Sequential Chance Optimization For Flow-Tube Based Control Of Probabilistic Nonlinear Systems
Name
1912.03572.pdf
Description
Accepted version
Size
1.54 MB
Format
Adobe PDF
Checksum (MD5)
edb277ba0067ac77ab6f7b58e0f5eda2
Author(s)
Jasour, Ashkan
Williams, Brian C
Date Issued
March 2020
Journal
Proceedings of the IEEE Conference on Decision and Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Jasour, Ashkan and Williams, Brian C. 2020. "Sequential Chance Optimization For Flow-Tube Based Control Of Probabilistic Nonlinear Systems." Proceedings of the IEEE Conference on Decision and Control, 2019-December.
Version
Author's final manuscript
Abstract
© 2019 IEEE. In this paper, we address the problem of closed-loop control of nonlinear dynamical systems subjected to probabilistic uncertainties. More precisely, we design time-varying polynomial feedback controllers to follow the given nominal trajectory and also, for safety purposes, remain in the tube around the nominal trajectory, despite all uncertainties. We formulate this problem as a chance optimization problem where we maximize the probability of achieving control objectives. To address control problems with long planning horizons, we formulate the single large chance optimization problem as a sequence of smaller chance optimization problems. To solve the obtained chance optimization problems, we leverage the theory of measures and moments and obtain convex relaxations in the form of semidefinite programs. We provide numerical examples on stabilizing controller design and motion planning of uncertain nonlinear systems to illustrate the performance of the proposed approach.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/CDC40024.2019.9030218
