Search

In this study, we propose a novel method that integrates Nonlinear Model Predictive Contour Control (NMPCC) with an Exponentially Stabilizing Control Lyapunov Function (ES-CLF) and Exponential Higher-Order Control Barrier Functions to achieve stable path-following and obstacle avoidance in UAV systems. This framework enables unmanned aerial vehicles (UAVs) to safely navigate around both static and dynamic obstacles while strictly adhering to desired paths. The quaternion-based formulation ensures precise orientation and attitude control, while a robust optimization solver enforces the constraints imposed by the Control Lyapunov Function (CLF) and Control Barrier Functions (CBF), ensuring reliable real-time performance. The method was validated in a Model-in-the-Loop (MiL) environment, demonstrating effective path tracking and obstacle avoidance. The results highlight the framework's ability to minimize both orthogonal and tangential errors, ensuring stability and safety in complex environments., Comment: Submitted to IEEE Access for review

This paper presents a comprehensive approach to nonlinear dynamics identification for UAVs using a combination of data-driven techniques and theoretical modeling. Two key methodologies are explored: Proportional-Derivative (PD) approximation and Sparse Identification of Nonlinear Dynamics (SINDy). The UAV dynamics are first modeled using the Euler-Lagrange formulation, providing a set of generalized coordinates. However, platform constraints limit the control inputs to attitude angles, and linear and angular velocities along the z-axis. To accommodate these limitations, thrust and torque inputs are approximated using a PD controller, serving as the foundation for nonlinear system identification. In parallel, SINDy, a data-driven method, is employed to derive a compact and interpretable model of the UAV dynamics from experimental data. Both identified models are then integrated into a Model Predictive Control (MPC) framework for accurate trajectory tracking, where model accuracy, informed by data-driven insights, plays a critical role in optimizing control performance. This fusion of data-driven approaches and theoretical modeling enhances the system's robustness and adaptability in real-world conditions, offering a detailed analysis of the UAV's dynamic behavior.

IJCLab has been leading the development and deployment of low-$\beta$ Superconducting Radio Frequency (SRF) cavities for proton and heavy ion accelerators. We are launching an electron accelerator project for sustainable Energy Recovery Linac (iSAS/PERLE) with state-of-the-art SRF cavities at 800~MHz. Our proposal includes advanced heat treatment of such cavities to reach an excellent quality factor of $3\times 10^{10}$ at $22$~MV/m. In this paper, we overview the status of this activity., Comment: Contribution to LCWS2024 proceedings

Not all logical systems can be captured using algebras. We see this in classical logic (formalized by Boolean algebras) and many-valued logics (like Lukasiewicz logic with MV-algebras). Even quantum mechanics, initially formalized with orthomodular lattices, benefits from a simpler approach using just partially ordered sets (posets). This paper explores how logical connectives are introduced in poset-based logics. Building on prior work by the authors, we delve deeper into "dynamic" logics where truth values can change over time. We consider time sets with a preference relation and propositions whose truth depends on time. Tense operators, introduced by J.Burgess and extended for various logics, become a valuable tool. This paper proposes several approaches to this topic, aiming to inspire a further stream of research.

The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on photogrammetry. However, the photogrammetry approach presents limitations, such as an increased amount of useless data during flights, potential issues related to image resolution, and the detection process during high-altitude flights. In this work, we develop a visual servoing control system applied to a UAV with dynamic compensation using a nonlinear model predictive control (NMPC) capable of accurately tracking the middle of the underlying PV array at different frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on the extraction of features using RGB-D images and the Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture. Our approach is available for the scientific community in: https://github.com/EPVelasco/VisualServoing_NMPC, Comment: This paper has been accepted for publication in IEEE Robotics and Automation Letters

Within the framework of the first phase of MYRRHA (Multi-purpose hYbrid Research Reactor for High-tech Applications) project, called MINERVA, IJCLab was in charge of a fully equipped Spoke cryomodule prototype development, tested at 2K. It integrates two superconducting single spoke cavities, the RF power couplers and the Cold Tuning Systems associated. On the control side, a MTCA.4-based Low Level Radio Frequency (LLRF) system prototype and the Software/EPICS developments has been realized by IJCLab and the SCK CEN in collaboration with the company IOxOS Technologies. The final version of the global system and the results of the tests at 2K will show with some perspectives., Comment: Poster pr\'esent\'e au LLRF Workshop 2023 (LLRF2023, arXiv : 2310.03199)

The G-NUMEN array is the future gamma spectrometer of the NUMEN experiment (Nuclear Matrix Element for the Neutrinoless double beta decay), to be installed around the object point of the MAGNEX magnetic spectrometer at the INFN-LNS laboratory. This project aims at exploring Double Charge Exchange (DCE) reactions in order to obtain crucial information about the neutrinoless double beta decay ($0\nu\beta\beta$). The primary objective of the G-NUMEN array is to detect the gamma rays emitted from the deexcitation of the excited states populated via DCE reactions with good energy resolution and detection efficiency, amidst a background composed of transitions from competing reaction channels with far higher cross sections. To achieve this, the G-NUMEN signals will be processed in coincidence with those generated by the detection of the reaction ejectiles in the MAGNEX Focal Plane Detector(FPD). Under the expected experimental conditions, G-NUMEN detectors will operate at high counting rates, of the order of hundreds of kHz per detector, while maintaining excellent energy and timing resolutions. The complete array will consist of over 100 LaBr$_3$(Ce) scintillators. Initial tests have been conducted on the first detectors of the array, allowing for the determination of their performance at high rates., Comment: 14 pages Submitted to Instruments

Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the dilatation caused by heat release does not have any relevant effect on turbulent kinetic energy with respect to non-reacting flow, while at low Karlovitz number, the mean shear is a sink of turbulent kinetic energy, and counter-gradient transport is observed. This latter phenomenon is not well captured by closure models commonly used in Large Eddy Simulations that are based on gradient diffusion. The massive amount of data available from Direct Numerical Simulation (DNS) opens the possibility to develop data-driven models able to represent physical mechanisms and non-linear features present in both these regimes. In this work, the databases are formed by DNSs of two planar hydrogen/air flames at different Karlovitz numbers corresponding to the two asymptotic regimes. In this context, the Generative Adversarial Network (GAN) gives the possibility to successfully recognize and reconstruct both gradient and counter-gradient phenomena if trained with databases where both regimes are included. Two GAN models were first trained each for a specific Karlovitz number and tested using the same dataset in order to verify the capability of the models to learn the features of a single asymptotic regime and assess its accuracy. In both cases, the GAN models were able to reconstruct the Reynolds stress subfilter scales accurately. Later, the GAN was trained with a mixture of both datasets to create a model containing physical knowledge of both combustion regimes. This model was able to reconstruct the subfilter scales for both cases capturing the interaction between heat release and turbulence closely to the DNS as shown from the turbulent kinetic budget and barycentric maps., Comment: 24 pages, 11 figures. This article has been submitted for publication in Combustion Science and Technology, published by Taylor & Francis

Using Pascal triangle, we give a simple generalization to the so-called STRAND Puzzle solved by Srinivasa Ramanujan. Thus we are interested in computing the median, first and third quartiles of some integer valued distributions, arising naturally when extending partial sums of the arithmetic progression (triangular numbers) to tetrahedral numbers and beyond. We show this reduces to equations of Pell-Fermat type of higher order, which admit very few integer solutions, but for which, following Ramanujan's original idea, we can always find integer sequences of best approximation, in the Diophantine sense. In absence of a general theory on Pell-Fermat equation of higher order, our procedure relies much on formal Calculus with Mathematica.

The first phase of the MYRRHA (Multi-purpose hYbrid Research Reactor for High-tech Applications) project, MINERVA, was launched in September 2018. Through collaboration with the SCK-CEN, IN2P3 laboratories take in charge the developments of several parts of the accelerator, including a fully equipped Spoke cryomodule prototype and a cold valves box. This cryomodule will integrate two superconducting single spoke cavities operating at 2K, the RF power couplers and the cold tuning systems associated. For control and regulation purpose, a mTCA LLRF system prototype is being implemented and will be presented here alongside with the hardware, VHDL and EPICS developments that aim to fulfil MYRRHA's ambitious requirements., Comment: Poster presented at LLRF Workshop 2019 (LLRF2019, arXiv:1909.06754)

The Planck satellite in orbit mission ended in October 2013. Between the end of Low Frequency Instrument (LFI) routine mission operations and the satellite decommissioning, a dedicated test was also performed to measure the Planck telescope emissivity. The scope of the test was twofold: (i) to provide, for the first time in flight, a direct measure of the telescope emissivity; and (ii) to evaluate the possible degradation of the emissivity by comparing data taken in flight at the end of mission with those taken during the ground telescope characterization. The emissivity was determined by heating the Planck telescope and disentangling the system temperature excess measured by the LFI radiometers. Results show End of Life (EOL) performance in good agreement with the results from the ground optical tests and from in-flight indirect estimations measured during the Commissioning and Performance Verification (CPV) phase. Methods and results are presented and discussed., Comment: 18 pages, 18 pictures. Results from the Planck Satellite "End Of Life" Test Campaign

We consider the ferromagnetic Ising model on the Cayley tree and we investigate the decomposition of the free state into extremal states below the spin glass temperature. We show that this decomposition has uncountably many components. The tail observable showing that the free state is not extremal is related to the Edwards-Anderson parameter, measuring the variance of the (random) magnetization obtained from drawing boundary conditions from the free state.

Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualising such variation in high dimensional data. We provide a detailed examination of these plots, with the aid of examples and simulation, explaining what they are and what they can reveal. RLE plots are particularly useful for assessing whether a procedure aimed at removing unwanted variation, i.e. a normalisation procedure, has been successful. These plots, while originally devised for gene expression data from microarrays, can also be used to reveal unwanted variation in many other kinds of high dimensional data, where such variation can be problematic., Comment: 9 pages, 3 figures

We investigate the dynamics of large-scale interacting neural populations, composed of conductance based, spiking model neurons with modifiable synaptic connection strengths, which are possibly also subjected to external noisy currents. The network dynamics is controlled by a set of neural population probability distributions ($\mathrm{PPD}$)which are constructed along the same lines as in the Klimontovich approach to the kinetic theory of plasmas. An exact non-closed, nonlinear, system of integro-partial differential equations is derived for the $\mathrm{PPD}$s. As is customary, a closing procedure leads to a mean field limit. The equations we have obtained are of the same type as those which have been recently derived using rigorous techniques of probability theory. The numerical solutions of these so called McKean-Vlasov-Fokker-Planck equations, which are only valid in the limit of infinite size networks, actually shows that the statistical measures as obtained from $\mathrm{PPD}$s are in good agreement with those obtained through direct integration of the stochastic dynamical system for large but finite size networks. Although numerical solutions have been obtained in the case of Fitzhugh-Nagumo model neurons, the theory can be readily applied to systems of Hodgkin-Huxley type model neurons of arbitrary dimension., Comment: 20 pages

We consider translation-invariant splitting Gibbs measures (TISGMs) for the $q$-state Potts model on a Cayley tree of order two. Recently a full description of the TISGMs was obtained, and it was shown in particular that at sufficiently low temperatures their number is $2^{q}-1$. In this paper for each TISGM $\mu$ we explicitly give the set of boundary conditions such that limiting Gibbs measures with respect to these boundary conditions coincide with $\mu$., Comment: 15 pages, 8 figures