This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed.

This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed.

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The variable rate fertilizer spreader was modified to control each pair of nozzles for spot-application of fertilizer only in plant areas of wild blueberry fields. The experiments were conducted to evaluate performance accuracy of modified variable rate granular (MVRG) fertilizer spreader. The results suggested that the MVRG fertilizer spreader performed efficiently in detecting bare spots/weed patches and clay filler application only in green grass/plant areas. Two wild blueberry fields were selected to evaluate the impact of MVRG spreader on nutrient leaching through small bare spots/weed patches. Management zones were delineated on the basis of slope variability. The MVRG spreader significantly reduced the nutrient loading in subsurface water samples collected from the bare spots/weed patches. Based on the results obtained, it can be concluded that the fertilization in wild blueberry fields using MVRG fertilizer spreader can result in the protection of subsurface water quality, thus protecting the environment.

This thesis describes an investigation on the blowout phenomenon in gas turbine combustors. The combustor primarily used for this study was a swirl- and dump-stabilized, atmospheric pressure device, which did not exhibit dynamic combustion instabilities. The first part of the thesis work concentrated on finding a sensing methodology to be able to predict the onset of approach of combustor blowout using optical methods. Temporary extinction-reignition events that occurred prior to blowout were found to be precursor events to blowout. A threshold based method was developed to identify these events in the time-resolved sensor output. The number and the average length of each event were found to increase as the LBO limit (fuel-air ratio) is approached. This behavior is used to predict the proximity to lean blowout. In the second part of this study, the blowout sensor was incorporated into a control system that monitored the approach of blowout and then actuated an alternate mechanism to stabilize the combustor near blowout. Enhanced stabilization was achieved by redirecting a part of the main fuel to a central preinjection pilot injection. The sensing methodology, without modification, was effective for the combustor with pilot stabilization. An event based control algorithm for controlling the combustor from blowing out was also developed in this study. The control system was proven to stabilize the combustor even when the combustor loading was rapidly changed. The final part of this study focused on understanding the physical mechanisms behind the precursor events. High speed movies of flame chemiluminescence and laser sheet scattering from oil droplets seeded into the reactants were analyzed to explain the physical processes that cause the extinction and the reignition of the combustor during a precursor event. A physical model for coupling of the fluid dynamics of vortex breakdown and combustion during precursor and blowout events is proposed. This model of blowout phenomenon, along with the sensing and control strategies developed in this study could enable the gas turbine combustor designers to design combustors with wider operability regimes. This could have significant payoffs in terms of reduction in NOx emissions from the combustor.

Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the data. Consequently, before the data can be used in ecological models, future experiments, or even policy decisions, it must be quality controlled (QC'd) to flag affected measurements and impute corrected values. This dissertation describes a probabilistic modeling approach for real-time automated QC that exploits the spatial and temporal correlations in the data to distinguish sensor failures from valid observations. The model adapts to a site by learning a Bayesian network structure that captures spatial relationships among sensors, and then extends this structure to a dynamic Bayesian network to incorporate temporal correlations. The final QC model contains both discrete and continuous variables, which makes inference intractable for large sensor networks. Consequently, we examine the performance of three approximate methods for inference in this probabilistic framework. Two of these algorithms represent contemporary approaches to inference in hybrid models, while the third is a greedy search-based method of our own design. We demonstrate the results of these algorithms on synthetic datasets and real environmental sensor data gathered from an ecological sensor network located in western Oregon. Our results suggest that we can improve performance over networks with less sensors that use exhaustive asynchronic inference by including additional sensors and applying approximate algorithms. / Graduation date: 2013