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Ehsan Taheri

Researcher at Auburn University

Publications -  67
Citations -  926

Ehsan Taheri is an academic researcher from Auburn University. The author has contributed to research in topics: Trajectory & Trajectory optimization. The author has an hindex of 12, co-authored 58 publications receiving 589 citations. Previous affiliations of Ehsan Taheri include K.N.Toosi University of Technology & University of Michigan.

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Shape-Based Approximation of Constrained Low-Thrust Space Trajectories Using Fourier Series

TL;DR: This paper builds upon existing shapebased techniques to present an alternative Fourier series approximation for rapid low-thrust-rendezvous/orbitraising trajectory construction with thrust-acceleration constraint-handling capability, and shows its ability to solve problems with a greater number of free parameters than in shape-based methods.
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Enhanced Smoothing Technique for Indirect Optimization of Minimum-Fuel Low-Thrust Trajectories

TL;DR: In this paper, the extended logarithmic smoothing technique is revisited and integrated with an indirect method to efficiently generate minimum-fuel time-fixed low-thrust rendezvous trajectories.
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Initial three-dimensional low-thrust trajectory design

TL;DR: The proposed method is capable of rapid generation of sub-optimal feasible trajectories that are totally different from and comparable to the solutions of the state-of-the-art three-dimensional shape-based methods.
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Aircraft Optimal Terrain/Threat-Based Trajectory Planning and Control

Abstract: In this paper, single- and multi-objective three-dimensional terrain- and threat-based trajectory planning and control are addressed for optimal time, fuel, and altitude scenarios. To obtain more realistic and feasible trajectories, a high-fidelity, six-degree-of-freedom dynamic model of the aircraft is used that includes accurate aerodynamic and propulsion models in all path-planning scenarios. The paper is composed of two parts. In the first part, optimal trajectories are generated using a global differential-evolution-based optimization algorithm. A comprehensive analysis of the resulting optimal trajectories reveals major characteristics of each scenario, which are generally different. In the second part of the paper, a multi-input, multi-output nonlinear model predictive controller is established to enable the aircraft to track the optimal paths in real time. The controller uses a neurofuzzy predictor model that is trained using the local linear model tree algorithm. A robustness analysis shows that ...