This thesis presents decentralized model reference adaptive control techniques for systems with full-state feedback and systems with output feedback. The controllers are strictly decentralized, that is, each local controller uses feedback from only local subsystems and no information is shared between local controllers. The full-state feedback decentralized controller is effective for multi-input systems, where the dynamics matrix and control-input matrix are unknown. The decentralized controller achieves asymptotic stabilization and command following in the presence of sinusoidal disturbances with known spectrum. We present a construction technique of the reference-model dynamics such that the decentralized controller is effective for systems with arbitrarily large subsystem interconnections. The output-feedback decentralized controller is effective for single-input single-output subsystems that are minimum phase and relative degree one. The decentralized controller achieves asymptotic stabilization and disturbance rejection in the presence of an unknown disturbance, which is generated by an unknown Lyapunov-stable linear system.

Vývoj v informačním systému neustále zvyšuje nároky na účinnou, efektivní a adaptivní spolupráci mezi komponenty systému, aby se vyrovnal se změnami v systému a zaručil tak nejlepší výkon. K dosažení těchto požadavků byly zavedeny dva hlavní přístupy. Přístup k adaptaci umožňuje informačnímu systému přizpůsobit se změnám v kontextu informací systému a jeho okolního prostředí na základě adaptační strategie. Přístup ke zvyšování informovanosti zase napomáhá sledovat informace o kontextu a rozpoznat změny, které mohou proces adaptace vyvolat. V této práci představujeme obecný kontextově orientovaný model vlastní adaptivní architektury pro podporu softwarového systému s adaptačními funkcemi, které zaručují dostupnost systému, provozní podmínky a výkon. Navíc poskytujeme dvě případové studie v reálném životě jako důkaz konceptu použitelnosti našeho navrhovaného adaptačního přístupu.

Les travaux de cette thèse portent sur l’estimation et la commande décentralisée des systèmes de grande dimension. L’objectif est de développer des capteurs logiciels pouvant produire une estimation fiable des variables nécessaires pour la stabilisation des systèmes non linéaires interconnectés. Une décomposition d’un tel système de grande dimension en un ensemble de n sous-systèmes interconnectés est primordiale. Ensuite, en tenant compte de la nature du sous-système ainsi que les fonctions d’interconnexions, des lois de commande décentralisées basées observateurs ont été synthétisées. Chaque loi de commande est associée à un sous-système qui permet de le stabiliser localement, ainsi la stabilité du système global est assurée. L’existence d’un observateur et d’un contrôleur stabilisant le système dépend de la faisabilité d’un problème d’optimisation LMI. La formulation LMI, basée sur l’approche de Lyapunov, est élaborée par l’utilisation de principe de DMVT sur la fonction d’interconnexion non linéaire supposée bornée et incertaine. Ainsi des conditions de synthèse non restrictives sont obtenues. Des méthodes de synthèse de loi de commande décentralisée basée observateur ont été proposées pour les systèmes non linéaires interconnectés dans le cas continu et dans le cas discret. Des lois de commande robuste H1 décentralisées sont élaborées pour les systèmes non linéaires interconnectés en présence de perturbations et des incertitudes paramétriques. L’efficacité et la validation des approches présentées sont testées sur un modèle de réseaux électriques composé de trois générateurs interconnectés / This thesis focuses on the decentralized estimation and control for large scale systems. The objective is to develop software sensors that can produce a reliable estimate of the variables necessary for the interconnected nonlinear systems stability analysis. A decomposition of a such large system into a set of n interconnected subsystems is paramount for model simplification. Then, taking into account the nature of the subsystem as well as the interconnected functions, observer-based decentralized control laws have been synthesized. Each control law is associated with a subsystem which allows it to be locally stable, thus the stability of the overall system is ensured. The existence of an observer and a controller gain matrix stabilizing the system depends on the feasibility of an LMI optimization problem. The LMI formulation, based on Lyapunov approach, is elaborated by applying the DMVT technique on the nonlinear interconnection function, assumed to be bounded and uncertain. Thus, non-restrictive synthesis conditions are obtained. Observer-based decentralized control schemes have been proposed for nonlinear interconnected systems in the continuous and discrete time. Robust Hinfini decentralized controllers are provided for interconnected nonlinear systems in the presence of perturbations and parametric uncertainties. Effectiveness of the proposed schemes are verified through simulation results on a power systems with interconnected machines

Dans cette thèse, nous nous intéressons au problème du suivi en convoi, désigné en anglais par le terme platooning, où un train de robots essaie de suivre un chemin décrit par le leader. Ce chemin, n'étant pas prédéfini mais généré au cours du suivi, est inconnu de tous les robots suiveurs. Dans ce travail, nous choisissons une approche décentralisée locale où chaque robot du convoi observe son voisinage et calcule son contrôle de façon à avoir un suivi stable (absence d'oscillations) et précis (erreur latérale aussi faible que possible). Cette thèse étudie plus précisément le comportement latéral d'un robot du convoi et propose deux contrôleurs s'appuyant sur la mémorisation du chemin suivi par son prédécesseur. Un premier algorithme de contrôle Memo-LAT (Memorization and Look-Ahead Target) calcule une commande latérale continue en utilisant une loi de contrôle analytique. La stabilité de Memo-LAT n'étant pas toujours garantie, nous proposons l'algorithme de contrôle NOC (Non-Oscillatory Convergence) qui prend en compte la courbure du chemin à suivre dans le calcul du comportement latéral. NOC combine une approche géométrique avec une recherche heuristique pour calculer une commande discrète permettant au robot de suivre avec précision le chemin de son prédécesseur sans oscillation. / This thesis deals with the platooning problem which aims to concieve a control algorithm allowing a convoy of vehicles to follow their leader's path. This path, which is initially undefined and unknown to all the following robots, is generated as the leader moves. In this thesis, we choose a local decentralized approach in which each robot of the platoon uses its local perceptions to compute its own commands aiming to achieve a stable (no oscillations) and precise (with a lateral error as small as possible) platooning. More precisely, this thesis studies the lateral behavior of a platoon's robot and introduces two controllers based on the memorization of the robot's predecessor's path. The first algorithm, Memo-LAT (Memorization and Look-Ahead Target), computes a continuous lateral command using an analytic control law. As the stability of Memo-LAT is not always guaranteed, we present NOC (Non-Oscillatory Convergence), a control algorithm which takes into account the path's curvature in the robot's lateral behavior's computation. NOC combines a geometric approach to a heuristic search method to compute a discrete command allowing the robot to follow precisely and without oscillations its predecessor's path

This dissertation presents a suite of design tools for multi-agent systems that address three main areas: network design, decentralized controller generation, and the synthesis of decentralized control strategies by combining individual decentralized controllers. First, a new metric for quantifying heterogeneity in multi-agent systems is presented based on combining different notions of entropy, and is shown to overcome the drawbacks associated with existing diversity metrics in various scientific fields. Moreover, a new method of controlling multi-agent networks through the single-leader network paradigm is presented where by directly exploiting the homogeneity of agent capabilities, a network which is not completely controllable can be driven closer to a desired target configuration than by using traditional control techniques. An algorithm is presented for generating decentralized control laws that allow for agents to best satisfy a desired global objective, while taking into account network topological constraints and limitations on how agents can compute their control signals. Then, a scripting tool is developed to aid in specifying sequences of decentralized controllers to be executed consecutively, while helping ensure that the required network topological requirements needed for each controller to execute properly are maintained throughout mode switches. Finally, the underlying concepts behind the developed tools are showcased in three example applications: distributed merging and spacing for heterogeneous aircraft during terminal approaches, collaborative multi-UAV convoy protection in dynamic environments, and an educational tool used to teach a graduate-level networked controls course at the Georgia Institute of Technology.

This thesis aims to develop control algorithms for irrigation canals in an experimental framework.These water transport systems are difficult to manage and present low efficiencies in practice. As a result, an important percentage of water is lost, maintenance costs increase and water users follow a rigid irrigation schedule.All these problems can be reduced by automating the operation of irrigation canals.In order to fulfil the objectives, a laboratory canal, called Canal PAC-UPC, was equipped and instrumented in parallel with the development of this thesis. In general, the methods and solutions proposed herein were extensively tested in this canal.In a broader context, three main contributions in different irrigation canal control areas are presented.Focusing on gate-discharge measurements, many submerged-discharge calculation methods are tested and compared using Canal PAC-UPC measurement data. It has been found that most of them present errors around ±10%, but there are notable exceptions. Specifically, using classical formulas with a constant 0.611 contraction value give very good results (error<±6%), but when data is available, a very simple calibration formula recently proposed in the literature significantly outperform the rest (error<±3%). As a consequence, the latter is encouragingly proposed as the basis of any gate discharge controller.With respect to irrigation canal modeling, a detailed procedure to obtain data-driven linear irrigation canal models is successfully developed. These models do not use physical parameters of the system, but are constructed from measurement data. In this case, these models are thought to be used in irrigation canal control issues like controller tuning, internal controller model in predictive controllers or simply as fast and simple simulation platforms. Much effort is employed in obtaining an adequate model structure from the linearized Saint-Venant equations, yielding to a mathematical procedure that verifies the existence of an integrator pole in any type of canal working under any hydraulic condition. Time-domain and frequency-domain results demonstrate the accuracy of the resulting models approximating a canal working around a particular operation condition both in simulation and experiment.Regarding to irrigation canal control, two research lines are exploited. First, a new water level control scheme is proposed as an alternative between decentralized and centralized control. It is called Semi-decentralized scheme and aims to resemble the centralized control performance while maintaining an almost decentralized structure. Second, different water level control schemes based on PI control and Predictive control are studied and compared. The simulation and laboratory results show that the response and performance of this new strategy against offtake discharge changes, are almost identical to the ones of the centralized control, outperforming the other tested schemes based on PI control and on Predictive control. In addition, it is verified that schemes based on Predictive control with good controller models can counteract offtake discharge variations with less level deviations and in almost half the time than PI-based schemes.In addition to these three main contributions, many other smaller developments, minor results and practical recommendations for irrigation canal automation are presented throughout this thesis.

Die Arbeit beschreibt ein Modell, dass den Materialfluss in Transport- und Puffersystemen mit Hilfe eines agenten-basierten Ansatzes realistisch abbildet. Auf der Basis von Produktionsprogrammen werden individuelle Güter in beliebigen Netzwerkstrukturen mit Hilfe direkter und indirekter Interaktionen behinderungsvermeidend gesteuert. / The thesis describes a model that realistically illustrates the material flow in transport and buffer systems by means of an agent-based approach. Individual goods are steered hindrance-avoiding in arbitrary network structures based on production programs by means of direct and indirect interactions.

Temporal and spatial distribution of incoming vehicular charging demand is a significant challenge for the future planning of power systems. In this thesis the vehicular loading is-sue is categorized into two classes of stationary and mobile; they are then addressed in two phases. The mobile vehicular load is investigated first; a location-based forecasting algorithm for the charging demand of plug-in electric vehicles at potential off-home charging stations is proposed and implemented for real-world case-studies. The result of this part of the re-search is essential to realize the scale of fortification required for a power grid to handle vehicular charging demand at public charging stations. In the second phase of the thesis, a novel decentralized control strategy for scheduling vehicular charging demand at residential distribution networks is developed. The per-formance of the proposed algorithm is then evaluated on a sample test feeder employing real-world driving data. The proposed charging scheduling algorithm will significantly postpone the necessity for upgrading the assets of the network while effectively fulfilling customers’ transportation requirements and preferences. / October 2014

Many control systems of practical importance are multivariable. In such systems, each manipulated variable (input signal) may affect several controlled variables (output signals) causing interaction between the input/output loops. For this reason, control of multivariable systems is typically much more difficult compared to the single-input single-output case. It is therefore of great importance to quantify the degree of interaction so that proper input/output pairings that minimize the impact of the interaction can be formed. For this, dedicated interaction measures can be used. The first part of this thesis treats interaction measures. The commonly used Relative Gain Array (RGA) is compared with the Gramian-based interaction measures the Hankel Interaction Index Array (HIIA) and the Participation Matrix (PM) which consider controllability and observability to quantify the impact each input signal has on each output signal. A similar measure based on the <img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?%5Cmathcal%20H_2" /> norm is also investigated. Further, bounds on the uncertainty of the HIIA and the PM in case of uncertain models are derived. It is also shown how the link between the PM and the Nyquist diagram can be utilized to numerically calculate such bounds. Input/output pairing strategies based on linear quadratic Gaussian (LQG) control are also suggested. The key idea is to design single-input single-output LQG controllers for each input/output pair and thereafter form closed-loop multivariable systems for each control configuration of interest. The performances of these are compared in terms of output variance. In the second part of the thesis, the activated sludge process, commonly found in the biological wastewater treatment step for nitrogen removal, is considered. Multivariable interactions present in this type of bioreactor are analysed with the tools discussed in the first part of the thesis. Furthermore, cost-efficient operation of the activated sludge process is investigated.

Multi-vehicle systems offer many advantages in engineering applications such as increased efficiency and robustness. However, the disadvantage of multi-vehicle systems is that they require a high level of organization and coordination in order to successfully complete a task. Formation control is a field of engineering that addresses this issue, and provides coordination schemes to successfully implement multi-vehicle systems. Two approaches to group coordination were proposed in this work: geometric and self-organizing formations. A geometric reconfiguring formation was developed using the leader-follower method, and the self-organizing formation was developed using the velocity potential equations from fluid flow theory. Both formation controllers were first tested in simulation in MATLAB, and then implemented on the X80 mobile robot units. Various experiments were conducted to test the formations under difficult obstacle scenarios. The robots successfully navigated through the obstacles as a coordinated as a team using the self-organizing and geometric formation control approaches.