ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
I operated the bench-scale isMBfR system in 7 different conditions to evaluate its nitrate-removal performance. When I supplied H2 with the isMBfR (stages 1 - 6), I observed at least 70% nitrate removal, and almost all of the denitrification occurred in the "MBfR zone." When I stopped the H2 supply in stage 7, the nitrate-removal percentage immediately dropped from 92% (stage 6) to 11% (stage 7). Denitrification raised the pH of the bulk liquid to ~ 9.0 for the first 6 stages, but the high pH did not impair the performance of the denitrifiers. Microbial community analyses indicated that DB were the dominant bacteria in the "MBfR zone," while photosynthetic Cyanobacteria were dominant in the "photo-zone".
I derived stoichiometric relationships among COD, alkalinity, H2, Dissolved Oxygen (DO), and nitrate to model the nitrate removal capacity of the "MBfR zone." The stoichiometric relationships corresponded well to the nitrate-removal capacity for all stages expect stage 3, which was limited by the abundance of Denitrifying Bacteria (DB) so that the H2 supply capacity could not be completely used.
Finally, I analyzed two case studies for the real-world application of the isMBfR to constructed wetlands. Based on the characteristics for the wetlands and the stoichiometric relationships, I designed a feasible operation condition (membrane area and H2 pressure) for each wetland. In both cases, the amount of isMBfR surface area was modest, from 0.022 to 1.2 m2/m3 of wetland volume.