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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.

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; A successful implementation of Model Predictive Control (MPC) requires appropriately tuned parameters. In This paper an Artificial Neural Network (ANN) based approach is presented. To build the data learning base of the ANN, we adopted the Particle Swarm Optimisation (PSO) method, and we used an Online Sequential Extreme-Learning-Machine (OS-ELM) algorithm to learn the ANN. The objective of this work is to to ensure robustness of the closed-loop system against neglecting dynamics, disturbances and sensor noises, and also show that good tuning of MPC parameters allows to reach closed-loop stability without using robustification approaches in addition to MPC. The effectiveness of our approach has been emphasized by comparing the obtained performances to Generalized Predictive Control (GPC) approach without robustification approach, and also to a robustified (GPC) using Youla parametrisation in the presence of disturbances.

International audience; A successful implementation of Model Predictive Control (MPC) requires appropriately tuned parameters. This paper presents a novel tuning approach based on Artificial Neural Network (ANN). To build the data learning base of the ANN, we adopted the Particle Swarm Optimisation (PSO) method, and we used the reliable algorithm, Online Sequential Extreme-Learning-Machine (OS-ELM) to learn the ANN. The objective of this work is to show that good tuning of MPC parameters makes it possible to reach closed-loop stability and ensure robustness against disturbances and sensor noises, without using robustification approaches in addition to MPC. The effectiveness of our approach is brought to light by comparing the obtained performances to other MPC tuning approaches without disturbances, and also to a robustified Generalized Predictive Control (GPC) using Youla parametrisation in the presence of disturbances.

International audience; This paper addresses the implementation of a novel approach to the design of Model Predictive Control (MPC) on a brandy distillation process. This new approach is based on multi-objective optimization with prioritization and switching between multiple models obtained at different operating phases. At each sampling step, several conflicting objectives are to be optimized under an order of priority that may be changed from one phase of the process working cycle to another. The proposed Switched Multi-Objective Prioritizing MPC (S-MOP-MPC) is compared with a PID-based control structure in terms of various performance indices. The results show that S-MOP-MPC provides better control and better performance than the PID-based control structure.