Neural network control of an optimized regenerative motor drive for a lower-limb prosthesis

Taylor Barto; Dan Simon

doi:10.23919/ACC.2017.7963783

Neural network control of an optimized regenerative motor drive for a lower-limb prosthesis

Taylor Barto
, Dan Simon

Electrical and Computer Engineering

Cleveland State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

A voltage source converter (VSC) is incorporated in an active prosthetic leg design. The VSC supplies power to the prosthesis motor and regenerates energy from the prosthesis motor for storage in a supercapacitor bank. An artificial neural network controls the VSC switching so that the prosthesis motor generates a knee torque that matches the torque that is output from a passivity-based controller (PBC). The neural network, PBC, and prosthesis motor parameters are optimized with an evolutionary algorithm to achieve knee angle tracking. Several reference trajectories from able-bodied walking were tracked with an RMS tracking error of less than 0.5° while regenerating up to 67 Joules of energy during four gait cycles.

Original language	English
Title of host publication	Proceedings of the American Control Conference
Place of Publication	usa
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5330-5335
Number of pages	6
ISBN (Electronic)	9781509059928
DOIs	https://doi.org/10.23919/ACC.2017.7963783
State	Published - Jun 29 2017
Event	2017 American Control Conference, ACC 2017 - Seattle, United States Duration: May 24 2017 → May 26 2017

Conference

Conference	2017 American Control Conference, ACC 2017
Country/Territory	United States
City	Seattle
Period	05/24/17 → 05/26/17

Access to Document

10.23919/ACC.2017.7963783

Cite this

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title = "Neural network control of an optimized regenerative motor drive for a lower-limb prosthesis",

abstract = "A voltage source converter (VSC) is incorporated in an active prosthetic leg design. The VSC supplies power to the prosthesis motor and regenerates energy from the prosthesis motor for storage in a supercapacitor bank. An artificial neural network controls the VSC switching so that the prosthesis motor generates a knee torque that matches the torque that is output from a passivity-based controller (PBC). The neural network, PBC, and prosthesis motor parameters are optimized with an evolutionary algorithm to achieve knee angle tracking. Several reference trajectories from able-bodied walking were tracked with an RMS tracking error of less than 0.5° while regenerating up to 67 Joules of energy during four gait cycles.",

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Barto, T & Simon, D 2017, Neural network control of an optimized regenerative motor drive for a lower-limb prosthesis. in Proceedings of the American Control Conference., 7963783, Institute of Electrical and Electronics Engineers Inc., usa, pp. 5330-5335, 2017 American Control Conference, ACC 2017, Seattle, United States, 05/24/17. https://doi.org/10.23919/ACC.2017.7963783

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N2 - A voltage source converter (VSC) is incorporated in an active prosthetic leg design. The VSC supplies power to the prosthesis motor and regenerates energy from the prosthesis motor for storage in a supercapacitor bank. An artificial neural network controls the VSC switching so that the prosthesis motor generates a knee torque that matches the torque that is output from a passivity-based controller (PBC). The neural network, PBC, and prosthesis motor parameters are optimized with an evolutionary algorithm to achieve knee angle tracking. Several reference trajectories from able-bodied walking were tracked with an RMS tracking error of less than 0.5° while regenerating up to 67 Joules of energy during four gait cycles.

AB - A voltage source converter (VSC) is incorporated in an active prosthetic leg design. The VSC supplies power to the prosthesis motor and regenerates energy from the prosthesis motor for storage in a supercapacitor bank. An artificial neural network controls the VSC switching so that the prosthesis motor generates a knee torque that matches the torque that is output from a passivity-based controller (PBC). The neural network, PBC, and prosthesis motor parameters are optimized with an evolutionary algorithm to achieve knee angle tracking. Several reference trajectories from able-bodied walking were tracked with an RMS tracking error of less than 0.5° while regenerating up to 67 Joules of energy during four gait cycles.

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Neural network control of an optimized regenerative motor drive for a lower-limb prosthesis

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