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.
Displaying 31 - 36 of 36
Description
Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees of freedom and prominent nonlinearities pose significant challenges in developing dynamic models and guiding the robots along desired paths. Additionally, soft robots may exhibit rigid behaviors and potentially collide with their surroundings during path tracking tasks, particularly when possible contact points are unknown. In this dissertation, reduced-order models are used to describe the behaviors of three different soft robot designs, including both linear parameter varying (LPV) and augmented rigid robot (ARR) models. While the reduced-order model captures the majority of the soft robot's dynamics, modeling uncertainties notably remain. Non-repeated modeling uncertainties are addressed by categorizing them as a lumped disturbance, employing two methodologies, $H_\infty$ method and nonlinear disturbance observer (NDOB) based sliding mode control, for its rejection. For repeated disturbances, an iterative learning control (ILC) with a P-type learning function is implemented to enhance trajectory tracking efficacy. Furthermore,for non-repeated disturbances, the NDOB facilitates the contact estimation, and its results are jointly used with a switching algorithm to modify the robot trajectories. The stability proof of all controllers and corresponding simulation and experimental results are provided. For a path tracking task of a soft robot with multi-segments, a robust control strategy that combines a LPV model with an innovative improved nonlinear disturbance observer-based adaptive sliding mode control (INASMC). The control framework employs a first-order LPV model for dynamic representation, leverages an improved disturbance observer for accurate disturbance forecasting, and utilizes adaptive sliding mode control to effectively counteract uncertainties. The tracking error under the proposed controller is proven to be asymptotically stable, and the controller's effectiveness is is validated with simulation and experimental results. Ultimately, this research mitigates the inherent uncertainty in soft robot modeling, thereby enhancing their functionality in contact-intensive tasks.
ContributorsQIAO, ZHI (Author) / Zhang, Wenlong (Thesis advisor) / Marvi, Hamidreza (Committee member) / Lee, Hyunglae (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Tire blowout often occurs during driving, which can suddenly disturb vehicle motions and seriously threaten road safety. Currently, there is still a lack of effective methods to mitigate tire blowout risks in everyday traffic, even for automated vehicles. To fundamentally study and systematically resolve the tire blowout issue for automated vehicles, a collaborative project between General Motors (GM) and Arizona State University (ASU) has been conducted since 2018. In this dissertation, three main contributions of this project will be presented. First, to explore vehicle dynamics with tire blowout impacts and establish an effective simulation platform for close-loop control performance evaluation, high-fidelity tire blowout models are thoroughly developed by explicitly considering important vehicle parameters and variables. Second, since human cooperation is required to control Level 2/3 partially automated vehicles (PAVs), novel shared steering control schemes are specifically proposed for tire blowout to ensure safe vehicle stabilization via cooperative driving. Third, for Level 4/5 highly automated vehicles (HAVs) without human control, the development of control-oriented vehicle models, controllability study, and automatic control designs are performed based on impulsive differential systems (IDS) theories.
Co-simulations Matlab/Simulink® and CarSim® are conducted to validate performances of all models and control designs proposed in this dissertation. Moreover, a scaled test vehicle at ASU and a full-size test vehicle at GM are well instrumented for data collection and control implementation. Various tire blowout experiments for different scenarios are conducted for more rigorous validations. Consequently, the proposed high-fidelity tire blowout models can correctly and more accurately describe vehicle motions upon tire blowout. The developed shared steering control schemes for PAVs and automatic control designs for HAVs can effectively stabilize a vehicle to maintain path following performance in the driving lane after tire blowout.
In addition to new research findings and developments in this dissertation, a pending patent for tire blowout detection is also generated in the tire blowout project. The obtained research results have attracted interest from automotive manufacturers and could have a significant impact on driving safety enhancement for automated vehicles upon tire blowout.
ContributorsLi, Ao (Author) / Chen, Yan (Thesis advisor) / Berman, Spring (Committee member) / Kannan, Arunachala Mada (Committee member) / Liu, Yongming (Committee member) / Lin, Wen-Chiao (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2023
Description
When solving analysis, estimation, and control problems for Partial Differential Equations (PDEs) via computational methods, one must resolve three main challenges: (a) the lack of a universal parametric representation of PDEs; (b) handling unbounded differential operators that appear as parameters; and (c), enforcing auxiliary constraints such as Boundary conditions and continuity conditions. To address these challenges, an alternative representation of PDEs called the `Partial Integral Equation' (PIE) representation is proposed in this work. Primarily, the PIE representation alleviates the problem of the lack of a universal parametrization of PDEs since PIEs have, at most, $12$ Partial Integral (PI) operators as parameters. Naturally, this also resolves the challenges in handling unbounded operators because PI operators are bounded linear operators. Furthermore, for admissible PDEs, the PIE representation is unique and has no auxiliary constraints --- resolving the last of the $3$ main challenges. The PIE representation for a PDE is obtained by finding a unique unitary map from the states of the PIE to the states of the PDE. This map shows a PDE and its associated PIE have equivalent system properties, including well-posedness, internal stability, and I/O behavior. Furthermore, this unique map also allows us to construct a well-defined dual representation that can be used to solve optimal control problems for a PDE. Using the equivalent PIE representation of a PDE, mathematical and computational tools are developed to solve standard problems in Control theory for PDEs. In particular, problems such as a test for internal stability, Input-to-Output (I/O) $L_2$-gain, $\hinf$-optimal state observer design, and $\hinf$-optimal full state-feedback controller design are solved using convex-optimization and Lyapunov methods for linear PDEs in one spatial dimension. Once the PIE associated with a PDE is obtained, Lyapunov functions (or storage functions) are parametrized by positive PI operators to obtain a solvable convex formulation of the above-stated control problems. Lastly, the methods proposed here are applied to various PDE systems to demonstrate the application.
ContributorsShivakumar, Sachin (Author) / Peet, Matthew (Thesis advisor) / Nedich, Angelia (Committee member) / Marvi, Hamidreza (Committee member) / Platte, Rodrigo (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2024
Description
Shape memory alloys (SMAs) are a class of smart materials that can recover their predetermined shape when subjected to an appropriate thermal cycle. This unique property makes SMAs attractive for actuator applications, where the material’s phase transformation can be used to generate controlled motion or force. The actuator design leverages the one-way shape memory effect of NiTi (Nickel-Titanium) alloy wire, which contracts upon heating and recovers its original length when cooled. A bias spring opposes the SMA wire contraction, enabling a cyclical actuation motion. Thermal actuation is achieved through joule heating by passing an electric current through the SMA wire. This thesis presents the design of a compact, lightweight SMA-based actuator, providing controlled and precise motion in various engineering applications. A design of a soft actuator is presented exploiting the responses of the shape memory alloy (SMA) to trigger intrinsically mono-stable shape reconfiguration. The proposed class of soft actuators will perform bending actuation by selectively activating the SMA. The transition sequences were optimized by geometric parameterizations and energy-based criteria. The reconfigured structure is capable of arbitrary bending, which is reported here. The proposed class of robots has shown promise as a fast actuator or shape reconfigurable structure, which will bring new capabilities in future long-duration missions in space or undersea, as well as in bio-inspired robotics.
ContributorsShankar, Kaushik (Author) / Ma, Leixin (Thesis advisor) / Berman, Spring (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2024
Incorporating Causal Information using Temporal-Logic-Based Causal Diagram in Reinforcement Learning
Description
In this thesis, I investigate a subset of reinforcement learning (RL) tasks where the objective for the agent is to achieve temporally extended goals. A common approach, in this setting, is to represent the tasks using deterministic finite automata (DFA) and integrate them in the state space of the RL algorithms, yet such representations often disregard causal knowledge pertinent to the environment. To address this limitation, I introduce the Temporal-Logic-based Causal Diagram (TL-CD) in RL.TL-CD encapsulates temporal causal relationships among diverse environmental properties. We leverage the TL-CD to devise an RL algorithm that significantly reduces environment exploration requirements. By synergizing TL-CD with task-specific DFAs, I identify scenarios wherein the agent can efficiently determine expected rewards early during the exploration phases. Through a series of case studies, I empirically demonstrate the advantages of TL-CDs, particularly highlighting the accelerated convergence of the algorithm towards an optimal policy facilitated by diminished exploration of the environment.
ContributorsPaliwal, Yash (Author) / Xu, Zhe (Thesis advisor) / Marvi, Hamidreza (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2024
Description
Origami, the Japanese art of paper folding, has come a long way from its traditionalroots. It’s now being used in modern engineering and design. In this thesis, I explored
multi-stable origami structures. These structures can hold multiple stable shapes, which
could have a big impact on various technologies. I aim to break down the complex ideas
behind these structures and explain their potential applications in a way that’s easy to understand.
In this research, I looked at the history of origami and recent developments in computational design to create and study multi-stable origami structures. I used computer tools like
parametric modeling software and finite element analysis to come up with new origami
designs. These tools helped me create, improve, and test these designs with a level of
accuracy and speed that hadn’t been possible before.
The process begins with the formulation of design principles rooted in the fundamental
geometry and mechanics of origami. Leveraging mathematical algorithms and optimization
techniques, diverse sets of origami crease patterns are generated, each tailored to exhibit
specific multi-stable behaviors. Through iterative refinement and simulation-driven design,
optimal solutions are identified, leading to the realization of intricate origami morphologies
that defy traditional design constraints.
Furthermore, the technological implications of multi-stable origami structures are explored across a spectrum of applications. In robotics, these structures serve as foundational
building blocks for reconfigurable mechanisms capable of adapting to dynamic environments and tasks. In aerospace engineering, they enable the development of lightweight,
deployable structures for space exploration and satellite deployment. In architecture, they
inspire innovative approaches to adaptive building envelopes and kinetic facades, enhancing sustainability and user experience.
In summary, this thesis presents a comprehensive exploration of multi-stable origami
structures, from their generation through computational design methodologies to their application across diverse technological domains. By pushing the boundaries of traditional
design paradigms and embracing the synergy between art, science, and technology, this
research opens new frontiers for innovation and creativity in the realm of origami-inspired
engineering.
ContributorsRayala, Sri Ratna Kumar (Author) / Ma, Leixin L (Thesis advisor) / Berman, Spring (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2024