Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving

We present an information-theoretic approach to stochastic optimal control problems that can be used to derive general sampling-based optimization schemes. This new mathematical method is used to develop a sampling-based model predictive control algorithm. We apply this information-theoretic model p...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on robotics Vol. 34; no. 6; pp. 1603 - 1622
Main Authors: Williams, Grady, Drews, Paul, Goldfain, Brian, Rehg, James M., Theodorou, Evangelos A.
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autonomous vehicles
Control algorithms
Control theory
Dirt
Entropy (Information theory)
Information theory
Mathematical models
Monte Carlo methods
Nonlinear control systems
Optimal control
parallel algorithms
Parallel processing
Predictive control
Sampling
Stochastic processes
ISSN:1552-3098, 1941-0468
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present an information-theoretic approach to stochastic optimal control problems that can be used to derive general sampling-based optimization schemes. This new mathematical method is used to develop a sampling-based model predictive control algorithm. We apply this information-theoretic model predictive control scheme to the task of aggressive autonomous driving around a dirt test track, and compare its performance with a model predictive control version of the cross-entropy method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2018.2865891