journal article·2011-11-01

Adaptive SMA actuator priming using resistance feedback

M El Dib, R Gorbet, E Kubica, X Gao, A L Browne, N L Johnson

Smart Materials and Structures· Volume 20· Issue 11
Publisher page

Abstract

Shape memory alloys (SMAs) are a group of alloys which demonstrate the unique ability of returning back to a previously defined shape or size if subjected to the appropriate thermal conditions. They have been implemented as actuators—where heat is controlled via applied current—in a wide range of applications spanning several fields such as robotics, aeronautics, automotive and medicine. SMA manufacturers specify what they refer to as the 'safe current' which is the maximum current that can be applied to the SMA wire indefinitely without damaging it by overheating. However, this current is typically specified at room temperature under natural convection conditions. The objective of this work is to develop controllers for SMA actuators in automotive applications and this requires predictable and consistent functionality across a wide range of ambient temperatures, typically from − 40 to 85 °C. Consequently, applying the safe current in cold ambient temperatures may not actuate the SMA whereas it could potentially over-heat the SMA at high ambient temperatures. In this paper, we use a novel approach involving resistance feedback to achieve more consistent actuation across a range of ambient temperatures and compare experimental results for several different control strategies. The results show that controller designs using an adaptive current to actuate the SMA wire achieved more consistent results across the desired range of ambient temperatures compared to using the fixed safe current. Of these designs, a controller strategy dubbed Minus 4.5% achieved the most consistent actuation results and was a significant improvement over conventional control strategies.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Shape Memory Alloy Transformations

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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