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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.

The current context of launchers reusability requires the improvement of control algorithms for their liquid-propellant rocket engines. Their transient phases are generally still performed in open loop. In this paper, it is aimed at enhancing the control performance and robustness during the fully continuous phase of the start-up transient of a generic gas-generator cycle. The main control goals concern end-state tracking in terms of combustion-chamber pressure and chambers mixture ratios, as well as the verification of a set of hard operational constraints. A controller based on a nonlinear preprocessor and on linear MPC (Model-Predictive Control) has been synthesised, making use of nonlinear state-space models of the engine. The former generates the full-state reference to be tracked while the latter achieves the aforementioned goals with sufficient accuracy and verifying constraints for the required pressure levels. Robustness considerations are included in the MPC algorithm via an epigraph formulation of the minimax robust optimisation problem, where a finite set of perturbation scenarios is considered., Comment: IFAC ACA 2019, Cranfield, UK. 6 pages