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The Essentials of Model Predictive Control Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Convexification and Real-Time Optimization for MPC with Aerospace Applications

Authors : Yuanqi Mao, Daniel Dueri, Michael Szmuk, Behçet Açıkmeşe

Published in: Handbook of Model Predictive Control

Publisher: Springer International Publishing

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Abstract

This chapter gives an overview of recent developments of convexification and real-time convex optimization based control methods, in the context of Model Predictive Control (MPC). Lossless Convexification is a technique that formulates a class of non-convex control constraints as equivalent convex ones, while Successive Convexification gives an algorithm that targets nonlinear dynamics and certain non-convex state constraints. A large class of real-world optimal control problems can be solved with either method or a combination of both. For some time-critical applications, such as autonomous vehicles, it is crucial to have real-time capabilities. The real-time solution to these problems requires highly efficient customized convex programming solvers, which is also discussed as a part of this chapter. The effectiveness of convexification methods and real-time computation is demonstrated by a planetary soft landing problem throughout the chapter.

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previous chapter Implicit Non-convex Model Predictive Control
next chapter Explicit (Offline) Optimization for MPC
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Metadata
Title
Convexification and Real-Time Optimization for MPC with Aerospace Applications
Authors
Yuanqi Mao
Daniel Dueri
Michael Szmuk
Behçet Açıkmeşe
Copyright Year
2019
Book
Handbook of Model Predictive Control

Print ISBN: 978-3-319-77488-6

Electronic ISBN: 978-3-319-77489-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-77489-3_15