Electrical and Computer Engineering ETDs

Publication Date

Summer 7-28-2026

Abstract

Autonomous systems operating in uncertain environments require methods that can rigorously quantify safety, predict future behavior, and compute optimal decisions tractably. The unifying challenge is reasoning about uncertainty propagation in constrained dynamical systems. Motivated by autonomous spacecraft inspection, this dissertation develops model-based and data-driven methods for stochastic optimal control and stochastic reachability. First, it studies optimal on-orbit inspection of an uncertain tumbling spacecraft in both single-agent and multi-agent settings, and derives computationally efficient reformulations for exploring trade-offs among sensing, trajectory deviation, and fuel use. Second, it develops a probabilistically safe penalty-based reinforcement learning method that preserves gradient information during training while steering policies toward safety. Finally, it formulates data-driven stochastic reachability in a probability-measure framework and constructs a neural network approximation of backward reachable sets using forward simulations, with applications to spacecraft autonomy and other safety-critical dynamical systems.

Keywords

stochastic optimal control, stochastic reachability, reinforcement learning, deep neural networks, spacecraft, trajectory planning

Project Sponsors

NSF, NASA, and AFRL

Document Type

Dissertation

Language

English

Degree Name

Electrical Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Meeko M. K. Oishi

Second Committee Member

Manel Martínez-Ramón

Third Committee Member

Wenbin Wan

Fourth Committee Member

Ali Bidram

Fifth Committee Member

Sean Phillips

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