Efficient Model Learning for Control

Efficient Model Learning for Control

Plane Cartpole Quad

We present a data-efficient algorithm for learning models for model-predictive control (MPC). Our approach, Jacobian-Regularized DMD (JDMD), offers improved sample efficiency over traditional Koopman approaches based on Dynamic-Mode Decomposition (DMD) by leveraging Jacobian information from an approximate prior model of the system, and improved tracking performance over traditional model-based MPC. We demonstrate JDMD’s ability to quickly learn bilinear Koopman dynamics representations across several realistic examples in simulation, including a perching maneuver for a fixed-wing aircraft with an experimentally derived high-fidelity physics model. In all cases, we show that the models learned by JDMD provide superior tracking and generalization performance in the presence of significant model mismatch within a model-predictive control framework, when compared to the approximate prior models used in training and models learned by standard extended DMD

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Related Papers

PDF Data-Efficient Model Learning for Model Predictive Control with Jacobian-Regularized Dynamic Mode Decomposition
Brian Jackson, Jeong Hun (JJ) Lee, Kevin Tracy, and Zac Manchester
The Conference on Robot Learning (CoRL)


Zac Manchester
Assistant Professor
Brian Jackson
Real-time motion planning
Jeong Hun (JJ) Lee
Swimming Dynamics and Control
Last updated: 2022-08-12