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

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

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 proceeding › Conference contribution

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 language	English (US)
Title of host publication	AIAA Information Systems-AIAA Infotech at Aerospace, 2017
Publisher	American Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)	9781624104497
State	Published - Jan 1 2017
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-18

Cite this

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title = "Validation of convex optimization algorithms and credible implementation for model predictive control",

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.",

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Validation of convex optimization algorithms and credible implementation for model predictive control. / Davy, Guillaume; Garoche, Pierre Loic; Cohen, Raphael et al.
AIAA Information Systems-AIAA Infotech at Aerospace, 2017. American Institute of Aeronautics and Astronautics Inc, AIAA, 2017.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Cohen, Raphael

AU - Feron, Eric

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PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

SN - 9781624104497

BT - AIAA Information Systems-AIAA Infotech at Aerospace, 2017

PB - American Institute of Aeronautics and Astronautics Inc, AIAA

ER -

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

Abstract

Bibliographical note

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