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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.

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

This paper develops a Super-Twisting Algorithm controller via a nonlinear disturbance observer for a DC motor system subject to matched and unmatched uncertainties. The proposed controller is based upon a novel Nonlinear Disturbance Observer structure which uses the concept of total disturbance estimation in order to estimate simultaneously the matched and the unmatched uncertainties in the system. This estimation is incorporated then in a composite observer based controller where the stability analysis is given. Simulations results illustrate the effectiveness of the proposed controller compared to other some existing control designs and show its advantages in terms of disturbance rejection, nominal performance recovery and chattering reduction.

In this paper, a time varying disturbance observer subject to sampled data's measurements is proposed for a class of non-linear systems. The proposed observer combines the advantage of a high gain structure in terms of convergence speed and an output predictor which remains continuous between the sampling times. The exponential convergence of the proposed observer is proved using a Lyapunov function adapted to impulsive systems.

In this paper we developed a passive fault tolerant control strategy for the diesel engine air path. This strategy is carried out under the concept of Higher Order Sliding Mode Control (HOSMC). The proposed fault tolerant strategy incorporates a super twisting controller which handles parametric uncertainties and actuator failures. In this paper we consider two types of actuator failures, additive and loss-of-effectiveness faults. The simulations of the proposed controller on a recently validated experimental air path diesel engine model, show good results for actuator failures conditions even in the presence of uncertainties on model parameters.

Depending strongly on the parameter tuning, the performances of model predictive control can be improved using the online process identification. Such an approach can't always ensure optimal results when the process parameters change with time. In this paper, an indirect adaptive model predictive control supervised by fuzzy logic is proposed to solve this issue. The predictive controller parameters are computed on line regarding measurable performance criteria and inequality constraints on control and output signals. A performance comparison in terms of overshoot, stability and rise time is carried out to highlight the relevance of this approach.

International audience; This paper investigates H∞ predictive control problem for a class of networked control system, and presents a method for quantitatively selecting the control weighting parameter. Based on Lyapunov function approach and H∞ control theory, a generalized predictive state feedback controller is derived to guarantee the closed-loop networked system stable. Then, an algebraic method for selecting a feasible GPC control-weighting parameter is proposed. The contribution realizes the adaptive parameter tuning for GPC with respect to response demands and performance. A simulation example demonstrates the feasibility and practicability of the proposed method.

In this paper, a fault-tolerant fuzzy model-predictive control with the integral action method for a class of nonlinear uncertain systems is proposed. Nonlinear uncertain systems subject to actuators and/or sensors faults are represented by the Takagi–Sugeno (T-S) fuzzy model. The objective is to design a stable, robust and efficient fault-tolerant controller based on a T-S fuzzy observer with measurable premise variables. The proposed T-S fuzzy observer estimates state vector and faults. Based on Lyapunov theory, the trajectory tracking performances and the closed-loop system stability are analysed. The gains of the fuzzy observer and the pre-stabilized control law are obtained by solving linear matrix inequalities. Simulation results illustrate the robustness of the proposed controller with respect to uncertainties on an academic mathematical system.

This paper proposes a Fuzzy Fault Tolerant Predictive Control (FFTPC) with integral action method for a class of nonlinear systems. The Takagi-Sugeno (T-S) fuzzy approach is introduced as a modelling technique in order to consider the active control methods adapted to linear models. The proposed control strategy is based on a combination between Parallel Distributed Compensation (PDC) control law and Model Predictive Control (MPC) where the T-S fuzzy aspect uses the Unmeasurable Premise Variables (UPV). A T-S fuzzy observer provides an L2 norm estimation of system state vector and faults. The controller and observer gains are obtained by solving Linear Matrix Inequalities (LMIs) derived from the Lyapunov theory. The validity of the proposed Fault Tolerant Control (FTC) strategy is illustrated through an application to a Diesel Engine Air Path (DEAP) system.

Due to the technological development, the electronic power progress and economic stake, through the use of Wound Rotor Induction Motor (WRIM) has taken more and more places in different domains (transport, energy production, electric drive..,) thanks to their robustness, efficiency and lower costs. Despite the performed work researches and the improvement that has been brought, these machines still remain the potential seats of failures both in stator and rotor levels. Consequently, WRIM faults detection is currently one of the centers of interest of several researches of both academic and industrial laboratories. In fact, this article addresses this problem by the use of Principal Components Analysis (PCA) for faults detection in electrical machine used to control the doors of the aircraft thrust reverser. An accurate analytic modeling of healthy and faulted WRIM is suggested to perform the data matrix needed for PCA method. Tests were achieved using a numeric simulator on Matlab/Simulink software. Analysis of WRIM simulation proves the efficiency of PCA method. Several simulation results will be presented and discussed.

International audience; In this paper a fuzzy controller is proposed to regulate the intake manifold pressure and the fresh mass airflow of diesel engines simultaneously. The instrumentation set usually embedded in a mass-produced passenger car has been considered. Unlike many multi-variable controllers, the proposed structure requires neither an internal model nor identification algorithms. In comparison to controllers embedded at present in standard engine control units (ECUs), it improves the trajectory tracking of desired outputs during simulation of EURO cycles. Because of its performance, the fuzzy controller has been implemented in an electronics control unit. Some real-time results are presented.

In this paper, a passive fault tolerant control strategy carried out under the concept of higher order sliding mode control is developed for the diesel engine air path. The proposed fault tolerant strategy incorporates a Super-Twisting algorithm controller which handles parametric uncertainties and actuator faults. In this paper we consider two types of actuator faults, additive and loss-of-effectiveness faults. Theoretical results on the convergence of the proposed controller based on the Lyapunov theory are presented. The simulations of the proposed controller on a recently validated experimental air path diesel engine model show good results under actuator faults conditions even in the presence of parametric uncertainties.

A new strategy based on a fuzzy multi-variable controller is proposed to regulate both the fresh airflow and the intake manifold pressure. The air system controller requires neither an internal model nor certain feed-forward maps. Taking only into account standard engine measurements, it is intrinsically robust and very easy to tune with respect to the PID strategy embedded in the electronic control unit of standard cars. A part of speed trajectory defined by the Euro 5 standard has been simulated to compare the both strategies.

This paper investigates second order sliding mode control. The use of the super twisting algorithm for the combustion engine air path is to achieve fault-tolerance and to enhance the chattering phenomenon. Comparing to the adaptive sliding mode controller (ASMC), the obtained results showed good performances for actuator fault-accommodation even in the presence of uncertainties on model parameters.

International audience; In this paper, with regards to discrete-time networked control systems with incomplete observations, a novel optimal linear estimator design is presented. Through a threshold-length buffer coupled to the estimator and a dropping over-delayed packet strategy, the packet arrival process is artificially limited to time pre-threshold at current moment. According to statistics and probability theory, we model the packet arrival probability subject to the choice of threshold. Based on linear matrix inequality (LMI) theorem and modified Riccati equation with packet arrival probability, with the existence of a critical bound of packet arrival rate, we can completely establish the relation between the filter convergence and the threshold-based packet arrival process as well as characterize the filter performance. Examples demonstrate the feasibility and practicability of the proposed method.

The paper develops a sliding mode controller via nonlinear disturbance observer for diesel engine air path system subject to matched and unmatched disturbances. The proposed controller is based upon a novel nonlinear disturbance observer (NDO) structure which uses the concept of total disturbance estimation in order to estimate simultaneously the matched and the unmatched disturbances in the system. This estimation is then incorporated in a composite controller which alleviates the chattering problem and maintains the nominal performance of the system in the absence of disturbances. Simulations results of the proposed controller on a recently validated experimental air path diesel engine model show that the proposed methods exhibit a better performances comparing to the baseline SMC in terms of reducing chattering and nominal performances recovery.

International audience; This paper deals with the problem of data validation of an instrumentation system applied in the aerospace area. The fault diagnosis method used for the validation is based on the principle of the parity space approach. Residuals are generated thanks to the analytical redundancy relations given by the model and the important number of sensors. Indeed, we propose a procedure, which permits us to compute systematically all the redundancy equations for the residual generation phase. The additional concept, called residuals structuration, is necessary to isolate the detected faults. Finally, the data validation task consists in isolating the failing data and in sending only the valid information to a control system. An application to an aerospace system illustrates the proposed algorithm.

This paper is concerned with the problem of data validation of an instrumentation system. The fault diagnosis method used for the validation is based on the principle of the parity space approach. Residuals are generated thanks to the Analytical Redundancy Relations ARR given by the model and the important number of sensors. The additional concept of residuals structuration is necessary to isolate the detected faults. Finally, data validation task consists in isolating the failing data and in sending only the valid information to control system. An application to an aerospace system illustrates the proposed algorithm.

The research work presented in the present PhD thesis focuses on the design and tuning of robust and intelligent control laws for a convertible Quad Tilt-Wings (QTW) drone. A fuzzy Takagi-Sugeno (T-S) based formulation of the studied drone was adopted for the modeling. Faced with the complexity of these ying systems, advanced linear and nonlinear control methods, allowing less complex tuning algorithms and reduced design time and resources, has been proposed and successfully applied to stabilize the attitude and altitude dynamics of the drone. To better manage this complexity, two effective control parameters' tuning methods are proposed. In the first approach, the parameters' selection problem is investigated via the reformulation in the form of static optimization problems under constraints of classical control approaches such as LQG, fuzzy, predictive MPC and nonlinear Backstepping. The principle consists in favoring performance indices such as ISE (Integral of Square Error) and MO (Maximum Overshoot) criteria derived from the theory of optimal control under various operational constraints. A second systematic and efficient tuning approach is proposed for the adaptive tuning of the predictive MPC parameters based on a dynamic metaheuristic algorithm such as a quantum particle swarm optimization (mQPSO). Such an online optimization algorithm makes an easy and systematic tuning of the main and effective parameters of the well-known MPC control approach. High performance specifications are satisfied and improved for the stabilization and tracking of the flight attitude and altitude dynamics., Ces travaux de recherche portent sur la synthèse de lois de commande robuste et intelligente pour le contrôle d'un drone convertible à ailes/rotors basculants QTW. Une formulation multimodèle floue de type Takagi-Sugeno (T-S) a été adoptée pour la modélisation du système complexe étudié. Face à la complexité de ces systèmes volants, la proposition de méthodes de commande avancée, permettant l'élaboration de lois de commande robuste et performante tout en réduisant au mieux le temps de conception et les ressources de calcul des paramètres de réglage, constitue une préoccupation d'actualité dans ce cadre. Pour mieux gérer cette complexité, deux méthodes de réglage et d'optimisation des paramètres de commande sont proposées dans cette thèse. Dans la première approche, le problème du choix de ces paramètres effectifs est contourné via la reformulation sous forme des problèmes d'optimisation statique sous contraintes des approches classiques de contrôle LQG, floue, prédictive MPC et non linéaire par Backstepping. Le principe consiste à favoriser des indices de performance tels que les critères ISE (Integral of Square Error) et MO (Maximum Overshoot) issus de la théorie de la commande optimale sous diverses contraintes opérationnelles. Une deuxième approche est proposée pour le réglage adaptatif en ligne des paramètres de la commande prédictive MPC basée sur un algorithme métaheuristique d'optimisation dynamique à comportement quantique (mQPSO). Cette dernière permet d'ajuster rapidement et avec une efficacité remarquable les paramètres effectifs de la commande MPC en assurant la stabilité optimale des sous modèles locaux issus d'une formulation multimodèle floue de type T-S (Takagi-Sugeno) du drone convertible QTW étudié.

Even though predictive control uses concrete parameters, the value of these latter has a strong impact on theobtained performances from the system to be controlled. Their tuning is not trivial. That is why the literaturereports a number of adjustment methods. However, these ones do not always guarantee optimal values. The goal of this thesis is to propose an analytical and original tuning tuning approach of these parameters. Initially applicable to linear MIMO systems, the proposed approach has been extended to non-linear systems with or without constraints and for which a Takagi-Sugeno (T-S) model exists. The class of nonlinear systems considered here is written in quasi-linear parametric form (quasi-LPV). Assuming that the system is controllable and observable, the proposed method guarantees the optimal stability of this closed-loop system.To do this, it relies, on the one hand, on a conditioning improving technique of the Hessian matrix and, on the other hand, on the concept of effective rank. It also has the advantage of requiring a lower computational load than the approaches identified in the literature.The interest of the proposed approach is shown through the simulation on different systems of increasing complexity. The work carried out has led to a self-adaptive predictive control strategy called "ATSMPC" (Adaptive Takagi-Sugeno Model-based Predictive Control)., Bien que la commande prédictive fasse appel à des paramètres ayant une signification concrète, la valeur de ces derniers impacte fortement les performances obtenues du système à contrôler. Leur réglage n’étant pas trivial, la littérature fait état d’un nombre conséquent de méthodes de réglage. Celles-ci ne garantissent cependant pas des valeurs optimales. L’objectif de cette thèse est de proposer une approche analytique et originale de réglage de ces paramètres. Initialement applicable aux systèmes MIMO linéaires, l’approche proposée a été étendue aux systèmes non linéaires avec ou sans contraintes et pour lesquels il existe un modèle Takagi-Sugeno (T-S). La classe des systemès non linéaires considérés ici est écrite sous la forme quasi-linéaire paramétrique (quasi-LPV). Sous l’hypothese que le système soit commandable et observable, la méthode proposée garantit la stabilité optimale de ce système en boucle fermée. Pour ce faire, elle s’appuie, d’une part, sur une technique d’amélioration du conditionnement de la matrice hessienne et, d’autre part, sur le concept de rang effectif. Elle présente également l’avantage de requérir une charge calculatoire moindre que celle des approches identifiées dans la littérature. L’intérêt de l’approche proposée est montré à travers l’application en simulation à différents systèmes de complexité croissante. Les travaux menés ont permis d’aboutir à une stratégie de commande prédictive auto-adaptative dénommée "ATSMPC" (Adaptive Takagi-Sugeno Model-based Predictive Control).

This thesis mainly focuses on Fuzzy Fault Tolerant Predictive Control for a class of nonlinear systems. The Takagi-Sugeno (T-S) fuzzy approach is introduced as a modelling technique in order to consider the active control methods adapted to linear models. To obtain the convex form, two approches are applied in this work, the proposed global non stationary linearization method and the sector nonlinearity approach. The contributions of this thesis and novelties with respect to other works are based on a combination between Parallel Distributed Compensation control law and Model Predictive Control where the T-S fuzzy aspect uses measured and unmeasured premise variables. The optimization problem is formulated as a quadratic programming problem. A nonlinear observer and A T-S fuzzy observer are designed for the proposed strategies, in order to estimate faults and system state variables. The controller and observer gains are obtained by solving Linear Matrix Inequalities (LMIs) derived from the Lyapunov theory. Convergences are performed by using Lyapunov asymptotic stability and L2 optimization. Actually, the use of the sector nonlinearity approach has reduced the conservatism related to the number of LMIs to solve. On top of that, the chosen form of the candidate function of Lyapunov and the T-S fuzzy structure have significantly decreased the pessimism of sufficient stability conditions derived from Lyapunov theories. The proposed Fuzzy model based predictive control is designed to achieve desired set points and control objectives in the the healthy operating and to accommodate and tolerate unexpected faults. Furthermore, the uncertain case and robustness with respect to constraints are investigated. The effectiveness and the validity of the proposed Fault Tolerant Control (FTC) strategies is illustrated through an application to an academic example and to a Diesel Engine Air Path (DEAP) system., Cette thèse porte sur la synthèse de lois de commande prédictive floue tolérante aux défauts pour les systèmes non linéaires modélisés selon l'approche dite T-S. Ma contribution est de proposer une FMPC (Fuzzy Model-based Predictive Control) visant à améliorer les performances d'un système non linéaire tout en respectant les contraintes sur la commande. L’optimisation de la commande nécessite la résolution d'un problème de programmation quadratique et une résolution d’inégalités matricielles linéaires (LMIs) dérivées des thèories de Lyapunov. La stratégie proposée a été appliquée en simulation à un système SISO non linéaire puis au système d'air d'un moteur Diesel en présence de défauts de type actionneur, capteur ou système, de perturbations et d'incertitudes de modélisation