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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.

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).

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 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 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.

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.

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.

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.