Document Type
Thesis
Date of Award
Summer 8-31-2017
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical and Computer Engineering
First Advisor
Cong Wang
Second Advisor
Bryan J. Pfister
Third Advisor
Xuan Liu
Abstract
Traumatic brain injury (TBI) afflicts over 10 million people around the world. Injury to the brain can occur from a variety of physical insults and the degree of disability can greatly vary from person to person. It is likely that the wide range of TBI outcomes may be due to the magnitude, direction, and forces of biomechanical insult acting on the head during such TBI events. Lateral Fluid Percussion (FPI) brain injury is one of the most commonly used and well-characterized experimental models of TBI. A Fluid Percussion Injury (FPI) device in the laboratory is used to replicate the injury but does not execute the desired pressure profile. The controller used is a QCI-S3-IG Silver Sterling from Quick Silver Controls. A limitation innate to the controller was a 3-millisecond sampling of the input signal that proved challenging for developing fast, accurate FPI pulses with periods as fast as 18-milliseconds. Iterative Learning Control is implemented which conditions the input signal to the open loop system offline such that the desired pressure profile is attained.
Recommended Citation
Susanibar, Steve, "Iterative learning control for improved tracking of fluid percussion injury device" (2017). Theses. 35.
https://digitalcommons.njit.edu/theses/35
