ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Mechanical Engineering
Description
Magnetic liquids called ferrofluids have been used in applications ranging from audio speaker cooling and rotary pressure seals to retinal detachment surgery and implantable artificial glaucoma valves. Recently, ferrofluids have been investigated as a material for use in magnetically controllable liquid droplet robotics. Liquid droplet robotics is an emerging technology that aims to apply control theory to manipulate fluid droplets as robotic agents to perform a wide range of tasks. Furthermore, magnetically controlled micro-robotics is another popular area of study where manipulating a magnetic field allows for the control of magnetized micro-robots. Both of these emerging fields have potential for impact toward medical applications: liquid characteristics such as being able to dissolve various compounds, be injected via a needle, and the potential for the human body to automatically filter and remove a liquid droplet robot, make liquid droplet robots advantageous for medical applications; while the ability to remotely control the torques and forces on an untethered microrobot via modulating the magnetic field and gradient is also highly advantageous. The research described in this dissertation explores applications and methods for the electromagnetic control of ferrofluid droplet robots. First, basic electrical components built from fluidic channels containing ferrofluid are made remotely tunable via the placement of ferrofluid within the channel. Second, a ferrofluid droplet is shown to be fully controllable in position, stretch direction, and stretch length in two dimensions using proportional-integral-derivative (PID) controllers. Third, control of a ferrofluid’s position, stretch direction, and stretch length is extended to three dimensions, and control gains are optimized via a Bayesian optimization process to achieve higher accuracy. Finally, magnetic control of both single and multiple ferrofluid droplets in two dimensions is investigated via a visual model predictive control approach based on machine learning. These achievements take both liquid droplet robotics and magnetic micro-robotics fields several steps closer toward real-world medical applications such as embedded soft electronic health monitors, liquid-droplet-robot-based drug delivery, and automated magnetically actuated surgeries.
ContributorsAhmed, Reza James (Author) / Marvi, Hamidreza (Thesis advisor) / Espanol, Malena (Committee member) / Rajagopalan, Jagannathan (Committee member) / Zhuang, Houlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2022
Description
This thesis presents a study on the user adaptive variable impedance control of a wearable ankle robot for robot-aided rehabilitation with a primary focus on enhancing accuracy and speed. The controller adjusts the impedance parameters based on the user's kinematic data to provide personalized assistance. Bayesian optimization is employed to minimize an objective function formulated from the user's kinematic data to adapt the impedance parameters per user, thereby enhancing speed and accuracy. Gaussian process is used as a surrogate model for optimization to account for uncertainties and outliers inherent to human experiments. Student-t process based outlier detection is utilized to enhance optimization robustness and accuracy. The efficacy of the optimization is evaluated based on measures of speed, accuracy, and effort, and compared with an untuned variable impedance controller during 2D curved trajectory following tasks. User effort was measured based on muscle activation data from the tibialis anterior, peroneus longus, soleus, and gastrocnemius muscles. The optimized controller was evaluated on 15 healthy subjects and demonstrated an average increase in speed of 9.85% and a decrease in deviation from the ideal trajectory of 7.57%, compared to an unoptimized variable impedance controller. The strategy also reduced the time to complete tasks by 6.57%, while maintaining a similar level of user effort.
ContributorsManoharan, Gautham (Author) / Lee, Hyunglae (Thesis advisor) / Berman, Spring (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2023
Description
Advanced driving assistance systems (ADAS) are one of the latest automotive technologies for improving vehicle safety. An efficient method to ensure vehicle safety is to limit vehicle states always within a predefined stability region. Hence, this thesis aims at designing a model predictive control (MPC) with non-overshooting constraints that always confine vehicle states in a predefined lateral stability region. To consider the feasibility and stability of MPC, terminal cost and constraints are investigated to guarantee the stability and recursive feasibility of the proposed non-overshooting MPC. The proposed non-overshooting MPC is first verified by using numerical examples of linear and nonlinear systems. Finally, the non-overshooting MPC is applied to guarantee vehicle lateral stability based on a nonlinear vehicle model for a cornering maneuver. The simulation results are presented and discussed through co-simulation of CarSim® and MATLAB/Simulink.
ContributorsSudhakhar, Monish Dev (Author) / Chen, Yan (Thesis advisor) / Ren, Yi (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2023
Description
In this thesis, the problem of designing model discrimination algorithms for unknown nonlinear systems is considered, where only raw experimental data of the system is available. This kind of model discrimination techniques finds one of its application in the estimation of the system or intent models under consideration, where all incompatible models are invalidated using new data that is available at run time. The proposed steps to reach the end goal of the algorithm for intention estimation involves two steps: First, using available experimental data of system trajectories, optimization-based techniques are used to over-approximate/abstract the dynamics of the system by constructing an upper and lower function which encapsulates/frames the true unknown system dynamics. This over-approximation is a conservative preservation of the dynamics of the system, in a way that ensures that any model which is invalidated against this approximation is guaranteed to be invalidated with the actual model of the system. The next step involves the use of optimization-based techniques to investigate the distinguishability of pairs of abstraction/approximated models using an algorithm for 'T-Distinguishability', which gives a finite horizon time 'T', within which the pair of models are guaranteed to be distinguished, and to eliminate incompatible models at run time using a 'Model Invalidation' algorithm. Furthermore, due the large amount of data under consideration, some computation-aware improvements were proposed for the processing of the raw data and the abstraction and distinguishability algorithms.The effectiveness of the above-mentioned algorithms is demonstrated using two examples. The first uses the data collected from the artificial simulation of a swarm of agents, also known as 'Boids', that move in certain patterns/formations, while the second example uses the 'HighD' dataset of naturalistic trajectories recorded on German Highways for vehicle intention estimation.
ContributorsBhagwat, Mohit Mukul (Author) / Yong, Sze Zheng (Thesis advisor) / Berman, Spring (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2021
Description
Animals have always been a source of inspiration for real-life problems. The octopus is one such animal that has a lot of untapped potential. The octopus’s arm is without solid joints or bone structure and despite this it can achieve many complicated movements with virtually infinite degrees of freedom. This ability is made possible through the unique morphology of the arm. The octopus’s arm is divided into transverse, longitudinal, oblique, and circular muscle groups and each one has a unique muscle fiber orientation. The octopus’s arm is classified as a hydrostat because it maintains a constant volume while contracting with the help of its different muscle groups. These muscle groups allow elongation, shortening, bending, and twisting of the arm when they work in combination with each other. To confirm the role of transverse and longitudinal muscle groups, an electromyography (EMG) recording of these muscle groups was performed while an amputated arm of an Octopus bimaculoides was stimulated with an electrical signal to induce movement. Statistical analysis was performed on these results to confirm the roles of each muscle group quantitatively. Octopus arm morphology was previously assumed to be uniform along the arm. Through a magnetic resonance imaging (MRI) study at the proximal, middle, and distal sections of the arm this notion was disproven, and a new pattern was discovered. Drawing inspiration from this finding and previous octopus arm prototypes, 4 bio-inspired designs were conceived and tested in finite element analysis (FEA) simulations. Four tests in elongation, shortening, bending, and transverse-assisted bending movements were performed on all designs to compare each design’s performance. The findings in this study have applications in engineering and soft robotics fields for use cases such as, handling fragile objects, minimally invasive surgeries, difficult-to-access areas that require squeezing through small holes, and other novel cases.
ContributorsAhmadi, Salaheddin (Author) / Marvi, Hamidreza (Thesis advisor) / Fisher, Rebecca (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2023
Description
Human-robot interactions can often be formulated as general-sum differential games where the equilibrial policies are governed by Hamilton-Jacobi-Isaacs (HJI) equations. Solving HJI PDEs faces the curse of dimensionality (CoD). While physics-informed neural networks (PINNs) alleviate CoD in solving PDEs with smooth solutions, they fall short in learning discontinuous solutions due to their sampling nature. This causes PINNs to have poor safety performance when they are applied to approximate values that are discontinuous due to state constraints. This dissertation aims to improve the safety performance of PINN-based value and policy models. The first contribution of the dissertation is to develop learning methods to approximate discontinuous values. Specifically, three solutions are developed: (1) hybrid learning uses both supervisory and PDE losses, (2) value-hardening solves HJIs with increasing Lipschitz constant on the constraint violation penalty, and (3) the epigraphical technique lifts the value to a higher-dimensional state space where it becomes continuous. Evaluations through 5D and 9D vehicle and 13D drone simulations reveal that the hybrid method outperforms others in terms of generalization and safety performance. The second contribution is a learning-theoretical analysis of PINN for value and policy approximation. Specifically, by extending the neural tangent kernel (NTK) framework, this dissertation explores why the choice of activation function significantly affects the PINN generalization performance, and why the inclusion of supervisory costate data improves the safety performance. The last contribution is a series of extensions of the hybrid PINN method to address real-time parameter estimation problems in incomplete-information games. Specifically, a Pontryagin-mode PINN is developed to avoid costly computation for supervisory data. The key idea is the introduction of a costate loss, which is cheap to compute yet effectively enables the learning of important value changes and policies in space-time. Building upon this, a Pontryagin-mode neural operator is developed to achieve state-of-the-art (SOTA) safety performance across a set of differential games with parametric state constraints. This dissertation demonstrates the utility of the resultant neural operator in estimating player constraint parameters during incomplete-information games.
ContributorsZhang, Lei (Author) / Ren, Yi (Thesis advisor) / Si, Jennie (Committee member) / Berman, Spring (Committee member) / Zhang, Wenlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2024