Les avionneurs expriment le besoin de développement de l’avion plus « électrique ». Cela se traduit par des besoins nouveaux en matière de systèmes de motorisation électrique, en termes de puissance à fournir et de partage de ressources matérielles en vue de minimiser la masse embarquée et les coûts. Parmi les études en cours sur l’évolution des machines tournantes, un intérêt particulier porte sur le développement des alterno-démarreurs de type machines synchrones sans balais et à plusieurs étages (BSSG). Celles-ci sont susceptibles de fournir un fort couple lors des phases de démarrage des réacteurs auxquelles elles sont associées. Pour ce faire, la connaissance, à tout moment, de la position du rotor est essentielle. Cependant, l'ajout d’un capteur dédié impacte la conception de la machine, rajoutant du volume, du câblage et augmentant le coût. La réalisation d'une commande dite « sensorless » permettrait de s'affranchir de l'utilisation d'un tel capteur et de simplifier le design des alterno-démarreurs.A partir d'une modélisation fine de la machine, cette thèse étudie les conditions dans lesquelles une telle commande est réalisable et analyse plusieurs techniques permettant d'y parvenir. Une nouvelle méthode d'estimation de la position du rotor, spécifique aux BSSG est proposée, puis illustrée avec des résultats expérimentaux. Cette technique est basée sur le traitement des composantes harmoniques existantes naturellement au stator de la machine et permet l'estimation de la position à l'arrêt et à très basse vitesse. Afin d'étendre l'estimation sur toute la plage de vitesse, une étude d'estimation de position par un observateur d'état à base du modèle complet de la machine en considérant les harmonique injectés (ou existantes) dans les courants du stator est proposée. Cet observateur peut s’appliquer à la machine synchrone à trois étages mais aussi à toute machine synchrone. Dans cette étude, son fonctionnement est illustré sur une machine synchrone à aimants permanents. / The aircraft manufacturers express the need to develop a more "electric" aircraft. This brings forward new requirements for the electric drive systems in terms of increasing the available on-board power and resource sharing in order to optimize the overall mass and cost. Among the ongoing studies on the evolution of motor drives, a particular interest is given to the development of multi-level brushless synchronous starter/generators (BSSG). These drives are likely to provide the high torque required to start-up the reactors to which they are associated. For this purpose, the knowledge, at any time, of the rotor position is essential. However, adding a dedicated sensor impacts on the design of the machine, increasing volume, cabling needs and cost. For this purpose, investigating on “sensorless” control laws will permit to avoid using such a sensor and to simplify the design of the Starter/Generators.Using a fine modelling of the machine, this work studies the conditions of feasibility for sensorless control and analyzes several techniques for this purpose. A new method of estimation of the shaft-position, particular to the BSSG architecture is proposed and then illustrated with experimental results. This technique is based on the processing of the existing harmonic components naturally in the stator of the machine and allows the estimation of the position at standstill and the very low speed. To extend the estimation to the whole speed range, a study of position estimation using a state observer using the complete model of the machine considering the knowledge of the existing (or injected) harmonic components in the stator currents is proposed. This observer can be applied to the brushless synchronous starter/generator but also on generic synchronous machines. In this study, its performance is illustrated on a permanent magnet synchronous machine.

With the emergence of Industry 4.0, there is a pronounced emphasis on the necessity for enhanced flexibility in assembly processes. In the domain of bolt-tightening, this transition is evident. Tools are now required to navigate a variety of bolts and unpredictable tightening methodologies. Each bolt, possessing distinct tightening parameters, necessitates a specific sequence to prevent issues like bolt cross-talk or unbalanced force. This thesis introduces an approach that integrates advanced computing techniques with machine learning to address these challenges in the tightening areas. The primary objective is to offer edge computation for bolt detection and tightening tools' precise localization. It is realized by leveraging visual-inertial data, all encapsulated within a System-on-Chip (SoC) Field Programmable Gate Array (FPGA). The chosen approach combines visual information and motion detection, enabling tools to quickly and precisely do the localization of the tool. All the computing is done inside the SoC FPGA. The key element for identifying different bolts is the YOLOv3-Tiny-3L model, run using the Deep-learning Processor Unit (DPU) that is implemented in the FPGA. In parallel, the thesis employs the Error-State Extended Kalman Filter (ESEKF) algorithm to fuse the visual and motion data effectively. The ESEKF is accelerated via a full implementation in Register Transfer Level (RTL) in the FPGA fabric. We examined the empirical outcomes and found that the visual-inertial localization exhibited a Root Mean Square Error (RMSE) position of 39.69 mm and a standard deviation of 9.9 mm. The precision in orientation determination yields a mean error of 4.8 degrees, offset by a standard deviation of 5.39 degrees. Notably, the entire computational process, from the initial bolt detection to its final localization, is executed in 113.1 milliseconds. This thesis articulates the feasibility of executing bolt detection and visual-inertial localization using edge computing within the SoC FPGA framework. The computation trajectory is significantly streamlined by harnessing the adaptability of programmable logic within the FPGA. This evolution signifies a step towards realizing a more adaptable and error-resistant bolt-tightening procedure in industrial areas. / Med framväxten av Industry 4.0, finns det en uttalad betoning på nödvändigheten av ökad flexibilitet i monteringsprocesser. Inom området bultåtdragning är denna övergång tydlig. Verktyg krävs nu för att navigera i en mängd olika bultar och oförutsägbara åtdragningsmetoder. Varje bult, som har distinkta åtdragningsparametrar, kräver en specifik sekvens för att förhindra problem som bultöverhörning eller obalanserad kraft. Detta examensarbete introducerar ett tillvägagångssätt som integrerar avancerade datortekniker med maskininlärning för att hantera dessa utmaningar i skärpningsområdena. Det primära målet är att erbjuda kantberäkning för bultdetektering och åtdragningsverktygs exakta lokalisering. Det realiseras genom att utnyttja visuella tröghetsdata, allt inkapslat i en System-on-Chip (SoC) Field Programmable Gate Array (FPGA). Det valda tillvägagångssättet kombinerar visuell information och rörelsedetektering, vilket gör det möjligt för verktyg att snabbt och exakt lokalisera verktyget. All beräkning sker inuti SoC FPGA. Nyckelelementet för att identifiera olika bultar är YOLOv3-Tiny-3L-modellen, som körs med hjälp av Deep-learning Processor Unit (DPU) som är implementerad i FPGA. Parallellt använder avhandlingen algoritmen Error-State Extended Kalman Filter (ESEKF) för att effektivt sammansmälta visuella data och rörelsedata. ESEKF accelereras via en fullständig implementering i Register Transfer Level (RTL) i FPGA-strukturen. Vi undersökte de empiriska resultaten och fann att den visuella tröghetslokaliseringen uppvisade en Root Mean Square Error (RMSE) position på 39,69 mm och en standardavvikelse på 9,9 mm. Precisionen i orienteringsbestämningen ger ett medelfel på 4,8 grader, kompenserat av en standardavvikelse på 5,39 grader. Noterbart är att hela beräkningsprocessen, från den första bultdetekteringen till dess slutliga lokalisering, exekveras på 113,1 millisekunder. Denna avhandling artikulerar möjligheten att utföra bultdetektering och visuell tröghetslokalisering med hjälp av kantberäkning inom SoC FPGA-ramverket. Beräkningsbanan är avsevärt effektiviserad genom att utnyttja anpassningsförmågan hos programmerbar logik inom FPGA. Denna utveckling innebär ett steg mot att förverkliga en mer anpassningsbar och felbeständig skruvdragningsprocedur i industriområden.

In More Electric Aircraft, different power system activities are attributed to electrical means such as the start-up of the main engine. In this context, the study of the sensorless control of the Brushless Synchronous Starter Generator (BSSG) that is used to electrically start the main engine is revealed to be a very interesting issue. For long time, the elimination of the mechanical sensor was highly recommended for reliability, cost, weight, integration issues.Hence, this work aims to transpose the results obtained in the research area to an avionic testbench. It presents an adaptive sensorless technique to use when electrically starting the main engine of the aircraft. This is achieved by elaborating three different methods selected depending on the speed of the machine and based on the :- injection of a high frequency signal- use of the back-emf of the Permanent Magnet Generator (PMG)- use of the extended Kalman Filter EKFIn this work, it is shown that the …first method gives good position estimation results from standstill up to 8% of the rated speed. Then, the back-emfs of the PMG are used to detect the position of the BSSG when the speed exceeds the 8% of the rated speed. Good results are observed with this method at medium and high speed.For redundancy reasons, the EKF was also used in this work. Thus, the estimated position can be delivered via two different estimation algorithms in medium and high speed region.The implementation of the algorithm was achieved on an FPGA board since the latter can ensure a very tiny execution time. The fastness of the treatment ensures quasi-instantaneous position estimation and does not practically introduce any phase lag in the position estimation. / Aujourd'hui, l'aviation est en train de vivre des évolutions technologiques concernant surtout l'attribution de différentes fonctionnalités aux équipements électriques et ceci au détriment d'équipements hydrauliques et mécaniques assurant les mêmes fonctionnalités.Dans le cadre de l'avion plus électrique, le démarrage électrique sans capteurs mécaniques de la turbine de l'avion préoccupe les avionneurs de nos jours. Les problèmes introduits par ce capteur ont été identifiés : problèmes de coût et de poids, problèmes de fiabilité et d'intégration.Ce travail présente alors une commande sans capteurs pour la machine synchrone à trois étages à utiliser durant le démarrage électrique de l'avion. Ceci est réalisé avec trois méthodes de détection de la position selon la vitesse de rotation, basées sur :- l'injection d'un signal à haute fréquence- l'utilisation d'un filtre de Kalman étendu FKE- les fém. du PMG (Permanent Magnet Generator) La première méthode donne de bons résultats d'estimation depuis l'arrêt jusqu'à 8% de la vitesse nominale de la machine. Au-delà de cette vitesse, es valeurs des fém. du PMG deviennent assez élevées pour être utilisées dans l'estimation de la position. De bons résultats sont obtenus à moyenne et haute vitesse.Pour des questions de redondance, le FKE est aussi utilisé. Ainsi, la position estimée peut être fournie par l'un des deux algorithmes à moyenne et haute vitesse.L'implémentation de ces algorithmes est réalisée via une carte FPGA étant donné que celui-ci garantit un temps d'exécution. La rapidité de traitement garantit une estimation de la position quasi-instantanée et donc n'introduit pratiquement pas des retards dans l'estimation.

Most micro air vehicles rely heavily on reliable GPS measurements for proper estimation and control, and therefore struggle in GPS-degraded environments. When GPS is not available, the global position and heading of the vehicle is unobservable. This dissertation establishes the theoretical and practical advantages of a relative navigation framework for MAV navigation in GPS-degraded environments. This dissertation explores how the consistency, accuracy, and stability of current navigation approaches degrade during prolonged GPS dropout and in the presence of heading uncertainty. Relative navigation (RN) is presented as an alternative approach that maintains observability by working with respect to a local coordinate frame. RN is compared with several current estimation approaches in a simulation environment and in hardware experiments. While still subject to global drift, RN is shown to produce consistent state estimates and stable control. Estimating relative states requires unique modifications to current estimation approaches. This dissertation further provides a tutorial exposition of the relative multiplicative extended Kalman filter, presenting how to properly ensure observable state estimation while maintaining consistency. The filter is derived using both inertial and body-fixed state definitions and dynamics. Finally, this dissertation presents a series of prolonged flight tests, demonstrating the effectiveness of the relative navigation approach for autonomous GPS-degraded MAV navigation in varied, unknown environments. The system is shown to utilize a variety of vision sensors, work indoors and outdoors, run in real-time with onboard processing, and not require special tuning for particular sensors or environments. Despite leveraging off-the-shelf sensors and algorithms, the flight tests demonstrate stable front-end performance with low drift. The flight tests also demonstrate the onboard generation of a globally consistent, metric, and localized map by identifying and incorporating loop-closure constraints and intermittent GPS measurements. With this map, mission objectives are shown to be autonomously completed.

Engineers have long been inspired by nature's flyers. Such animals navigate complex environments gracefully and efficiently by using a variety of evolutionary adaptations for high-performance flight. Biologists have discovered a variety of sensory adaptations that provide flow state feedback and allow flying animals to feel their way through flight. A specialized skeletal wing structure and plethora of robust, adaptable sensory systems together allow nature's flyers to adapt to myriad flight conditions and regimes. In this work, motivated by biology and the successes of bio-inspired, engineered aerial vehicles, linear quadratic control of a flexible, morphing wing design is investigated, helping to pave the way for truly autonomous, mission-adaptive craft. The proposed control algorithm is demonstrated to morph a wing into desired positions. Furthermore, motivated specifically by the sensory adaptations organisms possess, this work transitions to an investigation of aircraft wing load identification using structural response as measured by distributed sensors. A novel, recursive estimation algorithm is utilized to recursively solve the inverse problem of load identification, providing both wing structural and aerodynamic states for use in a feedback control, mission-adaptive framework. The recursive load identification algorithm is demonstrated to provide accurate load estimate in both simulation and experiment. / Graduation date: 2012

Traditionally, the task of determining aircraft position and attitude for automatic control has been handled by the combination of an inertial measurement unit (IMU) with a Global Positioning System (GPS) receiver. In this configuration, accelerations and angular rates from the IMU can be integrated forward in time, and position updates from the GPS can be used to bound the errors that result from this integration. However, reliance on the reception of GPS signals places artificial constraints on aircraft such as small unmanned aerial vehicles (UAVs) that are otherwise physically capable of operation in indoor, cluttered, or adversarial environments. Therefore, this work investigates methods for incorporating a monocular vision sensor into a standard avionics suite. Vision sensors possess the potential to extract information about the surrounding environment and determine the locations of features or points of interest. Having mapped out landmarks in an unknown environment, subsequent observations by the vision sensor can in turn be used to resolve aircraft position and orientation while continuing to map out new features. An extended Kalman filter framework for performing the tasks of vision-based mapping and navigation is presented. Feature points are detected in each image using a Harris corner detector, and these feature measurements are corresponded from frame to frame using a statistical Z-test. When GPS is available, sequential observations of a single landmark point allow the point's location in inertial space to be estimated. When GPS is not available, landmarks that have been sufficiently triangulated can be used for estimating vehicle position and attitude. Simulation and real-time flight test results for vision-based mapping and navigation are presented to demonstrate feasibility in real-time applications. These methods are then integrated into a practical framework for flight in GPS-denied environments and verified through the autonomous flight of a UAV during a loss-of-GPS scenario. The methodology is also extended to the application of vehicles equipped with stereo vision systems. This framework enables aircraft capable of hovering in place to maintain a bounded pose estimate indefinitely without drift during a GPS outage.

A main thrust of this thesis is to develop and explore linearization-based numeric-analytic integration techniques in the context of stochastically driven nonlinear oscillators of relevance in structural dynamics. Unfortunately, unlike the case of deterministic oscillators, available numerical or numeric-analytic integration schemes for stochastically driven oscillators, often modelled through stochastic differential equations (SDE-s), have significantly poorer numerical accuracy. These schemes are generally derived through stochastic Taylor expansions and the limited accuracy results from difficulties in evaluating the multiple stochastic integrals. We propose a few higher-order methods based on the stochastic version of transversal linearization and another method of linearizing the nonlinear drift field based on a Girsanov change of measures. When these schemes are implemented within a Monte Carlo framework for computing the response statistics, one typically needs repeated simulations over a large ensemble. The statistical error due to the finiteness of the ensemble (of size N, say)is of order 1/√N, which implies a rather slow convergence as N→∞. Given the prohibitively large computational cost as N increases, a variance reduction strategy that enables computing accurate response statistics for small N is considered useful. This leads us to propose a weak variance reduction strategy. Finally, we use the explicit derivative-free linearization techniques for state and parameter estimations for structural systems using the extended Kalman filter (EKF). A two-stage version of the EKF (2-EKF) is also proposed so as to account for errors due to linearization and unmodelled dynamics. In Chapter 2, we develop higher order locally transversal linearization (LTL) techniques for strong and weak solutions of stochastically driven nonlinear oscillators. For developing the higher-order methods, we expand the non-linear drift and multiplicative diffusion fields based on backward Euler and Newmark expansions while simultaneously satisfying the original vector field at the forward time instant where we intend to find the discretized solution. Since the non-linear vector fields are conditioned on the solution we wish to determine, the methods are implicit. We also report explicit versions of such linearization schemes via simple modifications. Local error estimates are provided for weak solutions. Weak linearized solutions enable faster computation vis-à-vis their strong counterparts. In Chapter 3, we propose another weak linearization method for non-linear oscillators under stochastic excitations based on Girsanov transformation of measures. Here, the non-linear drift vector is appropriately linearized such that the resulting SDE is analytically solvable. In order to account for the error in replacing of non-linear drift terms, the linearized solutions are multiplied by scalar weighting function. The weighting function is the solution of a scalar SDE(i.e.,Radon-Nikodym derivative). Apart from numerically illustrating the method through applications to non-linear oscillators, we also use the Girsanov transformation of measures to correct the truncation errors in lower order discretizations. In order to achieve efficiency in the computation of response statistics via Monte Carlo simulation, we propose in Chapter 4 a weak variance reduction strategy such that the ensemble size is significantly reduced without seriously affecting the accuracy of the predicted expectations of any smooth function of the response vector. The basis of the variance reduction strategy is to appropriately augment the governing system equations and then weakly replace the associated stochastic forcing functions through variance-reduced functions. In the process, the additional computational cost due to system augmentation is generally far less besides the accrued advantages due to a drastically reduced ensemble size. The variance reduction scheme is illustrated through applications to several non-linear oscillators, including a 3-DOF system. Finally, in Chapter 5, we exploit the explicit forms of the LTL techniques for state and parameters estimations of non-linear oscillators of engineering interest using a novel derivative-free EKF and a 2-EKF. In the derivative-free EKF, we use one-term, Euler and Newmark replacements for linearizations of the non-linear drift terms. In the 2-EKF, we use bias terms to account for errors due to lower order linearization and unmodelled dynamics in the mathematical model. Numerical studies establish the relative advantages of EKF-DLL as well as 2-EKF over the conventional forms of EKF. The thesis is concluded in Chapter 6 with an overall summary of the contributions made and suggestions for future research.

Development of dynamic state estimation techniques and their applications in problems of identification in structural engineering have been taken up. The thrust of the study has been the identification of structural systems that exhibit nonlinear behavior, mainly in the form of constitutive and geometric nonlinearities. Methods encompassing both linearization based strategies and those involving nonlinear filtering have been explored. The applications of derivative-free locally transversal linearization (LTL) and multi-step transversal linearization (MTrL) schemes for developing newer forms of the extended Kalman filter (EKF) algorithm have been explored. Apart from the inherent advantages of these methods in avoiding gradient calculations, the study also demonstrates their superior numerical accuracy and considerably less sensitivity to the choice of step sizes. The range of numerical illustrations covers SDOF as well as MDOF oscillators with time-invariant parameters and those with discontinuous temporal variations. A new form of the sequential importance sampling (SIS) filter is developed which explores the scope of the existing SIS filters to cover nonlinear measurement equations and more general forms of noise involving multiplicative and (or) Gaussian/ non-Gaussian noises. The formulation of this method involves Ito-Taylor’s expansions of the nonlinear functions in the measurement equation and the development of the ideal ispdf while accounting for the non-Gaussian terms appearing in the governing equation. Numerical illustrations on parameter identification of a few nonlinear oscillators and a geometrically nonlinear Euler–Bernoulli beam reveal a remarkably improved performance of the proposed methods over one of the best known algorithms, i.e. the unscented particle filter. The study demonstrates the applicability of diverse range of mathematical tools including Magnus’ functional expansions, theory of SDE-s, Ito-Taylor’s expansions and simulation and characterization of the non-Gaussian random variables to the problem of nonlinear structural system identification.

Ce mémoire est consacré à la modélisation des condensateurs électrolytiques aluminium dédiés à des applications à hautes températures ainsi qu’à la compréhension de leur vieillissement lors d’utilisations réalistes. En effet, dans le cas d’onduleur de traction de véhicule électrique, les sollicitations, notamment en température, peuvent être parfois très variables. Or, il se trouve que pour ce type d’applications, ces derniers sont la plupart du temps de type électrolytiques aluminium, technologie étant parmi les plus fragiles. Par conséquent, ce manuscrit propose tout d’abord une nouvelle modélisation électrique s’appuyant des phénomènes de diffusion permettant d’obtenir une représentation très précise de l’impédance de ces condensateurs. Compte-tenu de leur forte dépendance en température, la modélisation thermique couplée au modèle électrique est également traitée. Le but premier est de développer un outil permettant d’estimer précisément les pertes à chaque instant pour permettre au contrôleur d’ajuster la température de ce dernier par une modification de la stratégie MLI. Une méthode d’identification en ligne est alors proposée par l’utilisation de filtres de Kalman conjoints avec de très bons résultats obtenus en simulation. Le dimensionnement ainsi que la création d’un banc de cyclage accéléré est développé et une comparaison du vieillissement obtenu après 12 000 heures entre des composants cyclés thermiquement et d’autres non cyclés est donnée. Les résultats montrent une très bonne tenue dans le temps des condensateurs étudiés que ce soit au niveau de l’impédance ou bien visuellement avec néanmoins un impact du cyclage thermique non négligeable / This thesis is devoted to the modeling of aluminum electrolytic capacitors dedicatedto high temperatures. The purpose is also to understand their ageing while submitted to realistic use. Indeed, in the case of electric vehicle traction inverter, solicitations like temperature can vary a lot. This type of stress has already been studied for active components, but not yet on passive ones such as decoupling capacitors. However, it turns out that for this kind of application, they are most of the time aluminum electrolytic capacitors which is among the weakest technology. Consequently, this manuscript proposes at first a new electric model based on a diffusion phenomena which leads to a very accurate impedance description. It permits also a better understanding of the physical phenomena involved in these components. Because of their important temperature dependence, thermal modeling coupled to the electric model is also discussed. The very first purpose is to develop a tool that is able to estimate losses accurately at every moment. Thanks to it, the controller could so change the PWM strategy in order to act on the temperature. An online identification method is then proposed with the use of joint Kalman filters which led to very good results in simulation. The design and the creation of an accelerated cycling bench is developed and comparisons about the ageing obtained after 12 000 hours between thermally cycled components and others non-cycled are given. Results show a very good stability over time of the studied capacitors (PEG225MF470Q Kemet©) either on the impedance or visually. Nevertheless a significant impact can be observed on the cycled ones.

A new suboptimal guidance law design approach for aerospace vehicles is proposed in this thesis, followed by an advanced control design for supersonic air-breathing engines. The guidance law is designed using the newly developed Generalized Model Predictive Static Programming (G-MPSP), which is based on the continuous time nonlinear optimal control framework. The key feature of this technique is one-time backward propagation of a small-dimensional weighting matrix dynamics, which is used to update the entire control history. This key feature, as well as the fact that it leads to a static optimization problem, lead to its computational efficiency. It has also been shown that the existing model predictive static programming (MPSP), which is based on the discrete time framework, is a special case of G-MPSP. The G-MPSP technique is further extended to incorporate ‘input inequality constraints’ in a limited sense using the penalty function philosophy. Next, this technique has been developed also further in a ‘flexible final time’ framework to converge rapidly to meet very stringent final conditions with limited number of iterations. Using the G-MPSP technique in a flexible final time and input inequality constrained formulation, a suboptimal guidance law for a solid motor propelled carrier launch vehicle is successfully designed for a hypersonic mission. This guidance law assures very stringent final conditions at the injection point at the end of the guidance phase for successful beginning of the hypersonic vehicle operation. It also ensures that the angle of attack and structural load bounds are not violated throughout the trajectory. A second-order autopilot has been incorporated in the simulation studies to mimic the effect of the inner-loops on the guidance performance. Simulation studies with perturbations in the thrust-time behaviour, drag coefficient and mass demonstrate that the proposed guidance can meet the stringent requirements of the hypersonic mission. The G-MPSP technique in a fixed final time and input inequality constrained formulation has also been used for optimal guidance of an aerospace vehicle propelled by supersonic air-breathing engine, where the resulting thrust can be manipulated by managing the fuel flow and nozzle area (which is not possible in solid motors). However, operation of supersonic air-breathing engines is quite complex as the thrust produced by the engine is a result of very complex nonlinear combustion dynamics inside the engine. Hence, to generate the desired thrust, accounting for a fairly detailed engine model, a dynamic inversion based nonlinear state feedback control design has been carried out. The objective of this controller is to ensure that the engine dynamically produces the thrust that tracks the commanded value of thrust generated from the guidance loop as closely as possible by regulating the fuel flow rate. Simultaneously, by manipulating throat area of the nozzle, it also manages the shock wave location in the intake for maximum pressure recovery with sufficient margin for robustness. To filter out the sensor and process noises and to estimate the states for making the control design operate based on output feedback, an extended Kalman filter (EKF) based state estimation design has also been carried out and the controller has been made to operate based on estimated states. Moreover, independent control designs have also been carried out for the actuators so that their response can be faster. In addition, this control design becomes more challenging to satisfy the imposed practical constraints like fuel-air ratio and peak combustion temperature limits. Simulation results clearly indicate that the proposed design is quite successful in assuring the desired performance of the air-breathing engine throughout the flight trajectory, i.e., both during the climb and cruise phases, while assuring adequate pressure margin for shock wave management.