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International audience; Reusable liquid-propellant rocket engines (LPREs) imply more demanding robustness requirements than expendable ones due to their extended capabilities. Therefore, the goal of this work was to develop a control loop adapted to all the operating phases of LPRE, including transients, and robust to internal parametric variations. Firstly, thermo-fluid-dynamic simulators representative of the gas-generator-cycle engines were built. These simulators were subsequently translated into nonlinear state-space models. Based on these models, the continuous subphase of the start-up transient is controlled to track precomputed reference trajectories. Beyond the start-up, throttling scenarios are managed with end-state-tracking algorithm. Model Predictive Control has been applied in a linearised manner with robustness considerations to both scenarios, in which a set of hard state and control constraints must be respected. Tracking of pressure (thrust) and mixture-ratio operating points within the design envelope is achieved in simulation while respecting constraints. Robustness to variations in the predominant parameters, to external state perturbations and to the possible impact of an observer on the loop, is demonstrated.

Hydrogen (H) and sulfur (S) are light element candidates to enter the core of planetary bodies. Although Fe-S and Fe-H systems have been studied individually, the Fe-S-H ternary system has only bee...

Sensor or actuator faults occurring on a Unmanned Aerial Vehicle (UAV) can compromise the system integrity. Fault diagnosis methods is then becoming a required feature for those systems. In this paper, the focus is on fault estimation for a fixed-wing UAVs in the presence of simultaneous sensor faults. The altitude measurements of a UAV are commonly obtained from the combination of two different types of sensors: a Global Navigation Satellite System (GNSS) receiver and a barometer. Both sensors are subject to additive abrupt faults. To deal with the multimodal nature of the faulty modes, a Jump-Markov Regularized Particle Filter (JMRPF) is proposed in this paper to estimate the barometric altitude and GNSS altitude measurement faults, including the case when both faults occur simultaneously. This method is based on a regularization step that improves the robustness thanks to the approximation of the conditional density by a kernel mixture. In addition, the new jump strategy estimates the correct failure mode in 100 % of the 100 simulations performed in this paper. This approach is compared with an Interacting Multiple Model Kalman Filter (IMM-KF) and the results show that the JMRPF outperforms the IMM-KF approach, particularly in the ambiguous case when both sensors are simultaneously subject to additive abrupt faults.

This paper addresses the problem of searching and tracking of an a priori unknown number of targets spread over some geographical area using a fleet of UAVs. State perturbations and measurement noises are assumed to belong to bounded sets. In the monitored geographical area, some decoys may be erroneously considered as targets when observed under specific conditions. A robust bounded-error estimation approach is proposed to evaluate, at each time step, sets guaranteed to contain the actual state of already localized true targets and decoys. A set containing the states of targets still to be discovered is also evaluated. These sets are used to determine the control inputs of UAVs so as to minimize the estimation uncertainty at future time steps. Simulations involving several UAVs show that the proposed robust set-membership estimator is able to estimate the state of all actual targets and to efficiently identify and eliminate decoys.

The development of Particle Filters has made possible state estimation of dynamic systems presenting non-linear dynamics and potential multi-modalities. However, the efficiency of these approaches depends tightly of the required number of particles which may prove very high to approximate large range of uncertainty on the process or the measurements. To overcome this issue, the Box-Particle Filter (BPF) combines the versatility of the Particle Filter and the robustness of set-membership algorithms. The particles are replaced by boxes which represent in a compact way large variations of the estimates. Although this filter presents various advantages and requires a small number of boxes to estimate the state, the resulting estimates may prove pessimistic, as the uncertainty description as unions of axis-aligned intervals can be rather rough and doesn’t account for potential dependencies between the resulting estimate components. In the proposed paper, a new version of the BPF is proposed. Boxes are replaced by polytopes (multidimensional polygons) in the filter algorithm, so that they can adapt to represent state components dependency. This modification tends to ameliorate the estimation precision (i.e. the size of the final set that includes the true state decreases) while keeping the number of required polyhedrons small. Several examples illustrate the benefits of such an approach.

International audience; The main purpose of this survey paper is to review the field of convergence between the liquid-propellant rocket-propulsion and automatic-control disciplines. A comprehensive collection of academic works and some industrial developments are summarised and discussed, making the link to the current context of launcher reusability. The main control problem in liquid-propellant rocket engines (LPRE), the target of this survey, generally consists in tracking set-points in combustion-chamber pressure and mixture ratio, their main operating quantities. This goal is attained via the adjustment of flow-control valves while complying with operating constraints. Each aspect of control systems, ranging from modelling to the actual control techniques, is reviewed and subsequently related to the rest of aspects. The comparison of the different approaches points to the general use of linearised models about concrete operating points for deriving steady-state controllers, which in most cases are of PID type. Other more sophisticated approaches present in the literature, incorporating some nonlinear, hybrid or robust techniques, improve certain aspects of performance and robustness. Nevertheless, no fully nonlinear or hybrid frameworks, which may allow the control of a wider throttling domain, have been published. In addition, control during the thermally and structurally demanding transient phases is usually in open loop, and hence with low correction margins. This sort of control enhancements, probably together with more sophisticated monitoring architectures, would serve to extend the life of LPRE, a significant factor in their reusability. Therefore, this survey intends to draw a path for the future control design trends which will certainly be more suitable for reusable LPRE.

International audience; In this paper, the estimation of additive inertial navigation sensor faults with unknown dynamics is considered with application to the longitudinal navigation and control of a fixed wing unmanned aerial vehicle. The faulty measurement is on the pitch angle. A jump Markov regularized particle filter is proposed for fault and state estimation of the nonlinear aircraft dynamics, with a Markovian jump strategy to manage the probabilistic transitions between the fault free and faulty modes. The jump strategy uses a small number of sentinel particles to continue testing the alternate hypothesis under both fault free and faulty modes. The proposed filter is shown to outperform the regularized particle filter for this application in terms of fault estimation accuracy and convergence time for scenarios involving both abrupt and incipient faults, without prior knowledge of the fault models. The state estimation is also more accurate and robust to faults using the proposed approach. The root-mean-square error for the altitude is reduced by 77 % using the jump Markov regularized particle filter under a pitch sensor fault amplitude of up to 10 degrees. Performance enhancement compared to the regularized particle filter was found to be more pronounced when fault amplitudes increase.

Although high pressure promotes alloying between hydrogen and iron, it has been believed that H/Fe is limited to 1 in the close-packed alloys. We report a substantial increase in the H solubility for iron through stabilization of superstoichiometric close-packed alloys promoted by Ni and high temperature. We also observe Fe-H polyhydrides at temperatures over 2500 K at high pressures, stability of which has been unknown at temperatures relevant for the planetary interiors. Considering recent results, large storage of H in the planetary cores is possible from H2-rich to even H2O-rich conditions. The H storage in the core can change the mass-radius relations where H has been assumed to de-gas to the atmosphere. Such H solubility and storage in the core are important to consider for understanding the “diluted core” of Jupiter, the “radius cliff” in the sub-Neptune exoplanets population, and massive exoplanets with very thin atmospheres.