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Hauptverfasser: von Esch, Maximilian Pierer, Völz, Andreas, Graichen, Knut
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2512.08446
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author von Esch, Maximilian Pierer
Völz, Andreas
Graichen, Knut
author_facet von Esch, Maximilian Pierer
Völz, Andreas
Graichen, Knut
contents This paper presents a concise overview of sensitivity-based methods for solving large-scale optimization problems in distributed fashion. The approach relies on sensitivities and primal decomposition to achieve coordination between the subsystems while requiring only local computations with neighbor-to-neighbor communication. We give a brief historical synopsis of its development and apply it to both static and dynamic optimization problems. Furthermore, a real-time capable distributed model predictive controller is proposed which is experimentally validated on a coupled watertank system.
format Preprint
id arxiv_https___arxiv_org_abs_2512_08446
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control
von Esch, Maximilian Pierer
Völz, Andreas
Graichen, Knut
Optimization and Control
This paper presents a concise overview of sensitivity-based methods for solving large-scale optimization problems in distributed fashion. The approach relies on sensitivities and primal decomposition to achieve coordination between the subsystems while requiring only local computations with neighbor-to-neighbor communication. We give a brief historical synopsis of its development and apply it to both static and dynamic optimization problems. Furthermore, a real-time capable distributed model predictive controller is proposed which is experimentally validated on a coupled watertank system.
title An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control
topic Optimization and Control
url https://arxiv.org/abs/2512.08446