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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

12. Numerical Optimal Control of Nonlinear Systems

verfasst von : Lars Grüne, Jürgen Pannek

Erschienen in: Nonlinear Model Predictive Control

Verlag: Springer International Publishing

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Abstract

In this chapter, we focus on numerically solving the constrained finite horizon nonlinear optimal control problems occurring in each iterate of the NMPC procedure. To this end, we first state standard discretization techniques to obtain a nonlinear optimization problem in standard form. Utilizing this form, we outline basic versions of the two most common solution methods for such problems, that is, Sequential Quadratic Programming (SQP) and Interior Point Methods (IPM). Furthermore, we investigate interactions between the differential equation solver, the discretization technique, and the optimization method and present several NMPC specific details concerning the warm start of the optimization routine. Finally, we discuss NMPC variants relying on inexact solutions of the finite horizon optimal control problem.

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Vorheriges Kapitel Numerical Discretization
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Fußnoten
1
Appropriate step lengths \(\alpha _k\) will be added to these updates, below.
 
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Metadaten
Titel
Numerical Optimal Control of Nonlinear Systems
verfasst von
Lars Grüne
Jürgen Pannek
Copyright-Jahr
2017
Buch
Nonlinear Model Predictive Control

Print ISBN: 978-3-319-46023-9

Electronic ISBN: 978-3-319-46024-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-46024-6_12

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