In preparation for the influx of renewable energy sources that will be added to the electrical system, flexible and adaptable control schemes are necessary to accommodate the changing infrastructure. Microgrids have been gaining much attention as the main solution to the challenges of distributed and intermittent generation, but due to their low inertia, they need fast-acting control systems in order to maintain stability. Multi-Agent Systems have been proposed as dynamic control and communication frameworks. Decentralized arrangements of agents can provide resiliency and the much-desired “plug and play” behavior. This thesis describes a control system that implements droop control and the diffusion communication scheme without the need of a centralized controller to coordinate the Microgrid agents to maintain the frequency and stable operating conditions of the system. Moreover, the inter-agent communication is unaffected by changing network configurations and can achieve optimal economic dispatch through distributed optimization.

Target tracking is an important part of video surveillance from a UAV. Tracking a target in an urban environment can be difficult because of the number of occlusions present in the environment. If multiple UAVs are used to track a target and the target behavior is learned autonomously by the UAV then the task may become easier. This thesis explores the hypothesis that an existing cooperative control algorithm can be enhanced by a language modeling algorithm to improve over time the target tracking performance of one or more ground targets in a dense urban environment. Observations of target behavior are reported to the Sequence Memoizer which uses the observations to create a belief model of future target positions. This belief model is combined with a kinematic belief model and then used in a cooperative auction algorithm for UAV path planning. The results for tracking a single target using the combined belief model outperform other belief models and improve over the duration of the mission. Results from tracking multiple targets indicate that algorithmic enhancements may be needed to find equivalent success. Future target tracking algorithms should involve machine learning to enhance tracking performance.

On-orbit satellite servicing is critical to maximizing space utilization and sustainability and is of growing interest for commercial, civil, and defense applications. Reliance on astronauts or anchored robotic arms for the servicing of next-generation large, complex space structures operating beyond Low Earth Orbit is impractical. Substantial literature has investigated the mission design and analysis of robotic servicing missions that utilize a single servicing satellite to approach and service a single target satellite. This motivates the present research to investigate a fleet of servicing satellites performing several operations for a large, central space structure. This research leverages a distributed control approach, implemented using the Horizon Simulation Framework (HSF), to develop a tool capable of integrated mission modeling and task scheduling for a servicing satellite fleet. HSF is a modeling and simulation framework for verification of system level requirements with an emphasis on state representations, modularity, and event scheduling. HSF consists of two major modules: the main scheduling algorithm and the system model. The distributed control architecture allocates processing and decision making for this multi-agent cooperative control problem across multiple subsystem models and the main HSF scheduling algorithm itself. Models were implemented with a special emphasis on the dynamics, control, trajectory constraints, and trajectory optimization for the servicing satellite fleet. The integrated mission modeling and scheduling tool was applied to a sample scenario in which a fleet of 3 servicing assets is tasked with performing 12 servicing activities for a large satellite in Geostationary Orbit. The tool was able to successfully determine a schedule in which all 12 servicing activities were completed in under 32 hours, subject to the fuel and trajectory constraints of the servicing assets.

The upward trends in renewable energy deployment in recent years brings new challengesto the development of electrical networks. Interconnected microgrids appear as a novelbottom-up approach to the production and integration of renewable energy.Using model predictive control (MPC), the energy management of several interconnectedmicrogrids is investigated. An optimisation problem is formulated and distributed ontothe individual units using the alternating direction method of multipliers (ADMM). Themicrogrids cooperate to reach a global optimum using neighbour-to-neighbour communications.The benefits of using distributed operation control for microgrids are analysed and a controlarchitecture is proposed. Two algorithms are implemented to solve the optimisationproblem and their advantages or differences are confronted. / Förnybara energikällor har ökat under senaste åren. Det innebär nya utmaningar förevolutionen av elektriska nät. Microgrids är en bottom-up ansats för produktion ochintegrering av förnybar energi.Energiförsörjning av flera sammankoppladeMicrogrids studeras in detta arbete genommodellbaserad prediktiv kontroll (MPC). Ett optimeringsproblem formuleras på de enskildaenheterna med Alternating DirectionMethod ofMultipliers (ADMM) och parallellberäkningar härledas.Microgrids samarbetar för att nå en global lösning av neighbourto-neighbour kommunikation.Distribuerad energiförsörjning av microgrids analyseras och två kontroll algorithmerutformas.

Afin de rendre les machines synchrones à aimants permanents plus attractives pour l'aéronautique (actionneurs de commande de vol, systèmes de freinage, train d'atterrissage ...), il peut être intéressant de réduire le nombre de modules d'électronique de puissance utilisés en les mutualisant. De nombreuses études ont été réalisées pour des systèmes composés de plusieurs machines asynchrones et principalement en traction ferroviaire, mais peu concernent les machines synchrones. Après avoir étudié différentes structures envisageables, les travaux développés lors de cette thèse présentent une étude originale d'un système composé de deux machines synchrones à aimants permanents connectées en parallèle sur un onduleur unique mutualisé. Ces machines ont des caractéristiques identiques ou proches et doivent être pilotées à la même vitesse. La structure de commande retenue lors de cette thèse est de type maître-esclave: seule une des deux machines est autopilotée (la machine maître), l'autre (la machine esclave) fonctionnant en boucle ouverte. Afin d'assurer la stabilité d'un tel système, le synchronisme des deux moteurs doit toujours être respecté. Une stratégie de commande, qui choisit quel est le moteur maître, en prenant en compte la variation des paramètres internes et externes du système, est alors instaurée. Dans ces conditions, les évolutions théoriques des différentes variables sont déterminées. Pour valider ces résultats, un processus expérimental est mis en place. Les nombreux résultats obtenus en simulation et expérimentalement permettent alors de confirmer les résultats théoriques: que ce soient les paramètres mécaniques ou électriques qui varient, la stabilité du système est toujours garantie. Le cas supplémentaire où les deux machines déplacent une charge mécanique commune avec une liaison mécanique rigide entre les deux machines, est finalement développé. Une autre stratégie de commande, dont la structure et les résultats sont également présentés dans cette thèse, est alors nécessaire. / To make permanent magnet synchronous machines more attractive for aerospace (flight control actuators, braking systems, landing gear ...), it may be advantageous to reduce the number of power electronic modules used in the pooling. Many studies have been performed for systems composed of several machines and asynchronous traction primarily, but little concern synchronous machines. After considering various possible structures, this Ph.D. thesis presents an original study of a system consisting of two permanent magnet synchronous machines connected in parallel on a single shared inverter. These machines have characteristics identical or similar and must be driven at the same speed. The control structure chosen in this Ph.D. thesis is a master-slave : only one machine, called master machine is self-piloted, the other (the slave machine) operating in open loop. To ensure the stability of such a system, the timing of the two engines should always be respected. A control strategy, which selects which is the master motor, taking into account the variation of internal and external parameters of the system is then introduced. Under these conditions, the theoretical developments of the different variables are determined. To validate these results, an experimental process is established. The numerous results obtained in simulation and experiments are then used to confirm the theoretical results : whatever the mechanical or electrical parameters variation, system stability is always guaranteed. The additional case, when both machines move a mechanical load with a common rigid mechanical connection between two machines, is finally developed. Another required control strategy, the structure and the results are also presented in this Ph.D. thesis.

Το κυρίως αντικείμενο της διατριβής αυτής είναι ο σχεδιασμός και η ανάλυση αποκεντρωμένων τεχνικών ελέγχου για επίτευξη μέγιστης κάλυψης από κινούμενα δίκτυα αισθητήρων. Λόγω των πολλών εφαρμογών αυτών σε αποστολές σχετιζόμενες με εξερεύνηση περιοχών ενδιαφέροντος, περιβαλλοντική δειγματοληψία, φύλαξη ή ακόμα και θέματα ασφάλειας, μία μεγάλη μερίδα της επιστημονικής κοινότητας έχει στρέψει το ενδιαφέρον της στην ανάπτυξη μεθόδων για βέλτιστη (ει δυνατόν) περιβαλλοντική αντίληψη μέσω αισθητήρων από αυτόνομες ομάδες ρομποτικών συστημάτων. Τέτοιες ομάδες, συνήθως τοποθετούμενες αρχικώς στις περιοχές ενδιαφέροντος, σχεδιάζονται με στόχο τον αποκεντρωμένο έλεγχό τους, αντί ενός καθολικού εποπτικού συστήματος, με στόχο να επιτύχουν στην εκάστοτε αποστολή. Στα πρώτα στάδια της διατριβής αυτής, το πρόβλημα της κάλυψης μιας περιοχής ενδιαφέροντος από μία ομάδα όμοιων κόμβων αναλύεται από υπολογιστική σκοπιά. Οι κινούμενοι κόμβοι υποθέτονται ότι υπακούν σε απλοϊκό κινηματικό μοντέλο διακριτού χρόνου, ενώ η αισθητήρια επίδοσή τους θεωρείται ακτινική, περιορισμένης εμβέλειας, ομοιόμορφη γύρω από τον κόμβο. Σαν πρώτη προσέγγιση, η κατεύθυνση σε κάθε χρονική στιγμή για βέλτιστη κάλυψη καθορίζεται βάσει τεχνικών διαμέρισης του χώρου βασιζόμενες στην έννοια της απόστασης. Η αναπτυσσόμενη στρατηγική επιτρέπει σταδιακή αύξηση της καλυπτόμενης επιφάνειας μεταξύ διαδοχικών βημάτων, ενώ έχει ως απαίτηση την κίνηση ενός μόνο επιτρεπτού κόμβου τη φορά. Στη συνέχεια, το προαναφερθέν σχέδιο επεκτείνεται για την περίπτωση ετερογενών δικτύων, όπου η ετερογένεια αντικατοπτρίζεται στις άνισες εμβέλειες απόδοσης αίσθησης των κόμβων. Επιπροσθέτως, επέκταση σε μοντέλο συνεχούς χρόνου επιτρέπει την κίνηση όλων των κόμβων του δικτύου ταυτόχρονα, αυξάνοντας ιδιαίτερα τον χρόνο σύγκλισης προς την βέλτιστη κατάσταση, ειδικά για μεγάλης κλίμακας δίκτυα. Μία εναλλακτική διαμέριση του χώρου αναπτύσσεται, η οποία βασίζεται κυρίως στα αισθητήρια μοτίβα των κόμβων, παρά στις θέσεις των κόμβων καθεαυτές. Τα παραγόμενα κελιά του χώρου ανατιθέμενα στους κόμβους αποτελούν τον βασικό πυρήνα του αλγόριθμου οργάνωσης, με στόχο την αποκεντρωμένη οργάνωση της κινούμενης ομάδας, ώστε να επιτύχει βέλτιστη απόδοση κάλυψης. Υποκινούμενοι από την υψηλού–βαθμού ανισοτροπία που χαρακτηρίζει κάποιους τύπους αισθητήρων, όπως κατευθυντικά μικρόφωνα για ανίχνευση ήχου σε εφαρμογές ασφάλειας, ή ακόμα μοτίβα εκπομπής/λήψης κατευθυντικών κεραιών σε σενάρια τηλεπικοινωνιακής κάλυψης, η έρευνά μας επεκτείνεται πέραν του κλασσικού ακτινικού μοντέλου δίσκου αίσθησης. Βασιζόμενοι σε συγκεκριμένες ιδιότητες για επίπεδες κυρτές καμπύλες, μια αποκεντρωμένη στρατηγική οργάνωσης αναπτύχθηκε για δίκτυα που χαρακτηρίζονται από κυρτά αισθητήρια μοτίβα ίδιας κατευθυντικότητας. Παρότι η κυρτότητα των συνόλων αίσθησης φαίνεται να θέτει ένα μεγάλου βαθμού περιορισμό στο συνολικό πρόβλημα, στην πραγματικότητα προσπερνάται μέσω ανάθεσης αυτών ως το μέγιστο κυρτό χωρίο που εγγράφεται στο πρωταρχικώς ανισοτροπικό μοτίβο. Το σχήμα ελέγχου επεκτείνεται στη συνέχεια για την περίπτωση όπου εισάγουμε ένα επιπλέον βαθμό ελευθερίας στις κινηματικές ικανότητες των κόμβων, ενσωματώνοντας έτσι διαφορετικές και χρονικά μεταβαλλόμενες κατευθυντικότητες μεταξύ των μοτίβων αυτών. Το παραγόμενο πλάνο ελέγχου αποδεικνύεται ότι οδηγεί ανισοτροπικά δίκτυα σε βέλτιστες τοπολογίες, αναφορικά με τα αισθητήρια μοτίβα τους, ελέγχοντας κατάλληλα ταυτόχρονα την θέση και προσανατολισμό, μέσω ενός καινοτόμου σχήματος κατακερματισμού του χώρου βασιζόμενο στο εκάστοτε μοτίβο. Η διατριβή κλείνει με την μελέτη δικτύων με περιορισμούς στην εμβέλεια επικοινωνίας αναφορικά με την μετάδοση πληροφοριών μεταξύ των κόμβων. Στην πλειονότητα των σχετικών εργασιών, το ζήτημα αυτό προσπερνάται επιτρέποντας στην εμβέλεια επικοινωνίας να είναι τουλάχιστον διπλάσια αυτής της (ομοιόμορφης) αίσθησης, εγγυώντας έτσι την αποκεντρωμένη φύση των πλάνων ελέγχου. Ο προτεινόμενος έλεγχος επιτρέπει την αποσύζευξη μεταξύ των δύο αυτών εμβελειών, οδηγώντας το δίκτυο στην βέλτιστη κατάσταση, μέσω ταυτόχρονου σεβασμού του εκάστοτε, εκ των προτέρων δοσμένου, περιορισμού στην εμβέλεια επικοινωνίας. Συγκεντρωτικά συμπεράσματα και συγκριτική ανάλυση παρουσιάζονται στο τελευταίο κεφάλαιο, ενώ προτείνονται μελλοντικά πλάνα επέκτασης των τεχνικών αυτών. / The main scope of this thesis is the design and analysis of distributed control strategies for achieving optimum area coverage in mobile sensor networks. Due to the numerous applications of the latter in missions as area exploration, environmental sampling, patrolling, or even security, a large part of the scientific community has turned its interest on developing methods for achieving optimum, if possible, sensing environmental perception by groups of autonomous mobile agents. Such robotic teams, randomly deployed in areas of interest initially, are designed to coordinate their motion in a distributed manner, rather than via a global supervisory system, in order to succeed in the corresponding mission objective. At the first stages of this thesis, the coverage problem of an area of interest by a group of identical nodes is examined from a numerical point of view. The mobile nodes are considered to be governed by simple discrete–time kinodynamic motion, while their sensing performance is assumed radial, range–limited, uniform around the node. As a first approach, the optimum direction at each time step for optimum deployment achievement is determined based on proper distance–based space partitioning techniques. The developed concept allows for gradual increase in the covered area among consecutive steps, although suffers from allowing motion of one node at a time. In the sequel, the aforementioned concept is extended to the case of heterogeneous networks, where heterogeneity lays mainly in the unequal limited–range of the sensing performance of the nodes. In addition, extension to continuous–time allows for simultaneous motion of the nodes, increasing drastically the convergence time towards the optimal state, especially for large–scale networks. An alternate partitioning of the space is developed that is mainly based on the nodes’ footprints, rather than their spatial positions only. The resulting assigned cells form the main core for the coordination algorithm proposed, in order to distributedly organize the mobile swarm to achieve optimum sensing performance. Motivated by the high–degree anisotropy that governs the sensing domains of certain types of sensors, i.e. directional microphones for sound sensing mainly for security applications, or even the radiation patterns of directional antennas in communication–coverage scenarios, our research is extended beyond the standard disc model of sensing. Based on certain properties for planar convex curves, a distributed strategy is developed for networks characterized by convex sensing domains of same orientation. Although convexity of the sensing sets may seem to impose a high level restriction to the overall setup, in fact can be assigned as the maximal convex inscribed set in any (originally) anisotropic pattern. The control scheme is further extended, in the sequel, for the case of adding an extra degree of freedom to the node’s mobility abilities, incorporating different and time–varying orientations among the nodes patterns. The resulting scheme is proven to lead anisotropic networks in optimum configurations, considering their sensing footprints, by properly controlling both the nodes’ positions and orientations, via an innovative pattern–based partitioning scheme of the sensed space. The thesis ends by examining the case where radio–range constraints are imposed on inter–agents communication. In the majority of the related works, this issues is usually overcome by allowing RF range as double the sensing one, guaranteeing that way distributed nature of the control schemes. The proposed scheme allows for uncorrelated RF and sensing ranges in the network, while guarantees convergence of the network towards the optimal state, via simultaneous preservation of a–priori imposed radio–range constraints. Concluding remarks along with comparative discussion are presented in the last chapter, where future research plans and ways to improve the already developed schemes are proposed.

In this dissertation, we present several main research thrusts involving thermodynamic stabilization via energy dissipating hybrid controllers and nonlinear control of network systems. Specifically, a novel class of fixed-order, energy-based hybrid controllers is presented as a means for achieving enhanced energy dissipation in Euler-Lagrange, lossless, and dissipative dynamical systems. These dynamic controllers combine a logical switching architecture with continuous dynamics to guarantee that the system plant energy is strictly decreasing across switching. In addition, we construct hybrid dynamic controllers that guarantee that the closed-loop system is consistent with basic thermodynamic principles. In particular, the existence of an entropy function for the closed-loop system is established that satisfies a hybrid Clausius-type inequality. Special cases of energy-based hybrid controllers involving state-dependent switching are described, and the framework is applied to aerospace system models. The overall framework demonstrates that energy-based hybrid resetting controllers provide an extremely efficient mechanism for dissipating energy in nonlinear dynamical systems. Next, we present finite-time coordination controllers for multiagent network systems. Recent technological advances in communications and computation have spurred a broad interest in autonomous, adaptable vehicle formations. Distributed decision-making for coordination of networks of dynamic agents addresses a broad area of applications including cooperative control of unmanned air vehicles, microsatellite clusters, mobile robotics, and congestion control in communication networks. In this part of the dissertation we focus on finite-time consensus protocols for networks of dynamic agents with undirected information flow. The proposed controller architectures are predicated on the recently developed notion of system thermodynamics resulting in thermodynamically consistent continuous controller architectures involving the exchange of information between agents that guarantee that the closed-loop dynamical network is consistent with basic thermodynamic principles.

Un groupe de robots collaboratifs peut gérer des tâches qui sont difficiles, voire impossibles, à accomplir par un seul. On appelle un ensemble de robots coopérant un système multi-agents (SMA). L'interaction entre agents est un facteur clé dans la commande coopérative qui pose d'importants défis théoriques et pratiques. L'une des tâches du contrôle coopératif est le consensus dont l'objectif est de concevoir des protocoles de commande afin de parvenir à un accord entre leurs états respectifs. Cette thèse améliore la navigation pour les SMA, tout en tenant compte de certaines contraintes pratiques (modèle du robot et contraintes temporelles) dans la conception de contrôleurs coopératifs pour chaque agent, de manière décentralisée. Dans cette thèse, deux directions sont étudiées. D'une part, le taux de convergence est une spécification de performance importante pour la conception du contrôleur pour un système dynamique. La convergence rapide est toujours recherchée pour améliorer les performances et la robustesse. La plupart des algorithmes de consensus existants se concentrent sur la convergence asymptotique, où le temps d'établissement est infini. Cependant, de nombreuses applications nécessitent une convergence rapide généralement caractérisée par une stratégie de commande à temps fini. De plus, la commande à temps fini autorise certaines propriétés intéressantes, mais le temps de stabilisation dépend des conditions initiales des agents. L'objectif ici est de concevoir un protocole de consensus leader-follower à temps fixe pour les SMA décrits en temps continu. Ce problème est étudié en utilisant la théorie de la stabilisation à temps fixe, qui garantit que le temps de stabilisation est borné quelles que soient les conditions initiales. Les contrôleurs et les observateurs à modes glissants sont conçus pour que chaque agent résolve le problème du consensus à temps fixe lorsque le leader est dynamique. D'autre part, par rapport aux systèmes à temps continu, le problème du consensus dans un cadre à temps discret convient mieux aux applications pratiques en raison de la limitation des ressources de calcul pour chaque agent. Le modèle de commande prédictive (MPC) permet de gérer les contraintes de commande et d'état des systèmes. Dans cette thèse, cette méthode est appliquée pour traiter le problème du consensus en temps discret en laissant chaque agent résoudre, à chaque étape, un problème de commande optimale contraint impliquant uniquement l'état des agents voisins. Les performances de suivi sont également améliorées dans cette thèse en ajoutant de nouveaux termes à partir du MPC classique. Les contrôleurs proposés sont simulés et implémentés sur un groupe composé de plusieurs robots réels en utilisant ROS (Robotic Operating System). Dans cette thèse, quelques solutions correspondant au problème de la connexion entre plusieurs robots mobiles de manière décentralisée, du réglage des périodes d'échantillonnage et des paramètres de contrôle sont également abordées. / Nowadays, robots have become increasingly important to investigate hazardous and dangerous environments. A group of collaborating robots can often deal with tasks that are difficult, or even impossible, to be accomplished by a single robot. Multiple robots working in a cooperative manner is called as a Multi-Agent System (MAS). The interaction between agents to achieve a global task is a key in cooperative control. Cooperative control of MASs poses significant theoretical and practical challenges. One of the fundamental topics in cooperative control is the consensus where the objective is to design control protocols between agents to achieve a state agreement. This thesis improves the navigation scheme for MASs, while taking into account some practical constraints (robot model and temporal constraints) in the design of cooperative controllers for each agent, in a fully decentralized way. In this thesis, two directions are investigated. On one hand, the convergence rate is an important performance specification to design the controller for a dynamical system. As an important performance measure for the coordination control of MASs, fast convergence is always pursued to achieve better performance and robustness. Most of the existing consensus algorithms focus on asymptotic convergence, where the settling time is infinite. However, many applications require a high speed convergence generally characterized by a finite-time control strategy. Moreover, finite-time control allows some advantageous properties but the settling time depend on the initial states of agents. The objective here is to design a fixed-time leader-follower consensus protocol for MASs described in continuous-time. This problem is studied using the powerful theory of fixed-time stabilization, which guarantee that the settling time is upper bounded regardless to the initial conditions. Sliding mode controllers and sliding mode observers are designed for each agent to solve the fixed-time consensus tracking problem when the leader is dynamic. On the other hand, compared with continuous-time systems, consensus problem in a discrete-time framework is more suitable for practical applications due to the limitation of computational resources for each agent. Model Predictive Control (MPC) has the ability to handle control and state constraints for discrete-time systems. In this thesis, this method is applied to deal with the consensus problem in discrete-time by letting each agent to solve, at each step, a constrained optimal control problem involving only the state of neighboring agents. The tracking performances are also improved in this thesis by adding new terms in the classical MPC technique. The proposed controllers will be simulated and implemented on a team of multiple Mini-Lab Enova Robots using ROS (Robotic Operating System) which is an operating system for mobile robots. ROS provides not only standard operating system services but also high-level functionalities. In this thesis, some solutions corresponding to problem of connection between multiple mobile robots in a decentralized way for a wireless robotic network, of tuning of the sampling periods and control parameters are also discussed.

Cooperative control problems for multiple vehicle systems can be categorized as either formation control problems with applications to mobile robots, unmanned air vehicles, autonomous underwater vehicles, satellites, aircraft, spacecraft, and automated highway systems, or non-formation control problems such as task assignment, cooperative transport, cooperative role assignment, air traffic control, cooperative timing, and cooperative search. The cooperative control of multiple vehicle systems poses significant theoretical and practical challenges. For cooperative control strategies to be successful, numerous issues must be addressed. We consider three important and correlated issues: consensus seeking, formation keeping, and trajectory tracking. For consensus seeking, we investigate algorithms and protocols so that a team of vehicles can reach consensus on the values of the coordination data in the presence of imperfect sensors, communication dropout, sparse communication topologies, and noisy and unreliable communication links. The main contribution of this dissertation in this area is that we show necessary and/or sufficient conditions for consensus seeking with limited, unidirectional, and unreliable information exchange under fixed and switching interaction topologies (through either communication or sensing). For formation keeping, we apply a so-called "virtual structure" approach to spacecraft formation flying and multi-vehicle formation maneuvers. As a result, single vehicle path planning and trajectory generation techniques can be employed for the virtual structure while trajectory tracking strategies can be employed for each vehicle. The main contribution of this dissertation in this area is that we propose a decentralized architecture for multiple spacecraft formation flying in deep space with formation feedback introduced. This architecture ensures the necessary precision in the presence of actuator saturation, internal and external disturbances, and stringent inter-vehicle communication limitations. A constructive approach based on the satisficing control paradigm is also applied to multi-robot coordination in hardware. For trajectory tracking, we investigate nonlinear tracking controllers for fixed wing unmanned air vehicles and nonholonomic mobile robots with velocity and heading rate constraints. The main contribution of this dissertation in this area is that our proposed tracking controllers are shown to be robust to input uncertainties and measurement noise, and are computationally simple and can be implemented with low-cost, low-power microcontrollers. In addition, our approach allows piecewise continuous reference velocity and heading rate and can be extended to derive a variety of other trajectory tracking strategies.

This thesis presents a human interface which helps users efficiently allocate multiple unmanned ground vehicles (UGVs) cooperating to accomplish timing-sensitive missions in an urban environment. The urban environment consists of obstacles and a hazardous region. The obstacles represent a "no-go zone" while the hazardous region represents a high-risk area. The main object of this problem is to minimize the team operational cost while satisfying timing constraints. Operational costs for individual vehicles are based on risk and power consumption, and are calculated using path length and vehicle velocity. In this thesis, three types of timing constraints are considered: simultaneous arrival, tight sequential arrival, and loose sequential arrival. Coordination variables and functions are the strategy by which both temporal and spatial information is used to achieve cooperative timing at a minimum cost. Specifically, coordination variables and functions are used to plan trajectories for a team of UGVs that satisfy timing constraints. The importance of properly representing information to users, allowing them to make efficient decisions, is also discussed. Four different control interfaces (temporal, spatial, cost, and coordination variable/function control) were tested. A full factorial design of experiments was performed with response time, workload, and quality of decision as metrics used to evaluate the quality and effectiveness of each interface. Based on the results of this experiment, a final graphical user interface (GUI) was designed and is described. It incorporates a combination of coordination variable/function control and cost control. This GUI is capable of planning paths for vehicles based on cooperative timing constraints and enables users to make high quality decisions in deploying a group of vehicles.