Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Model-Predictive Optimal Control of Ferrofluid Droplet Microrobots

Description
Ferrofluidic microrobots have emerged as promising tools for minimally invasive medical procedures, leveraging their unique properties to navigate through complex fluids and reach otherwise inaccessible regions of the human body, thereby enabling new applications in areas such as targeted drug delivery, tissue engineering, and diagnostics. This dissertation develops a…

Ferrofluidic microrobots have emerged as promising tools for minimally invasive medical procedures, leveraging their unique properties to navigate through complex fluids and reach otherwise inaccessible regions of the human body, thereby enabling new applications in areas such as targeted drug delivery, tissue engineering, and diagnostics. This dissertation develops a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots. Several experiments are performed to illustrate the adaptability and generalizability of the control algorithm to changes in system parameters, including the three-dimensional reference trajectory, the velocity of the workspace fluid, and the size, orientation, deformation, and velocity of the microrobotic droplet. A linear time-invariant control system governing the dynamics of locomotion is derived and used as the constraints of a least squares optimal control algorithm to minimize the projected error between the actual trajectory and the desired trajectory of the microrobot. The optimal control problem is implemented after time discretization using quadratic programming. In addition to demonstrating generalizability and adaptability, the accuracy of the control algorithm is analyzed for several different types of experiments. The experiments are performed in a workspace with a static surrounding fluid and extended to a workspace with fluid flowing through it. The results suggest that the proposed control algorithm could enable new capabilities for ferrofluidic microrobots, opening up new opportunities for applications in minimally invasive medical procedures, lab-on-a-chip, and microfluidics.
ContributorsSkowronek, Elizabeth Olga (Author) / Marvi, Hamidreza (Thesis advisor) / Berman, Spring (Committee member) / Platte, Rodrigo (Committee member) / Xu, Zhe (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2023
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Robust Extended Kalman Filter Based Sensor Fusion for Soft Robot State Estimation and Control

Description
Soft robots provide an additional measure of safety and compliance over traditionalrigid robots. Generally, control and modelling experiments take place using a motion capture system for measuring robot configuration. While accurate, motion capture systems are expensive and require re-calibration whenever the cameras are adjusted. While advances in soft sensors contribute to a potential…

Soft robots provide an additional measure of safety and compliance over traditionalrigid robots. Generally, control and modelling experiments take place using a motion capture system for measuring robot configuration. While accurate, motion capture systems are expensive and require re-calibration whenever the cameras are adjusted. While advances in soft sensors contribute to a potential solution to sensing outside of a lab environment, most of these sensing methods require the sensors to be embedded into the soft robot arm. In this work, a more practical sensing method is proposed using off-the-shelf sensors and a Robust Extended Kalman Filter based sensor fusion method. Inertial measurement unit sensors and wire draw sensors are used to accurately estimate the state of the robot. An explanation for the need for sensor fusion is included in this work. The sensor fusion state estimate is compared to a motion capture measurement along with the raw inertial measurement unit reading to verify the accuracy of the results. The potential for this sensing system is further validated through Linear Quadratic Gaussian control of the soft robot. The Robust Extended Kalman Filter based sensor fusion shows an error of less than one degree when compared to the motion capture system.
ContributorsStewart, Kyle James (Author) / Zhang, Wenlong (Thesis advisor) / Yong, Sze Zheng (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022