Electrical and Electronic Engineering - Theses

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Type: PhD thesis × Subject: Mmltitarget-multisensor tracking × Subject: autonomous UAVs × Subject: hierarchical control ×

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    Hierarchical model predictive control of an unmanned-aerial-vehicle based multitarget-multisensor data fusion system
    Sarunic, Peter William ( 2011)
    There has been rapidly growing interest in the development of increased automation in the military in recent years. In particular, the number of unmanned aircraft and ground vehicles being put into use is rapidly increasing. Concurrently, there has been an associated increase in the amount of research being performed to develop increased autonomy, moving from relatively simple remotely controlled devices to autonomous systems that are able to operate in a sense-think-act paradigm, i.e., robots. An application of robotic technology that is of considerable military significance is that of detection and tracking of enemy assets. A key advantage of using autonomous vehicles in this application is that the locations and details of potential threats can be determined using relatively inexpensive unmanned vehicles with the operator of the system standing back at a safe distance. One example is the use of teams of unmanned aerial vehicles (UAVs) carrying passive direction-of-arrival sensors to detect and track enemy emitters such as radar-carrying platforms so as to enable reaction to the threats with other resources which could, say, include jammers or missile-carrying aircraft. In this thesis the problem of how to adaptively control the trajectories of UAVs in such an application in order to optimize performance in response to target measurements, while avoiding no-fly zones, is considered and a solution is developed. Because of the complexity of situations that are encountered, a major issue is how to formulate the problem in a manner which enables efficient computation of optimal behaviours for the platforms. In fact, an optimal solution cannot in practice be found by any physically implementable method. Hence, in this thesis an approach will be developed that enables implementation of a computationally feasible, albeit suboptimal solution, that takes into account both short-term and long-term goals. To this end, the problem will be addressed by developing a hierarchical control approach, incorporating an automated planner and a low-level (short-term) control algorithm. A key aim is to use a consistent mathematical framework that can be generalized to a range of optimal control problems. As a result, all components of the controllers that are developed are based on concepts from estimation theory, dynamic programming and optimal control, giving a mathematically coherent and scalable solution. To evaluate the effectiveness of the approach, first a controller is developed using an idealized UAV model and simulations are performed. Its performance is compared with a commonly used "myopic" control approach and found to give important improvements. Subsequently an improved planner is incorporated and tested, and then a version of the controller using a fixed-wing aircraft model for the UAVs is implemented. This version is also tested by simulation and found to perform successfully. Finally, a mathematical analysis of stability is commenced and significant headway made towards a stability proof.