Neural Network Control of an Optimized Regenerative Motor Drive for a Lower-limb Prosthesis
Document Type
Conference Proceeding
Publication Date
2017
Publication Title
American Control Conference (ACC)
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.
Repository Citation
Barto, Taylor and Simon, Daniel J., "Neural Network Control of an Optimized Regenerative Motor Drive for a Lower-limb Prosthesis" (2017). Electrical and Computer Engineering Faculty Publications. 421.
https://engagedscholarship.csuohio.edu/enece_facpub/421
Original Citation
T. Barto and D. Simon, “Neural network control of an optimized regenerative motor drive for a lower-limb prosthesis,” 2017, pp. 5330–5335.
DOI
10.23919/ACC.2017.7963783
