Challenges in set-valued model-predictive control

Jonathan Sprinkle, Nathalie Risso, Berk Altin, Ricardo Sanfelice

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

Abstract

In this abstract we describe a framework for computationally-aware computing through set-valued model predictive control. Model-predictive control (MPC) can enable multi-objective optimization in real-time, though it depends on accurate models through which future state values can be predicted. This abstract improves upon existing MPC approaches in that it considers the state to be a set (rather than a singleton in the state), allowing the trajectories to be given by a sequence of sets. The framework is beneficial for physical systems control where the uncertainty in future projection can be attributed to both model error, and environmental or sensor uncertainty, thus providing guarantees of performance, robustly. We provide an overview of the framework, and include discussion for its advantages.

Original languageEnglish (US)
Title of host publicationProceedings of 2021 Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems, CAADCPS 2021
PublisherAssociation for Computing Machinery, Inc
Pages13-14
Number of pages2
ISBN (Electronic)9781450383998
DOIs
StatePublished - May 19 2021
Event021 Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems, CAADCPS 2021 - Nashville, United States
Duration: May 18 2021May 21 2021

Publication series

NameProceedings of 2021 Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems, CAADCPS 2021

Conference

Conference021 Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems, CAADCPS 2021
Country/TerritoryUnited States
CityNashville
Period5/18/215/21/21

Fingerprint

Dive into the research topics of 'Challenges in set-valued model-predictive control'. Together they form a unique fingerprint.

Cite this