In multi-agent systems [1], coordinated resource sharing is indispensable for a set of autonomous agents, which are running in the same execution space, to accomplish their computational objectives. This research presents a new approach to network resource control in multi-agent systems, based on the CyberOrgs [2] model. This approach aims to offer a mechanism to reify network resource control in multi-agent systems and to realize this mechanism in a prototype system. <p>In order to achieve these objectives, a uniform abstraction vLink (Virtual Link) is introduced to represent network resource, and based on this abstraction, a coherent mechanism of vLink creation, allocation and consumption is developed. This mechanism is enforced in the network by applying a fine-grained flow-based scheduling scheme. In addition, concerns of computations are separated from those of resources required to complete them, which simplifies engineering of network resource control. Thus, application programmers are enabled to focus on their application development and separately declaring resource request and defining resource control policies for their applications in a simplified way. Furthermore, network resource is bounded to computations and controlled in a hierarchy to coordinate network resource usage. A computation and its sub-computations are not allowed to consume resources beyond their resource boundary. However, resources can be traded between different boundaries. <p> In this thesis, the design and implementation of a prototype system is described as well. The prototype system is a middleware system architecture, which can be used to build systems supporting network resource control. This architecture has a layered structure and aims to achieve three goals: (1) providing an interface for programmers to express resource requests for applications and define their resource control policies; (2) specializing the CyberOrgs model to control network resource; and (3) providing carefully designed mechanisms for routing, link sharing and packet scheduling to enforce required resource allocation in the network.

In multi-agent systems [1], coordinated resource sharing is indispensable for a set of autonomous agents, which are running in the same execution space, to accomplish their computational objectives. This research presents a new approach to network resource control in multi-agent systems, based on the CyberOrgs [2] model. This approach aims to offer a mechanism to reify network resource control in multi-agent systems and to realize this mechanism in a prototype system. <p>In order to achieve these objectives, a uniform abstraction vLink (Virtual Link) is introduced to represent network resource, and based on this abstraction, a coherent mechanism of vLink creation, allocation and consumption is developed. This mechanism is enforced in the network by applying a fine-grained flow-based scheduling scheme. In addition, concerns of computations are separated from those of resources required to complete them, which simplifies engineering of network resource control. Thus, application programmers are enabled to focus on their application development and separately declaring resource request and defining resource control policies for their applications in a simplified way. Furthermore, network resource is bounded to computations and controlled in a hierarchy to coordinate network resource usage. A computation and its sub-computations are not allowed to consume resources beyond their resource boundary. However, resources can be traded between different boundaries. <p> In this thesis, the design and implementation of a prototype system is described as well. The prototype system is a middleware system architecture, which can be used to build systems supporting network resource control. This architecture has a layered structure and aims to achieve three goals: (1) providing an interface for programmers to express resource requests for applications and define their resource control policies; (2) specializing the CyberOrgs model to control network resource; and (3) providing carefully designed mechanisms for routing, link sharing and packet scheduling to enforce required resource allocation in the network.

In the wake of rising energy costs, there is a critical need for sustainable energy management of commercial and residential buildings. Buildings consume approximately 40% of total energy consumed in the US, and current methods to reduce this level of consumption include energy monitoring, smart sensing, and advanced integrated building control. However, the building industry has been slow to replace current PID and rule-based control strategies with more advanced strategies such as model-based building control. This is largely due to the additional cost of accurately modeling the dynamics of the building and the general uncertainty that model-based controllers can be reliably used in real conditions. The first half of this thesis addresses the challenge of constructing accurate grey-box building models for control using model identification. Current identification methods poorly estimate building model parameters because of the complexity of the building model structure, and fail to do so quickly because these methods are not scalable for large buildings. Therefore, we introduce the notion of parameter identifiability to determine those parameters in the building model that may not be accurately estimated and we use this information to strategically improve the identifiability of the building model. Finally, we present a decentralized identification scheme to reduce the computational effort and time needed to identify large buildings. The second half of this thesis discusses the challenge of using uncertain building models to reliably control building temperature. Under real conditions, building models may not match the dynamics of the building, which directly causes increased building energy consumption and poor thermal comfort. To reduce the impact of model uncertainty on building control, we pose the model-based building control problem as a robust control problem using well-known H1 control methods. Furthermore, we introduce a tuning law to reduce the conservativeness of a robust building control strategy in the presence of high model uncertainty, both in a centralized and decentralized building control framework.

Human flexible behaviour is often seen in everyday life tasks. These tasks (e.g., making coffee) are composed of actions (e.g., pouring sugar) that are typically nested within a large task structures made of superordinate components and nested subcomponents. Task switching has been adopted widely as an effective tool to explore the mechanisms of flexible behaviour and can be easily adapted to real-life situations. The core hypothesis explored in this thesis was that there might be another level of control that is responsible for coordinating and scheduling actions in task switching, which I label "meta-control". My first project aimed to establish robust behavioural evidence of meta-control. To test this hypothesis, switching efficiency was treated as a measure of meta-control influence. In a novel design, participants' beliefs about switch probability were manipulated through explicit instruction, allowing manipulation of meta-level control independent of the specific sequence of tasks required. The first three behavioural experiments demonstrated behaviorally that instructions influenced the efficiency of switching even when low-level (bottom-up) experience was matched, and that this effect was motivation-dependent. In follow-up studies, this meta-control influence was found to bias voluntary task selection. Two EEG studies aimed to characterize the level at which instruction affected processing. Motor and task levels were examined with lateralized motor potentials and oscillatory alpha power, respectively. Effects of instruction only existed on alpha power. Overall, the results suggest that expectancy prompts the adoption of distinct control modes across sequences of trials. The second project explored meta-control in a task switching paradigm with a hierarchical task structure in very short (2-4 trial) sequences that were preceded by instructions about switch frequency or switch position. The experiments focused on the benefits and costs of sequence structure, based on the hypothesis that trial sequences are treated as coherent units. Three behavioural studies were conducted for testing this hypothesis. The first utilized instructions about switch frequency to test for sequence transition effects and their influence on switching performance at the trial level. In two subsequent experiments, instructions made explicit the position of a task switch. The results confirmed that instructions about switch position helped participants to build useful sequence representations, and that alternating between sequences influenced trial-level switch processes. Generally, sequence representations have a persisting influence across trials and a pronounced impact at the first trial position of sequences. The experiments in this thesis provide evidence of meta-control in task switching. The first conclusion is that meta-control can be studied with the novel experimental design introduced in this thesis and was found to be reward dependent. The second conclusion is that meta-control acts as a set of parameters that can modulate the mode of control in a sustained way across trials.

To reinforce its Inez network, which was operated close to its stability limits, American Electric Power (AEP) undertook two major developments, one being the installation of a Static Var Compensator (SVC) in November, 1980 and the other one being the installation of the world's first Unified Power Flow Controller (UPFC) in 1998. The controllers in the system include the Automatic Voltage Regulators (AVRs) of the generators, the controllers of the SVC, and UPFC. To coordinate the control actions of these controllers and prevent voltage instability resulting from their fighting against each other, a two level hierarchical control scheme using fuzzy logic has been developed and its performance was assessed via simulations. The second level of the hierarchy determines the set points of the local controllers of the AVRs, SVC, and UPFC and defines the switching sequences of the capacitor banks, the goal being to maximize the reactive reserve margins of the Inez subsystem. Numerous simulations were carried out on this system to determine the actions of the fuzzy controller required to prevent the occurrence of voltage collapse under double contingency. Simulations have revealed the occurrence of nonlinear interactions between the machines resulting in stable limit cycles, nonlinear oscillations undergoing period doubling leading to chaos and possible voltage collapse. The proposed fuzzy scheme provides a fast, simple and effective way to stretch the stability limit of the system for double contingency conditions, up to 175 MW in some cases. This is a significant increase in the system capacity. / Master of Science

A flexible workcell controller has been developed using a three level control hierarchy (workcell, workstation, equipment). The cell controller is automatically generated from a model input by the user. The model consists of three sets of graphs. One set of graphs describes the process plans of the parts produced by the manufacturing system, one set describes movements into, out of and within workstations, and the third set describes movements of parts/transporters between workstations. The controller uses an event driven Petri net to maintain state information and to communicate with lower level controllers. The control logic is contained in an artificial neural network. The Petri net state information is used as the input to the neural net and messages that are Petri net events are output from the neural net. A genetic algorithm was used to search over alternative operation choices to find a "good" solution. The system was fully implemented and several test cases are described.

A flexible workcell controller has been developed using a three level control hierarchy (workcell, workstation, equipment). The cell controller is automatically generated from a model input by the user. The model consists of three sets of graphs. One set of graphs describes the process plans of the parts produced by the manufacturing system, one set describes movements into, out of and within workstations, and the third set describes movements of parts/transporters between workstations. The controller uses an event driven Petri net to maintain state information and to communicate with lower level controllers. The control logic is contained in an artificial neural network. The Petri net state information is used as the input to the neural net and messages that are Petri net events are output from the neural net. A genetic algorithm was used to search over alternative operation choices to find a "good" solution. The system was fully implemented and several test cases are described.

No description available.

This thesis outlines the evolution of computer hardware and software architectures which are suitable for the programming and control of modular robots and distributed manipulators. Fundamental aspects of automating manufacturing functions are considered and the use of flexible machines, constructed from components of a family of mechanical modules and associated control system elements, are proposed. Many of the features of these flexible machines can be identified with those of conventional industrial robots. However a broader class of manufacturing machine is represented in as much as the industrial user defines the kinematics and dynamics of the manipulator. Such flexible machines can be referred to as "modular robots" or, where the mechanical modules are arranged in concurrently operating but mechanically decoupled groups, as "distributed manipulators". The main body of the work reported centred on the design of a family of computer control system elements which can serve a range of distributed manipulator and modular robot forms. These control system elements, whose cost is commensurate with the size and complexity of the manipulator's mechanical configuration, necessarily have many of the features found in robot controllers but also require properties of reconfigurability, programmability, and control system performance for the considerable array of manipulator configurations which can be constructed.

La production intermittente et aléatoire des énergies renouvelables, sources photovoltaïques et éoliennes, est toujours un problème pour leur intégration massive dans le réseau public. L'une des solutions est de grouper des sources renouvelables, des sources traditionnelles, des dispositifs de stockage et des charges locales, et les traiter comme une seule unité dans le réseau public. Il s'agit du concept "micro-réseau". Un micro-réseau a des potentiels pour mieux répondre aux besoins de l'utilisateur final et du réseau public, et il facilite la mise en œuvre de futur smart grid, soit le réseau intelligent.Basé sur un micro-réseau représentatif en zone urbaine et intégré aux bâtiments, cette thèse propose une supervision multicouche, afin d'effectuer une étude systémique en mettant en exergue un verrou scientifique concernant l'implémentation d'une optimisation dans l'exploitation en temps réel.La supervision traite un ensemble d’opérations telles que : l'équilibré des puissances,l'optimisation des coûts énergétiques, utilisation de métadonnées, et échange d'informations avec le réseau intelligent et avec l'utilisateur final. Cette supervision a été validée par des tests expérimentaux. Malgré les incertitudes concernant les prévisions météorologiques, la faisabilité d'implémentation de l'optimisation dans l'exploitation réelle est vérifiée. La supervision proposée est en mesure de gérer efficacement les flux en assurant l'équilibre des puissances dans tous les cas. Néanmoins, la performance d'optimisation est liée aux précisions de prédiction. Ce problème peut être amélioré dans les travaux futurs par la mise à jour des résultats d'optimisation en temps réel. / The intermittent and random production of renewable sources, such as photovoltaic and wind turbine, is always a problem for their large-scale integration in the utility grid. One of the solutions is to group renewable sources, traditional sources, storage and local consumption and treat it as a single unit in the utility grid. This is the concept of microgrid. A microgrid has the potentials of better responding both grid and end-user requirement, it facilitate the implementation of future smart grid. Based on a representative microgrid in urban area and integrated in buildings, this thesis proposes a multi-layer supervision, in order to realise a systemic study while particularly attempting to cover the research gap of implementing optimisation in realtimeoperation. The supervision handles together power balancing, energetic cost optimisation, metadata using, and information exchanges from both end-users and the smart grid. The supervision has been validated by experimental tests. The feasibility of implementing optimisation in real-time operation is validated even with uncertainties. The supervision is able to manage efficiently the power flow while maintaining power balancing in any case. Nevertheless, optimization effect relies on prediction precision. This problem can be improved in future works by updating optimization in real-time.