Abstract

International audience Business continuity of critical infrastructures (CIs) is exposed to various hazards including random failures, malicious threats, natural disasters and human errors, which could generate accidents with serious consequences (fatality, injury, environmental damage, business interruption and company reputation loss). We conceptualize the business continuity management (BCM) process as the integration of four active stages: prevention, mitigation, emergency and recovery. Integrated assessment and management is needed on all stages. On the contrary, the current approaches of BCM have not considered all phases in an integrated man-ner. We propose a new framework, which stands on an extension of the Bow-Tie model, to efficiently and effectively prevent and mitigate the potential consequences of an accident by properly designing and strengthening safety barriers for preventing and mitigating accidents, and making safety decisions for emer-gency and recovery. The proposed framework allows considering safety barriers and decisions in an integrated way. For operationalization, we explore the use of two complementary quantitative methods, Bayesian Network (BN) and Constraint Goal Method. BN takes the “negative” viewpoint of failure to determine the causes which lead to the final damage. CGM employs the positive perspective of the goal achievement process. An oil pipeline system is considered to show the application of the proposed approaches. Document type: Part of book or chapter of book

Abstract

International audience The two first CEAS (Council of European Aerospace Societies) Specialist Conferences on Guidance, Navigation and Control (CEAS EuroGNC) were held in Munich, Germany in 2011 and in Delft, The Netherlands in 2013. ONERA The French Aerospace Lab, ISAE (Institut Supérieur de l’Aéronautique et de l’Espace) and ENAC (Ecole Nationale de l’Aviation Civile) accepted the challenge of jointly organizing the 3rd edition. The conference aims at promoting new advances in aerospace GNC theory and technologies for enhancing safety, survivability, efficiency, performance, autonomy and intelligence of aerospace systems. It represents a unique forum for communication and information exchange between specialists in the fields of GNC systems design and operation, including air traffic management. This book contains the forty best papers and gives an interesting snapshot of the latest advances over the following topics:l Control theory, analysis, and designl Novel navigation, estimation, and tracking methodsl Aircraft, spacecraft, missile and UAV guidance, navigation, and controll Flight testing and experimental resultsl Intelligent control in aerospace applicationsl Aerospace robotics and unmanned/autonomous systemsl Sensor systems for guidance, navigation and controll Guidance, navigation, and control concepts in air traffic control systemsFor the 3rd CEAS Specialist Conference on Guidance, Navigation and Control the International Program Committee conducted a formal review process. Each paper was reviewed in compliance with standard journal practice by at least two independent and anonymous reviewers. The papers published in this book were selected from the conference proceedings based on the results and recommendations from the reviewers. Document type: Part of book or chapter of book

Abstract

International audience; The Cooperative Adaptive Cruise Control (CACC) is one of the most promising aiding systems to improve traffic flow in highways. When it comes to design a proper control algorithm, robustness against non-modeled dynamics and noise plays a key role not only for improving controller performance but also for increasing the ability of handling heterogeneous vehicle strings. This paper proposes a fractional order controller that is able to deal with non-modeled dynamics whereas keeping simplicity and a low computational cost. System robustness and string stability responses are analyzed for a string of six vehicles, showing a good performance.

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International audience; It is shown that the queuing delay involved in the congestion control algorithm is state-dependent and does not depend on the current time. Then, using an accurate formulation for buffers, networks with arbitrary topologies can be built. At equilibrium, our model reduces to the widely used setup by Paganini et al. Using this model, the delay-derivative is analyzed and it is proved that the delay time-derivative does not exceed one for the considered topologies. It is then shown that the considered congestion control algorithm globally stabilizes a delay-free single buffer network. Finally, using a specific linearization result for systems with state-dependent delays from Cooke and Huang, we show the local stability of the single bottleneck network.

Abstract

International audience; A nonlinear problem formulation of an energy saving model-predictive eco-cruise controller for electric vehicles is presented. With regard to the intended application in real world tests, the model has to include the specific properties of a serial electric vehicle such as energy-recovery and a discontinuous accelerator input giving rise to a binary control variable. These specific features and the nonlinearity of the system dynamics make it a challenging task to formulate the optimisation problem in a way that allows a fast computation in real-time application. The challenges are addressed by using a model of the vehicle dynamics that is formulated in terms of the vehicle position instead of time and by considering the kinetic energy instead of the velocity. Furthermore, various constraints on the input and state variables are introduced for a realistic representation of the vehicle characteristics. A special focus is put on the treatment of a binary input variable in the optimisation. Here, in order to avoid a mixed-integer formulation of the problem, a continuous variable is introduced which is forced to take only discrete values by a penalty term. Finally, first simulation results underline the feasibility of this control approach.

Abstract

International audience; This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. The controller and the underlying dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified by measurements. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. In this work, a coordinate transformation is used which leads to a linear differential motion equation without loss of information. The energy consumption map is approximated by the maximum of a set of linear functions which is implicitly determined in the optimisation problem. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the MPC controller and the simulation model in closed-loop operation finally provide a proof of concept.

Abstract

International audience; Modern embedded processors have gone through multiple internal optimization to speed-up the average execution time e.g., caches, pipelines, branch prediction. Besides, internal communication mechanisms and shared resources like caches or buses have a significant impact on Worst-Case Execution Times (WCETs). Having an accurate estimate of a WCET is now a challenge. Probabilistic approaches provide a viable alternative to single WCET estimation. They consider WCET as a probabilistic distribution associated to uncertainty or risk.In this paper, we present synthetic benchmarks and associated analysis for several LEON3 configurations on FPGA targets. Benchmarking exposes key parameters to execution time variability allowing for accurate probabilistic modeling of system dynamics. We analyze the impact of architecture-level configurations on average and worst-case behaviors.; Les processeurs embarqués modernes sont passés par optimisation interne multiple pour accélérer l'exécution moyenne par exemple le temps, les caches, les pipelines, la prédiction de branchement. Par ailleurs, mécanismes de communication et de partage des ressources comme des caches ou bus ont un impact significatif sur le temps d'exécution pire des cas (WCETs). Avoir une estimation précise d'un WCET est maintenant en défi. Les approches probabilistes fournies pour des alternatives viables à Singles WCET estimation. Ils considèrent comme un WCET probabiliste la distribution associée à l'incertitude ou le risque du pire cas.Dans cet article, nous présentons des benchmarks synthétiques et associés analyse pour plusieurs configurations LEON3 sur FPGA cibles. Benchmarking expose l'effet de certains paramètres sur la variabilité de l'exécution des taches afin de permettre à la modélisation probabiliste précise de la dynamique du système. Nous analysons l'impact des configurations sur les comportements moyens et pire cas du systeme.

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The exponential growth in air traffic and unplanned events on the day of flight schedules such as adverse weather conditions led to initial flight disruptions. In this paper, we propose a decision support framework for flight rescheduling in air traffic management based on ground delay, changing the initial flight and flight canceling. This approach is based on using Time Petri Nets (TPN) to model air traffic networks. We introduce a Binary Decision Diagram based tool to represent the state space of a class of Time Petri Nets. This tool, called Time Reduced Ordered Binary Decision Diagram (T-ROBDD) is able to represent a large state space of a TPN with a small data structure and enable the efficient manipulation of this set. In what follows, we developed a technique to generate all the rescheduling flights plans taking into account the temporal and spatial constraints.

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International audience; Semi-autonomous driving assistance systems have a high potential to improve the safety and efficiency of the battery electric vehicles that are enduring limited cruising range. This paper presents an ecologically advanced driver assistance system to extend the functionality of the adaptive cruise control system. A real-time stochastic non-linear model predictive controller with probabilistic constraints is presented to compute on-line the safe and energy-efficient cruising velocity profile. The individual chance-constraint is reformulated into a convex second-order cone constraint which is robust for a general class of probability distributions. Finally, the performance of proposed approach in terms of states regulation, constraints fulfilment, and energy efficiency is evaluated on a battery electric vehicle.

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International audience; Nowadays pedestrian detectors are fast, scale-robust and quite efficient. Embedded within a UAV such a detector would open new possibilities. In this paper the very well known HOG detector is adapted for UAV use and a new kind of training dataset is proposed in order to increase the detector's angular robustness. A more appropriate set of detection windows, together with a new detection pipeline, is proposed in order to reduce the search space and consequently reduce the computation time. Tests conducted using the improved detector show significantly better results on aerial images.