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Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization

Tsukamoto, Hiroyasu and Chung, Soon-Jo (2020) Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization. IEEE Transactions on Automatic Control . ISSN 0018-9286. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20191029-154243537

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Abstract

This paper presents ConVex optimization-based Stochastic steady-state Tracking Error Minimization (CV-STEM), a new state feedback control framework for a class of Ito stochastic nonlinear systems and Lagrangian systems. Its strength lies in computing the control input by an optimal contraction metric, which greedily minimizes an upper bound of the steady-state mean squared tracking error of the system trajectories. Although the problem of minimizing the bound is nonlinear, its equivalent convex formulation is proposed utilizing state-dependent coefficient parameterizations of the nonlinear system equation. It is shown using stochastic incremental contraction analysis that the CV-STEM provides a sufficient guarantee for exponential boundedness of the error for all time with L₂-robustness properties. For the sake of its sampling-based implementation, we present discrete-time stochastic contraction analysis with respect to a state- and time-dependent metric along with its explicit connection to continuous-time cases. We validate the superiority of the CV-STEM to PID, H∞, and given nonlinear control for spacecraft attitude control and synchronization problems.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/TAC.2020.3038402DOIArticle
https://arxiv.org/abs/2006.04359arXivDiscussion Paper
https://github.com/astrohiro/cvstemRelated ItemCode
ORCID:
AuthorORCID
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2020 IEEE. Date of Publication: 16 November 2020. This work was in part funded by the Jet Propulsion Laboratory, California Institute of Technology and the Raytheon Company.
Group:GALCIT, Center for Autonomous Systems and Technologies (CAST)
Funders:
Funding AgencyGrant Number
JPL/CaltechUNSPECIFIED
Raytheon CompanyUNSPECIFIED
Subject Keywords:Stochastic optimal control, Optimization algorithms, Robust control, Nonlinear systems, LMIs
Record Number:CaltechAUTHORS:20191029-154243537
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191029-154243537
Official Citation:H. Tsukamoto and S. -J. Chung, "Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization," in IEEE Transactions on Automatic Control, doi: 10.1109/TAC.2020.3038402
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99546
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Oct 2019 22:48
Last Modified:19 Nov 2020 19:14

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