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
CountryUnited States
CityPalo Alto, CA
Period10/21/1310/23/13

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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