Document Type

Article

Publication Date

2023

Department

Engineering

Keywords

analytical models, computational modeling, Gaussian processes, length measurement, aerospace electronics, probabilistic logic, trajectory

Abstract

Gaussian process state space models are becoming common tools for the analysis and design of nonlinear systems with uncertain dynamics. When designing control policies for these systems, safety is an important property to consider. In this paper, we provide safety guarantees by computing finite-horizon forward reachable sets for Gaussian process state space models. We use data-driven reachability analysis to provide exact probability measures for state trajectories of arbitrary length, even when no data samples are available. We investigate two numerical examples to demonstrate the power of this approach, such as providing highly non-convex reachable sets and detecting holes in the reachable set.

Comments

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Source Publication Title

2023 62nd IEEE Conference on Decision and Control (CDC)

Publisher

IEEE

First Page

4100

DOI

10.1109/CDC49753.2023.10383270

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