This dissertation aims at solving the consensus control problem of multi-agent systems with Lipschitz nonlinearity. This depends on the design of the controller that enables each agent or subsystem in multi-agent systems with Lipschitz nonlinearity to reach consensus; using the understanding of the agents' connection network from the knowledge of graph theory as well as the control system design strategy. The objective is achieved by designing a type of distributed control, namely the consensus control, which manipulates the relative information of each agent in a multi-agent systems in order to arrive at a single solution. In addition, containment control is also developed to solve containment problem. It is an extension of consensus control via leader-follower configuration, aimed at having each agent contained by multiple leaders in a multi-agent systems with Lipschitz nonlinearity. Four types of controllers are proposed - state-feedback consensus controller, observer-based consensus controller, state-feedback containment controller and observer-based containment controller; each provides the stability conditions based on Lyapunov stability analysis in time domain which enabled each agent or subsystem to reach consensus. The observer-based controllers are designed based on the consensus observer that is related to Luenberger observer. Linear Matrix Inequality (LMI) and Algebraic Riccati Equation (ARE) are utilized to obtain the solutions for the stability conditions. The simulation results of the proposed controllers and observers have been carried out to prove their theoretical validity. Several practical examples of flexible robot arm simulations are included to further validate the theoretical aspects of the thesis.

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The presented research concerns the control of unmanned vehicles. The results introduced in this dissertation provide a solid control framework for a wide class of nonlinear uncertain systems, with a special emphasis on issues related to implementation, such as control input amplitude and rate saturation, or partial state measurements availability. More specifically, an adaptive control framework, allowing to enforce amplitude and rate saturation of the command, is developed. The motion control component of this framework, which works in conjunction with a saturation algorithm, is then specialized to different types of vehicles. Vertical take-off and landing aerial vehicles and a general class of autonomous marine vehicles are considered. A nonlinear control algorithm addressing the tracking problem for a class of underactuated, non-minimum phase marine vehicles is then introduced. This motion controller is extended, using direct and indirect adaptive techniques, to handle parametric uncertainties in the system model. Numerical simulations are used to illustrate the efficacy of the algorithms. Next, the output feedback control problem is treated, for a large class of nonlinear and uncertain systems. The proposed solution relies on a novel nonlinear observer which uses output measurements and partial knowledge of the system's dynamics to reconstruct the entire state for a wide class of nonlinear systems. The observer is then extended to operate in conjunction with a full state feedback control law and solve both the output feedback control problem and the state observation problem simultaneously. The resulting output feedback control algorithm is then adjusted to provide a high level of robustness to both parametric and structural model uncertainties. Finally, in a natural extension of these results from motion control of a single system to collaborative control of a group of vehicles, a cooperative control framework addressing limited communication issues is introduced. / Ph. D.

Functional Electrical Stimulation (FES) is a technique that applies electrical currents to nervous tissue in order to actively induce muscle contraction. Recent research has shown that FES provides a promising treatment to restore functional tasks due to paralysis caused by spinal cord injury, head injury, and stroke, to mention a few. Therefore, the overarching goal of this research work is to develop FES controllers to enable patients with movement-disorder to control their limbs in a desired manner and, in particular, to aid Parkinson's patients to suppress hand tremor. In our effort to develop strategies for muscle stimulation control, we first implement a model-based control technique assuming that all the states are measurable. The Hill-type muscle model coupled with a simplified 2DoF model of the arm is used to study the performance of our proposed adaptive sliding mode controller for simulation purpose. However, in the more practical situations, human limb dynamics are extremely complicate and it is inadequate to use model based controllers, especially considering there are still technical limitations that allow in vivo measurements of muscle activity. To tackle these challenges, we have developed output feedback adaptive control approaches for a class of unknown multi-input multi-output systems. Such control strategies are first developed for linear systems, and then extended to the nonlinear case. The proposed controllers, supported by experimental results, require minimum knowledge of the system dynamics and avoid many restrictive assumptions typically found in the literature. Therefore, we expect that the results introduced in this dissertation can provide a solution for a wide class of nonlinear uncertain systems, with focus on practical issues such as partial state measurement and the presence of mismatched uncertainties. / Ph. D.

Model Predictive Control (MPC) is a control strategy based on optimization that handles system constraints explicitly, making it a popular feedback control method in real industrial processes. However, designing this control policy is an expensive operation since an explicit model of the process is required when re-tuning the controller. Another common practical challenge is that not all states are available, which calls for an observer in order to estimate the states, and imposes additional challenges such as satisfying the constraints and conditions that follow. This thesis attempts to address these challenges by extending the novel Adversarial Adaptive Model Predictive Control (AAMPC) algorithm with output-feedback for linear plants without explicit identification. The AAMPC algorithm is an adaptive MPC framework, where results from an adversarial Multi-Armed Bandit (MAB) are applied to a basic model predictive control formulation. The algorithm of the project, Adversarial Adaptive Output-Feedback Model Predictive Control (AAOFMPC), is derived by extending the standard MPC formulation with output-feedback, i.e, to an Output-Feedback Model Predictive Control (OFMPC) scheme, where a Kalman filter is implemented as the observer. Furthermore, the control performance of the extended algorithm is demonstrated with the problem of driving the state to a given reference, in which the performance is evaluated in terms of regret, state estimation errors, and how well the states track their given reference. Experiments are conducted on two discrete-time Linear Time- Invariant (LTI) systems, a second order system and a third order system, that are perturbed with different noise sequences. It is shown that the AAOFMPC performance satisfies the given theoretical bounds and constraints despite larger perturbations. However, it is also shown that the algorithm is not very robust against noise since offsets from the reference values for the state trajectories are observed. Furthermore, there are several tuning parameters of AAOFMPC that need further investigation for optimal performance. / Modell Prediktiv Reglering (MPC) är en optimeringsbaserad reglertekniksmetod som hanterar processbegränsingar på ett systematiskt sätt, vilket gör den till en populär metod inom återkopplad reglering i processindustrin. Denna metod medför dock höga beräkningskostnader eftersom det krävs en explicit modell varje gång regulatorn justeras online. I praktiken är det också vanligt att alla tillståndsvariabler inte är tillgängliga, vilket kräver en observatör för att rekonstruera alla tillståndsvariabler. Detta leder till fler utmaningar som att uppfylla ytterligare systembegränsingar och villkor som följer. Detta projekt adresserar dessa utmaningar genom att förlänga den nya algoritmen Adversarial Adaptiv Modell Prediktiv Reglering (AAMPC) med output-feedback för linjära system utan explicit modellidentifiering. AAMPC-algoritmen är en adaptiv reglerstrategi där resultat från en adversarial multiarmed bandit (MAB) appliceras i en standard MPC-formulering. Denna MPC-formulering är förlängd med output-feedback dvs. Output-Feedback Modell Predktiv Reglering (OFMPC) där ett Kalman filter är implementerad som en observatör och resulterar i projektets algoritm: Adversarial Adaptiv Output- Feedback Modell Prediktiv Reglering (AAOFMPC). Vidare demonstreras den utökade algoritmens prestanda med problemet att driva tillståndsvariablerna till ett givet referensvärde, där prestandan evalueras i termer av regret, skattningsfel och hur väl tillståndsvariablerna följer de givna referensvärdena. Experiment utförs på två tidsdiskreta tidsinvarianta (LTI) system, ett andraordningssystem och ett tredjeordningssystem, som är perturberade med olika värden av brus. Resultaten visar att AAOFMPC:s prestanda uppfyller de givna teoretiska begränsningarna trots större störningar. Det visar sig dock att algoritmen inte är särskilt robust mot brus eftersom det sker avvikelser från de givna referensvärdena för tillståndsvariablerna. Dessutom finns det flera parametrar i algoritmen som kräver ytterligare utredningar för optimal prestanda.

Dans cette thèse, le canal Gaussien à interférence à deux utilisateurs avec voie de retour dégradée par un bruit additif (GIC-NOF) est étudié sous deux perspectives : les réseaux centralisés et décentralisés. Du point de vue des réseaux centralisés, les limites fondamentales du GIC-NOF sont caractérisées par la région de capacité. L’une des principales contributions de cette thèse est une approximation à un nombre constant de bits près de la région de capacité du GIC-NOF. Ce résultat est obtenu grâce à l’analyse d’un modèle de canal plus simple, le canal linéaire déterministe à interférence à deux utilisateurs avec voie de retour dégradée par un bruit additif (LDIC-NOF). L’analyse pour obtenir la région de capacité du LDIC-NOF fournit les idées principales pour l’analyse du GIC-NOF. Du point de vue des réseaux décentralisés, les limites fondamentales du GIC-NOF sont caractérisées par la région d’η-équilibre de Nash (η-EN). Une autre contribution de cette thèse est une approximation de la région η-EN du GIC-NOF, avec η > 1. Comme dans le cas centralisé, le cas décentralisé LDIC-NOF (D-LDIC-NOF) est étudié en premier et les observations sont appliquées dans le cas décentralisé GIC-NOF (D-GIC-NOF). La contribution finale de cette thèse répond à la question suivante : “À quelles conditions la voie de retour permet d’agrandir la région de capacité, la région η-EN du GIC-NOF ou du D-GIC-NOF ? ”. La réponse obtenue est de la forme : L’implémentation de la voie de retour de la sortie du canal dans l’émetteur-récepteur i agrandit la région de capacité ou la région η-EN si le rapport signal sur bruit de la voie de retour est supérieure à SNRi* , avec i ∈ {1, 2}. La valeur approximative de SNRi* est une fonction de tous les autres paramètres du GIC-NOF ou du D-GIC-NOF. / In this thesis, the two-user Gaussian interference channel with noisy channel-output feedback (GIC-NOF) is studied from two perspectives: centralized and decentralized networks. From the perspective of centralized networks, the fundamental limits of the two-user GICNOF are characterized by the capacity region. One of the main contributions of this thesis is an approximation to within a constant number of bits of the capacity region of the two-user GIC-NOF. This result is obtained through the analysis of a simpler channel model, i.e., a two-user linear deterministic interference channel with noisy channel-output feedback (LDIC-NOF). The analysis to obtain the capacity region of the two-user LDIC-NOF provides the main insights required to analyze the two-user GIC-NOF. From the perspective of decentralized networks, the fundamental limits of the two-user decentralized GIC-NOF (D-GIC-NOF) are characterized by the η-Nash equilibrium (η-NE) region. Another contribution of this thesis is an approximation of the η-NE region of the two-user GIC-NOF, with η> 1. As in the centralized case, the two-user decentralized LDIC-NOF (D-LDIC-NOF) is studied first and the lessons learnt are applied in the two-user D-GIC-NOF. The final contribution of this thesis consists in a closed-form answer to the question: “When does channel-output feedback enlarge the capacity or η-NE regions of the two-user GIC-NOF or two-user D-GIC-NOF?”. This answer is of the form: Implementing channel-output feedback in transmitter-receiver i enlarges the capacity or η-NE regions if the feedback SNR is beyond SNRi* , with i ∈ {1, 2}. The approximate value of SNRi* is shown to be a function of all the other parameters of the two-user GIC-NOF or two-user D-GIC-NOF.

Le contexte général de cette thèse est l’étude de problèmes inverses et directs en théorie du contrôle. Plus précisément, les trois problèmes étudiés sont les suivants.Le premier est un problème de contrôle optimal (approche directe). Il s’agit de fournir la synthèse temps minimum du modèle cinématique d'un drone volant à altitude constante, de vitesse linéaire non nécessairement constante voulant rejoindre une trajectoire circulaire de rayon de courbure minimum.Le deuxième problème concerne une approche inverse du contrôle optimal. Il s’agit d’élaborer des méthodes théoriques de reconstruction du critère optimisé dans un problème de contrôle optimal à partir d’un ensemble de solutions à ce problème, ainsi que caractériser les "bons" ensembles de trajectoires permettant la reconstruction du critère. Le contrôle optimal inverse connait un regain d’intérêt depuis une quinzaine d’années, en particulier dans l’étude des comportements moteurs humains. En effet, selon un paradigme largement accepté en neurophysiologie, parmi tous les mouvements possibles ceux effectivement réalisés sont solutions d’un processus d’optimisation.Le troisième problème traite de stabilisation par retour de sortie. Nous analysons, à travers un exemple académique tiré du contrôle quantique, le problème de stabilisation par retour de sortie (à l’aide d’un observateur) lorsque le point où l'on souhaite stabiliser le système correspond à un contrôle qui rend le système inobservable. L’idée générale est de perturber le retour d’état stabilisant afin de garantir l’observabilité du système tout en stabilisant le système sur la cible. L’analyse de cet exemple académique nous permet dans un second temps de dégager une méthode générale pouvant s’appliquer à une classe de système beaucoup plus large. / The overall context of this thesis is the study of inverse and direct problems in control theory. More specifically, the following three problems are studied.The first one is an optimal control problem (direct approach). The aim is to give a time minimum systhesis fora kinematic model of a UAV flying at constant altitude with positive (non-necessarily constant) linear velocityin order to steer it to a fixed circle of minimum turning radius.The second problem deals with an inverse approach of optimal control. The aim is to develop theoretical methods in order to reconstruct the minimized criterion in an optimal control problem from a set of solution to this problem. The aim is also to characterize the « good » sets of trajectories leading to the reconstruction of the criterion. In the last fifteen years, there has been a renewed interest in inverse optimal control, especially inhuman motor behavior. Indeed, according to a well accepted paradigm in neurophysiology, among all possible movements, those actually accomplished are solutions of an optimization process.The third problem tackles output feedback stabilization. We analyze, via a simple academic example from quantum control, the problem of dynamic output feedback stabilization, when the point where we want to stabilize corresponds to a control value that makes the system unobservable. The general idea is to perturb the stabilizing state feedback in order to ensure the observability of the system while stabilizing it to the target.The analysis of this example allows, secondly, to identify a general procedure that can be applied to a widerclass of systems.

Cette thèse a pour sujet la stabilisation de systèmes non-linéaires avec des mesures partielles et des entrées contraintes. Les deux premiers chapitres traitent du problème des entrées saturées dans le contexte des systèmes de dimension infinie pour des équations nonlinéaires abstraites et une équation aux dérivées partielles nonlinéaire particulière, l'équation de Korteweg-de Vries. Les outils mathématiques utilisés pour obtenir des résultats Le troisième chapitre propose une méthode de synthèse de retour de sortie pour deux équations de Korteweg-de Vries. Le quatrième chapitre concerne la synthèse d'un retour de sortie pour des systèmes non-linéaires de dimension finie pour lequel il existe un contrôle hybride. Une stratégie basée sur des observateurs grand gain est utilisée. / This thesis is about the stabilization of nonlinear systems with partial measurements and constrained input. The two first chapters deals with saturated inputs in the contex of infinite-dimensional systems for nonlinear abstract equations and for a particular partial differential equation, the Korteweg-de Vries equation. The third chapter provides an output feedback design for two Korteweg-de Vries equations using the backstepping method. The fourth chapter is about the output feedback design of nonlinear finite-dimensional systems for which there exists a hybrid controller. A high-gain observer strategy is used.

AN ABSTRACT OF THE THESIS OF PRASHANTHI BANALA, for the Master of Science degree in ELECTRICAL AND COMPUTER ENGINEERING, presented on 31 October 2011, at Southern Illinois University Carbondale. TITLE: OUTPUT FEEDBACK H-inf CONTROL DESIGN FOR MULTI-AGENT SYSTEMS MAJOR PROFESSOR: Dr. Farzad Pourboghrat In this thesis, the design of distributed control for identical multi-agent systems is considered based on the optimization of H-inf cost function. Identical dynamically coupled but interacting systems (agents) are considered where control action of each agent is based on relative output measurement of their neighboring agents and a subset of their own output. The agents communicate with each other to achieve a common goal. A distributed dynamic output feedback control strategy that satisfies H-inf performance for multi-agent systems is developed and corresponding H-inf performance region is analyzed. An example illustrates the necessary and sufficient condition for dynamic output feedback controller synthesis to obtain desired H-inf performance.