Equilibrio del robot AIBO usando DMPs

El trabajo presentado se enmarca en una iniciativa global que tiene como objeto la recuperacion de la plataforma robotica AIBO de Sony. Para demostrar las prestaciones de la arquitectura propuesta y la viabilidad del robot AIBO como plataforma robotica util, se han escogido algoritmos de aprendizaje por refuerzo muy novedosos como implementacion en la tarea de mantener el equilibrio ante movimientos indeseados en la base de apoyo del robot. El robot AIBO puede ser controlado de forma permanente con un tiempo de respuesta adecuado para la tarea.Postprint (published version

Sincronización de robots AIBO. Un Estudio comparativo

El trabajo tiene por objetivo implementar un conjunto de modulos de software que permitan integrar la plataforma robotica AIBO en el marco de trabajo ROS (Robot Operating System). El problema principal radica en el metodo utilizado para comunicarlos. Inicialmente se estudiaron los diferentes software alternativos con los que poder operar. Luego se compararon varios metodos de programacion, de forma que un analisis cuantitativo permitio decidir el metodo mas adecuado. Finalmente se implementaron los modulos que facilitan la integracion con ROS. Por un lado el modulo de control, al que se le han aplicado unos criterios de valoracion y se ha puesto a prueba su funcionamiento, y por otro el modulo que facilita la integracion de un modelo de visualizacion 3D. Con todo este trabajo desarrollado, se realizo la aplicacion de sincronizacion de movimientos de un par de robots AIBO y se realizaron experimentaciones que muestran la bondad del trabajo desarrollado.Postprint (published version

On Lyapunov sampling for event-driven controllers

This paper investigates an event condition for event-driven controllers based on Lyapunov functions. Considering that constant values of a Lyapunov function define contour curves that form closed regions around the equilibrium point, in this paper we present a sampling mechanism that enforces job executions (sampling, control algorithm computation and actuation) each time the system trajectory reaches a given contour curve. By construction, the sequence of generated samples is stable in the discrete Lyapunov sense. However, in order to ensure that the system trajectory will tend to zero as time tends to infinity, it must be ensured that the sequence of samples is infinite. We provide conditions to ensure this property. The approach is illustrated by simulated examples.Peer ReviewedPostprint (published version

Analysis of gas turbine compressor performance after a major maintenance operation using an autoencoder architecture

Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version

Machine-learning-based condition assessment of gas turbine: a review

Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machinelearning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version

Conectando el Robot AIBO a ROS: Extracción de imágenes

En este documento se realiza una introduccion al trabajo realizado con el robot AIBO, de Sony, para la obtencion de imagenes, asi como su tratamiento con el objetivo que el robot navegue de forma autonoma mediante algoritmos de vision artificial. A nivel practico, el principal punto de interes del articulo se centra en el entorno de programacion, que se ha exportado al marco de ROS (Robot Operating System), de forma que se pueda hacer uso de la extensa biblioteca de algoritmos ya desarrollados en este entorno.Postprint (published version

Analysis of the effect of clock drifts on frequency regulation and power sharing in inverter-based islanded microgrids

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Local hardware clocks in physically distributed computation devices hardly ever agree because clocks drift apart and the drift can be different for each device. This paper analyses the effect that local clock drifts have in the parallel operation of voltage source inverters (VSIs) in islanded microgrids (MG). The state-of-the-art control policies for frequency regulation and active power sharing in VSIs-based MGs are reviewed and selected prototype policies are then re-formulated in terms of clock drifts. Next, steady-state properties for these policies are analyzed. For each of the policies, analytical expressions are developed to provide an exact quantification of the impact that drifts have on frequency and active power equilibrium points. In addition, a closed-loop model that accommodates all the policies is derived, and the stability of the equilibrium points is characterized in terms of the clock drifts. Finally, the implementation of the analyzed policies in a laboratory MG provides experimental results that confirm the theoretical analysis.Peer ReviewedPostprint (author's final draft

Performance evaluation of secondary control policies with respect to digital communications properties in inverter-based islanded microgrids

A key challenge for inverted-based microgrids working in islanded mode is to maintain their own frequency and voltage to a certain reference values while regulating the active and reactive power among distributed generators and loads. The implementation of frequency and voltage restoration control policies often requires the use of a digital communication network for real-time data exchange (tertiary control covers the coordi- nated operation of the microgrid and the host grid). Whenever a digital network is placed within the loop, the operation of the secondary control may be affected by the inherent properties of the communication technology. This paper analyses the effect that properties like transmission intervals and message dropouts have for four existing representative approaches to secondary control in a scalable islanded microgrid. The simulated results reveals pros and cons for each approach, and identifies threats that properly avoided or handled in advance can prevent failures that otherwise would occur. Selected experimental results on a low- scale laboratory microgrid corroborate the conclusions extracted from the simulation study.Peer ReviewedPostprint (author's final draft

Efficient utilization of bus idle times in CAN-based networked control systems

This paper presents a novel approach to networked control systems (NCS) analysis and design that provides increased control performance for a set of control loops that exchange control data over the Controller Area Network (CAN). This is achieved by enabling the following functionality for each control loop: first, standard periodic messaging is guaranteed to ensure stability, and second, non-periodic additional messaging is added whenever the bus is idle in such a way that the aggregated control performance for all control loops is improved. The proposed approach, named Maximum Difference (MD) policy, is computable in a distributed manner, and is practically feasible (computationally efficient and CAN-implementable). We theoretically prove that the MD policy behaves better than static strategies. Simulation results complement the theoretical derivations and show that the MD policy outperforms static, random and Largest Error First policies.Postprint (author’s final draft

Embedding Kalman techniques in the one-shot task model when non-uniform samples are corrupted by noise

The performance of several closed-loop systems whose controllers concurrently execute in a multitasking realtime system may be deteriorated due to timing uncertainties in tasks´executions, problem known as scheduling jitters. Recently, the one-shot task model, that combines irregular sampling, a predictor observer, and strictly periodic actuation, was presented in order to remove the negative effects of jitters. However, its successful application required noise-free samples. In this paper we extend the one-shot task model to the case of noisy measurements. In particular, we embed a Kalman filter into the model taking into account that the available measurements are not periodic. This poses the problem of adapting the standard discrete-time Kalman filter to the case under study, and decide when to apply the prediction and the correction phase. Two different strategies are presented, and their control performance and computation demand are analyzed through real experiments.Peer ReviewedPostprint (published version