TR2019-047
Data-Driven Control Policies for Partially Known Systems via Kernelized Lipschitz Learning
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- "Data-Driven Control Policies for Partially Known Systems via Kernelized Lipschitz Learning", American Control Conference (ACC), DOI: 10.23919/ACC.2019.8815325, July 2019, pp. 4192-4197.BibTeX TR2019-047 PDF
- @inproceedings{Chakrabarty2019jul,
- author = {Chakrabarty, Ankush and Jha, Devesh K. and Wang, Yebin},
- title = {Data-Driven Control Policies for Partially Known Systems via Kernelized Lipschitz Learning},
- booktitle = {American Control Conference (ACC)},
- year = 2019,
- pages = {4192--4197},
- month = jul,
- publisher = {IEEE},
- doi = {10.23919/ACC.2019.8815325},
- url = {https://www.merl.com/publications/TR2019-047}
- }
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- "Data-Driven Control Policies for Partially Known Systems via Kernelized Lipschitz Learning", American Control Conference (ACC), DOI: 10.23919/ACC.2019.8815325, July 2019, pp. 4192-4197.
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MERL Contacts:
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Research Areas:
Abstract:
Generating initial stabilizing control policies that satisfy operational constraints in the absence of full model information remains an open but critical challenge. In this paper, we propose a systematic framework for constructing constraint enforcing initializing control policies for a class of nonlinear systems based on archival data. Specifically, we study systems for which we have linear components that are modeled and nonlinear components that are unmodeled, but satisfy a local Lipschitz condition. We employ kernel density estimation (KDE) to learn a local Lipschitz constant from data (with high probability), and compute a constraint enforcing control policy via matrix multipliers that utilizes the learned Lipschitz constant. We demonstrate the potential of our proposed methodology on a nonlinear system with an unmodeled local Lipschitz nonlinearity.
Related News & Events
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NEWS MERL researchers presented 8 papers at American Control Conference Date: July 10, 2019 - July 12, 2019
Where: Philadelphia
MERL Contacts: Mouhacine Benosman; Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference, MERL presented 8 papers on subjects including model predictive control applications, estimation and motion planning for vehicles, modular control architectures, and adaptation and learning.