Fast and guaranteed safe controller synthesis for aerial vehicle models

Chuchu Fan, Kristina Miller, Dawei Sun, Sayan Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the problem of synthesizing a controller for nonlinear aerial vehicle models with reach-avoid requirements. Our controller consists of a reference controller and a tracking controller which drives the actual trajectory to follow the reference trajectory or waypoints. We employ a machine learning-based framework to learn a tracking controller that can track any reference trajectory and simultaneously learn a tracking certificate such that the tracking error between the actual trajectory of the closed-loop system and the reference trajectory can be bounded. Moreover, such a bound on the tracking error is independent of the reference trajectory. Using such bounds on the tracking error, we propose a method that can find a reference trajectory by solving a satisfiability problem over linear constraints. Our overall algorithm guarantees that the resulting controller can make sure every trajectory from the initial set of the aerial vehicle satisfies the given reach-avoid requirement.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2021 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-8
Number of pages8
ISBN (Print)9781624106095
StatePublished - 2021
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online
Duration: Jan 11 2021Jan 15 2021

Publication series

NameAIAA Scitech 2021 Forum

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
CityVirtual, Online
Period1/11/211/15/21

ASJC Scopus subject areas

  • Aerospace Engineering

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