Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators
Tecnol Monterrey, Escuela Ingn & Ciencias, Ave Epigmenio Gonzalez 500, Fracc San Pablo 76130, Queretaro, Mexico..
KTH.
Univ Mondragon, Escuela Politecn Super, Pais Vasco 20500, Spain..ORCID iD: 0000-0002-0074-1816
Tecnol Monterrey, Escuela Ingn & Ciencias, Ave Epigmenio Gonzalez 500, Fracc San Pablo 76130, Queretaro, Mexico..ORCID iD: 0000-0003-4324-3558
Show others and affiliations
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 11, article id 2576Article in journal (Refereed) Published
Abstract [en]

New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83 degrees was achieved when validated against several set-points within the possible range.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 19, no 11, article id 2576
Keywords [en]
shape memory alloys, artificial neural networks, control, manipulators
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-255213DOI: 10.3390/s19112576ISI: 000472133300154PubMedID: 31174288Scopus ID: 2-s2.0-85067537158OAI: oai:DiVA.org:kth-255213DiVA, id: diva2:1348177
Note

QC 20190903

Available from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-09-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Sundin, Roberto Castro

Search in DiVA

By author/editor
Sundin, Roberto CastroLoidi Eguren, IonCuan-Urquizo, Enrique
By organisation
KTH
In the same journal
Sensors
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 2 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf