A controller for improving lateral stability in a dynamically stable gait

Zhenglong Sun; Nico Roos

doi:10.1007/978-3-319-44944-9_32

A controller for improving lateral stability in a dynamically stable gait

Zhenglong Sun^*, Nico Roos

^*Corresponding author for this work

Robots, Agents, Interaction

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

Abstract

Due to the technological advances in computer hardware and software tools, structural optimization has been gaining continuously increasing interest over the last two decades. The purpose of the present work is to quantitatively compare three metaheuristic optimization algorithms, namely the Differential Evolution, Harmony Search and Particle Swarm Optimization methods, in the framework of structural optimization. The comparison of the optimizers is performed with reference to their efficiency (overall computing demands) and robustness (capability to detect near-optimal solutions). The optimum design of a real-world overhead traveling crane is used as the test bed application for conducting optimization test runs. © 2010 IFIP.

Original language	English
Title of host publication	IFIP Advances in Information and Communication Technology
Publisher	Springer Verlag
Pages	371-383
Number of pages	13
ISBN (Print)	9783319449432
DOIs	https://doi.org/10.1007/978-3-319-44944-9_32
Publication status	Published - 2016

Publication series

Series	IFIP Advances in Information and Communication Technology
Volume	475

Access to Document

10.1007/978-3-319-44944-9_32Licence: Unspecified

http://www.mendeley.com/research/controller-improving-lateral-stability-dynamically-stable-gait

Cite this

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AB - Due to the technological advances in computer hardware and software tools, structural optimization has been gaining continuously increasing interest over the last two decades. The purpose of the present work is to quantitatively compare three metaheuristic optimization algorithms, namely the Differential Evolution, Harmony Search and Particle Swarm Optimization methods, in the framework of structural optimization. The comparison of the optimizers is performed with reference to their efficiency (overall computing demands) and robustness (capability to detect near-optimal solutions). The optimum design of a real-world overhead traveling crane is used as the test bed application for conducting optimization test runs. © 2010 IFIP.

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