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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 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 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.