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Robust and Adaptive Optimal Control Methods for a Hybrid Neuroprosthesis

Bao, Xuefeng (2019) Robust and Adaptive Optimal Control Methods for a Hybrid Neuroprosthesis. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Functional electrical stimulation (FES) is an external application of electrical pulses to skeletal
muscles to produce desired limb movements. It is prescribed as a rehabilitation intervention
to restore standing and walking functions in people with paraplegia. However, its
clinical implementation is hindered by a rapid onset of muscle fatigue that limits its use for
longer durations. To overcome the FES-induced muscle fatigue, hybrid neuroprostheses that
combine FES with powered exoskeletons were proposed recently. However, how to coordinate
FES and powered exoskeleton in a hybrid neuroprosthesis still remains an open issue.
The long-term goal of this research is to develop control methods that can optimally
coordinate FES and the powered exoskeleton by considering muscle fatigue dynamics during
standing and walking activities. The research objective in this dissertation was to derive
robust and adaptive optimal control methods for two hybrid neuroprostheses: a hybrid leg
extension machine (HLEM) and a full lower-body neuroprosthesis (FLBN).
Firstly, a model predictive control (MPC) method that coordinates FES and an electric
motor in the HLEM is developed. However, due to inaccurate system identification, day-today
variations in the model, and partially measurable state, it is challenging to implement
this method in a clinical setting. Therefore, robust and adaptive versions of the MPC method
were derived. To overcome modeling uncertainties, a tube-based robust MPC was derived.
This MPC has a feedback controller that can drive the actual state into a region centered by
the nominal state. This ensures recursive feasibility and stability despite disturbances. Later,
a recurrent neural network (RNN) was developed to capture the non-autonomous behavior in the musculoskeletal system, and then a nonlinear MPC and a reinforcement learning (RL)
method were derived to sub-optimally compute the control actions for the system. To achieve
a standing-up motion, a ratio-allocation method was developed to determine the ratio of the
FES-induced torque to the motor torque at the knee joint. The dynamically varied estimated
muscle fatigue was used as an index that guided the optimal allocation. Experiments were
performed to validate the robust and adaptive methods. The results show a potential of the
proposed methods for clinical implementation.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Bao, Xuefengxub3@pitt.eduxub3
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSharma, NitinNIS62@pitt.eduNIS620000-0003-1872-0156
Committee MemberClark, WWwclark@pitt.eduWCLARK0000-0002-2165-8448
Committee MemberMao, ZHzhm4@pitt.eduZHM4
Committee MemberVipperman, Jefferyjsv@pitt.eduJSV
Committee MemberMunro, Paulpwm@pitt.eduPWM
Date: 19 June 2019
Date Type: Publication
Defense Date: 25 March 2019
Approval Date: 19 June 2019
Submission Date: 28 March 2019
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 129
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering and Materials Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: optimal control
Date Deposited: 19 Jun 2019 15:34
Last Modified: 19 Jun 2021 05:15


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