Shape memory alloy (SMA) actuators exhibit considerable hysteresis between the supply voltage (conventionally used in resistive heating) and strain characteristics of the SMA. Hence, it is not easy to control the strain of a thin-SMA wire, unless a model is developed that can match the actuator's nonlinearities for predicting the supply voltage required by the SMA system accurately. The work presented in this paper proposes the use of a black-box technique called the adaptive neurofuzzy inference system (ANFIS) to study the hysteretic behavior of SMAs. The input parameters for such an ANFIS model would be a physical variable at time t and at a time t + n, where n is a time shift. The present work studies the effect of a time shift on the actuator nonlinearities for two ANFIS models. One of the models studies the relationship between the desired displacement of an SMA and the supply voltage across the SMA, while the other model predicts the actual displacement of an SMA from the feedback temperature. A novel SMA–Constantan thermocouple records the feedback temperature.

Issue Section:
Research Papers
Keywords:
shape memory alloy (SMA), ANFIS, fuzzy, thermocouple, time-delay
Topics:
Displacement, Temperature, Thermocouples, Wire, Shape memory alloys, Feedback, Sensors

References

1.
Menciassi
,
A.
,
Gorini
,
S.
,
Pernorio
,
G.
, and
Dario
,
P.
,
2004
, “
A SMA Actuated Artificial Earthworm
,”
IEEE International Conference on Robotics and Automation
(
ICRA'04
), New Orleans, LA, Apr. 26–May 1, pp.
3282
–
3287
.10.1109/ROBOT.2004.1308760
2.
Menciassi
,
A.
,
Park
,
J. H.
,
Lee
,
S.
,
Gorini
,
S.
,
Dario
,
P.
, and
Park
,
J.-O.
,
2002
, “
Robotic Solutions and Mechanisms for a Semi-Autonomous Endoscope
,”
IEEE/RSJ International Conference on Intelligent Robots and Systems
, Lausanne, Switzerland, Sept. 30–Oct. 4, pp.
1379
–
1384
.10.1109/IRDS.2002.1043947
3.
Maeda
,
S.
,
Abe
,
K.
,
Yamamoto
,
K.
,
Tohyama
,
O.
, and
Ito
,
H.
,
1996
, “
Active Endoscope With SMA (Shape Memory Alloy) Coil Springs
,”
IEEE Ninth Annual International Workshop on Micro Electro Mechanical Systems
, (
MEMS'96
), San Diego, CA, Feb. 11–15, pp.
290
–
295
.10.1109/MEMSYS.1996.493996
4.
Hirose
,
S.
,
Ikuta
,
K.
, and
Umetani
,
Y.
,
1988
, “
Development of Shape-Memory Alloy Actuators. Performance Assessment and Introduction of a New Composing Approach
,”
Adv. Rob.
,
3
(
1
), pp.
3
–
16
.10.1163/156855389X00145
5.
Avirovik
,
D.
,
Kumar
,
A.
,
Bodnar
,
R. J.
, and
Priya
,
S.
,
2013
, “
Remote Light Energy Harvesting and Actuation Using Shape Memory Alloy—Piezoelectric Hybrid Transducer
,”
Smart Mater. Struct.
,
22
(
5
), p.
052001
.10.1088/0964-1726/22/5/052001
6.
Selden
,
B.
,
Cho
,
K.
, and
Asada
,
H. H.
,
2006
, “
Segmented Shape Memory Alloy Actuators Using Hysteresis Loop Control
,”
Smart Mater. Struct.
,
15
(
2
), p.
642
.10.1088/0964-1726/15/2/048
7.
Elzey
,
D. M.
,
Sofla
,
A. Y.
, and
Wadley
,
H. N.
,
2005
, “
A Shape Memory-Based Multifunctional Structural Actuator Panel
,”
Int. J. Solids Struct.
,
42
(
7
), pp.
1943
–
1955
.10.1016/j.ijsolstr.2004.05.034
8.
Ma
,
N.
,
Song
,
G.
, and
Lee
,
H.
,
2004
, “
Position Control of Shape Memory Alloy Actuators With Internal Electrical Resistance Feedback Using Neural Networks
,”
Smart Mater. Struct.
,
13
(
4
), p.
777
.10.1088/0964-1726/13/4/015
9.
Barbarino
,
S.
,
Ameduri
,
S.
, and
Lecce
,
L.
,
2009
, “
Wing Shape Control Through an SMA-Based Device
,”
J. Intell. Mater. Syst. Struct.
,
20
(
3
), pp.
283
–
296
.10.1177/1045389X08093825
10.
Craciunescu
,
C.
, and
Wuttig
,
M.
,
2000
, “
Extraordinary Damping of Ni–Ti Double Layer Films
,”
Thin Solid Films
,
379
(
1
), pp.
173
–
175
.10.1016/S0040-6090(00)01556-X
11.
Krulevitch
,
P.
,
Lee
,
A. P.
,
Ramsey
,
P. B.
,
Trevino
,
J. C.
,
Hamilton
,
J.
, and
Northrup
,
M. A.
,
1996
, “
Thin Film Shape Memory Alloy Microactuators
,”
J. Microelectromech. Syst.
,
5
(
4
), pp.
270
–
282
.10.1109/84.546407
12.
Hassan
,
M. R.
,
Scarpa
,
F. L.
, and
Mohamed
,
N. A.
, “
Shape Memory Alloys Honeycomb: Design and Properties
,”
Proc. SPIE
,
5387
, pp.
557
–
564
.10.1117/12.555597
13.
Ikuta
,
K.
,
Tsukamoto
,
M.
, and
Hirose
,
S.
,
1991
, “
Mathematical Model and Experimental Verification of Shape Memory Alloy for Designing Micro Actuator
,”
IEEE Micro Electro Mechanical Systems, An Investigation of Micro Structures, Sensors, Actuators, Machines and Robots
(
MEMS'91
), Nara, Japan, Jan. 30–Feb. 2, pp.
103
–
108
.10.1109/MEMSYS.1991.114778
14.
Bertram
,
A.
,
1983
, “
Thermo-Mechanical Constitutive Equations for the Description of Shape Memory Effects in Alloys
,”
Nucl. Eng. Des.
,
74
(
2
), pp.
173
–
182
.10.1016/0029-5493(83)90054-7
15.
Popov
,
P.
, and
Lagoudas
,
D. C.
,
2007
, “
A 3-D Constitutive Model for Shape Memory Alloys Incorporating Pseudoelasticity and Detwinning of Self-Accommodated Martensite
,”
Int. J. Plast.
,
23
(
10
), pp.
1679
–
1720
.10.1016/j.ijplas.2007.03.011
16.
Lee
,
J.
,
Jin
,
M.
, and
Ahn
,
K. K.
,
2013
, “
Precise Tracking Control of Shape Memory Alloy Actuator Systems Using Hyperbolic Tangential Sliding Mode Control With Time Delay Estimation
,”
Mechatronics
,
23
(
3
), pp.
310
–
317
.10.1016/j.mechatronics.2013.01.005
17.
Tai
,
N. T.
, and
Ahn
,
K. K.
,
2012
, “
A Hysteresis Functional Link Artificial Neural Network for Identification and Model Predictive Control of SMA Actuator
,”
J. Process Control
,
22
(
4
), pp.
766
–
777
.10.1016/j.jprocont.2012.02.007
18.
Tan
,
X.
, and
Baras
,
J. S.
,
2005
, “
Adaptive Identification and Control of Hysteresis in Smart Materials
,”
IEEE Trans. Autom. Control
,
50
(
6
), pp.
827
–
839
.10.1109/TAC.2005.849215
19.
Kumagai
,
A.
,
Hozian
,
P. A.
, and
Kirkland
,
M.
, “
Neurofuzzy-Model-Based Feedback Controller for Shape Memory Alloy Actuators
,”
Proc. SPIE
,
3984
, pp.
291
–
299
.10.1117/12.388772
20.
Malladi
,
V. V. N. S.
, and
Tarazaga
,
P. A.
, “
Sensorless Control of SMA Using Seebeck Voltage
,”
ASME
Paper No. SMASIS2012-8062.10.1115/SMASIS2012-8062
21.
Malladi
,
V. V. N. S.
, and
Tarazaga
,
P. A.
,
2013
, “
Thermoelectric Property of SMA–Constantan Thermocouple
,”
J. Intell. Mater. Syst. Struct.
(unpublished).
22.
Zadeh
,
L. A.
,
1965
, “
Fuzzy Sets
,”
Inf. Control
,
8
(
3
), pp.
338
–
353
.10.1016/S0019-9958(65)90241-X
23.
Zadeh
,
L. A.
,
1975
, “
The Concept of a Linguistic Variable and Its Application To Approximate Reasoning—I
,”
Inf. Sci.
,
8
(
3
), pp.
199
–
249
.10.1016/0020-0255(75)90036-5
24.
Zadeh
,
L. A.
,
1973
, “
Outline of a New Approach to the Analysis of Complex Systems and Decision Processes
,”
IEEE Trans. Syst., Man Cybern.
,
SMC-3
(
1
), pp.
28
–
44
.10.1109/TSMC.1973.5408575
25.
Bellman
,
R. E.
, and
Zadeh
,
L. A.
,
1970
, “
Decision-Making in a Fuzzy Environment
,”
Manage. Sci.
,
17
(
4
), pp.
B-141
–
B-164
.10.1287/mnsc.17.4.B141
26.
Gupta
,
M.
, and
Rao
,
D.
,
1994
, “
On the Principles of Fuzzy Neural Networks
,”
Fuzzy Sets Syst.
,
61
(
1
), pp.
1
–
18.
10.1016/0165-0114(94)90279-8
27.
Jang
,
J. S. R.
,
1993
, “
ANFIS: Adaptive-Network-Based Fuzzy Inference System
,”
IEEE Trans. Syst., Man Cybern.
,
23
(
3
), pp.
665
–
685
.10.1109/21.256541
28.
Jang
,
J.-S. R.
,
Sun
,
C.-T.
, and
Mizutani
,
E.
,
1997
, “
Neuro-Fuzzy and Soft Computing—A Computational Approach to Learning and Machine Intelligence (Book Review)
,”
IEEE Trans. Autom. Control
,
42
(
10
), pp.
1482
–
1484
.10.1109/TAC.1997.633847
29.
Guély
,
F.
, and
Siarry
,
P.
,
1993
, “
Gradient Descent Method for Optimizing Various Fuzzy Rule Bases
,”
2nd IEEE International Conference on Fuzzy Systems
, San Francisco, CA, Mar. 28–Apr. 1, pp.
1241
–
1246
.10.1109/FUZZY.1993.327570
30.
Rutkowska
,
D.
,
2002
,
Neuro-Fuzzy Architectures and Hybrid Learning
,
Physica-Verlag
, Heidelberg, Germany.
31.
Yoshida
,
I.
,
Ono
,
T.
, and
Asai
,
M.
,
2000
, “
Internal Friction of Ti–Ni Alloys
,”
J. Alloys Compd.
,
310
(
1
), pp.
339
–
343
.10.1016/S0925-8388(00)00957-9
32.
DYNALLOY
,
2011
, “
Technical Characteristics of FLEXINOL Actuator Wires
,” DYNALLOY Inc., Irvine, CA, see http://www.dynalloy.com/pdfs/TCF1140.pdf
Copyright © 2015 by ASME
You do not currently have access to this content.