Shape Memory Alloy actuators can be used for morphing, or shape change, by
controlling their temperature, which is effectively done by applying a voltage difference
across their length. Control of these actuators requires determination of the relationship
between voltage and strain so that an input-output map can be developed. In this
research, a computer simulation uses a hyperbolic tangent curve to simulate the
hysteresis behavior of a virtual Shape Memory Alloy wire in temperature-strain space,
and uses a Reinforcement Learning algorithm called Sarsa to learn a near-optimal
control policy and map the hysteretic region. The algorithm developed in simulation is
then applied to an experimental apparatus where a Shape Memory Alloy wire is
characterized in temperature-strain space. This algorithm is then modified so that the
learning is done in voltage-strain space. This allows for the learning of a control policy
that can provide a direct input-output mapping of voltage to position for a real wire.
This research was successful in achieving its objectives. In the simulation phase,
the Reinforcement Learning algorithm proved to be capable of controlling a virtual
Shape Memory Alloy wire by determining an accurate input-output map of temperature to strain. The virtual model used was also shown to be accurate for characterizing Shape
Memory Alloy hysteresis by validating it through comparison to the commonly used
modified Preisach model. The validated algorithm was successfully applied to an
experimental apparatus, in which both major and minor hysteresis loops were learned in
temperature-strain space. Finally, the modified algorithm was able to learn the control
policy in voltage-strain space with the capability of achieving all learned goal states
within a tolerance of +-0.5% strain, or +-0.65mm. This policy provides the capability of
achieving any learned goal when starting from any initial strain state. This research has
validated that Reinforcement Learning is capable of determining a control policy for
Shape Memory Alloy crystal phase transformations, and will open the door for research
into the development of length controllable Shape Memory Alloy actuators.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-05-632 |
| Date | 16 January 2010 |
| Creators | Kirkpatrick, Kenton C. |
| Contributors | Valasek, John |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis |
| Format | application/pdf |
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