Browsing by Subject "Optimization algorithms"

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    Radar deception through phantom track generation
    (Texas A&M University, 2006-04-12) Maithripala, Diyogu Hennadige Asanka
    This thesis presents a control algorithm to be used by a team of ECAVs (Electronic Combat Air Vehicle) to deceive a network of radars through the generation of a phantom track. Each ECAV has the electronic capability of intercepting and introducing an appropriate time delay to a transmitted pulse of a radar before transmitting it back to the radar, thereby deceiving the radar into seeing a phantom target at a range beyond that of the ECAV. A radar network correlates targets and target tracks to detect range delay based deception. A team of cooperating ECAVs, however, precisely plans their trajectories in a way all the radars in the radar network are deceived into seeing the same phantom. Since each radar in the network confirms the target track of the other, the phantom track is considered valid. An important feature of the algorithm achieving this is that it translates kinematic constraints on the ECAV dynamic system into constraints on the phantom point. The phantom track between two specified way points then evolves without violating any of the system constraints. The evolving phantom track in turn generates the actual controls on the ECAVs so that ECAVs have flyable trajectories. The algorithms give feasible but suboptimal solutions. The main objectives are algorithm development for phantom track generation through a team of cooperating ECAVs, development of the algorithms to be finite dimensional searches and determining necessary conditions for feasible solutions in the immediate horizon of the searches of the algorithm. Feasibility of the algorithm in deceiving a radar network through phantom track generation is demonstrated through simulation results.
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    Theoretical modeling of single-phase power electronics loads to predict harmonic distortion at a distribution feeder network using a reverse optimization solution
    (2009-12) Kapur, Virat; Grady, W. M.
    Proliferation of non-linear, single-phase power electronics loads, such as personal computers, television sets, CFLs, has resulted in thousands of individual small harmonic current injectors connected to a distribution feeder network. Harmonic standard: IEC 1000-3-2 classifies such loads as Class D, “low-voltage” equipment with current emissions limited to 16A/Phase. Individual harmonic contributions of such loads appear insignificant; their collective contribution, however, is a matter of concern. The average order of voltage distortion usually varies between 4-6%; current distortion, however, is usually of the order of 100%. Limitations and high-costs associated with conventional harmonic mitigation measures, has furthered the need for regulation and alternative strategies. The objective of this research is to predict, and mitigate the effects of harmonic proliferation in the main supply current measured at the point of common coupling (PCC). An equivalent circuit model – an aggregation of single phase power electronics loads connected to the distribution feeder network is proposed as a part of a forward solution. Each load, individually, behaves as a harmonic current source; the proposed model combines these individual harmonic current injectors into a single harmonic source connected at the PCC and their collective contribution as a single composite harmonic signal. It represents harmonic conditions at the PCC and provides a theoretical measure of harmonic distortion in the supply current. Such a model finds application during harmonic compliance testing for single-phase power electronics loads; it simulates and predicts the harmonic response of such loads using a theoretical pure 60 Hz sine wave as the supply voltage diffcult to obtain physically, yet critical to such tests. The accuracy of the equivalent circuit model in predicting a harmonic response is pivotal to a successful forward solution. A feed-backwards mechanism is proposed. For a given harmonic supply voltage and circuit configuration of the equivalent circuit model, the feed-backwards method generates the modeled response and compares it to a reference physical response. Finally, it optimizes the circuit configuration to a unique Correction Factor that facilitates an accurate modeled response. Three optimization algorithms, labeled as Response Optimization algorithms have been developed to execute the feed-backwards mechanism. These algorithms are written in FORTRAN-90.