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Trajectory optimization using colloc...

The University of Texas at Arlington., Aerospace Engineering.

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Trajectory optimization using collocation and evolutionary programming for constrained nonlinear dynamical systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Trajectory optimization using collocation and evolutionary programming for constrained nonlinear dynamical systems./
作者:	Shippey, Brandon Merle.
面頁冊數:	119 p.
附註:	Adviser: Kamesh Subbarao.
Contained By:	Masters Abstracts International46-06.
標題:	Engineering, Aerospace. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1453338
ISBN:	9780549557029

Trajectory optimization using collocation and evolutionary programming for constrained nonlinear dynamical systems.
Shippey, Brandon Merle.

Trajectory optimization using collocation and evolutionary programming for constrained nonlinear dynamical systems. - 119 p.

Adviser: Kamesh Subbarao.

Thesis (M.S.)--The University of Texas at Arlington, 2008.

The framework is applied to three minimum-time case studies: (i) a path planning problem for two mobile robots with obstacle avoidance; (ii) an aircraft turning maneuver; (iii) a low-thrust interplanetary transfer.

ISBN: 9780549557029Subjects--Topical Terms:

1018395
Engineering, Aerospace.

Trajectory optimization using collocation and evolutionary programming for constrained nonlinear dynamical systems.
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100 1 $a Shippey, Brandon Merle. $3 1058044
245 1 0 $a Trajectory optimization using collocation and evolutionary programming for constrained nonlinear dynamical systems.
300 $a 119 p.
500 $a Adviser: Kamesh Subbarao.
500 $a Source: Masters Abstracts International, Volume: 46-06, page: 3334.
502 $a Thesis (M.S.)--The University of Texas at Arlington, 2008.
520 $a The framework is applied to three minimum-time case studies: (i) a path planning problem for two mobile robots with obstacle avoidance; (ii) an aircraft turning maneuver; (iii) a low-thrust interplanetary transfer.
520 $a Trajectory design and optimization has a broad variety of applications in fields such as aerospace and electrical engineering. The solution of a trajectory that minimizes a cost function subject to nonlinear differential equations of motion and various types of constraints may be obtained by the methods of optimal control theory.
520 $a A framework is presented for numerical solution of the optimal control problem. The solution is converted to that of a constrained discrete parameter optimization problem. Direct collocation and nonlinear programming are used to perform a local gradient-based search for the optimal solution. A genetic algorithm combined with a shooting method conducts a global search of the solution space to provide a near-optimal, near-feasible initialization for the nonlinear program.
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