Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil

Jari Ahola, Tomi Makkonen, Kalervo Nevala, Tomi Lindroos, Pekka Isto

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

2 Citations (Scopus)

Abstract

This paper describes the development of a neural network control for shape memory alloy (SMA) actuators as well as the control tests. Precise control of SMA actuators that are integrated in the composite structure is difficult because of the hysteretic behaviour of SMA and time delays in the control system. The weakness of the static model of SMA is that it does not take into account the actuator dynamics. The NARMA-L2 neural network model enables us to solve the inverse dynamic model of the nonlinear discrete-time dynamical system. In this study the NARMA-L2 model of the airfoil (a cross section of wind turbine blade) was created using Matlab Simulink software and neural networks. Data for network training was measured from the specimen. The performance of the constituted NARMA-L2 controller was tested in control tests. According to the control tests the NARMA-L2 controller is well suited for controlling embedded SMA actuators. The constituted controller enabled fast and stable actuation in step responses and it was also able to compensate for changes in cooling conditions.
Original languageEnglish
Title of host publicationMechatronic Systems and Materials III
EditorsZdislaw Gosiewski, Zbigniew Kulesza
Pages278-283
DOIs
Publication statusPublished - 2009
MoE publication typeA3 Part of a book or another research book

Publication series

SeriesSolid State Phenomena
Volume147-149
ISSN1012-0394

Fingerprint

Recurrent neural networks
Shape memory effect
Airfoils
Actuators
Neural networks
Controllers
Step response
Composite structures
Wind turbines
Turbomachine blades
Dynamic models
Time delay
Dynamical systems
Cooling
Control systems

Keywords

  • neural network (NN)
  • shape control
  • shape memory alloy
  • system identification

Cite this

Ahola, J., Makkonen, T., Nevala, K., Lindroos, T., & Isto, P. (2009). Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil. In Z. Gosiewski, & Z. Kulesza (Eds.), Mechatronic Systems and Materials III (pp. 278-283). Solid State Phenomena, Vol.. 147-149 https://doi.org/10.4028/www.scientific.net/SSP.147-149.278
Ahola, Jari ; Makkonen, Tomi ; Nevala, Kalervo ; Lindroos, Tomi ; Isto, Pekka. / Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil. Mechatronic Systems and Materials III. editor / Zdislaw Gosiewski ; Zbigniew Kulesza. 2009. pp. 278-283 (Solid State Phenomena, Vol. 147-149).
@inbook{7fd8293ae45544ba9eee8097769c75cd,
title = "Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil",
abstract = "This paper describes the development of a neural network control for shape memory alloy (SMA) actuators as well as the control tests. Precise control of SMA actuators that are integrated in the composite structure is difficult because of the hysteretic behaviour of SMA and time delays in the control system. The weakness of the static model of SMA is that it does not take into account the actuator dynamics. The NARMA-L2 neural network model enables us to solve the inverse dynamic model of the nonlinear discrete-time dynamical system. In this study the NARMA-L2 model of the airfoil (a cross section of wind turbine blade) was created using Matlab Simulink software and neural networks. Data for network training was measured from the specimen. The performance of the constituted NARMA-L2 controller was tested in control tests. According to the control tests the NARMA-L2 controller is well suited for controlling embedded SMA actuators. The constituted controller enabled fast and stable actuation in step responses and it was also able to compensate for changes in cooling conditions.",
keywords = "neural network (NN), shape control, shape memory alloy, system identification",
author = "Jari Ahola and Tomi Makkonen and Kalervo Nevala and Tomi Lindroos and Pekka Isto",
year = "2009",
doi = "10.4028/www.scientific.net/SSP.147-149.278",
language = "English",
isbn = "978-3-908451-65-5",
series = "Solid State Phenomena",
publisher = "Trans Tech Publications",
pages = "278--283",
editor = "Zdislaw Gosiewski and Zbigniew Kulesza",
booktitle = "Mechatronic Systems and Materials III",

}

Ahola, J, Makkonen, T, Nevala, K, Lindroos, T & Isto, P 2009, Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil. in Z Gosiewski & Z Kulesza (eds), Mechatronic Systems and Materials III. Solid State Phenomena, vol. 147-149, pp. 278-283. https://doi.org/10.4028/www.scientific.net/SSP.147-149.278

Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil. / Ahola, Jari; Makkonen, Tomi; Nevala, Kalervo; Lindroos, Tomi; Isto, Pekka.

Mechatronic Systems and Materials III. ed. / Zdislaw Gosiewski; Zbigniew Kulesza. 2009. p. 278-283 (Solid State Phenomena, Vol. 147-149).

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

TY - CHAP

T1 - Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil

AU - Ahola, Jari

AU - Makkonen, Tomi

AU - Nevala, Kalervo

AU - Lindroos, Tomi

AU - Isto, Pekka

PY - 2009

Y1 - 2009

N2 - This paper describes the development of a neural network control for shape memory alloy (SMA) actuators as well as the control tests. Precise control of SMA actuators that are integrated in the composite structure is difficult because of the hysteretic behaviour of SMA and time delays in the control system. The weakness of the static model of SMA is that it does not take into account the actuator dynamics. The NARMA-L2 neural network model enables us to solve the inverse dynamic model of the nonlinear discrete-time dynamical system. In this study the NARMA-L2 model of the airfoil (a cross section of wind turbine blade) was created using Matlab Simulink software and neural networks. Data for network training was measured from the specimen. The performance of the constituted NARMA-L2 controller was tested in control tests. According to the control tests the NARMA-L2 controller is well suited for controlling embedded SMA actuators. The constituted controller enabled fast and stable actuation in step responses and it was also able to compensate for changes in cooling conditions.

AB - This paper describes the development of a neural network control for shape memory alloy (SMA) actuators as well as the control tests. Precise control of SMA actuators that are integrated in the composite structure is difficult because of the hysteretic behaviour of SMA and time delays in the control system. The weakness of the static model of SMA is that it does not take into account the actuator dynamics. The NARMA-L2 neural network model enables us to solve the inverse dynamic model of the nonlinear discrete-time dynamical system. In this study the NARMA-L2 model of the airfoil (a cross section of wind turbine blade) was created using Matlab Simulink software and neural networks. Data for network training was measured from the specimen. The performance of the constituted NARMA-L2 controller was tested in control tests. According to the control tests the NARMA-L2 controller is well suited for controlling embedded SMA actuators. The constituted controller enabled fast and stable actuation in step responses and it was also able to compensate for changes in cooling conditions.

KW - neural network (NN)

KW - shape control

KW - shape memory alloy

KW - system identification

U2 - 10.4028/www.scientific.net/SSP.147-149.278

DO - 10.4028/www.scientific.net/SSP.147-149.278

M3 - Chapter or book article

SN - 978-3-908451-65-5

T3 - Solid State Phenomena

SP - 278

EP - 283

BT - Mechatronic Systems and Materials III

A2 - Gosiewski, Zdislaw

A2 - Kulesza, Zbigniew

ER -

Ahola J, Makkonen T, Nevala K, Lindroos T, Isto P. Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil. In Gosiewski Z, Kulesza Z, editors, Mechatronic Systems and Materials III. 2009. p. 278-283. (Solid State Phenomena, Vol. 147-149). https://doi.org/10.4028/www.scientific.net/SSP.147-149.278