Document Type
Article
Publication Date
8-2016
Abstract
The DoD has introduced the concept of Manned-Unmanned Teaming, a subset of which is the loyal wingman. Optimal control techniques have been proposed as a method for rapidly solving the intermediate-target (mid-point constraint) optimal control problem. Initial results using direct orthogonal collocation and a gradient-based method for solving the resulting nonlinear program reveals a tendency to converge to or to get `stuck’ in locally optimal solutions. The literature suggested a hybrid technique in which a particle swarm optimization is used to quickly find a neighborhood of a more globally minimal solution, at which point the algorithm switches to a gradient-based nonlinear programming solver to converge on the globally optimal solution. The work herein applies the hybrid optimization technique to rapidly solve the loyal wingman optimal control problem. After establishing the background and describing the loyal wingman particle swarm optimization algorithm, the problem is solved first using the gradient-based direct orthogonal collocation method, then re-solved using a hybrid approach in which the results of the particle swarm optimization algorithm are used as the initial guess for the gradient-based direct orthogonal collocation method. Results comparing the final trajectory and convergence time, demonstrate the hybrid technique as a reliable method for producing rapid, autonomous, and feasible solutions to the loyal wingman optimal control problem.
DOI
10.4172/2229-8711.1000200
Source Publication
Global Journal of Technology and Optimization
Recommended Citation
Humphreys, C. J., Cobb, R. G., Jacques, D. R., & Reeger, J. A. (2016). A Hybrid Optimization Technique Applied to the Intermediate-Target Optimal Control Problem. Global Journal of Technology and Optimization, 7(2). https://doi.org/10.4172/2229-8711.1000200 Issue table of contents in lieu of DOI: https://www.hilarispublisher.com/archive/gjto-volume-7-issue-2-year-2016.html
Comments
This is an open access article published by OMICS and distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CC BY 4.0
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