Real-Time Trajectory Optimization for High-Performance Guidance & Control

Abstract

Autonomous systems of today rely on trajectory planning to achieve complex tasks. With the increasing capabilities of such systems, there is a need for a framework that not only allows for accurate modeling of these tasks, but also enables real-time generation of feasible trajectories to achieve them. This dissertation presents trajectory generation methods, using gradient-based optimization and set-based dynamic programming, for a large class of optimal, robust, and resilient control problems. These methods are intended for adoption onboard agile autonomous systems—such as reusable rockets—that mandate high-performance guidance & control.