Focussed on application-oriented research and development, ECHORD++ (E++) is being funded by the European Comission in the 7PM for five years to improve and increase the innovation in robotic technology. Activities include small-scale projects and a “structured dialogue” incorporating public entities and citizens. Three instruments and processes are being developed under the ECHORD++ project: experiments (EXP), Research Innovation Facilities (RIF) and Public end-user Driven Technological Innovation (PDTI), all of them aimed at improving and increasing the innovation in robotic technology within SMEs companies and addressing answers to societal and industrial needs in different scenarios. This paper describes the outcomes and results of the project, the tasks of communication and dissemination and the structured dialogue between all the involved stakeholders.
Soldevila, A.; Blesa, J.; Tornil-Sin, S.; Rosa M. Fernandez-Canti; Puig, V. Computers & chemical engineering Vol. 108, num. 4 January 2018, p. 152-162 DOI: 10.1016/j.compchemeng.2017.09.002 Data de publicació: 2018-01-04 Article en revista
This paper presents a sensor placement approach for classifier-based leak localization in water distribution networks. The proposed method is based on a hybrid feature selection algorithm that combines the use of a filter based on relevancy and redundancy with a wrapper based on genetic algorithms. This algorithm is applied to data generated by hydraulic simulation of the considered water distribution network and it determines the optimal location of a prespecified number of pressure sensors to be used by a leak localization method based on pressure models and classifiers proposed in previous works by the authors. The method is applied to a small-size simplified network (Hanoi) to better analyze its computational performance and to a medium-size network (Limassol) to demonstrate its applicability to larger real-size networks.
Identification and bi-manual handling of deformable objects, like textiles, is one of the most challenging tasks in the field of industrial and service robotics. Their unpredictable shape and pose makes it very difficult to identify the type of garment and locate the most relevant parts that can be used for grasping. In this paper, we propose an algorithm that first, identifies the type of garment and second, performs a search of the two grasping points that allow a robot to bring the garment to a known pose. We show that using an active search strategy it is possible to grasp a garment directly from predefined grasping points, as opposed to the usual approach based on multiple re-graspings of the lowest hanging parts. Our approach uses a hierarchy of three Convolutional Neural Networks (CNNs) with different levels of specialization, trained both with synthetic and real images. The results obtained in the three steps (recognition, first grasping point, second grasping point) are promising. Experiments with real robots show that most of the errors are due to unsuccessful grasps and not to the localization of the grasping points, thus a more robust grasping strategy is required.
This work reports research on mapping, path planning, and autonomous exploration. These are classical problems in robotics, typically studied independently, and here we link such problems by framing them within a common SLAM approach, adopting Pose SLAM as the basic state estimation machinery. The main contribution of this work is an approach that allows a mobile robot to plan a path using the map it builds with Pose SLAM and to select the appropriate actions to autonomously construct this map.
Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. In Pose SLAM, observations come in the form of relative-motion mea- surements between robot poses. With regards to extending the original Pose SLAM formulation, this work studies the computation of such measurements when they are obtained with stereo cameras and develops the appropriate noise propagation models for such case. Furthermore, the initial formulation of Pose SLAM assumes poses in SE(2) and in this work we extend this formulation to SE(3), parameterizing rotations either with Euler angles and quaternions. We also introduce a loop closure test that exploits the information from the filter using an independent measure of information content between poses. In the application domain, we present a tech- nique to process the 3D volumetric maps obtained with this SLAM methodology, but with laser range scanning as the sensor modality, to derive traversability maps that were useful for the navigation of a heterogeneous fleet of mobile robots in the context of the EU project URUS.
Aside from these extensions to Pose SLAM, the core contribution of the work is an approach for path planning that exploits the modeled uncertainties in Pose SLAM to search for the path in the pose graph with the lowest accumulated robot pose uncertainty, i.e., the path that allows the robot to navigate to a given goal with the least probability of becoming lost. An added advantage of the proposed path planning approach is that since Pose SLAM is agnostic with respect to the sensor modalities used, it can be used in different environments and with different robots, and since the original pose graph may come from a previous mapping session, the paths stored in the map already satisfy constraints not easy modeled inthe robot controller, such as the existence of restricted regions, or the right of way along paths. The proposed path planning methodology has been extensively tested both in simulation and with a real outdoor robot.
Our path planning approach is adequate for scenarios where a robot is initially guided during map construction, but autonomous during execution. For other scenarios in which more autonomy is required, the robot should be able to explore the environment without any supervision. The second core contribution of this work is an autonomous exploration method that complements the aforementioned path planning strategy. The method selects the appropriate actions to drive the robot so as to maximize coverage and at the same time minimize localization and map uncertainties. An occupancy grid is maintained for the sole purpose of guaranteeing coverage. A significant advantage of the method is that since the grid is only computed to hypothesize entropy reduction of candidate map posteriors, it can be computed at a very coarse resolution since it is not used to maintain neither the robot localization estimate, nor the structure of the environment. Our technique evaluates two types of actions: exploratory actions and place revisiting actions. Action decisions are made based on entropy reduction estimates. By maintaining a Pose SLAM estimate at run time, the technique allows to replan trajectories online should significant change in the Pose SLAM estimate be detected.
Xu, F.; Tan, J.; Wang, X.; Puig, V.; Liang, B.; Yuan, B. IEEE transactions on automation science and engineering Vol. PP, num. 99, p. 1-9 DOI: 10.1109/TASE.2017.2776998 Data de publicació: 2017-12-19 Article en revista
IEEE This paper proposes a robust fault detection and isolation (FDI) approach that combines active and passive robust FDI approaches. Standard active FDI approaches obtain robustness by using the unknown input observer (UIO) to decouple unknown inputs from residuals. Differently, standard passive FDI approaches achieve robustness by using the set theory to bound the effect of uncertain factors (disturbances and noises). In this paper, we combine the UIO-based and the set-based approaches to produce a mixed robust FDI, which can mitigate the disadvantages and exert the advantages of the two robust FDI approaches. In order to emphasize the role of set theory, the UIO design based on the set theory is named as the set-theoretic UIO (SUIO). A quadrotor subsystem is used to illustrate the effectiveness of the proposed FDI approach.
Diaz, J.; Ocampo-Martinez, C.A.; Alvarez, H. Industrial & engineering chemistry research Vol. 56, num. 49, p. 14592-14603 DOI: 10.1021/acs.iecr.7b04393 Data de publicació: 2017-12-13 Article en revista
This paper proposes a moving horizon estimator for nonlinear systems with unknown inputs, which do not comply with the model structures proposed in the literature for the design of nonlinear observers. The estimator is designed as an optimization problem over a moving horizon, constrained to process model equations and considering the unknown inputs as random inputs among their operating bounds. This proposal is applied to the transport of mineral slurries among process units, typically present in chemical and biological processes. There, to have the slurry properties as online measurements is vital to an efficient control of those processing units. The performance of the proposed estimator is evaluated by simulation with data from a real processing plant, and its performance is compared with a linear estimator executing the same estimation task. Better results are obtained using the proposed estimator by considering the nonlinearities of the process.
The combination of visual and inertial sensors for state estimation has recently found wide echo in the robotics community, especially in the aerial robotics field, due to the lightweight and complementary characteristics of the sensors data. However, most state estimation systems based on visual-inertial sensing suffer from severe processor requirements, which in many cases make them impractical. In this paper, we propose a simple, low-cost and high rate method for state estimation enabling autonomous flight of micro aerial vehicles, which presents a low computational burden. The proposed state estimator fuses observations from an inertial measurement unit, an optical flow smart camera and a time-of-flight range sensor. The smart camera provides optical flow measurements up to a rate of 200 Hz, avoiding the computational bottleneck to the main processor produced by all image processing requirements. To the best of our knowledge, this is the first example of extending the use of these smart cameras from hovering-like motions to odometry estimation, producing estimates that are usable during flight times of several minutes. In order to validate and defend the simplest algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside with a large number of algorithm modifications defended in earlier literature on visual-inertial odometry, showing that their impact on filter performance is minimal. To close the control loop, a non-linear controller operating in the special Euclidean group SE(3) is able to drive, based on the estimated vehicle’s state, a quadrotor platform in 3D space guaranteeing the asymptotic stability of 3D position and heading. All the estimation and control tasks are solved on board and in real time on a limited computational unit. The proposed approach is validated through simulations and experimental results, which include comparisons with ground-truth data provided by a motion capture system. For the benefit of the community, we make the source code public.
The final publication is available at link.springer.com
The objective of this paper is to explain the importance of research in wastewater transportation (sewage systems) using new technologies such as robotics systems and information and communication technologies. ECHORD++ (European Coordination Hub for Open Robotics Development) is a very useful tool to foster this research and to meet needs and solutions. In this paper, authors explain the tool as well as the methodology to promote robotics research in urban environments, and the on-going experience will demonstrate that huge advances are made in this field.
Searching and tracking people in crowded urban areas where they can be occluded by static or dynamic obstacles is an important behavior for social robots which assist humans in urban outdoor environments. In this work, we propose a method that can handle in real-time searching and tracking people using a Highest Belief Particle Filter Searcher and Tracker. It makes use of a modified Particle Filter (PF), which, in contrast to other methods, can do both searching and tracking of a person under uncertainty, with false negative detections, lack of a person detection, in continuous space and real-time. Moreover, this method uses dynamic obstacles to improve the predicted possible location of the person. Comparisons have been made with our previous method, the Adaptive Highest Belief Continuous Real-time POMCP Follower, in different conditions and with dynamic obstacles. Real-life experiments have been done during two weeks with a mobile service robot in two urban environments of Barcelona with other people walking around.
The idea of the ECHORD project was born before the economic crisis had its maximum impact on the robotics industry. Therefore, the concept of a project with the clear goal to strengthen the collaboration between academia and industry was a good opportunity to support the industry by offering funding opportunities and fostering already existing networks and creating new partnerships with the academic world taking into account the circular economy in the productive cycle of the intelligent robotics solutions to solve the challenges of the modern cities. One of the most innovative part of this project is to foster the participation of public investment in new robotic projects mainly in urban robotics. At this moment, more than 40 european cities have been participating in the challenge that ECHORD++ proposed.
Rotondo, D.; Cristofaro, A.; Johansen , T. A.; Nejjari, F.; Puig, V. Journal of intelligent and robotic systems: theory and applications (Online) p. 1-15 DOI: 10.1007/s10846-017-0716-1 Data de publicació: 2017-10-05 Article en revista
This paper proposes a discrete-time linear parameter varying (LPV) unknown input observer (UIO) for the diagnosis of actuator faults and ice accretion in unmanned aerial vehicles (UAVs). The proposed approach, which is suited to an implementation on-board, exploits a complete 6-degrees of freedom (DOF) UAV model, which includes the coupled longitudinal/lateral dynamics and the impact of icing. The LPV formulation has the advantage of allowing the icing diagnosis scheme to be consistent with a wide range of operating conditions. The developed theory is supported by simulations illustrating the diagnosis of actuator faults and icing in a small UAV. The obtained results validate the effectiveness of the proposed approach.
Nejjari, F.; Puig, V.; Quevedo, J.; Pascual, J.; de Campos, S. European Symposium on Computer Aided Process Engineering p. 1579-1584 DOI: https://doi.org/10.1016/B978-0-444-63965-3.50265-8 Data de presentació: 2017-10-05 Presentació treball a congrés
This work presents an economic model predictive control (EMPC) strategy for the control of the dissolved oxygen concentrations in the aerated reactors of a wastewater treatment plant located in Girona, Spain. The control strategy is investigated and evaluated based on the ASM1 simulation benchmark for performance assessment. In addition, the effect of the EMPC controller parameters such as the predictive horizon, the control horizon, the weights of input and the sample time are also investigated and studied. The obtained results applying the EMPC strategy for the control of the aeration system in the wastewater treatment plant show its effectiveness.
Puig, V.; Romera, J.; Nejjari, F.; Quevedo, J.; de Campos, S. European Symposium on Computer Aided Process Engineering p. 865-870 DOI: 10.1016/B978-0-444-63965-3.50146-X Data de presentació: 2017-10-04 Presentació treball a congrés
n this paper, a methodology and software tool for the optimal design of advanced waste treatment plants is presented. The problem of the design of the WWTP is translated into a discrete optimization problem. As a result, an optimal sequence of treatment technologies to obtain the output water quality from a given inlet water containing a certain input contaminant concentrations is provided. The discrete optimization problem is solved using Genetic and Pattern Search Algorithms available at the Global Optimization Toolbox of MATLAB. A typical design scenario is used to illustrate the validity and performance of the proposed approach and tool.
Karimi Pour, F.; Puig, V.; Ocampo-Martinez, C.A. European Symposium on Computer Aided Process Engineering p. 1573-1578 DOI: 10.1016/B978-0-444-63965-3.50264-6 Data de presentació: 2017-10-03 Presentació treball a congrés
This paper proposes the application of Economic Model Predictive Control (EMPC) for a scale pilot pasteurization plant that is modelled by means of a linear parameter varying (LPV) model. The LPV model considers the variation of the model parameters with the operating point defined by the scheduling variables. The variation of the parameters is modeled experimentally by deriving an expression for each parameter in function of the scheduling variables. The functional relation is included as an additional constraint into the MPC optimization problem. In this way, the MPC controller is able to predict the change of the varying parameters over the prediction horizon. Moreover, the MPC objective function is not defined in terms of the classic standard tracking objective but defined as an economic objective that aims to optimize the cost operation of the system. Finally, the proposed approach is tested in simulation utilizing a high-fidelity simulator using a model calibrated with data from the real pasteurization plant.
In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H8 performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.
Rodriguez-Rangel, H.; Flores, J.; Puig, V.; Morales, L.; Guerra, A.; Calderon, F. International Conference on New Energy and Future Energy Sytem p. 1-9 DOI: 10.1088/1755-1315/93/1/012020 Data de presentació: 2017-09-24 Presentació treball a congrés
Currently, electric energy is used in practically all modern human activities. Most of the energy produced came from fossil fuels, making irreversible damage to the environment. Lately, there has been an effort by nations to produce energy using clean methods, such as solar and wind energy, among others. Wind energy is one of the cleanest alternatives. However, the wind speed is not constant, making the planning and operation at electric power systems a difficult activity. Knowing in advance the amount of raw material (wind speed) used for energy production allows us to estimate the energy to be generated by the power plant, helping the maintenance planning, the operational management, optimal operational cost. For these reasons, the forecast of wind speed becomes a necessary task. The forecast process involves the use of past observations from the variable to forecast (wind speed). To measure wind speed, weather stations use devices called anemometers, but due to poor maintenance, connection error, or natural wear, they may present false or missing data. In this work, a hybrid methodology is proposed, and it uses a compact genetic algorithm with an artificial neural network to reconstruct wind speed time series. The proposed methodology reconstructs the time series using a ANN defined by a Compact Genetic Algorithm.
A pesar de los avances actuales, la tecnología de celdas de hidrógeno tipo PEM no está suficientemente preparada para ser ampliamente introducida en el mercado energético. Rendimiento, durabilidad y costo son los mayores retos.El rendimiento y la durabilidad de las celdas dependen significativamente de las variaciones en las concentraciones de hidrógeno y oxígeno en los canales de alimentación de gases, la humedad relativa en las capas catalizadoras, el contenido de agua de la membrana polimérica, así como la temperatura, entre otras variables. Dichas variables presentan dependencia espacial interna en la dirección del flujo de gases del ánodo y del cátodo. Distribuciones espaciales altamente no uniformes en algunas variables de la celda resultan en sobrecalentamiento local, inundación, degradación acelerada y menor potencia de la requerida.Muy pocos trabajos disponibles en la literatura se ocupan del control de perfiles espaciales. La mayoría de los diseños orientados a control usan modelos de parámetros concentrados que ignoran la dependencia espacial de variables internas de la celda, debido a la complejidad que añaden al funcionamiento de controladores. En contraste, esta Tesis Doctoral trata la modelización y control de parámetros distribuidos en las celdas de hidrógeno tipo PEM.En la parte de modelización, esta tesis presenta el desarrollo detallado de un modelo no lineal de parámetros distribuidos para una sola celda, el cual incorpora las variaciones espaciales de todas las variables que son relevantes para su correcto funcionamiento. El modelo se usa primero para analizar importantes perfiles espaciales internos, y luego se simplifica para reducir su complejidad computacional y adecuarlo a propósitos de control. En esta tarea se usan y se comparan dos técnicas de reducción de orden de modelos.El propósito de la parte de control es abordar la gestión de agua y el suministro de reactantes, que son dos grandes retos en el funcionamiento de las celdas con importantes consecuencias para su vida útil. En esta parte de la tesis, dos estrategias de control descentralizadas, basadas en controladores predictivos de modelos de referencia con parámetros distribuidos, son diseñadas, implementadas y analizadas en un entorno de simulación. Estas tareas incluyen también el diseño de observadores de estado que estiman los perfiles espaciales internos necesarios para la acción de control.El objetivo de la primera estrategia es monitorear y controlar perfiles espaciales observados de la humedad relativa en las capas catalizadoras para mantenerlos en niveles apropiados. Estos niveles son escogidos cuidadosamente para combinar la correcta humidificación de la membrana y las capas catalizadoras, reduciendo la velocidad de acumulación de agua líquida. El objetivo clave de este enfoque es disminuir la frecuencia de las acciones de remoción de agua dentro de la celda, ya que estas acciones causan interrupción en la potencia suministrada, aumento de las cargas parasitarias y disminución de la eficiencia.La segunda estrategia es una variación de la estrategia anterior que considera adicionalmente el control de la distribución espacial de los gases en los canales del ánodo y cátodo. Esta solución integrada tiene como objetivo evitar la ausencia local de reactantes mediante el control de perfiles espaciales de concentración de gases. Este enfoque pretende prevenir la degradación de las celdas debido a mecanismos de corrosión.Los resultados muestran un mayor rendimiento de la celda considerando los enfoques de control de perfiles espaciales propuestos en esta tesis, en comparación con técnicas de control que ignoran dichos perfiles. Además, la característica descentralizada de los esquemas de control, combinada con el uso de modelos reducidos dentro de los controladores predictivos, tiene un impacto positivo importante en el rendimiento general del control.
La cerca i el seguiment de persones són comportaments importants per un robot mòbil de servei per poder assistir, trovar i ajudar als humans, i en general, per localitzar objectes, animals o vianants. Tot i que, la cerca és fàcil per als humans, per un robot no ho és, ja que requereix exploració, maneig de soroll de sensors, fer front als obstacles dinàmics, i la coordinació en el cas de múltiples agents.En aquesta tesi, presentem diferents mètodes per a buscar i seguir a una persona en un entorn urbà. Tots els mètodes han estat provats extensivaments en simulació i després en el món real, utilitzant dos robots mòbils de servei, la Tibi i en Dabo. Els robots utilitzen sensors làsers per a navegar, detectar obstacles i detectar les cames de les persones. Com que en aquest treball ens centrem en mètodes de cerca-i-seguiment, fem servir mètodes existents per a la navegació del robot, la detecció i el reconeixement de persones.Primer, fem proves amb el conegut joc de fet-i-amagar, on el robot aprèn a captar l'amagador. Es fa servir el model Mixed Observable Markov Decision Process (MOMDP), on la posició del trobador és completament visible i la posició de l'amagador és parcialment visible. Perquè la complexitat computacional depèn del nombre d'estats, proposem un mètode jeràrquic en línia que redueix l¿espai d'estats agrupant-los. Encara que el mètode va funcionar correctament en simulació, en els experiments reals els resultats no van ser satisfectoris i el càlcul de la política no va ser prou ràpid per treballar en temps real.Per tal de fer front a entorns més grans, treballar en l'espai continu i executar en temps real, proposem un nou enfocament, el CR-POMCP, que fa simulacions de Monte-Carlo per aprendre una política. El mètode va funcionar correctament en simulació, però el robot real feía moviments ziga-zaga lents. Per tant, es proposa un mètode nou, que utilitza els llocs amb més alts probababilitat, d'acord amb el seu mapa de probabilitats (belief). Atès que la propagació de les probabilitats en el POMCP s'assembla al funcionament d'un filtre de partícules (PF), també proposem un mètode que utilitza un PF per mantenir el belief. El mètode de PF ha de manejar la manca d'observacions, per tant, introduïm una funció del pes especial. Tots dos mètodes de creences tenen en compte el soroll dels sensors i actuadors, la deteccions de falsos negatius i positius (per a un curt període de temps) i els obstacles dinàmics.Finalment, es presenta un mètode multi-agent distribuït cooperatiu, que fa ús de les anteriors funcions d'actualització de la creencia (belief), i a més utilitza totes les observacions dels agents. En el proper pas, les ubicacions de cerca s'assignen mitjançant l'exploració de l'entorn de treball, tenint en compte: la creença, la distància a la ubicació de cerca i si un altre agent ja buscarà a prop d'ella.En resum, les principals contribucions d'aquesta tesi són diversos mètodes per a la cerca i seguiment d'una persona en un entorn urbà amb un o més robots de serveis mòbils. Tots aquests mètodes han demostrat que funcionen a través d'un conjunt de simulacions i experiments en la entorn real dinàmics.
Lopez, J.; Ocampo-Martinez, C.A.; Alvarez, J.A.; Moreno-Eguilaz, J.M.; Ruiz Mansilla, Rafael IEEE transactions on vehicular technology Vol. 66, num. 9, p. 7751-7760 DOI: 10.1109/TVT.2017.2678921 Data de publicació: 2017-09-15 Article en revista
A real-time nonlinear model predictive control (NMPC) for the thermal management (TM) of the electrical components cooling circuit in a Plug-In Hybrid Electric Vehicle (PHEV) is presented. The electrical components are highly temperature-sensitive and therefore working out of the ranges recommended by the manufacturer can lead to their premature aging or even failure. Consequently, the goals for an accurate and efficient TM are two: to keep the main component, the Li-ion battery, within optimal working temperatures, and to consume the minimum possible electrical energy through the cooling circuit actuators. This multi-objective requirement is formulated as a finite-horizon optimal control problem (OCP) that includes a multi-objective cost function, several constraints and a prediction model especially suitable for optimization. The associated NMPC is performed on real-time by the optimization package MUSCOD-II and is validated in three different repeatable test-drives driven with a PHEV. Starting from identical conditions, each cycle is driven once being the cooling circuit controlled with NMPC and once with a conventional approach based on a finite-state machine. Compared to the conventional strategy, the NMPC proposed here results in a more accurate and healthier temperature performance, and at the same time, leads to reductions in the electrical consumption up to 8%.
This chapter describes the development of an autonomous fluid sampling system for outdoor facilities, and the localization solution to be used. The automated sampling system will be based on collaborative robotics, with a team of a UAV and a UGV platform travelling through a plant to collect water samples. The architecture of the system is described, as well as the hardware present in the UAV and the different software frameworks used. A visual simultaneous localization and mapping (SLAM) technique is proposed to deal with the localization problem, based on authors’ previous works, including several innovations: a new method to initialize the scale using unreliable global positioning system (GPS) measurements, integration of attitude and heading reference system (AHRS) measurements into the recursive state estimation, and a new technique to track features during the delayed feature initialization process. These procedures greatly enhance the robustness and usability of the SLAM technique as they remove the requirement of assisted scale initialization, and they reduce the computational effort to initialize features. To conclude, results from experiments performed with simulated data and real data captured with a prototype UAV are presented and discussed.
En este trabajo se presenta una plataforma de prácticas de control automático robusto y de bajo coste. El trabajo describe los componentes y la arquitectura propuestos, además de presentar los modelos utilizados para describir su comportamiento. Finalmente se muestran algunos resultados experimentales.
Barreiro, J.; Ocampo-Martinez, C.A.; Quijano, N.; Maestre, J. Journal of the Franklin Institute Vol. 354, num. 14, p. 5571-5796 DOI: 10.1016/j.jfranklin.2017.06.021 Data de publicació: 2017-09-01 Article en revista
This paper solves a data-driven control problem for a flow-based distribution network with two objectives: a resource allocation and a fair distribution of costs. These objectives represent both cooperation and competition directions. It is proposed a solution that combines either a centralized or distributed cooperative game approach using the Shapley value to determine a proper partitioning of the system and a fair communication cost distribution. On the other hand, a decentralized non-cooperative game approach computing the Nash equilibrium is used to achieve the control objective of the resource allocation under a non-complete information topology. Furthermore, an invariant-set property is presented and the closed-loop system stability is analyzed for the non-cooperative game approach. Another contribution regarding the cooperative game approach is an alternative way to compute the Shapley value for the proposed specific characteristic function. Unlike the classical cooperative-games approach, which has a limited application due to the combinatorial explosion issues, the alternative method allows calculating the Shapley value in polynomial time and hence can be applied to large-scale problems.
Villamizar, M.A.; Garrell, A.; Sanfeliu, A.; Moreno-Noguer, F. Neural computing and applications Vol. 28, num. 9, p. 2445-2460 DOI: 10.1007/s00521-016-2284-x Data de publicació: 2017-09-01 Article en revista
We propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficiently object instances in images and discover simultaneously the most prominent appearance modes of the object through tree-structured visual words. In particular, we use boosted random ferns and probabilistic latent semantic analysis to obtain a discriminative and multimodal classifier that automatically clusters the response of its randomized trees in function of the visual object appearance. The proposed method is validated extensively in synthetic and real experiments, showing that the method is capable of detecting objects with diverse and complex appearance distributions in real-time performance.
The final publication is available at link.springer.com
This paper proposes an integrated actuator and sensor active fault-tolerant model predictive control scheme. In this scheme, fault detection is implemented by using a set-valued observer, fault isolation (FI) is performed by set manipulations, and fault-tolerant control is carried out through the design of a robust model predictive control law. In this paper, a set-valued observer is used to passively complete the fault detection task, while FI is actively performed by making use of the constraint-handling capability of robust model predictive control. The set-valued observer is chosen to implement fault detection and isolation (FDI) because of its simple mathematical structure that is not affected by the type of faults such as sensor, actuator, and system-structural faults. This means that only one set-valued observer is needed to monitor all considered actuator and sensor statuses (health and fault) and to carry out the fault detection and isolation task instead of using a bank of observers (each observer matching a health/fault status). Furthermore, in the proposed scheme, the advantage of robust model predictive control is that it can effectively deal with system constraints, disturbances, and noises and allow to implement an active FI strategy, which can improve FI sensitivity when compared with the passive FI methods. Finally, a case study based on the well-known two-tank system is used to illustrate the effectiveness of the proposed fault-tolerant model predictive control scheme.
Electrostatic parallel-plate actuators are a common way of actuating microelectromechanical systems, both statically and dynamically. Nevertheless, actuation voltages and oscillations are limited by the nonlinearity of the actuator that leads to the pull-in phenomena. This work presents a new approach to obtain the electrostatic parallel-plate actuation voltage, which allows to freely select the desired frequency and amplitude of oscillation. Harmonic Balance analysis is used to determine the needed actuation voltage and to choose the most energy-efficient actuation frequency. Moreover, a new two-sided actuation approach is presented that allows to actuate the device in all the stable range using the Harmonic Balance Voltage.
This is the peer reviewed version of the following article: “Fargas Marques, A., Costa Castelló, R. (2017) Energy-efficient full-range oscillation analysis of parallel-plate electrostatically actuated MEMS resonators, 1-13.” which has been published in final form at [doi: 10.1007/s11071-017-3633-8]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."
This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flow-based networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (all-to-all) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of non-sparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a large-scale complex flow-based network: the Barcelona drinking water supply system.
Carignano, M.; Costa-Castelló, R.; Roda, V.; Nigro, N.; Junco, S.; Feroldi, D. Journal of power sources Vol. 360, p. 419-433 DOI: 10.1016/j.jpowsour.2017.06.016 Data de publicació: 2017-08-31 Article en revista
Offering high efficiency and producing zero emissions Fuel Cells (FCs) represent an excellent alternative to internal combustion engines for powering vehicles to alleviate the growing pollution in urban environments. Due to inherent limitations of FCs which lead to slow transient response, FC-based vehicles incorporate an energy storage system to cover the fast power variations. This paper considers a FC/supercapacitor platform that configures a hard constrained powertrain providing an adverse scenario for the energy management strategy (EMS) in terms of fuel economy and drivability. Focusing on palliating this problem, this paper presents a novel EMS based on the estimation of short-term future energy demand and aiming at maintaining the state of energy of the supercapacitor between two limits, which are computed online. Such limits are designed to prevent active constraint situations of both FC and supercapacitor, avoiding the use of friction brakes and situations of non-power compliance in a short future horizon. Simulation and experimentation in a case study corresponding to a hybrid electric bus show improvements on hydrogen consumption and power compliance compared to the widely reported Equivalent Consumption Minimization Strategy. Also, the comparison with the optimal strategy via Dynamic Programming shows a room for improvement to the real-time strategies.
In this study, actuator fault diagnosis of singular delayed linear parameter varying (SDLPV) systems is considered. The considered system has a time-varying state delay and its matrices are dependent on some parameters that are measurable online. It is assumed that the measured parameters are inexact due to the existence of noise in real situations. The system with inexact measured parameters is converted to an uncertain system. Actuator fault diagnosis is carried out based on fault size estimation. For this purpose, the system is transformed to a polytopic representation and then a polytopic proportional integral unknown input observer (PI-UIO) is designed. The proposed observer provides simultaneous state and actuator fault estimation while attenuating, in the H8H8 sense, the effects of input disturbance, output noise and the uncertainty caused by inexact measured parameters. The design procedure of PI-UIO is formulated as a convex optimisation problem with a set of Linear Matrix Inequality (LMI) constraints in the vertices of the parameter domain, guaranteeing robust exponential convergence of the PI-UIO. The efficiency of the proposed method is illustrated with an electrical circuit example modelled as an SDLPV system.
The aim of this research is to develop and test in a simulation environment an advanced model-based control solution for a proton exchange membrane fuel cell (PEMFC) system. A nonlinear model-predictive control (NMPC) strategy is proposed to maximize the active catalytic surface area at the cathode catalyst layer to increase the available reaction area of the stack and to avoid starvation at the catalyst sites. The PEMFC stack model includes a spatial discretization that permits the control strategy to take into account the internal conditions of the system. These internal states are estimated and fed to the NMPC via a nonlinear distributed parameters observer. The air-fed cathode of the PEMFC simulation model includes a two-phase water model for better representation of the stack voltage. The stack temperature is regulated through the use of an active cooling system. The control strategy is evaluated in an automotive application using a driving cycle based on the New European Driving Cycle profile as the case study.
Policy search (PS) algorithms are widely used for their simplicity and effectiveness in finding solutions for robotic problems. However, most current PS algorithms derive policies by statistically fitting the data from the best experiments only. This means that experiments yielding a poor performance are usually discarded or given too little influence on the policy update. In this paper, we propose a generalization of the relative entropy policy search (REPS) algorithm that takes bad experiences into consideration when computing a policy. The proposed approach, named dual REPS (DREPS) following the philosophical interpretation of the duality between good and bad, finds clusters of experimental data yielding a poor behavior and adds them to the optimization problem as a repulsive constraint. Thus, considering that there is a duality between good and bad data samples, both are taken into account in the stochastic search for a policy. Additionally, a cluster with the best samples may be included as an attractor to enforce faster convergence to a single optimal solution in multimodal problems. We first tested our proposed approach in a simulated reinforcement learning setting and found that DREPS considerably speeds up the learning process, especially during the early optimization steps and in cases where other approaches get trapped in between several alternative maxima. Further experiments in which a real robot had to learn a task with a multimodal reward function confirm the advantages of our proposed approach with respect to REPS.
This paper addresses a non-linear economic model predictive control (EMPC) strategy for water distribution networks (WDNs). A WDN could be considered as a non-linear system described by differential-algebraic equations (DAEs) when flow and hydraulic head equations are considered. As in other process industries, the main operational goal of WDNs is the minimisation of the economic costs associated to pumping and water treatment, while guaranteeing water supply with required flows and pressures at all the control/demand nodes in the network. Other operational goals related to safety and reliability are usually sought. From a control point of view, EMPC is a suitable control strategy for WDNs since the optimal operation of the network cannot be established a priori by fixing reference volumes in the tanks. Alternatively, the EMPC strategy should determine the optimal filling/emptying sequence of the tanks taking into account that electricity price varies between day and night and that the demand also follows a 24-hour repetitive pattern. On the other hand, as a result of the ON/OFF operation of parallel pumps in pumping stations, a two-layer control scheme has been used: a non-linear EMPC strategy with hourly control interval is chosen in the upper layer and a pump scheduling approach with one-minute sampling time in the lower layer. Finally, closed-loop simulation results of applying the proposed control strategy to the D-Town water network are shown.
Santamaria, A.; Grosch, P.; Lippiello, V.; Solá, J.; Andrade-Cetto, J. IEEE-ASME transactions on mechatronics Vol. 22, num. 4, p. 1610-1621 DOI: 10.1109/TMECH.2017.2682283 Data de publicació: 2017-08-01 Article en revista
This paper addresses the problem of autonomous servoing an unmanned redundant aerial manipulator using computer vision. The overactuation of the system is exploited by means of a hierarchical control law, which allows to prioritize several tasks during flight. We propose a safety-related primary task to avoid possible collisions. As a secondary task, we present an uncalibrated image-based visual servo strategy to drive the arm end-effector to a desired position and orientation by using a camera attached to it. In contrast to the previous visual servo approaches, a known value of camera focal length is not strictly required. To further improve flight behavior, we hierarchically add one task to reduce dynamic effects by vertically aligning the arm center of gravity to the multirotor gravitational vector, and another one that keeps the arm close to a desired configuration of high manipulability and avoiding arm joint limits. The performance of the hierarchical control law, with and without activation of each of the tasks, is shown in simulations and in real experiments confirming the viability of such prioritized control scheme for aerial manipulation.
Aguilar, W.; Angulo, C.; Costa-Castelló, R. International Conference on Intelligent Robotics and Applications p. 287-297 DOI: 10.1007/978-3-319-65298-6_27 Data de presentació: 2017-08 Presentació treball a congrés
In this paper, we describes a novel proposal for the autonomous navigation control of quadrotor micro aerial vehicles for trajectories tracking in the XY plane. The quadrotor vehicle is an AR.Drone 1.0 from the company Parrot with a nonlinear behavior. The proposal includes system modeling, controller design, planning and simulation of the results. In our approach, we separate the model into two primary models: A linearity for the steady state and a nonlinearity for the dynamic transition.
Busqué , R.; Torres, R.; Grau, J.; Roda, V.; Husar, A. International journal of hydrogen energy Vol. 42, num. 30, p. 19114-19125 DOI: 10.1016/j.ijhydene.2017.06.125 Data de publicació: 2017-07-27 Article en revista
A two-dimensional axisymmetric model is developed to study the hydrogen absorption reaction and resultant mass and heat transport phenomena inside a metal hydride canister. The model is compared against published literature and experimental data. Experimental tests are performed on an in-house fabricated setup using different cooling scenarios. An extensive study on the effects of the metal properties on charging performance is carried out through non-destructive testing (NDT). Results show that the properties that most influence the charging performance are: absorption rate constant (Ca), activation energy (Ea) and thermal conductivity (km). A Higher porosity (e) reduces charging time and amount of hydrogen stored while a higher cooling level produces a faster charging process. These results can be used to select metal hydride materials but also to estimate the metal hydride internal state and the process can be used for future evaluation of metal hydride degradation.
Fault estimation plays an important role in the fault diagnosis system since provides information about the fault magnitude and temporal evolution. In this paper, we present an approach that allows to obtain a simultaneous estimation of the fault, state and associated uncertainty intervals of a uncertain Takagi-Sugeno (TS) system. The fault estimation is obtained using a TS interval observer augmenting the system state with the fault and considering the system uncertainty in a bounded context. A set of Linear Matrix Inequalities (LMIs) have
been derived to design the TS interval observer. With the purpose of illustrating the performance of TS interval observer for fault and state estimation, a case study based on a Proton Exchange Membrane (PEM)
fuel cell is used.
Segovia, P.; Horvath, K.; Rajaoarisoa, L.H.; Nejjari, F.; Puig, V.; Duviella, E. Internacional Conference on Informatics in Control, Automation and Robotics p. 459-467 Data de presentació: 2017-07-26 Presentació treball a congrés
Inland navigation networks are large-scale systems that can be described by using the nonlinear Saint-Venant partial differential equations. However, as there is no analytical solution for them, simplified models are used instead for modeling purposes. This work addresses the modeling of two sub-reach systems by means of the
well-known Integrator Delay Zero model. Two main scenarios are considered: in the first one, the two partial models are independently computed one from each other; the second one uses previous knowledge of the whole two sub-reach system in order to ensure the flow consistency along the system. The application of these two methodologies to a part of the navigation network in the north of France serves as the case study for this work.
Els components dels aerogeneradors estan sotmesos a considerable estrès i fatiga, degut a les condicions ambientals extremes a les quals estan exposats, especialment els localitzats en alta mar. Per aquest motiu, al comunitat científica durant els últims anys ha investigat les averies més comunes presents en els aerogeneradors, fet que ha portat a proposar un cas d'estudi de diagnosi i control tolerant de fallades que inclou un conjunt de fallades que afecten a diversos components dels aerogeneradors.Aquesta tesi presenta algunes contribucions en els camps de la diagnosi de fallades, el control tolerant de fallades i la prognosi, així com la seva integració amb el control d'aerogeneradors, fet que ha portat a proposar una tècnica de control anomenada control predictiu basada en models conscients de la salut del sistema (HAMPC). Concretament les aportacions es poden resumir en:-Diagnosi de fallades basada en models: per a la detecció s'utilitzen observadors intervalars i l'aïllament de la fallada es fa en base el conjunt d'ARRs obtinguts de l'anàlisi estructural i de la matriu de signatures de fallades que relaciona les ARRs amb les fallades.-Control tolerant de fallades: es proposa un esquema de control tolerant a fallades que integra la detecció de fallades i algoritme d'acomodació de fallades, i té per objectiu evitar l'augment de càrregues en la pala i la torre quan es produeix una fallada en el sensor azimuth quan es fa un control individual de la inclinació de les pales (IPC).-Prognosi de la fatiga i la degradació de les pales: la fatiga s'avalua amb un algorisme denominat "rainflow counting" amb el qual es fa estimació del dany acumulat i per a la degradació es fa servir un model de degradació de la rigidesa del material amb el qual es fan prediccions de la vida útil restant (RUL).-Control de la salut d'aerogeneradors: s'ha integrat la gestió de la salut del sistema basat en danys per fatiga o prediccions de RUL amb control predictiu basat en models (MPC) donant lloc al control que anomenem HAMPC.Les contribucions presentades en aquesta tesi han sigut validades en un cas d'estudi d'aerogeneradors basat en un aerogenerador de referència de 5MW de potència implementat en el simulador d'aerogeneradors d'alta fidelitat conegut amb el nom de FAST.