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

Grady Williams, Paul Drews, Brian Goldfain, James M. Rehg, IEvangelos A. Theodorou

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number8558663
Pages (from-to)1603-1622
Number of pages20
JournalIEEE Transactions on Robotics
Volume34
Issue number6
DOIs
StatePublished - Dec 2018
Externally publishedYes

Keywords

  • Autonomous vehicles
  • Monte-Carlo methods
  • nonlinear control systems
  • optimal control
  • parallel algorithms

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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