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© 2022 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. Direct collocation methods are powerful tools to solve trajectory optimization problems in robotics. While their resulting trajectories tend to be dynamically accurate, they may also present large kinematic errors in the case of constrained mechanical systems, i.e., those whose state coordinates are subject to holonomic or nonholonomic constraints, such as loop-closure or rolling-contact constraints. These constraints confine the robot trajectories to an implicitly-defined manifold, which complicates the computation of accurate solutions. Discretization errors inherent to the transcription of the problem easily make the trajectories drift away from this manifold, which results in physically inconsistent motions that are difficult to track with a controller. This article reviews existing methods to deal with this problem and proposes new ones to overcome their limitations. Current approaches either disregard the kinematic constraints (which leads to drift accumulation) or modify the system dynamics to keep the trajectory close to the manifold (which adds artificial forces or energy dissipation to the system). The methods we propose, in contrast, achieve full drift elimination on the discrete trajectory, or even along the continuous one, without artificial modifications of the system dynamics. We illustrate and compare the methods using various examples of different complexity. This work was supported in part by the Spanish Ministry of Science, Innovation, and Universities under Project DPI2017-88282-P and Project PID2020-117509GBI00/AEI/10.13039/50110001103 and in part by the German Federal Ministry for Economic Affairs and Energy (BMWi) via DyConPV (0324166B)

This paper considers the problem of optimal actuator dimensioning for LTI systems, in the sense of choosing appropriate saturation limits for a given set of admissible initial conditions and for a predefined integral state-feedback control law. By using an invariant ellipsoid argument, it is shown that this problem can be described as a linear matrix inequality (LMI)-based optimization that can be solved efficiently. Moreover, the paper shows that the optimal actuator dimensioning is connected to the choice of the initial conditions of the integral states of the controller, which can be included in the overall optimization to improve further the results. Two different methods are described and analyzed by means of simulation results. This work has been supported by the Spanish State Research Agency (AEI)through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656)and the grant Juan de la Cierva-Formacion (FJCI-2016-29019), by the DAADfunding programme: Research Grants - Short-Term Grants, 2019 (57440917),personal reference: 91725219, and by the University of Stavanger through theproject IN-12267.

In order to reduce the global energy consumption and avoid highest power peaks during operation of manufacturing systems, an optimization-based controller for selective switching on/off of peripheral devices in a test bench that emulates the energy consumption of a periodic system is proposed. First, energy consumption models for the test-bench devices are obtained based on data and subspace identification methods. Next, a control strategy is designed based on both optimization and receding horizon approach, considering the energy consumption models, operating constraints, and the real processes performed by peripheral devices. Thus, a control policy based on dynamical models of peripheral devices is proposed to reduce the energy consumption of the manufacturing systems without sacrificing the productivity. Afterward, the proposed strategy is validated in the test bench and comparing to a typical rule-based control scheme commonly used for these manufacturing systems. Based on the obtained results, reductions near 7% could be achieved allowing improvements in energy efficiency via minimization of the energy costs related to nominal power purchased., This work has been funded by the project IKERCON (ref. C10683) and partially supported by the Spanish State Research Agency, Spain through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Jenny Díaz has been supported by FI-AGAUR scholarship programme of the Catalan Government, Spain .

In this paper, we propose a novel interpretable shape model to encode object nonrigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these precomputed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and nonrigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods., This work is supported in part by a Google Faculty Research Award, by the MINECO projects HuMoUR TIN2017-90086-R and Mar´ıa de Maeztu Seal of Excellence MDM-2016-0656.

Trabajo presentado en la 7th International Conference on Control, Decision and Information Technologies (CoDIT), celebrada en Praga (República Checa), del 29 de junio al 2 de julio de 2020, The intrinsic redundancy of large-scale control systems allows to differentiate between the nominal system configuration, where the dynamics of the plant are controlled through a specific subset of actuators, and the possible configurations that arise from the combination of all the existing actuators (nominal plus back-up ones). After the occurrence of an unexpected event (component fault, isolation of a specific area due to security concerns, etc), some of the back-up system actuators can be used in order to guarantee that the system performance will remain within some admissible margins. Thus, given a scenario that causes a non-admissible system operation with the nominal configuration, the possibility to use back-up actuators in order to improve the system performance expands the traditional fault tolerant control scenario defining a new problem. Accordingly, this work is devoted to the introduction of the problem which is solved by formulating the actuator selection as a mixed-integer programming optimization. A water distribution network is used with the aim to illustrate the new problem implications as well as to demonstrate the validity of the proposed solution.

In this technical report we present a framework for the empirical validation of a physical cloth model. First, we introduce the model to be validated and its parameters, next we explain how to obtain real world data of the motion of a textile and finally we show how to fit the model to the experimental data.

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and effciently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases., This work has been funded by the Ministerio de Economa, Industria y Competitividad (MEINCOP) of the Spanish Government through the project DEOCS (ref. DPI2016-76493) by MEICOMP and FEDER, through the grant IJCI-2014-20801 and by the Catalan Agency for Management of University and Research Grants (AGAUR), the European Social Fund (ESF) and the Secretary of University and Research of the Department of Companies and Knowledge of the Government of Catalonia through the grants FI-DGR 2015 (ref. 2015 FI_B 00591). Besides, this work has also been supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656).

Letter: Communicated by Masa-aki Sato., Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concentrated in particular convergence regions, which in the long term tend to dominate the approximation in the less sampled regions. The nonstationarity comes from the recursive nature of the estimations typical of temporal difference methods. This nonstationarity has a local profile, varying not only along the learning process but also along different regions of the state space.We propose to deal with these problems using an estimation of the probability density of samples represented with a gaussian mixture model. To deal with the nonstationarity problem, we use the common approach of introducing a forgetting factor in the updating formula. However, instead of using the same forgetting factor for the whole domain, we make it dependent on the local density of samples, which we use to estimate the nonstationarity of the function at any given input point. To address the biased sampling problem, the forgetting factor applied to each mixture component is modulated according to the new information provided in the updating, rather than forgetting depending only on time, thus avoiding undesired distortions of the approximation in less sampled regions.

Trabajo presentado en el 6th International Conference on Control, Decision and Information Technologies (CoDIT), celebrado en París (Francia), del 23 al 26 de abril de 2019, The solar PV power sector in Spain has been developing at a spectacular rate in recent years. The energy cost and the dependence on fossil fuels can be reduced by improving the efficiency of photovoltaic energy production. The performance of a solar radiation conversion system is affected by a tilt angle with the horizontal plane. Thus, the photovoltaic array needs to be tilted at the correct angle to maximize the performance of the system. In this paper, we found the optimum tilt angle and applied for Barcelona, Spain, located at latitude 41o 22' 56'' North and longitude 2o 6'56'' East. The optimal tilt angle for Winter (December, January, February) is 56.46 and the optimum tilt angle for Spring (March, April, May Ls 2 .11¿and the optimum tilt angle for Summer(Jun, July, August Ls 13.76¿ and the optimum tilt angle for Autumn (September, 2ctober, November Ls 8.1 ¿. finally, the annual optimum tilt angle for our latitude Ls 36.87¿, ZLth thLs optimal slope angle, maximum monthly and annual solar radiation is calculated. In this way, we can increase the energy generation when achieved to maximum solar radiation. With this process, we were able to increase 10.54% of energy., This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the projects DEOCS (ref. MINECO DPI2016-76493) and SCAV (ref. MINECO DPI2017-88403-R).This work has also been partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482).

Proton Exchange Membrane Fuel Cells (PEMFC) are a promising technology for substitution of hydrocarbon powerdrives and battery based electrical supply systems. However, below-required durability is still an important factor that hinders its widespread use, thus, novel operating approaches are required to surpass this shortcoming. The present abstract presents a line of research directed towards the of analysis and definition of load profile characteristics in order to design controllers and operation strategies that optimize cell efficiency and durability. Degradation of the catalyst layer, commonly composed of platinum supported on carbon (Pt/C), mainly due to platinum loss by oxidation/dissolution and the resulting reduction of Electrochemical Active Surface Area (ECSA), is the main issue reducing the durability of PEMFC.

This paper proposes robust economic model predictive control based on a periodicity constraint for linear systems subject to unknown-but-bounded additive disturbances. In this economic MPC design, a periodic steady-state trajectory is not required and thus assumed unknown, which precludes the use of enforcing terminal state constraints as in other standard economic formulations. Instead, based on the desired periodicity of system operation, we optimize the economic performance over a set of periodic trajectories that include the current state. To achieve robust constraint satisfaction, we use a tube-based technique in the economic MPC formulation. The mismatches between the nominal model and the closed-loop system with perturbations are limited using a local control law. With the proposed robust tube-based strategy, recursive feasibility is guaranteed. Moreover, under a convexity assumption, the closed-loop convergence of the closed-loop system is analyzed, and an optimality certificate is provided to check if the closed-loop trajectory reaches a neighborhood of the optimal nominal periodic steady trajectory using Karush-Kuhn-Tucker optimality conditions. Finally, through numerical examples, we show the effectiveness of the proposed approach., This work was supported in part by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through projects DEOCS (DPI2016‐76493‐C3) and SCAV (DPI2017‐88403‐R), the FPI grant (BES‐2014‐068319), and in part by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group under grant (2017‐SGR‐482).

This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image. To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles., This work has been partially supported by the Spanish Ministry of Science and Innovation under projects FireDMMI TIN2014-56919-C3-2- R, BOSSS TIN2017-89723-P, and HuMoUR TIN2017- 90086-R; by the CSIC project R3OBJ 201850I099, and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI MDM2016-0656.

Trabajo presentado en el 18th European Control Conference (ECC), celebrado en Nápoles (Italia), del 25 al 28 de junio de 2019, The aim of this work is to develop the phenomenological based modeling of a self-pressurized alkaline electrolyser with the objective of predicting the cross-contamination of the gases produced. A proposed model, built in Matlab®, represents the dynamical evolution in real electrolysers, and anticipates operational variables: level, pressure and all concentrations. Dynamic responses in the concentrations of the electrolytic cell, and variations in both level and pressure at the chamber due to the change in current and diffusivity, are reported. The equations by which the variables can be computed are also presented. The proposed model is ready for the corresponding adjustment of parameters based on experimental measurements taken from an available prototype and through a suitable identification process.

Trabajo presentado en el 16th International Symposium on Advances in Robot Kinematics (ARK), celebrado en Bolonia (Italia), del 1 al 5 de julio de 2018, The main non-singular alternative to 3×3 proper orthogonal matrices, for representing rotations in R3, is quaternions. Thus, it is important to have reliable methods to pass from one representation to the other. While passing from a quaternion to the corresponding rotation matrix is given by Euler-Rodrigues formula, the other way round can be performed in many different ways. Although all of them are algebraically equivalent, their numerical behavior can be quite different. In 1978, Shepperd proposed a method for computing the quaternion corresponding to a rotation matrix which is considered the most reliable method to date. Shepperd’s method, thanks to a voting scheme between four possible solutions, always works far from formulation singularities. In this paper, we propose a new method which outperforms Shepperd’s method without increasing the computational cost., This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through projects DPI2014-57220-C2-2-P, DPI2017-88282- P, and MDM-2016-0656. We also want to express our gratitude to Antonio B. Martınez for the financial support of the first author, and to the anonymous reviewers whose comments have led to a number of valuable improvements in the text

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case-study network as virtual reality., This work has been partially funded by the research project ECOCIS (DPI-2013-48243-C2-1-R).

Trabajo presentado a la IEEE 3rd Colombian Conference on Automatic Control (CCAC), celebrada en Cartagena (Colombia) del 18 al 20 de octubre de 2017., Due to the growth of computational capabilities and the proliferation of field-programmable gate arrays (FPGA), the utilization of model predictive control (MPC) for embedded applications in the industry has become a possibility and a fact. This paper presents and discusses the possibilities of the use of online MPC, embedded in an educational device from National Instruments, using two different optimization algorithms and code generators, which have come out in recent years by the academia: CVXGEN, which implements a primal-dual interior-point algorithm, and qpOASES, which relies on the online active-set strategy algorithm. Both algorithms have been tested both in simulation and in real-time experimentation to control a four-tank pilot plant., This work has been supported by the project DEOCS (Ref. DPI2016-76493-C3-3-R) from the Spanish Ministry of Economy and Competitiveness.

Trabajo presentado al 20th IFAC (International Federation of Automatic Control) World Congress, celebrado en Toulouse (Francia) del 9 al 14 de julio de 2017., Population games have become a powerful tool for solving resource-allocation problems in a distributed manner, and for the design of non-centralized optimization-based controllers. The aim of this paper is to illustrate the advantages of two recently introduced population-game approaches in comparison to other classical optimization methods. More specically, the discussion is mainly devoted to the communication requirements. Finally, an illustrative example shows with more detail the advantages highlighted throughout the comparative discussion, i.e., fewer communications links are required for resource allocation problems, and there is not need of additional computation stages to solve the problem in a distributed manner., Authors would like to thank COLCIENCIAS (grant 6172) and the Agencia de Gestio d’Ajust Universitaris i de Recerca, AGAUR, for supporting J. Barreiro-Gomez. Authors would also like to thank the project DEOCS (Ref. DPI2016-76493-C3-3-R), which have partially supported this work.

Trabajo presentado a la 13th Asian Conference on Computer Vision (ACCV), celebrada en Taipei (Taiwan) del 20 al 24 de noviembre de 2016., In recent years, there has been a growing interest on tackling the Non-Rigid Structure from Motion problem (NRSfM), where the shape of a deformable object and the pose of a moving camera are simultaneously estimated from a monocular video sequence. Existing solutions are limited to single objects and continuous, smoothly changing sequences. In this paper we extend NRSfM to a multi-instance domain, in which the images do not need to have temporal consistency, allowing for instance, to jointly reconstruct the face of multiple persons from an unordered list of images. For this purpose, we present a new formulation of the problem based on a dual low-rank shape representation, that simultaneously captures the between- and within-individual deformations. The parameters of this model are learned using a variant of the probabilistic linear discriminant analysis that requires consecutive batches of expectation and maximization steps. The resulting approach estimates 3D deformable shape and pose of multiple instances from only 2D point observations on a collection images, without requiring pre-trained 3D data, and is shown to be robust to noisy measurements and missing points. We provide quantitative and qualitative evaluation on both synthetic and real data, and show consistent benefits compared to current state of the art., This work has been partially supported by the Spanish Ministry of Science and Innovation under project RobInstruct TIN2014-58178-R; by the ERA-net CHISTERA projects VISEN PCIN-2013-047 and I-DRESS PCIN-2015-147.

© 20xx 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. This work presents a solution to automatize the water sampling process of outdoor basins in a wastewater treatment plant. The system proposed is based on the utilization of collaborative robotics: a team of an UAV and a terrestrial robotic platform make a route along the plant collecting and storing the water samples. The architecture of the designed system is described in terms of functional blocks, and implementation details including software frameworks and hardware on the UAV are provided. As the objective of the system is industry levels of robustness and performance, the UAV use is minimized and subjected to control from the robotic ground platform, reducing risks associated with autonomous UAV. To conclude, results from experiments performed to validate the viability of the system and study several design decisions are presented and briefly discussed, including: estimation of the accuracy of several GNSS technologies on the plant, viability of the landing operation over a mobile robotic platform and controlling a quadrotor over waters.

Trabajo presentado a la American Control Conference (ACC), celebrada en Boston (US) del 6 al 8 de julio de 2016., Urban drainage systems (UDSs) are complex large-scale systems that carry stormwater and wastewater throughout urban areas. During heavy rain scenarios, UDSs are not able to handle the amount of extra water that enters the network and flooding occurs. Usually, this might happen because the network is not being used efficiently, i.e., some structures remain underused while many others are overused. This paper proposes a control methology based on differential game theory that aims to efficiently use the existing network elements in order to minimize overflows and properly manage the water resource. The proposed controller is tested on a typical UDS and is compared with a centralized MPC achieving similar results in terms of flooding minimization and network usage, but only using local information on distributed controllers., Authors would like to thank Mexichem (Colombia) for supporting this research through the project “Drenaje Urbano y Cambio Climatico: hacia los sistemas de alcantarillado del futuro.” Fase II. COLCIENCIAS 633/2013. Also, this work has been partially supported by the spanish project ECOCIS (Ref. DPI2013-48243-C2-1-R).

Trabajo presentado al 26th International Conference on Automated Planning and Scheduling (ICAPS) celebrado en Londres (UK) del 12 al 17 de junio de 2016., Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work., This work has been supported by the MINECO project RobInstruct TIN2014-58178-R and the ERA-Net CHISTERA project I-DRESS PCIN-2015-147. D. Martínez is also supported by the Spanish Ministry of Education, Culture and Sport via a FPU doctoral grant (FPU12-04173).

Póster presentado a la 14th European Conference on Computer Vision (ECCV), celebrada en Amsterdam (Netherlands) del 11 al 14 de octubre de 2016., The Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3D-to-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms, the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP., This work is partly funded by the Russian MES grant RFMEFI61516X0003; by the Spanish MINECO project RobInstruct TIN2014-58178-R and by the ERA-Net Chistera project I-DRESS PCIN-2015-147.

This paper presents a path planning method for actuated tensegrity structures with quasi-static motion. The valid configurations for such structures lay on an equilibrium manifold, which is implicitly defined by a set of kinematic and static constraints. The exploration of this manifold is difficult with standard methods due to the lack of a global parameterization. Thus, this paper proposes the use of techniques with roots in differential geometry to define an atlas, i.e., a set of coordinated local parameterizations of the equilibrium manifold. This atlas is exploited to define a rapidly-exploring random tree, which efficiently finds valid paths between configurations. However, these paths are typically long and jerky and, therefore, this paper also introduces a procedure to reduce their control effort. A variety of test cases are presented to empirically evaluate the proposed method., This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under Project DPI2014-57220-C2-2-P.

Trabajo presentado al 19th IFAC World Congress celebrado del 24 al 29 de agosto de 2014 en Cape Town (Sudafrica)., Drinking Water Networks (DWN) are large-scale multiple-input multiple-output systems with uncertain disturbances (such as the water demand from the consumers) and involve components of linear, non-linear and switching nature. Operating, safety and quality constraints deem it important for the state and the input of such systems to be constrained into a given domain. Moreover, DWNs' operation is driven by time-varying demands and involves an considerable consumption of electric energy and the exploitation of limited water resources. Hence, the management of these networks must be carried out optimally with respect to the use of available resources and infrastructure, whilst satisfying high service levels for the drinking water supply. To accomplish this task, this paper explores various methods for demand forecasting, such as Seasonal ARIMA, BATS and Support Vector Machine, and presents a set of statistically validated time series models. These models, integrated with a Model Predictive Control (MPC) strategy addressed in this paper, allow to account for an accurate on-line forecasting and flow management of a DWN., This work was financially supported by the EU FP7 research project EFFINET “Efficient Integrated Real-time monitoring and Control of Drinking Water Networks,” grant agreement no. 318556.

Trabajo presentado al 19th IFAC World Congress celebrado del 24 al 29 de agosto de 2014 en Cape Town (Sudafrica)., Water systems are a challenging problem because of their size and exposure to uncertain influences such as the unknown demands or the meteorological phenomena. In this paper, two different stochastic programming approaches are assessed when controlling a drinking water network: chance-constrained model predictive control (CC-MPC) and tree-based model predictive control (TB-MPC). Under the former approach, the disturbances are modeled as stochastic variables with non-stationary uncertainty description, unbounded support and quasi concave probabilistic distribution. A deterministic equivalent of the related stochastic problem is formulated using Boole¿s inequality and a uniform allocation of risk. In the later approach, water demand is modelled as a disturbance rooted tree where branches are formed by the most probable evolutions of the demand. In both approaches, a model predictive controller is used to optimise the expectation of the operational cost of the disturbed system., This research has been supported by the EU Project EFFINET (FP7-ICT-2011-8-31855), the EU Network of Excellence Highly Complex and Networked Control Systems (HYCON2) under grant agreement no. 257462, and the DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491).

Trabajo presentado a la 11th International Conference on Hydroinformatics celebrada en New York (US) del 17 al 21 de agosto de 2014., This research has been partially funded by the DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491), Doctorat Industrial AGAUR-2013-DI-041 and by EFFINET: Efficient Integrated Real-time Monitoring and Control of Drinking Water Networks (FP7-ICT2011-8-318556).

We are interested in the optimal control of sewage networks. It is of high public interest to minimize the overflow of sewage onto the streets and to the natural environment that may occur during periods of heavy rain. The assumption of linear flow in a discrete time setting has proven to be adequate for the practical control of larger systems. However, the possibility of overflow introduces a nonlinear and nondifferentiable element to the formulation, by means of a maximum of linear terms. This particular challenge can be addressed by smoothing methods that result in a nonlinear program (NLP) or by logical constraints that result in a mixed integer linear program (MILP). We discuss both approaches and present a novel tailored branch-and-bound algorithm that outperforms competing methods from the literature for a set of realistic rain scenarios. © 2013 Springer Science+Business Media Dordrecht., This work has been partially funded by the EU Project EFFINET (FP7-ICT-2011-8- 318556) and DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491). Financial support of the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences and of the EU project EMBOCON under grant FP7-ICT-2009-4 248940 is gratefully acknowledged.

We deal with the problem of representing a bivariate density function by level sets. The choice of which levels are used in this representation are commonly arbitrary (most usual choices being those with probability contents .25, .5 and .75). Choosing which level is (or which levels are) of most interest is an important practical question which depends on the kind of problem one has to deal with as well as the kind of feature one wishes to highlight in the density. The approach we develop is based on minimum distance ideas.

Trabajo presentado a la 53rd IEEE Conference on Decision and Control (CDC 2014), celebrada del 15 al 17 de diciembre en Los Angeles, California (US).-- et al., Inventory management is one of the main tasks that the pharmacy department has to carry out in a hospital. It is a complex problem that requires to establish a tradeoff between different and contradictory optimization criteria. The complexity of the problem is increased due to the constraints that naturally arise in this type of applications. In this paper, which corresponds to preliminary works performed to implement advanced control techniques for pharmacy management in two Spanish hospitals, we propose and assess chance-constrained model predictive control (CC-MPC) as a mean to relieve this issue., The authors would like to acknowledge Junta de Andalucía (Pharmacon-trol Project, P12-TIC-2400), for funding this work.

Many of the existing molecular simulation tools require the efficient identification of the set of nonbonded interacting atoms. This is necessary, for instance, to compute the energy values or the steric contacts between atoms. Cell linked-lists can be used to determine the pairs of atoms closer than a given cutoff distance in asymptotically optimal time. Despite this long-term optimality, many spurious distances are anyway computed with this method. Therefore, several improvements have been proposed, most of them aiming to refine the volume of influence for each atom. Here, we suggest a different improvement strategy based on avoiding to fill cells with those atoms that are always at a constant distance of a given atom. This technique is particularly effective when large groups of the particles in the simulation behave as rigid bodies as it is the case in simplified models considering only few of the degrees of freedom of the molecule. In these cases, the proposed technique can reduce the number of distance computations by more than one order of magnitude, as compared with the standard cell linked-list technique. The benefits of this technique are obtained without incurring in additional computation costs, because it carries out the same operations as the standard cell linked-list algorithm, although in a different order. Since the focus of the technique is the order of the operations, it might be combined with existing improvements based on bounding the volume of influence for each atom. Copyright © 2011 Wiley Periodicals, Inc., Funded by: Spanish Ministry of Science and Innovation. Grant Number: DPI2010-18449.

The purpose of the field of statistical disclosure control is to avoid that no confidential information can be derived from statistical data released by, mainly, national statistical agencies. Controlled tabular adjustment (CTA) is an emerging technique for the protection of statistical tabular data. Given a table to be protected, CTA looks for the closest safe table. In this work we focus on CTA for three-dimensional tables using the L1 norm for the distance between the original and protected tables. Three L1-CTA models are presented, giving rise to six different primal block-angular structures of the constraint matrices. The resulting linear programming problems are solved by a specialized interior-point algorithm for this constraints structure, which solves the normal equations by a combination of Cholesky factorization and preconditioned conjugate gradients (PCG). In the past this algorithm shown to be one of the most efficient approaches for some classes of block-angular problems. The effect of quadratic regularizations is also analyzed, showing that for three of the six primal block-angular structures the performance of PCG is guaranteed to improve. Computational results are reported for a set of large instances, which provide linear optimization problems of up to 50 millions of variables and 25 millions of constraints. The specialized interior-point algorithm is compared with the state-of-the-art barrier solver of the CPLEX 12.1 package, showing to be a more efficient choice for very large L1-CTA instances.

8 páginas, 7 figuras.-- Trabajo presentado al IASTED 2010 celebrado en Cambridge del 1 al 3 de noviembre., This paper presents a design method for a reconfigurable single degree-of-freedom mechanism for robotic assisted finger therapy following a stroke. The mechanism is a four-bar linkage that in combination with variable link lengths is capable of reproducing a power grasp finger motion for a wide variety of finger sizes. This is accomplished through an optimization procedure that determines the parameters of the four-bar linkage needed to fit the sampled range of finger trajectories. The linkage is located behind the hand and attaches to the medial phalanx of the finger just above the distal interphalangeal joint. In addition, the mechanism is designed so that it does not interfere with finger motion and so that the subject‘s fingertips and palm are free to touch real objects and experience tactile feedback. In future implementations, the mechanism could be used for a single finger or in parallel with other similar mechanisms to exercise multiple fingers simultaneously. Although the specific application presented here is the four-bar mechanism and finger power grasp motion, the developed design methods may be applied to a much broader range of mechanisms and applications where scalability for human-machine interface is required.

6 páginas, 7 figuras.-- Trabajo presentado a la IROS 2010 celebrada en Taipei (Taiwan) del 18 al 22 de Octubre., In this paper we propose an efficient preconditioned conjugate gradients (PCG) approach to solving large-scale SLAM problems. While direct methods, popular in the literature, exhibit quadratic convergence and can be quite efficient for sparse problems, they typically require a lot of storage and efficient elimination orderings to be found. In contrast, iterative optimization methods only require access to the gradient and have a small memory footprint, but can suffer from poor convergence. Our new method, subgraph preconditioning, is obtained by re-interpreting the method of conjugate gradients in terms of the graphical model representation of the SLAM problem. The main idea is to combine the advantages of direct and iterative methods, by identifying a sub-problem that can be easily solved using direct methods, and solving for the remaining part using PCG. The easy sub-problems correspond to a spanning tree, a planar subgraph, or any other substructure that can be efficiently solved. As such, our approach provides new insights into the performance of state of the art iterative SLAM methods based on re-parameterized stochastic gradient descent. The efficiency of our new algorithm is illustrated on large datasets, both simulated and real., Gratefully acknowledge the support from the National Science Foundation, Awards 0713162 and 0448111 (CAREER). Viorela Ila has been partially supported by the Spanish Ministry of Science and Innovation under the Programa Nacional de Movilidad de Recursos Humanos de Investigación.

Hybridization with high specific energy storage devices like supercapacitors has important advantages in fuel cell-based systems. This paper presents an approach for the design and analysis of Fuel Cell Hybrid Systems (FCHS) oriented to automotive applications. FCHS is considered the most attractive long-term option for propulsion of passenger cars. The design stage includes the determination of the electrical topology and the determination of the hybridization degree according to drivability conditions. With the selected design, the optimal hydrogen consumption for different driving cycles and the energy flows in the hybrid vehicle are analysed. The entire study is performed with a detailed model of the FCHS in ADVISOR: the determination of the hybridization degree according to drivability requirements, the analysis of the energy flows, and the computation of the optimal hydrogen consumption. The results show that hybridization allows a significant improvement in the hydrogen economy through the recovered energy from breaking. At the same time, the results suggest a conflict between a design according to drivability conditions and a design for the highest efficiency. The conclusion is that hybridization with supercapacitors in fuel cell-based vehicles is a meaningful procedure to enhances the performance., This work was supported by the project 'Avances en el modelo y diseño de controladores para sistemas basados en pila de combustible PEM' (4800). This work was supported in part by the Spanish Government and the Department of Universities, Investigation, and Society of Information of the Generalitat de Catalunya under Project CICYT DPI2007-62966.

This paper presents a stationary and dynamic study of the advantages of using a regulating valve for the cathode outlet flow in combination with the compressor motor voltage as manipulated variables in a fuel cell system. At a given load current, the cathode input and output flowrate determine the cathode pressure and stoichiometry, and consequently determine the oxygen partial pressure, the generated voltage and the compressor power consumption. In order to maintain a high efficiency during operation, the cathode output regulating valve has to be adjusted to the operating conditions, specially marked by the current drawn from the stack. Besides, the appropriate valve manipulation produces an improvement in the transient response of the system. The influence of this input variable is exploited by implementing a predictive control strategy based on dynamic matrix control (DMC), using the compressor voltage and the cathode output regulating valve as manipulated variables. The objectives of this control strategy are to regulate both the fuel cell voltage and oxygen excess ratio in the cathode, and thus, to improve the system performance. All the simulation results have been obtained using the MATLAB-Simulink environment., This work was supported by the project 'Diseño de controladores para el proceso electroquímico en pilas de combustible de tipo PEM' (4796). This work has been funded partially by the project CICYT DPI2004-06871-C02-01 of the Spanish Government, and the support of the Department of Universities, Investigation and Society of Information of the Generalitat de Catalunya.

Under a Creative Commons license.-- Trabajo presentado a la 13th Computing and Control for the Water Industry (CCWI2015): "Sharing the best practice in water management", celebrada en De Montfort University (UK) del 2 al 4 de septiembre de 2015., This paper proposes a method to combine linear Model Predictive Control (MPC), a Constraint Satisfaction Problem (CSP)formulation and a Network Aggregation Method (NAM) for the predictive operational control of water pumping in DWNs. The proposed method can produce optimal pumping strategies for complex DWNs in short computation times, while avoiding the need for non-linear programming techniques to cater for non-linear flow-head equations. The proposed approach is simulated using Epanet to represent the hydraulic DWNs. The D-Town benchmark water network is used as a case study., This research has been funded by DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491), ECOCIS (DPI2013-48243-C2-1-R), EFFINET (FP7-ICT-2011-8-31855) and an FI grant by AGAUR.

Trabajo presentado a la 11th International Conference on Hydroinformatics celebrada en New York (US) en Octubre de 2014., Management and operation of a multiple-objective multisource water supply system from the point of view of the conjunctive use of water sources is a very complex problem whose solution is not just obtained using analytical models but also through a negotiation process among stakeholders and in which Public Bodies have a main role. For these reasons, this problem has been addressed using conservative approaches based on simulation models or simulation–linear optimization models parameterized using few parameters which, in general, are already covered by existing generalized modelling tools using a longer or shorter trial and error process. However, these conservative approaches have drawbacks and constraints when dealing with certain complexities of water supply systems (i.e.: non-linearity, uncertainty or stochastic nature) that may prevent them of finding an optimal solution. This paper identifies and tests suitable simulation-optimization approaches found in existing generalized modeling tools for optimizing operating rules of multisource water supply systems in terms of system guarantee and resulting operating costs. The main purpose is to find out whether these approaches are already covering the decision support needs of managers, Public Bodies or other stakeholders involved in the operation of these systems, or ‘ad-hoc’ tools may be needed.

Trabajo presentado en la Annual American Control Conference (ACC), celebrada en Milwaukee (WI, USA), del 27 al 29 de junio de 2018, This paper presents a model predictive control (MPC) strategy aimed to regulate the total power delivered to the grid while maximizing the power reserve. Nowadays, the high participation of wind energy in the electricity generation requires that wind power plants (WPPs) also provide ancillary services. This fact implies that WPPs must be capable of temporally increasing the power generation to help, for instance, the primary-frequency control. To this end, WPPs work below the maximum generation capacity keeping some power reserves (difference between available and generated powers). The available power depends on the wind conditions that each turbine is facing but these conditions are also affected by the wakes produced by upstream turbines. In order to satisfy the aforementioned objectives, this work proposes to cast the MPC strategy as a multi-objective optimization problem solved using a lexicographic approach in order to consider the hierarchy of the control objectives. The performance of the control scheme is evaluated by simulations for the case of a WPP with three turbines taking into account the variation of wind speed faced by downstream turbines due to the wake effect.

Presentado al 12th IFAC Symposium on Large-Scale Systems: Theory and Applications celebrado en Francia en 2010., In the present work, distributed control and artificial intelligence are combined in a control architecture for Large Scale Systems (LSS). The aim of this architecture is to provide a general structure and methodology to perform optimal control in networked distributed environments where multiple dependencies between sub-systems are found. Often these dependencies or connections represent control variables so the distributed control has to be consistent for both subsystems and the optimal value of these variables has to accomplish a common goal. The aim of the research described in this paper is to exploit the attractive features of MPC (meaningful objective functions and constraints) in a distributed implementation combining learning techniques to perform the negotiation of these variables in a cooperative Multi Agent environment and over a Multi Agent platform to provide speed, scalability, and computational effort reduction. This approach is based on negotiation, cooperation and learning. Results of the application of this architecture to a small drinking water network show that the resulting trajectories of the levels in tanks (control variables) can be acceptable compared to the centralized solution. The application to a real network (the Barcelona case) is currently under development., This work was supported by the project 'Analysis and Design of distributed optimal control strategies applied to large-scale WATer systems MANagement' (4808). The authors wish to thank the support received by WIDE-224168-FP7-ICT-2007-2 project and WATMAN ref. DPI2009-13744 of Spanish Ministry of Education. First author is supported by CONACYT and partially supported by ITESCA.

Trabajo presentado a la 26th Chinese Control and Decision Conference (2014 CCDC) celebrada del 31 de mayo al 2 de junio en Changsha (China)., In this paper, coordinating technique for the multi-layer model predictive control (MPC) of complex water network is proposed. A multi-layer topology structure resulting from a functional decomposition of water network is briefly presented. Inside each layer, a MPC based controller is used. Between the related two layers, a multi-layer coordinating mechanism is provided to generate control strategies which consider objectives and time scales of both layers. Optimal Path Method (OPM) is used to estimate aggregated water price for the whole layer. The case study is the Catalunya Regional Water Network. Simulation results based on an aggregate model of this network are provided., This research has been partially funded by CDTI (MCyT) project HIDROPTIM IDI-20100722, the DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491), the AGAUR by an FI grant and by EFFINET grant FP7-ICT-2012-318556 of the European Commission.

We propose a new approach to simultaneously recover camera pose and 3D shape of non-rigid and potentially extensible surfaces from a monocular image sequence. For this purpose, we make use of the Extended Kalman Filter based Simultaneous Localization And Mapping (EKF-SLAM) formulation, a Bayesian optimization framework traditionally used in mobile robotics for estimating camera pose and reconstructing rigid scenarios. In order to extend the problem to a deformable domain we represent the object's surface mechanics by means of Navier's equations, which are solved using a Finite Element Method (FEM). With these main ingredients, we can further model the material's stretching, allowing us to go a step further than most of current techniques, typically constrained to surfaces undergoing isometric deformations. We extensively validate our approach in both real and synthetic experiments, and demonstrate its advantages with respect to competing methods. More specifically, we show that besides simultaneously retrieving camera pose and non-rigid shape, our approach is adequate for both isometric and extensible surfaces, does not require neither batch processing all the frames nor tracking points over the whole sequence and runs at several frames per second., This work was partly funded by the MINECO projects Abdomesh DPI2011-27939-C02-01, RobInstruct TIN2014-58178-R and SVMap DPI2012-32168; by the ERA-net CHISTERA project VISEN PCIN-2013-047; and by a scholarship FPU12/04886 from the Spanish MECD.