllers of a single lower limb robotic exoskeleton. To achieve this, a control and communication topology of the exoskeleton is established based on a master node and three slave nodes. The communication system employs CAN protocol with Process Data Objects (PDO), which reduces communication times by not using acknowledgement (ACK). Comparing it with SDO, there is a significant improvement, so it demonstrates the potential of PDO to take full advantage of the CAN bus. This system is implemented in the central node utilizing CAN reading and writing functions for frame management. The control system is also implemented in this node, which sends the necessary orders to each of the EPOS4 distributed controllers with an internal speed and position controller for the application of gait trajectory. Furthermore, it includes a heuristic-based optimizer that analyzes the error to determine when a correction of the control signal is necessary to apply to minimize the trajectory tracking error. The result is a functional single lower limb exoskeleton with three joints: hip, knee, and ankle. Control and communication algorithms showed an improvement over previous versions regarding the time of sending and receiving data through CAN bus, as well as its processing in the central node. In consequence, this allows planning an extension of the algorithms for an additional limb. This project makes it possible to clarify the capabilities of distributed nodes to establish solid bases for future research with the prototype. In this way, the subsequent studies could focus on improving th

In mid-December 2019, in the city of Wuhan, China, the first cases of the infectious respiratory virus COVID-19 were reported. COVID-19, according to the World Health Organization (WHO), is an infectious respiratory disease caused by a virus belonging to the large CoV family (Coronavirus). The symptoms vary in intensity for each human being, from a mild clinical picture that does not require specific care, to severe symptoms that could lead to a series of serious diseases that require intensive respiratory treatment or in the worst cases, death. This situation forces the medical staff to decide if patients are suitable to access the different life support treatments according to the "severity and intensity of the resources", including the use of mechanical ventilators. However, the creation of new medical devices to alleviate this saturation is possible, which is why the construction of low-cost portable ventilators is presented as an important and feasible solution. This document shows the process followed in order to build an assisted mechanical ventilator model. The model was developed based on the research collected during the pandemic by Italian physicians and MIT. This ventilator allows patients with less severe Covid-19 symptoms to be cared for by less experienced healthcare specialists. All this helps while the rest of the resources are used in patients with severe clinical case. In the implementation, a satisfactory operation of the device was obtained in addition to having reduced costs in its construction. Compression of the manual resuscitation bag was achieved by varying the tidal volume, which is designed to vary in a range between 100 and 800 ml. Likewise, the assisted mode has greater precision for tidal volume values between 400 and 500 ml and a RR of 1.2 Brpm

Human beings, in their quest to improve the quality of life in people who suffer from gait disturbances, have developed technologies capable of identifying patterns and characteristics of different conditions that deteriorate mobility. Although, some studies explore invasive methods such as electromyography, the use of sensors and/or markers for the analysis and evaluation of pathological gaits, there is still little research that addresses methods that do not invade the body, given that in current times it is an essential approach. The purpose of this study is to develop a non-invasive system, based on vision techniques and artificial intelligence capable of generating spatio-temporal parameters of the biomechanics of movement of the lower extremities from normal or pathological gaits such as hemiparetic and paraparetic, as well as the analysis and classification of these gaits. The methodology used consists of capturing RGB images in people who perform several cycles of the normal, hemiparetic and paraparetic gaits. These images are processed by using models like OpenPose and PoseNet to estimate the pose. Then, cutting, synchronization, filtering, normalization and 2D analysis techniques are applied, as well as new approaches such as Skeleton Gait Energy Image (SGEI) to characterize the gait. Finally, through algorithms such as Convolutional Neural Network (CNN) or Support Vector Machine (SVM), the system is trained to classify the analyzed gaits. As a result, it is possible to generate the parameters of stride length, cadence, stride width, step time, gait speed, front body posture inclination and angles of the lower extremities of the human body of the 3 gaits using a non-invasive system approach, additionally experimental results show high efficiency in the classification of the gaits with a 98.57 % using OpenPose and a 98.15 % with PoseNet.

This work is part of the partial results of the project “Robotic exoskeleton for functional assistance in walking of patients with incomplete spinal cord injuries: design and application”, which is currently under development by the University of Cuenca. The motivation for the development of this project has research purposes and to support people who, due to some condition or circumstance, have suffered the loss of some of their extremities, specifically their hands. This project aims to develop a system for detecting and classifying the opening and closing movements of the hand, and an inactive state by acquiring electroencephalographic (EEG) signals through the use of the Emotiv EPOC+ device. The methodology proposed in this project consists of an acquisition stage through the Emotiv EPOC+ device. This is followed by a stage of pre-processing the EEG signal by applying an oset filter, a bandpass filter and a Common Average Reference (CAR) filter. Subsequently, a processing stage is introduced for analysis in the frequency and frequency-time domains with the application of the Fast Fourier Transform (FFT) and the Discrete Wavelet Transform (DWT) respectively. In the final stage, the most relevant characteristics in each transformed domain are extracted to guarantee a classifier performance using neural networks. At the end of this project, a data set of 18 records corresponding to 8 patients is conformed, 3 of them shown pathology. It was determined that the result of the characterization of EEG signals through DWT provides greater accuracy and information compared to processing through FFT with percentages of 82% and 77% respectively. After the classification using (Artificial Neuronal Networks (ANN)) for the classes open hand, closed hand and inactivity, it is possible to move a robot hand via serial communication between the Arduino and Python programs.

In networks that cover wide geographic areas, scalability and distance between nodes make it difficult to transfer data to information management points. Generally, these types of networks are used for monitoring and are deployed using a Wireless Sensor Network (WSN). Precision agriculture is a common and timely scenario for the deployment of these types of networks. These networks collect information from the environment, transmit it and process it. The low performance of the network nodes requires the use of various methods and structures that facilitate the transfer of information. A Delay Tolerant Network (DTN) is presented as a timely solution to ensure the transfer of information through store and forward. Within this scenario, a DTN can employ an unmanned aerial vehicle as a mule node to transport the information. The development and implementation of computer vision algorithms can improve the performance of the actions and processes developed by the unmanned vehicle. The present work addresses the mobile node landing stage as an alternative to improve data collection in WSNs using a DTN. The adjustment and customization provided by the autopilot allows total control of the movement and actions carried out by the vehicle. The difficulty in developing algorithms for this type of system lies in the possible errors that the vehicle experiences before and during the flight. Although the calibration and configuration of the vehicle provide a degree of confidence in its operation, ArduPilot does not guarantee that errors will not occur due to either the software or the hardware. Therefore, it is essential to take into account the challenges linked to the behavior of the vehicle and its environment. This degree work is a first approach for the development of applications that exploit the adaptability of an unmanned aerial vehicle in applications that deploy WSNs. In this first approach, it was possible to use a camera to obtain characteristics of a marker ArUco, and

The risk of injury to the locomotor system is common due to the physical activity to which the lower extremities are subjected, whether in daily activities, recreation or competition. In this sense, to prolong the motor independence of the individual within a normal life, rehabilitation therapies are essentials in the maintenance and even in the improvement of motor skills. Nowadays, there are many researches interested in solving and reduce these traumatological limitations, under the scientific foundation and technology development, thus achieving the exploration of robotic prototypes for assistance and rehabilitation called exoskeletons. This research work offers an alternative to rehabilitation therapies based on a system of instrumentation, control and communications of a robotic exoskeleton for lower extremities. The instrumentation system is responsible for collecting the required information through the communications network formed by the CAN protocol, so that the PID control scheme makes decisions in reference to a trajectory extracted from subjects without pathologies. The design and implementation of the device is applied to the left lower extremity and it has three degrees of freedom corresponding to the joints of the hip, knee and ankle, which move in the sagittal plane. In this first version, gearboxes of cycloidal and planetary type have been occupied to increase the torque in the joints, the first being of greater relevance due to the increase in the performance of the control. Future research will focus on improving transmission rates and processing times.

This project arises from the need to satisfy the requirements of individuals who, have lost the ability to control their limbs and are in rehabilitation. The aim of this project is to develop an algorithm capable of identifying the intention of movement in the upper and lower extremities, starting from electroencephalographic signals (EEG). The acquisition of signals EEG is performed in subjects without apparent pathology and subjects with different pathologies that impede their mobility, for which the Emotiv EPOC+ acquisition device is used. A stage of conditioning and pre-processing of the signal is implemented, in order to improve the signal-to-noise ratio (SNR). Subsequently, frequency and time-frequency analyzes are applied by the fast Fourier transform (FFT) and the discrete wavelet transform (DWT), respectively; to extract characteristics related to the intention of movement. For the classification of signals, we experiment with artificial neural networks (ANN), fuzzy inference systems (FIS) and adaptive neuro-fuzzy inference systems (ANFIS). As a result of this project we have a data set with 40 records of upper extremities and 6 records of lower extremities, corresponding to 10 subjects (4 records per subject) and 3 subjects (2 records per subject), respectively. In addition, it is inferred that the characterization with DWT provides more information, which facilitates the recognition of patterns in the signals EEG. Finally, performance analysis yields 92.39 %, 86.52 %, 72.13 % and 94.73 % for the neural, fuzzy, neuro-fuzzy and parallel neural-fuzzy classifiers, respectively.

This project presents a methodology for designing and building an ankle exoskeleton prototype to help patients with non-invaliding disability in physical rehabilitation. This methodology includes, among others: kinematic and dynamic analyzes, mechanical design by using a CAD, materials resistance study, actuator sizing, sEMG signals processing, and control system deployment in an embedded microcontroller platform. The prototype is tested in two healthy subjects, in order to evaluate the potential benefit for non-healthy patients. The results show a good insight of the multiple advantages of the exoskeleton in the gait rehabilitation process, while set a future path for improving the prototype.

In this document, an application of optimal control is presented to achieve the stability of the dynamics of a biped robot with 10 degrees of freedom (10 DOF). The trajectory of the robot is generated following the pattern based on human gait, modifying the angles of the articulations to create steps that consider the zero moment point (ZMP). The goals are to model, simulate and control the stability of the robot utilizing a prototype whose mass and center of mass (CoM) of each joint are known. The gait cycle is proposed for the sagittal plane, and it is built following the ZMP of the biped, considering the polygon of support. This process has been divided into two stages: single support phase and double support phase. The linear inverted pendulum model (LIPM) is used to design the controller with feedback and future predictions of the behavior of the system. The corrective action applies for both stages mentioned before. In the software tests, it was shown that the control is robust when perturbations are present. With this result, the trajectory generated, the control algorithm and methodology used in the software is applied successfully to the real robot

Currently, the use of wind energy as a source of pollution-free energy is an attractive alternative. For a wind farm to work properly, it is necessary to take different measures (wind speed, direction, etc.) with the intention of optimizing the energy generation process. In addition, it seeks to protect the structure of the wind turbine before adverse environmental conditions, such as continuous gusts of wind or with variations where the structure can reach a resonance regime. One of the approaches to reduce the cost of corrective maintenance is the supervision of the state of the blade for the early detection of faults. The purpose of this project is the design and implementation of an instrumentation system to measure the effort in the blade of a wind turbine using voltage meters. This in order to monitor the structural health (avoid fractures or ruptures), looking to increase the life of the blade in a wind turbine. For which, a wind turbine was implemented as a prototype. In this structure are the main cable and wireless communication modules and the instrumentation system. The latter processes the measurements that reflect the strain gauges placed on the blade in question. In addition, of two secondary modules, the first one processes the voltage obtained by a generator, due to the movement of the blades. The second module is a tripping angle control; this has the purpose of modifying the wind speed of a fan. That will be used as a disturbance in the blades

Three motion intention detectors of the muscles involved in flexion-extension of the knee during gait were studied, based on surface electromyography (sEMG). The purpose of this study is to estimate the detection threshold of the movement intention of nine specific muscles. We used a sEMG acquisition system formed by an EKG / EMG shield based on Arduino Mega. Participants were 21 subjects (14 men), age (21.52 ± 2.4 years) without pathologies at the level of the knee. sEMG of 9 muscles were recorded at 1 KHz and 58 ± 3 seconds in duration. Subsequently, a study of three detectors was carried out: Single Threshold (D1), Double Threshold (D2) and Double Statistical Threshold (D3). The comparison was made based on the distance to the perfect detection point (DDP) and time delay (Td), using a cross-validation technique. D3 presented a better performance (SEN: 85.88 ± 2.18%, ESP: 86.11 ± 3.55% Td: 6.24 ± 2.42 ms). Additionally, the minimum / desirable value of the SNR was determined for a reliable measurement of the Medium Frequency (MNF) of the sEMG. The signals were contaminated with different noise levels. The thresholds were determined using the K-means algorithm (Minimum: 5.51 dB, Desirable 12.28 dB). The beginning of muscle contraction oversees activating an orthopedic device, therefore, an accurate detection of this episode of muscular contraction is of crucial importance to ensure the proper operation of the device. The D3 detector fulfills these characteristics and this finding constitutes a contribution to the development of the exoskeleton prototype proposed by the DEET.

The solution to several problems facing the traditional electric model is intended to be the incorporation of micro networking. The micro networking looks for creating a competitive, efficient electrical system and to reduce the environmental damage throughout the incorporation of renewable energies. The evolution of electric networks leads to new ways of managing the networks. For this reason, this thesis project proposes the simulation in a steady state of the different technologies that make up the Scientific, Technological and Research Center BALZAY, specifically in the Laboratory of the Electrical Micro Networking in order to have models that allow starting studies on the behavior of each of the elements involved and their interaction. The modeling using computational tools is divided into two parts: The first consists on the individual simulation of each of the generation and storage equipment; the second part integrates all the equipment through two different ways of operation: operation with connection to the public electric network and operation in island mode. In both cases, Simulink's SimPowerSystems test platform is used. Finally, in the economic dispatch analysis, case studies are carried out using the Homer Energy tool where optimization takes into account some variables such as production and maintenance costs. Additionally, the main conclusions of this project are presented, and some future research is proposed starting from basic problems that must be solved.

This work present the design methodology of two fault tolerant proportional-integral-derivative (PID) controllers. The first controller proposes a passive fault tolerant control strategy (PFTC) through online calculation of controller parameters using an ADALINE neural network whose learning rate is adjusted with a diffuse inference system. The second controller is a Fuzzy Gain Scheduling PID whose ranges of variation of parameters are adjust online with the information from a fault detection and diagnostic (FDD) module. The proposed scheme’s architecture forms an active fault tolerant controller (AFTC). The detection and diagnosis of faults is achieved by parity equations. The validation of the control algorithms is performed in a multiple-input multiple-output system (MIMO) of three tank, including the leakages and obstructions models. The simulations allow us to compare the performance of the proposed approaches with the classic PID control algorithm.

In industry, specially manufacturing, it is important to supervise and control all processes involved in production. To achieve this goal, it is usually necessary to make use of proprietary software whose costs are high in most cases. Therefore, to communicate the devices of the plant and to perform a proper supervision of the processes it is necessary to have a SCADA system with the field networks to communicate several devices, which implies the acquisition of hardware and software licenses, impliying an important investment and thus limiting its penetration on small and medium enterprises. This project proposes the development of a system of supervision, communication and fault tolerance control that make use of programmable logic controllers (PLCs) and low cost platforms to perform this task. A direct current (DC) motor is used as the case study. The speed measurement is performed through an incremental encoder. In addition, it is proposed the development of a SCADA system that integrates the industrial communication protocol (OPC), and in the level of execution of manufacturing systems, an active control algorithm is developed with the parity space method for the DC motor, In order to monitor dynamic changes and accommodate faults in real time.

This research presents the methodology for the design and construction of a prototype knee exoskeleton for rehabilitation of patients with crippling disabilities. Kinematic and dynamic analysis of the lower extremity is performed using the method proposed by Denavit-Hartenberg and LaGrange-Euler, respectively. The stress analysis of the materials used to develop the exoskeleton is performed in Autodesk Inventor. The actuator sizing was performed using an analysis of effort with the SimMechanics tool of MATLAB. The operation of the exoskeleton is monitored by an external control system that connects to a database to decide routine or routines to be executed along rehabilitation therapy. Routines are stored in various movement patterns, depending on the angular velocity, both assisted walking as muscle relaxation cycles. A classic control system, proportional-integral-derivative controller, is developed to perform a reference following strategy for every rehabilitation routine to be performed by the exoskeleton. Finally, by the prototype is validated by laboratory test and the Department of Physical Therapy at the University of Cuenca, which evaluated the response of the control system the prototype is validated by laboratory test.

Using OPC servers allow a better industrial process control. However, nowadays their high cost is a limitation for many industries. Another barrier is that the classic OPC protocols are still limited in communication capabilities. Because of this, OPC Foundation has developed the new OPC Protocol version named OPC UA. This research work presents the development of an OPC UA server implemented in a low cost embedded platform. The Raspberry Pi has been chosen because of its processing characteristics, and its capabilities of communication with other devices. The OPC server is implemented in Python, so it is multiplatform. The prototype developed in this work offers interesting advantages for industries, such as easiness in development in OPC standard communication with a low investment

This research project presents the methodology of design of a domotic solution that facilitates the integration of persons with disabilities into the home. Four interfaces that allow the interaction of the user with the facilities in the home are developed: web-based interface, remote control interface, RFID interface, and voice commands interface. Two different technologies have been used for developing this solution. The first one is a proprietary technology, which implements a single communication bus for interconnec¬ting intelligent devices that manage the home facilities based on the previously programmed commands. The second one is an open hardware solution, developed by using the Arduino technology. Both technologies offer good results for the purposes of helping with the inte¬gration of persons with disabilities into the home. The main differences are the throughput and the price. The trade off between price and throughput is analyzed, and allows a proper selection of the technology that best meets the requirements of every person.

This paper describes the design methodology of a smart meter (SM) with functions of demand response (DR). The smart meter prototype's hardware architecture uses a central processing unit with a dedicated ADE7753 integrated circuit to calculate the energy consumption. The smart meter prototype integrates itself to a distribution system simulation, interacting with an energy management system (EMS) that monitors the consumption of the network, forming a loop simulation hardware. The EMS makes optimal consumption decisions and sends it to the smart meter that manages the use of programmable and dimmable charges as part of the concept of a demanded response. The goal is to change the way you use electricity and to improve energy efficiency in order to optimize the management of demand.

Nowadays, health services strive to provide optimal rehabilitation therapies for people who have suffered a stroke or some kind of accident that could cause paralysis or loss of strength in an upper limb. This has motivated the scientific community to explore the use of electronic devices (exoskeleton) to provide rehabilitation therapies and/or assistance to the movement.This project presents an alternative to conventional rehabilitation therapies, through a prototype exoskeleton for upper limb (elbow). This prototype is focused on improving strength and motor movement of the limb. The exoskeleton consists of two degrees of freedom, allowing them to strengthen flexion-extension movements between the arm and forearm, and pronation-supination movements of the forearm. The prototype designed allows patient mobility while doing therapy, because the structure is not a fixed base. The exoskeleton has a standard measure, therefore difficult to fit different lengths of the extremity of patients, however, after being evaluated by a specialist in physical therapy, it is useful for patients with neurological problems because it allows a passive-assisted rehabilitation and for patients with orthopedic problems allows active-assisted rehabilitation.

Haptic interfaces allow human-machine interaction in virtual environments that can be both simulated or remote. Technological advances, both in electronics and in informatic tools have allowed important developments of these devices. This research presents the results of the implementation of a haptic interface of one degree of freedom using classic controllers, such as PID, and modern controllers designed in state-space that require a parametric identification of the system. The design of a neural PID controller, which tunes their parameters depending on the operating point of the system as it would do an adaptive control, is also proposed. Finally, a pair of applications are presented: one in virtual reality of a dynamic system, which simulates a virtual pendulum as well as its controller and its closed-loop behavior; and a motion follower that reproduces the joint movement of a human knee.

Cement grinding is the responsible for the fineness characteristics of the final product, typically measured by two quality variables: specific surface or Blaine (cm2/g), and the percentage passing the sieve No. 325. It is a common practice in cement plants to perform laboratory tests of the fineness of the cement every two hours, leading to a gap of what produces an interval of two hours of blind operation regarding cement quality. As a response to this problematic, in this paper, it will be developed models of prediction of the quality variables, which allows minimization of the final product variable or increased the energy efficiency. The implemented models are: statistics, autoregressive, neuronal, and neuro-fuzzy. A statistical analysis of the database is performed, provided by the UCEM-Guapán factory, corresponding to the operation and quality variables of cement grinding. The selected variables for the development of the models are made through the treatment and analysis of the correlating data. The validation takes place in with operation values in the plants of UCEM. For the selection of the best model, it uses the residual error technique. The model with the best results is implemented in the LabVIEW software, for both the variable of Blaine and for percentage passing the sieve.

The computer integrated manufacturing (CIM) approach allows the possibility to remotely and optimally control the entire production process within a plant. The implementation of CIM architectures demands the installation of several software platforms, which most of the cases are commercial and have high licensing prices. This research presents the development of open software for advanced CIM (OSACIM) by using two open software development platforms: Java and Python. The use of open software in the development of the solution allows the creation of a low price CIM approach. The results of the use of OSCACIM in laboratory tests show good results in comparison with a commercial SCADA system that performs the same features of the proposed software. A detailed methodology of the software development is presented, as well as the main features and limitations of the system.