Programación de un robot planar para que dibuje

Duración (en horas): De 41 a 50 horas. Destinatario: Estudiante y DocenteEl proyecto propuesto consiste en la construcción y programación de un robot planar (robot SCARA de dos grados de libertad) que actúa como plotter. El proyecto está diseñado para la asignatura de Informática Industrial de 3er curso del Grado en Ingeniería en Electrónica Industrial y Automática. Se trata de un proyecto pensado para ser realizado en grupos de 3 personas que requiere bastante interdependencia positiva debido a su elevada carga de trabajo. El proyecto se divide en dos fases principales: (1) Un estudio de viabilidad consistente en desarrollar un programa que simule el movimiento, punto a punto, que debe seguir un robot para desarrollar una trayectoria específica y la escriba en un fichero de texto; y (2) La programación del controlador del robot implementado sobre una CPU LEGO mindstorms que mueva la estructura mecánica del robot de forma que ésta realice la trayectoria deseada. En el desarrollo del proyecto se utiliza el lenguaje de programación C y la plataforma LEGO Mindstorms para la construcción del robot

Design and experimental validation of a piezoelectric actuator tracking control based on fuzzy logic and neural compensation

This work proposes two control feedback-feedforward algorithms, based on fuzzy logic in combination with neural networks, aimed at reducing the tracking error and improving the actuation signal of piezoelectric actuators. These are frequently used devices in a wide range of applications due to their high precision in micro- and nanopositioning combined with their mechanical stiffness. Nevertheless, the hysteresis is one the main phenomenon that degrades the performance of these actuators in tracking operations. The proposed control schemes were tested experimentally in a commercial piezoelectric actuator. They were implemented with a dSPACE 1104 device, which was used for signal generation and acquisition purposes. The performance of the proposed control schemes was compared to conventional structures based on proportional-integral-derivative and fuzzy logic in feedback configuration. Experimental results show the advantages of the proposed controllers, since they are capable of reducing the error to significant magnitude orders.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputación Foral de Álava (DFA), through the project CONAVANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work

Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules

This paper presents a deep analysis of different feed-forward (FF) techniques combined with two different proportional-integral-derivative (PID) control to guide a real piezoelectric actuator (PEA). These devices are well known for a non-linear effect called “hysteresis” which generates an undesirable performance during the device operation. First, the PEA was analysed under real experiments to determine the response with different frequencies and voltages. Secondly, a voltage and frequency inputs were chosen and a study of different control approaches was performed using a conventional PID in close-loop, adding a linear compensation and a FF with the same PID and an artificial neural network (ANN). Finally, the best result was contrasted with an adaptive PID which used a single neuron (SNPID) combined with Hebbs rule to update its parameters. Results were analysed in terms of guidance, error and control signal whereas the performance was evaluated with the integral of the absolute error (IAE). Experiments showed that the FF-ANN compensation combined with an SNPID was the most efficient.The authors wish to express their gratitude to the Basque Government through the project SMAR3NAK (ELKARTEK KK-2019/00051), to the Diputación Foral de Álava (DFA) through the project CONAVAUTIN 2 and to the UPV/EHU for supporting this work

Sliding Mode-Based Robust Control for Piezoelectric Actuators with Inverse Dynamics Estimation

This paper presents an improved control approach to be used for piezoelectric actuators. The proposed approach is based on sliding mode control with estimation perturbation (SMCPE) techniques. Also, a proportional-integral-derivative (PID)-type sliding surface is proposed for position tracking. The proposed approach has been studied and implemented in a commercial actuator. A model for the system is introduced, which includes the Bouc-Wen (BW) model to represent the hysteresis, and it is identified by means of the System Identification Toolbox in Matlab/Simulink. Experimental data show that the proposed controller has a better performance when compared to a proportional-integral (PI) controller or a conventional SMCPE in motion tracking. Furthermore, a sub-micrometer accuracy tracking can be obtained while compensating for the hysteresis effect.This research was partially funded by the Basque Government through the project ETORTEK KK-2017/00033, and by the UPV/EHU through the projects PPGA18/04 and UFI 11/07

Adaptive Sliding Mode Control for a Double Fed Induction Generator Used in an Oscillating Water Column System

Wave power conversion systems are nonlinear dynamical systems that must endure strong uncertainties. Efficiency is a key issue for these systems, and the application of robust control algorithms can improve it considerably. Wave power generation plants are typically built using variable speed generators, such as the doubly fed induction generator (DFIG). These generators, compared with fixed speed generators, are very versatile since the turbine speed may be adjusted to improve the efficiency of the whole system. Nevertheless, a suitable speed controller is required for these systems, which must be able to avoid the stalling phenomenon and track the optimal reference for the turbine. This paper proposes a sliding mode control scheme aimed at oscillating water column (OWC) generation plants using Wells turbines and DFIGs. The contributions of the paper are (1) an adaptive sliding mode control scheme that does not require calculating the bounds of the system uncertainties, (2) a Lyapunov analysis of stability for the control algorithm against system uncertainties and disturbances, and (3) a validation of the proposed control scheme through several simulation examples with the Matlab/Simulink suite. The performance results, obtained by means of simulations, for a wave power generation plant (1) evidence that this control scheme improves the power generation of the system and (2) prove that this control scheme is robust in the presence of disturbances.This research was partially funded by the Basque Government through the project ETORTEK KK-2017/00033 and by the UPV/EHU through the project PPGA18/04

Provision of Frequency Response from Wind Farms: A Review

Renewable sources of energy play a key role in the process of decarbonizing modern electric power systems. However, some renewable sources of energy operate in an intermittent, non-dispatchable way, which may affect the balance of the electrical grid. In this scenario, wind turbine generators must participate in the system frequency control to avoid jeopardizing the transmission and distribution systems. For that reason, additional control strategies are needed to ensure the frequency response of variable-speed wind turbines. This review article analyzes diverse control strategies at different levels which are aimed at contributing to power balancing and system frequency control, including energy storage systems.This research was funded by the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), Diputación Foral de Álava (DFA) through the project CONAVANTER, and UPV/EHU through the project GIU20/063

Experimental Validation of a Sliding Mode Control for a Stewart Platform Used in Aerospace Inspection Applications

The authors introduce a new controller, aimed at industrial domains, that improves the performance and accuracy of positioning systems based on Stewart platforms. More specifically, this paper presents, and validates experimentally, a sliding mode control for precisely positioning a Stewart platform used as a mobile platform in non-destructive inspection (NDI) applications. The NDI application involves exploring the specimen surface of aeronautical coupons at different heights. In order to avoid defocusing and blurred images, the platform must be positioned accurately to keep a uniform distance between the camera and the surface of the specimen. This operation requires the coordinated control of the six electro mechanic actuators (EMAs). The platform trajectory and the EMA lengths can be calculated by means of the forward and inverse kinematics of the Stewart platform. Typically, a proportional integral (PI) control approach is used for this purpose but unfortunately this control scheme is unable to position the platform accurately enough. For this reason, a sliding mode control (SMC) strategy is proposed. The SMC requires: (1) a priori knowledge of the bounds on system uncertainties, and (2) the analysis of the system stability in order to ensure that the strategy executes adequately. The results of this work show a higher performance of the SMC when compared with the PI control strategy: the average absolute error is reduced from 3.45 mm in PI to 0.78 mm in the SMC. Additionally, the duty cycle analysis shows that although PI control demands a smoother actuator response, the power consumption is similar.This research was funded by the Basque Government through the project SMAR3NAK (ELKARTEK KK-2019/00051), by the Ministerio de Economía y Competitividad (RTI2018-094669-B-C31) and by Aernnova and the Diputación Foral de Álava (DFA) through the project CONAVAUTIN 2 (Collaboration Agreement)

Architecture for Smart Buildings Based on Fuzzy Logic and the OpenFog Standard

The combination of Artificial Intelligence and IoT technologies, the so-called AIoT, is expected to contribute to the sustainability of public and private buildings, particularly in terms of energy management, indoor comfort, as well as in safety and security for the occupants. However, IoT systems deployed on modern buildings may generate big amounts of data that cannot be efficiently analyzed and stored in the Cloud. Fog computing has proven to be a suitable paradigm for distributing computing, storage control, and networking functions closer to the edge of the network along the Cloud-to-Things continuum, improving the efficiency of the IoT applications. Unfortunately, it can be complex to integrate all components to create interoperable AIoT applications. For this reason, it is necessary to introduce interoperable architectures, based on standard and universal frameworks, to distribute consistently the resources and the services of AIoT applications for smart buildings. Thus, the rationale for this study stems from the pressing need to introduce complex computing algorithms aimed at improving indoor comfort, safety, and environmental conditions while optimizing energy consumption in public and private buildings. This article proposes an open multi-layer architecture aimed at smart buildings based on a standard framework, the OpenFog Reference Architecture (IEEE 1934–2018 standard). The proposed architecture was validated experimentally at the Faculty of Engineering of Vitoria-Gasteiz to improve indoor environmental quality using Fuzzy logic. Experimental results proved the viability and scalability of the proposed architecture.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ II; to the Diputación Foral de Álava (DFA), through the project CONAVANTER; to the UPV/EHU, through the projects GIU20/063 and CBL 22APIN; and to the MobilityLab Foundation (CONV23/12), for supporting this work

Building IoT Applications with Raspberry Pi and Low Power IQRF Communication Modules

Typical Internet of Things (IoT) applications involve collecting information automatically from diverse geographically-distributed smart sensors and concentrating the information into more powerful computers. The Raspberry Pi platform has become a very interesting choice for IoT applications for several reasons: (1) good computing power/cost ratio; (2) high availability; it has become a de facto hardware standard; and (3) ease of use; it is based on operating systems with a big community of users. In IoT applications, data are frequently carried by means of wireless sensor networks in which energy consumption is a key issue. Energy consumption is especially relevant for smart sensors that are scattered over wide geographical areas and may need to work unattended on batteries for long intervals of time. In this scenario, it is convenient to ease the construction of IoT applications while keeping energy consumption to a minimum at the sensors. This work proposes a possible gateway implementation with specific technologies. It solves the following research question: how to build gateways for IoT applications with Raspberry Pi and low power IQRF communication modules. The following contributions are presented: (1) one architecture for IoT gateways that integrates data from sensor nodes into a higher level application based on low-cost/low-energy technologies; (2) bindings in Java and C that ease the construction of IoT applications; (3) an empirical model that describes the consumption of the communications at the nodes (smart sensors) and allows scaling their batteries; and (4) validation of the proposed energy model at the battery-operated nodes.This work was supported in part by the University of the Basque Country (UPV/EHU) under projects EHU13/42 and UFI11/28 and by the Basque Government (GV/EJ) under projects CPS4PSS ETORTEK14/10 and Thinking Factory ETORGAI14

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained