Due to continued miniaturization, semiconductor-based components used in high-performance digital microelectronics are becoming increasingly sensitive to cosmic rays and solar particle events. In the context of high-altitude flight control systems based on fly-by-wire techniques, this may produce sensor noise or affect actuator control signals. Although the consequences so far have been simply reductions in aircraft performance, catastrophic scenarios may be envisioned. In this article, we propose a novel architecture for a fault-tolerant flight control system able to detect and compensate for cosmic ray-induced multiple-bit upsets that affect actuator control signals in modern fly-by-wire avionics systems while assuming that the actuator itself remains healthy. A fault detection and diagnosis procedure was designed using a geometric approach combined with an extended multiple-model adaptive estimation technique. This procedure is able to process multiple faulty actuator-control signals and identify their parameters. The parameters thus obtained are then used with a reconfigurable sliding-mode control to compensate for such errors by mobilizing the remaining actuators' healthy control signals. Lyapunov stability theory is used to analyze the closed-loop system stability. Simulation results using Matlab R /Simulink R showed the effectiveness of the proposed approach in the case of a system challenged with double faults. Manuscript

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ternary U-7.5 wt%-2.5 wt% Zr alloy are presented. Two hundred sixty-four different combinations of compositiontime-temperature representing 527 tests are included. Impact properties of gamma-quenched and aged binary U-4 and U-6 wt% Nb alloys, and the ternary U-7.5 Nb-2.5 Zr alloy are presented. Thirty-six different combinations of composition-time-temperature representing 106 tests are included.

Within the strongly regulated avionic engineering field, conventional graphical desktop hardware and software application programming interface (API) cannot be used because they do not conform to the avionic certification standards. We observe the need for better avionic graphical hardware, but system engineers lack system design tools related to graphical hardware. The endorsement of an optimal hardware architecture by estimating the performance of a graphical software, when a stable rendering engine does not yet exist, represents a major challenge. As proven by previous hardware emulation tools, there is also a potential for development cost reduction, by enabling developers to have a first estimation of the performance of its graphical engine early in the development cycle. In this paper, we propose to replace expensive development platforms by predictive software running on a desktop computer. More precisely, we present a system design tool that helps predict the rendering performance of graphical hardware based on the OpenGL Safety Critical API. First, we create nonparametric models of the underlying hardware, with machine learning, by analyzing the instantaneous frames per second (FPS) of the rendering of a synthetic 3D scene and by drawing multiple times with various characteristics that are typically found in synthetic vision applications. The number of characteristic combinations used during this supervised training phase is a subset of all possible combinations, but performance predictions can be arbitrarily extrapolated. To validate our models, we render an industrial scene with characteristic combinations not used during the training phase and we compare the predictions to those real values. We find a median prediction error of less than 4 FPS.