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La commande H∞ est de nos jours utilisée pour la régulation de nombreux systèmes. Cette technique de contrôle permet de synthétiser des lois de commande robustes, dans le sens où le comportement du système régulé est peu sensible aux perturbations externes. De plus, la commande H∞ permet de prendre en compte des incertitudes liés au modèle décrivant le système à réguler. Par conséquence, cette technique de contrôle est robuste vis-à-vis des perturbations et des incertitudes de modèle. Afin de synthétiser une loi de commande robuste, les spécifications des performances du système en boucle fermée sont traduites en critères H∞ à partir desquels est formulé un problème d'optimisation. La loi de commande est une solution de ce problème, qui est non convexe dans le cas général. Les deux principales approches pour la résolution de ce problème sont basées sur la reformulation convexe et les méthodes d'optimisations locales, mais ne garantissent pas l'optimalité de la loi de commande vis-à-vis des critères H∞. Cette thèse propose une approche de la commande H∞ par des méthodes d'optimisation globales, rarement considérées jusqu'à présent. Contrairement aux approches classiques, bien qu'au prix d'une complexité algorithmique supérieure, la convergence vers la loi de commande optimale est garantie par les méthodes globales. De plus, les incertitude de modèle sont prises en compte de manière garantie, ce qui n'est pas nécessairement le cas avec les approches convexes et locales. / H∞ control is nowadays used in many applications. This control technique enables to synthesize control laws which are robust with respect to external disturbances. Moreover, it allows to take model uncertainty into account in the synthesis process. As a consequence, H∞ control laws are robust with respect to both external disturbances and model uncertainty. A robust control law is a solution to an optimization problem, formulated from H∞ criteria. These criteria are the mathematical translations of the desired closed loop performance specifications. The two classical approaches to the optimization problem rely on the convex reformulation and local optimization methods. However, such approaches are unable to guarantee the optimality, with respect to the H∞ criteria, of the control law. This thesis proposes to investigate a global optimization approach to H∞ control. Contrary to convex and local approaches, global optimization methods enable to guarantee the optimality of the control, and also to take into account model uncertainty in a reliable way.

Observers are used in the monitoring and control of dynamical systems to deduce the values of unmeasured states. Designing an observer requires having an accurate model of the plant — if the model parameters are characterized imprecisely, the observer may not provide reliable estimates. An interval observer, which comprises an upper and lower observer, bounds the plant's states from above and below, given the range of values of the imprecisely characterized parameters, i.e., it defines an interval in which the plant's states must lie at any given instant. We propose a linear programming-based method of interval observer design for two cases: 1) only the initial conditions of the plant are uncertain; 2) the dynamical parameters are also uncertain. In the former, we optimize the transient performance of the interval observers, in the sense that the volume enclosed by the interval is minimized. In the latter, we optimize the steady state performance of the interval observers, in the sense that the norm of the width of the interval is minimized at steady state. Interval observers are typically designed to characterize the widest interval that bounds the states. This thesis proposes an interval observer design method that utilizes additional, but still-incomplete information, that enables the designer to identify tighter bounds on the uncertain parameters under certain operating conditions. The number of bounds that can be refined defines a class of systems. The definition of this class is independent of the specific parameters whose bounds are refined. Applying robust optimization techniques, under a cardinality constrained model of uncertainty, we design a single observer for an entire class of systems. These observers guarantee a minimum level of performance with respect to the aforementioned metrics, as we optimize the worst-case performance over a given class of systems. The robust formulation allows the designer to tune the level of uncertainty in the model. If many of the uncertain parameter bounds can be refined, the nominal performance of the observer can be improved, however, if few or none of the parameter bounds can be refined, the nominal performance of the observer can be designed to be more conservative.

Observers are used in the monitoring and control of dynamical systems to deduce the values of unmeasured states. Designing an observer requires having an accurate model of the plant — if the model parameters are characterized imprecisely, the observer may not provide reliable estimates. An interval observer, which comprises an upper and lower observer, bounds the plant's states from above and below, given the range of values of the imprecisely characterized parameters, i.e., it defines an interval in which the plant's states must lie at any given instant. We propose a linear programming-based method of interval observer design for two cases: 1) only the initial conditions of the plant are uncertain; 2) the dynamical parameters are also uncertain. In the former, we optimize the transient performance of the interval observers, in the sense that the volume enclosed by the interval is minimized. In the latter, we optimize the steady state performance of the interval observers, in the sense that the norm of the width of the interval is minimized at steady state. Interval observers are typically designed to characterize the widest interval that bounds the states. This thesis proposes an interval observer design method that utilizes additional, but still-incomplete information, that enables the designer to identify tighter bounds on the uncertain parameters under certain operating conditions. The number of bounds that can be refined defines a class of systems. The definition of this class is independent of the specific parameters whose bounds are refined. Applying robust optimization techniques, under a cardinality constrained model of uncertainty, we design a single observer for an entire class of systems. These observers guarantee a minimum level of performance with respect to the aforementioned metrics, as we optimize the worst-case performance over a given class of systems. The robust formulation allows the designer to tune the level of uncertainty in the model. If many of the uncertain parameter bounds can be refined, the nominal performance of the observer can be improved, however, if few or none of the parameter bounds can be refined, the nominal performance of the observer can be designed to be more conservative.

Η παρούσα διπλωματική εργασία ασχολείται με τη μελέτη και το σχεδιασμό ενός άμεσου προσαρμοστικού ελεγκτή, για μη γραμμικά συστήματα με αβεβαιότητα και εξωγενείς διαταραχές. Αρχικά παρουσιάζονται οι έννοιες του προσαρμοστικού ελέγχου, των μη γραμμικών συστημάτων και διερευνώνται οι έννοιες της ευστάθειας και των διαταραχών, με ιδιαίτερη έμφαση στην ευστάθεια κατά Lyapunov. Στη συνέχεια προτείνεται ένας προσαρμοστικός ελεγκτής για άμεσο προσαρμοστικό έλεγχο, προσαρμοστική ευστάθεια, απόρριψη διαταραχών και εκτέλεση εντολών, σε μη γραμμικά δυναμικά συστήματα πολλαπλών μεταβλητών, με αβεβαιότητα και εξωγενείς διαταραχές, ο οποίος εγγυάται μερική ασυμπτωτική ευστάθεια του συστήματος κλειστού βρόχου. Το προτεινόμενο πλαίσιο, ειδικεύεται περαιτέρω για τις περιπτώσεις όπου το μη γραμμικό σύστημα παρουσιάζεται σε κανονική μορφή, με ευσταθείς input-to-state internal dynamics, έχει μια είσοδο ή έχει εξωγενείς L2 διαταραχές. Τέλος ο προτεινόμενος ελεγκτής εφαρμόζεται σε αρκετά πειραματικά συστήματα, καθώς και στον έλεγχο της αστάθειας θερμοακουστικής καύσης. / This diploma thesis features the analysis and design of a direct adaptive controller, for nonlinear uncertain systems with exogenous disturbances. At first, we introduce the concept of adaptive control and we underline its’ need in modern applications. Also, we include a brief presentation of nonlinear systems and we inquire into the theory of stabilization and disturbances, with significant emphasis in Lyapunov’s methods. Furthermore we develop a direct adaptive control framework for adaptive stabilization, disturbance rejection, and command following of multivariable nonlinear uncertain dynamical systems with exogenous disturbances, which guarantees partial asymptotic stability of the closed-loop system. The proposed framework is further specialized for the cases where the nonlinear system is represented in normal form with input-to-state stable internal dynamics, has single-input with uncertain dynamics, or exogenous L2 disturbances .Finally we present several illustrative numerical examples and we apply our framework to the control of thermoacoustic combustion instabilities.

Orientador: Paulo Augusto Valente Ferreira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-06T11:17:31Z (GMT). No. of bitstreams: 1 Benjovengo_FabioPereira_M.pdf: 2854202 bytes, checksum: b1bccce6d2ea6efa1b55778d0fba1399 (MD5) Previous issue date: 2006 / Resumo: Esta Dissertação tem como objetivos o projeto de sistemas de controle e a análise de estabilidade de plantas lineares e invariantes no tempo imprecisamente conhecidas (com incertezas do tipo intervalar). O problema de projeto de controladores é tratado através da técnica de alocação de pólos. A técnica de alocação robusta de pólos proposta nesta Dissertação é uma extensão da técnica de projeto clássica e utiliza resultados de Análise Intervalar. A extensão proposta resulta numa equação Diofantina intervalar. Busca-se, então, a redução do raio de sua solução através do tratamento de multiincidências de elementos intervalares. Outro tema tratado é o projeto de controladores que visem o rastreamento de sinais de referência e a rejeição de distúrbios, assintoticamente. Estuda-se ainda o problema da estabilidade de sistemas intervalares formulado como o problema de encontrar uma matriz intervalar simétrica definida positiva como solução de uma equação de Lyapunov intervalar / Abstract: This Dissertation is focused on control system design and stability analysis of linear timeinvariant uncertain plants, with uncertainties of interval nature. The control system design problem is treated in the context of the pole placement technique. An extension of the classical technique to robust pole placement based on Interval Analysis is proposed. The main objective of the work is to handle multiple incidences of plant parameters in order to reduce the radii of the solutions of the resulting interval Diophantine equations. Another subject treated is the design of robust controllers for interval plants which guarantee reference tracking and disturbance rejection asymptotically. The stability of linear interval systems as the problem of finding a symmetric positive definite interval solution of an interval Lyapunov equation is also considered. / Mestrado / Engenharia Eletrica / Mestre em Engenharia Elétrica

Les dernières années ont permis des développements très rapides, tant au niveau de l’élaboration que de l’application, d’algorithmes de commande prédictive non linéaire (CPNL), avec une gamme relativement large de réalisations industrielles. Un des obstacles les plus significatifs rencontré lors du développement de cette commande est lié aux incertitudes sur le modèle du système. Dans ce contexte, l’objectif principal de cette thèse est la conception de lois de commande prédictives non linéaires robustes vis-à-vis des incertitudes sur le modèle. Classiquement, cette synthèse peut s’obtenir via la résolution d’un problème d’optimisation min-max. L’idée est alors de minimiser l’erreur de suivi de la trajectoire optimale pour la pire réalisation d'incertitudes possible. Cependant, cette formulation de la commande prédictive robuste induit une complexité qui peut être élevée ainsi qu’une charge de calcul importante, notamment dans le cas de systèmes multivariables, avec un nombre de paramètres incertains élevé. Pour y remédier, une approche proposée dans ces travaux consiste à simplifier le problème d’optimisation min-max, via l’analyse de sensibilité du modèle vis-à-vis de ses paramètres afin d’en réduire le temps de calcul. Dans un premier temps, le critère est linéarisé autour des valeurs nominales des paramètres du modèle. Les variables d’optimisation sont soit les commandes du système soit l’incrément de commande sur l’horizon temporel. Le problème d’optimisation initial est alors transformé soit en un problème convexe, soit en un problème de minimisation unidimensionnel, en fonction des contraintes imposées sur les états et les commandes. Une analyse de la stabilité du système en boucle fermée est également proposée. En dernier lieu, une structure de commande hiérarchisée combinant la commande prédictive robuste linéarisée et une commande par mode glissant intégral est développée afin d’éliminer toute erreur statique en suivi de trajectoire de référence. L'ensemble des stratégies proposées est appliqué à deux cas d'études de commande de bioréacteurs de culture de microorganismes. / The last few years have led to very rapid developments, both in the formulation and the application of Nonlinear Model Predictive Control (NMPC) algorithms, with a relatively wide range of industrial achievements. One of the most significant challenges encountered during the development of this control law is due to uncertainties in the model of the system. In this context, the thesis addresses the design of NMPC control laws robust towards model uncertainties. Usually, the above design can be achieved through solving a min-max optimization problem. In this case, the idea is to minimize the tracking error for the worst possible uncertainty realization. However, this robust approach tends to become too complex to be solved numerically online, especially in the case of multivariable systems with a large number of uncertain parameters. To address this shortfall, the proposed approach consists in simplifying the min-max optimization problem through a sensitivity analysis of the model with respect to its parameters, in order to reduce the calculation time. First, the criterion is linearized around the model parameters nominal values. The optimization variables are either the system control inputs or the control increments over the prediction horizon. The initial optimization problem is then converted either into a convex optimization problem, or a one-dimensional minimization problem, depending on the nature of the constraints on the states and commands. The stability analysis of the closed-loop system is also addressed. Finally, a hierarchical control strategy is developed, that combines a robust model predictive control law with an integral sliding mode controller, in order to cancel any tracking error. The proposed approaches are applied through two case studies to the control of microorganisms culture in bioreactors.

The control of systems with uncertain nonlinear dynamics is an important field of control science attracting decades of focus. In this dissertation, four different control strategies are presented using sliding mode control, adaptive control, dynamic compensation, and neural network for a nonlinear aeroelastic system with bounded uncertainties and external disturbance. In Chapter 2, partial state feedback adaptive control designs are proposed for two different aeroelastic systems operating in unsteady flow. In Chapter 3, a continuous robust control design is proposed for a class of single input and single output system with uncertainties. An aeroelastic system with a trailingedge flap as its control input will be considered as the plant for demonstration of effectiveness of the controller. The controller is proved to be robust by both athematical proof and simulation results. In Chapter 3, a robust output feedback control strategy is discussed for the vibration suppression of an aeroelastic system operating in an unsteady incompressible flowfield. The aeroelastic system is actuated using a combination of leading-edge (LE) and trailing-edge (TE) flaps in the presence of different kinds of gust disturbances. In Chapter 5, a neural-network based model-free controller is designed for an aeroelastic system operating at supersonic speed. The controller is shown to be able to effectively asymptotically stabilize the system via both a Lyapunov-based stability proof and numerical simulation results.

No / An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.