Inverse optimization of discrete-time systems applied to human locomotion

The problem of inverse optimization is to find the objective function that is being minimized, given knowledge of the constraints and observations of local minima. In this thesis, we consider the special case in which the objective function is a linear combination of known basis functions weighted by unknown parameters. Therefore the aim is to recover the weights governing the objective function. We propose a solution approach in this case that is based on the application of necessary conditions for optimality. We begin with a review of how these necessary conditions arise, with a particular focus on the relationship between duality theory and inverse optimization. We then proceed to describe our solution approach. Finally, we apply our approach to find a model of goal-directed human walking from experimental data with human subjects.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Aerospace Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2012

Author dc:creator

• Puydupin, Anne-Sophie

Contributors dc:contributor

• Bretl, Timothy W.

• Inverse optimization
• optimization
• necessary conditions for optimality
• duality theory
• human locomotion.

Statement dc:rights

• Copyright 2012 Anne-Sophie Gaby Marthe Puydupin.

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/31127

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/31127