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arXiv · 2512.08446

An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control

Maximilian Pierer von Esch·Andreas Völz·Knut Graichen
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Abstract

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.

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Maximilian Pierer von Esch, Andreas Völz, Knut Graichen. 2025-12-09. An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control. https://arxiv.org/abs/2512.08446

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