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Application of Cerebellum Inspired Controllers to Balance Related Tasks

Mota, Ricardo Evora
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1671190327774901

Abstract Details

2022, Master of Science in Electrical Engineering, University of Dayton, Electrical Engineering.
Despite impressive advancements in the field of robotics, tasks such quick reaching movements, bipedal locomotion, and balance maintenance have shown to be a challenge. A possible reason for this is the predominance of feedback controls in robotics, which provide robust controllers at the expense of a slower response. The part of the human brain responsible for the performance of such tasks is the cerebellum, which functions exclusively in a feedforward way. Prior studies have shown cerebellum inspired controller’s capabilities in movement learning, performing quick reaching movements, and functioning in uncertain environments. This thesis focuses on supervised learning cellular-level cerebellum computational models and its capability of performing balance related tasks. Through computer simulations, the innovative design was tested for the first time on the balancing of the inverted pendulum and double inverted pendulum. Another concept investigated in this work is the effect of cerebellum network size on performance, where among four different network sizes, the largest network ever simulated by the EDLUT spiking neural network simulator was created. Lastly, the controller's capability to transfer knowledge to another model performing the same task with different dynamics was evaluated. All controller sizes tested displayed impressive results on the inverted pendulum, quickly learning how to balance the pole. For the double inverted pendulum, all but the smaller sized network were able to achieve learning. The larger networks displayed better performance in both tasks, but the creation of even larger networks might be necessary to properly define the cerebellum network size effect on performance. The bio-inspired design was also shown to be capable of transferring knowledge, with an initially trained controller outperforming an initially naïve controller on inverted pendulum models with different dynamics. The findings of this experiment show that cerebellum computational models are capable of learning balance related tasks and of transferring knowledge.
Raúl Ordóñez (Advisor)
Terek Taha (Committee Member)
Temesguen Kebede (Committee Member)
85 p.
Electrical Engineering
adaptive controller; cerebellum computational model; non-linear control; biologically inspired controller; inverted pendulum; double inverted pendulum; knowledge transfer

Recommended Citations

Citations

  • Mota, R. E. (2022). Application of Cerebellum Inspired Controllers to Balance Related Tasks [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1671190327774901

    APA Style (7th edition)

  • Mota, Ricardo. Application of Cerebellum Inspired Controllers to Balance Related Tasks. 2022. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1671190327774901.

    MLA Style (8th edition)

  • Mota, Ricardo. "Application of Cerebellum Inspired Controllers to Balance Related Tasks." Master's thesis, University of Dayton, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1671190327774901

    Chicago Manual of Style (17th edition)

dayton1671190327774901
212
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This open access ETD is published by University of Dayton and OhioLINK.
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