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Kamran Turkoglu

Researcher at San Jose State University

Publications -  51
Citations -  151

Kamran Turkoglu is an academic researcher from San Jose State University. The author has contributed to research in topics: Wind power & Nonlinear system. The author has an hindex of 6, co-authored 50 publications receiving 130 citations. Previous affiliations of Kamran Turkoglu include University of Connecticut & University of Minnesota.

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PID Parameter Optimization of an UAV Longitudinal Flight Control System

TL;DR: An automatic control system design based on Integral Squared Error parameter optimization technique has been implemented on longitudinal flight dynamics of an UAV to minimize the error function between the reference signal and the output of the plant.
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Nonlinear consensus strategies for multi-agent networks under switching topologies: Real-time receding horizon approach

TL;DR: Based on real-time nonlinear receding horizon control methodology, a novel scheme is developed for multi-agent nonlinear consensus problem under jointly connected switching topologies and the stability is proved without the assumption that the topology is connected for all the time.
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Distributed Real-Time Non-Linear Receding Horizon Control Methodology for Multi-Agent Consensus Problems

TL;DR: This work investigates the consensus problem for multi-agent nonlinear systems through the real-time nonlinear receding horizon control methodology and develops a scheme to reach the consensus under fixed directed/undirected graph(s) without any linearization technique.
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Adaptive Differential Thrust Methodology for Lateral/Directional Stability of an Aircraft with a Completely Damaged Vertical Stabilizer

TL;DR: In this article, the authors investigated the utilization of differential thrust to help a commercial aircraft with a damaged vertical stabilizer regain its lateral/directional stability, and a unique differential thrust based adaptive control approach is proposed to achieve a stable flight envelope.
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Real-Time Insitu Strategies for Enhancing UAV Endurance by Utilizing Wind Energy

TL;DR: In this paper, the authors present real-time practical strategies for enhancing the endurance of UAVs by utilizing wind energy, which relies solely on making "optimal" decisions at each successive time instant, given the instantaneous values of estimated local wind speeds.