NDLI: Optimal control of multi-input SMA actuator arrays using graph theory

Content Provider	IEEE Xplore Digital Library
Author	Flemming, L.J. Johnson, D.E. Mascaro, S.A.
Copyright Year	2011
Description	Author affiliation: School of Computing, University of Utah, Salt Lake City USA (Johnson, D.E.) \|\| Mechanical Engineering, University of Utah, Salt Lake City, 84112 USA (Flemming, L.J.; Mascaro, S.A.)
Abstract	Shape memory alloy (SMA) actuators are compact and have high force-to-weight ratios, making them strong candidates to actuate robots, exoskeletons, and prosthetics. To optimize speed and energy consumption, SMA actuators have been embedded in an N×N vascular network that can deliver electric and thermofluidic energy to the each actuator. The scalable architecture of the vascular network allows for 2N control devices (valves, transistors) to be shared amongst $N^{2}$ actuators, so that as the number of actuators increases, the number of required control devices scales at a smaller rate. This Network Array Architecture (NAA) allows for each actuator to be controlled individually or in discrete subarrays. However, not all combinations of actuators can be activated simultaneously; therefore in general, a sequence of control commands will be need to be executed in order to achieve the desired actuation. By treating each actuator as having a binary state, the combined states of the actuator array can be represented by graph theory, where states are nodes and the transitions between states are graph edges. By properly weighting the costs of the transitions, graph search techniques can be used to find optimal sets of control commands for desired state changes. This paper formulates the control of NAA actuators systems as a graph theory problem, and characterizes the ability of search algorithms to optimize a weighted combination of speed and energy usage, while minimizing computational cost.
Starting Page	6109
Ending Page	6114
File Size	805469
Page Count	6
File Format	PDF
ISBN	9781612843865
ISSN	10504729
e-ISBN	9781612843858
DOI	10.1109/ICRA.2011.5980563
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Actuators Fluids Graph theory Humans Muscles Heuristic algorithms Robots
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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