Smart materials have
received increasing attention of wide range of researchers because of their
great scientific and technological significance. Among the group of smart
materials, shape memory alloy (SMA) are most popular because of their unusual
properties such as shape memory effect and pseudo elasticity, which are due to
the reversible phase transition in the materials. However, they exhibit some
rather surprising phenomena such as nonlinearities, hysteresis as well as
extreme sensitivities to testing conditions. These materials exhibit phase
transition when subjected to an external stimuli in the form of temperature or
stress. Many studies have indicated that SMA in addition to these properties
also possesses a large fatigue life. The above features have enormous potential
for low frequency applications. These fascinating capabilities of SMAs have
motivated many researchers worldwide to consider them as actuators in smart
structures applications.  In the last two
decades, SMAs have found a large number of potential applications in several
fields ranging from medical devices to aerospace industries. One such relevant
areas of applications of SMA as actuators are deployment of aircraft control
surfaces, morphing of wing, structural damping application etc. There have been
few such attempts made at CSMST, CSIR-NAL to demonstrate SMAs potential
actuator for active shape control as well as damping applications.

In most of these
applications, shape memory alloys are often used under various thermo-mechanical
cyclic loading condition, however accurate control, degradation and fatigue life
are of important concerns. An effective position control and failure prediction
of shape memory alloy necessitates a model which represents the dynamic
characteristic of the system. Good models for phase transitions are very
difficult to obtain precisely because formulations of adequate constitutive
laws are very complex. This paper narrates the signal processing based
different modeling techniques such as mathematical model based on heat
conduction, Hammerstein wiener, feed forward neural network and recurrent neural
network for controlling of shape memory alloy and Hammerstein wiener and sub
space model for failure prediction.

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This paper narrates
the different modeling techniques such as mathematical model based on heat
conduction, Hammerstein wiener, feed forward neural network and recurrent neural
network for controlling of shape memory alloy. Also deals with Hammerstein
wiener and sub space model for failure prediction. The typical results obtained
with some of these approaches are presented in Figure 1 to Figure 3.

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