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Technical Brief

Trajectory Optimization Using Analytical Target Cascading

[+] Author and Article Information
Xiang Li, Yuheng Guo

School of Aerospace Engineering,
Beijing Institute of Technology,
Beijing 100081, China

Xiaonpeng Wang

Shanghai Electro-Mechanical Engineering Institute,
Shanghai 201109, China

Houjun Zhang

Beijing System Design Institute of Electro-Mechanic
Engineering,
Beijing 100854, China

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 30, 2016; final manuscript received August 12, 2017; published online October 3, 2017. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 139(12), 124501 (Oct 03, 2017) (5 pages) Paper No: MD-16-1802; doi: 10.1115/1.4037714 History: Received November 30, 2016; Revised August 12, 2017

In the previous reports, analytical target cascading (ATC) is generally applied to product optimization. In this paper, the application area of ATC is expanded to trajectory optimization. Direct collocation method is utilized to convert a trajectory optimization into a nonlinear programing (NLP) problem. The converted NLP is a large-scale problem with sparse matrix of functional dependence table (FDT) suitable for the application of ATC. Three numerical case studies are provided to show the effects of ATC in solving trajectory optimization problems.

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Figures

Grahic Jump Location
Fig. 1

Optimal trajectories of x(τ) obtained from two sets of weighting coefficients: (a) trajectory of x(τ) for weighting coefficients set 1 and (b) trajectory of x(τ) for weighting coefficients set 2

Grahic Jump Location
Fig. 2

Optimal trajectories of x1(τ), x2(τ), and x3(τ): (a) trajectory of x1(τ), (b) trajectory of x2(τ), and (c) trajectory of x3(τ)

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