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Variable Step Length Control for a Hybrid Neuroprosthesis Using Dynamic Movement Primitives and Model Predictive Control

AHMAD, ABDULLAH (2021) Variable Step Length Control for a Hybrid Neuroprosthesis Using Dynamic Movement Primitives and Model Predictive Control. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

There are approximately 17,000 people that are diagnosed with spinal cord injury (SCI) each year, where a considerable number of persons are diagnosed with paraplegia. Efforts to restore walking include the use of Functional Electrical Stimulation (FES) and power-exoskeletons. FES applies electrical current to motor nerves to create muscle contraction that produce desired limb movement. FES has been shown to recreate lower-limb functions in paraplegic subjects. However, the method suffers from rapid onset of muscle fatigue caused by non-physiological stimulation and unreliable muscle force generation. A powered exoskeleton can be combined with FES to overcome its limitations. The combined system, which is called a hybrid exoskeleton, has been recently used to restore lower-limb functions of subjects with paraplegia.
The control methods in these hybrid exoskeletons produce a quasi-static gait with a constant step length. A variable step length adjustment is needed to meet a user’s preference to walk at his/her desired step length. Almost all gait algorithms rely on off-line trajectory generation and are incapable to change step length on demand, in real-time. Therefore, a need exists to design real-time gait trajectory modification system and gait controller that produce different step lengths.
This thesis investigates a variable step length algorithm and an optimal gait controller that combines Dynamic Movement Primitives (DMPs), muscle synergies, and Model Predictive Control (MPC). Discrete DMPs were used to spatially and temporally scale previous dynamically optimized trajectories to various step lengths. The gait controller is a muscle synergy-inspired control scheme that facilitate the use of dynamic postural synergies. Dynamic postural synergies were found using off-line optimizations that distribute the actuator effort among the available actuators to produce the withdrawal reflex and knee extension. Using dynamic postural synergies, it was also possible to minimize co-activation due to FES between antagonistic muscle pairs. A MPC method was used to optimally compute the synergy activation coefficients that drive the system to the desired postures for different step lengths. Simulation results indicate a good tracking performance of the MPC method to produce a desired step length and the feasibility of the DMPs to produce different step lengths.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
AHMAD, ABDULLAHaba81@pitt.eduaba81
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSharma, Nitinnsharm23@ncsu.edu
Committee MemberVipperman, Jeffreyjsv@pitt.edu
Committee MemberMao, Zhi-Hongzhm4@pitt.edu
Date: 26 January 2021
Date Type: Publication
Defense Date: 9 November 2020
Approval Date: 26 January 2021
Submission Date: 16 November 2020
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 58
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: DMP, MPC, Trajectory Planning, Exoskeleton
Date Deposited: 26 Jan 2021 20:16
Last Modified: 26 Jan 2021 20:16
URI: http://d-scholarship.pitt.edu/id/eprint/39894

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