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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
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2019 (Engelska)Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 11, artikel-id 2576Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
MDPI , 2019. Vol. 19, nr 11, artikel-id 2576
Nyckelord [en]
shape memory alloys, artificial neural networks, control, manipulators
Nationell ämneskategori
Reglerteknik
Identifikatorer
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
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QC 20190903

Tillgänglig från: 2019-09-03 Skapad: 2019-09-03 Senast uppdaterad: 2019-09-03Bibliografiskt granskad

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Sundin, Roberto Castro

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Sundin, Roberto CastroLoidi Eguren, IonCuan-Urquizo, Enrique
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