TR2022-054
Robust Model Predictive Control with Data-Driven Koopman Operators
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- "Robust Model Predictive Control with Data-Driven Koopman Operators", American Control Conference (ACC), June 2022, pp. 3885-3892.BibTeX TR2022-054 PDF Video
- @inproceedings{Mamakoukas2022jun,
- author = {Mamakoukas, Giorgos and Di Cairano, Stefano and Vinod, Abraham P.},
- title = {Robust Model Predictive Control with Data-Driven Koopman Operators},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- pages = {3885--3892},
- month = jun,
- url = {https://www.merl.com/publications/TR2022-054}
- }
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- "Robust Model Predictive Control with Data-Driven Koopman Operators", American Control Conference (ACC), June 2022, pp. 3885-3892.
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MERL Contacts:
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Research Areas:
Abstract:
This paper presents robust Koopman model predictive control (RK-MPC), a framework that leverages the training errors of data-driven models to improve constraint satisfaction. Koopman-based control has enabled fast nonlinear feedback using linear tools, but existing approaches ignore the modeling error during control, which can lead to constraint violations. Our approach assumes that the unknown dynamics are Lipschitz-continuous and uses the training error of datadriven Koopman models to approximate a Lipschitz constant for the state- and control-dependent model error. We then use the Lipschitz constant to bound the prediction error along the planning horizon and formulate a convex, robust finite-horizon optimal control problem that is real-time implementable. We demonstrate the efficacy of this approach with simulation results using the dynamics of a forced Duffing oscillator and a quadrotor. Our Python implementation can run in real-time at 66Hz for the 17-dimensional duffing oscillator and at 12Hz for the 44-dimensional quadrotor on a standard laptop.
Related News & Events
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NEWS MERL researchers presented 9 papers at the American Control Conference (ACC) Date: June 8, 2022 - June 10, 2022
Where: Atlanta, GA
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.