Fast modeling and identification of robot dynamics using the lasso

Cong Wang, Xiaowen Yu, Masayoshi Tomizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper presents an approach for fast modeling and identification of robot dynamics. By using a data-driven machine learning approach, the process is simplified considerably from the conventional analytical method. Regressor selection using the Lasso (l1-norm penalized least squares regression) is used. The method is explained with a simple example of a two-link direct-drive robot. Further demonstration is given by applying the method to a three-link belt-driven robot. Promising result has been demonstrated.

Original languageEnglish (US)
Title of host publicationNonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing;
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Print)9780791856147
DOIs
StatePublished - Jan 1 2013
Externally publishedYes
EventASME 2013 Dynamic Systems and Control Conference, DSCC 2013 - Palo Alto, CA, United States
Duration: Oct 21 2013Oct 23 2013

Publication series

NameASME 2013 Dynamic Systems and Control Conference, DSCC 2013
Volume3

Other

OtherASME 2013 Dynamic Systems and Control Conference, DSCC 2013
Country/TerritoryUnited States
CityPalo Alto, CA
Period10/21/1310/23/13

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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