Validation of convex optimization algorithms and credible implementation for model predictive control

Guillaume Davy, Pierre Loic Garoche, Raphael Cohen, Eric Feron

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Advanced real-time embedded algorithms are growing in complexity and length, related to the growth in autonomy, which allows vehicles to plan paths of their own. However, this promise cannot happen without proper attention to the considerably stronger operational constraints that real time, safety-critical applications must meet. This paper discusses the formal verification for optimization algorithms with a particular emphasis on receding-horizon controllers. Following a brief historical overview, a prototype autocoder for embedded convex optimization algorithms is discussed. Options for encoding code properties and proofs, and their applicability and limitations is detailed as well.
Original languageEnglish (US)
Title of host publicationAIAA Information Systems-AIAA Infotech at Aerospace, 2017
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624104497
StatePublished - Jan 1 2017
Externally publishedYes

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