Robotics: Science and Systems XV
An Online Learning Approach to Model Predictive Control
Nolan Wagener, Ching-an Cheng, Jacob Sacks, Byron BootsAbstract:
Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In this paper, we show that there exists a close connection between MPC and online learning, an abstract theoretical framework for analyzing online decision making in the optimization literature. This new perspective provides a foundation for leveraging powerful online learning algorithms to design MPC algorithms. Specifically, we propose a new algorithm based on dynamic mirror descent (DMD), an online learning algorithm that is designed for non-stationary setups. Our algorithm, Dynamic Mirror Descent Model Predictive Control (DMD-MPC), represents a general family of MPC algorithms that includes many existing techniques as special instances. DMD-MPC also provides a fresh perspective on previous heuristics used in MPC and suggests a principled way to design new MPC algorithms. In the experimental section of this paper, we demonstrate the flexibility of DMD-MPC, presenting a set of new MPC algorithms on a simple simulated cartpole and a simulated and real-world aggressive driving task. A video of the real-world experiment can be found at https://youtu.be/vZST3v0_S9w.
Bibtex:
@INPROCEEDINGS{Boots-RSS-19, AUTHOR = {Nolan Wagener AND Ching-an Cheng AND Jacob Sacks AND Byron Boots}, TITLE = {An Online Learning Approach to Model Predictive Control}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2019}, ADDRESS = {FreiburgimBreisgau, Germany}, MONTH = {June}, DOI = {10.15607/RSS.2019.XV.033} }