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.
Displaying 111 - 116 of 116
Description
Local interactions drive emergent collective behavior, which pervades biological and social complex systems. These behaviors are scalable and robust, motivating biomimicry: engineering nature-inspired distributed systems. But uncovering the interactions that produce a desired behavior remains a core challenge. In this thesis, I present EvoSOPS, an evolutionary framework that searches landscapes of stochastic distributed algorithms for those that achieve a mathematically specified target behavior. These algorithms govern self-organizing particle systems (SOPS) comprising individuals with strictly local sensing and movement and no persistent memory. For aggregation, phototaxing, and separation behaviors, EvoSOPS discovers algorithms that achieve 4.2–15.3% higher fitness than those from the existing “stochastic approach to SOPS” based on mathematical theory from statistical physics. EvoSOPS is also flexibly applied to new behaviors such as object coating where the stochastic approach would require bespoke, extensive analysis. Across repeated runs, EvoSOPS explores distinct regions of genome space to produce genetically diverse solutions. Finally, I provide insights into the best-fitness genomes for object coating, demonstrating how EvoSOPS can bootstrap future theoretical investigations into SOPS algorithms for challenging new behaviors.
ContributorsParkar, Devendra Rajendra (Author) / Daymude, Joshua (Thesis advisor) / Richa, Andrea (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2024
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
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
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