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The paper proposes an alternative methodology to build Linear Fractional Transformation (LFT) models of uncertain aeroelastic systems described by Fluid-Structure Interaction (FSI) solvers with the aim of studying flutter with the μ analysis technique from robust control. Two main issues can be identified for the fulfillment of this task. On the one hand, there is the difficult reconciliation between sources of physical uncertainty (well distinguishable in the original high-order system) and the abstracted uncertainties (defined in the reduced-order size representation used for the robust analyses). On the other hand, the large size of the resulting LFT model can prevent the application of robust analysis techniques. The solution proposed here consists of a symbolic LFT algorithm applied at FSI solver level, which guarantees the connection between the physical uncertainties and the parameters captured by the LFT. It also alleviates the final size of the LFT by exploiting the modal-oriented approach taken in introducing the uncertainties. Application of the framework using an unconventional aircraft layout as case study is finally discussed. Proceedings of the 18th European Control Conference (ECC 2019) ISBN:978-3-907144-00-8 ISBN:978-3-907144-01-5 ISBN:978-1-7281-1314-2

Autonomous vehicles (AVs) must interact with other road users including pedestrians. Unlike passive environments, pedestrians are active agents having their own utilities and decisions, which must be inferred and predicted by AVs in order to control interactions with them and navigation around them. In particular, when a pedestrian wishes to cross the road in front of the vehicle at an unmarked crossing, the pedestrian and AV must compete for the space, which may be considered as a game-theoretic interaction in which one agent must yield to the other. To inform AV controllers in this setting, this study collects and analyses data from real-world human road crossings to determine what features of crossing behaviours are predictive about the level of assertiveness of pedestrians and of the eventual winner of the interactions. It presents the largest and most detailed data set of its kind known to us, and new methods to analyze and predict pedestrian-vehicle interactions based upon it. Pedestrian-vehicle interactions are decomposed into sequences of independent discrete events. We use probabilistic methods - logistic regression and decision tree regression - and sequence analysis to analyze sets and sub-sequences of actions used by both pedestrians and human drivers while crossing at an intersection, to find common patterns of behaviour and to predict the winner of each interaction. We report on the particular features found to be predictive and which can thus be integrated into game-theoretic AV controllers to inform real-time interactions.

This study presents a methodology for collaboratively designing laboratory experiments and developing key performance indicators for the testing and validation of novel power system control architectures in multiple laboratory environments. The contribution makes use of the smart grid architecture model as it facilitates the integration of individually developed control functions into a consolidated solution for laboratory validation and testing. The experimental results obtained across multiple laboratories can be efficiently compared, when the proposed methodology is adopted and thus the study offers means of support for improved cooperation in smart grid validation and round robin testing. The work in this paper has been supported by the European Commission under the FP7 ELECTRA IRP (grant no: 609687).

The aim of the paper is to present offshore wind farm wake observed from satellite Synthetic Aperture Radar (SAR) wind fields from RADARSAT-1/-2 and Envisat and to compare these wakes qualitatively to wind farm wake model results. From some satellite SAR wind maps very long wakes are observed. These extend several tens of kilometres downwind e.g. 70 km. Other SAR wind maps show near-field fine scale details of wake behind rows of turbines. The satellite SAR wind farm wake cases are modelled by different wind farm wake models including the PARK microscale model, the Weather Research and Forecasting (WRF) model in high resolution and WRF with coupled microscale parametrization. International audience

Present study has been financed by the EU-project PreventEscape (Project number: 226885 www.preventescape.eu) and FATFISH project, funded by Spanish Ministry of Science and Innovation (CTM2009-14362-CO2-01). Descripción de la problemática de los escapes de peces desde la jaulas flotantes en el Mediterráneo.