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Unified Multi-Rate Control: From Low-Level Actuation to High-Level Planning

Rosolia, Ugo and Singletary, Andrew and Ames, Aaron D. (2022) Unified Multi-Rate Control: From Low-Level Actuation to High-Level Planning. IEEE Transactions on Automatic Control . ISSN 0018-9286. doi:10.1109/tac.2022.3184664. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20220721-7910000

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Abstract

In this paper we present a hierarchical multi-rate control architecture for nonlinear autonomous systems operating in partially observable environments. Control objectives are expressed using syntactically co-safe Linear Temporal Logic (LTL) specifications and the nonlinear system is subject to state and input constraints. At the highest level of abstraction, we model the system-environment interaction using a discrete Mixed Observable Markov Decision Process (MOMDP), where the environment states are partially observed. The high-level control policy is used to update the constraint sets and cost function of a Model Predictive Controller (MPC) which plans a reference trajectory. Afterwards, the MPC planned trajectory is fed to a low-level high-frequency tracking controller, which leverages Control Barrier Functions (CBFs) to guarantee bounded tracking errors. Our strategy is based on model abstractions of increasing complexity and layers running at different frequencies. We show that the proposed hierarchical multi-rate control architecture maximizes the probability of satisfying the high-level specifications while guaranteeing state and input constraint satisfaction. Finally, we tested the proposed strategy in simulations and experiments on examples inspired by the Mars exploration mission, where only partial environment observations are available.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/TAC.2022.3184664DOIArticle
https://arxiv.org/abs/2012.06558arXivDiscussion Paper
ORCID:
AuthorORCID
Rosolia, Ugo0000-0002-1682-0551
Singletary, Andrew0000-0001-6635-4256
Ames, Aaron D.0000-0003-0848-3177
Additional Information:© 2022 IEEE. The authors would like to acknowledge the support by the National Science Foundation award #1932091. The authors would like to thank Geoffroy le Courtois du Manoir for helping with experiments and anonymous reviewers for constructive suggestions.
Funders:
Funding AgencyGrant Number
NSFCNS-1932091
Subject Keywords:partially observable, noisy observations, predictive control, control barrier function, multi-rate control, hierarchical control
DOI:10.1109/tac.2022.3184664
Record Number:CaltechAUTHORS:20220721-7910000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220721-7910000
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115713
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:22 Jul 2022 21:06
Last Modified:22 Jul 2022 21:06

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