An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control
Optimization and Control
2025-12-10 v1
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.
Cite
@article{arxiv.2512.08446,
title = {An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control},
author = {Maximilian Pierer von Esch and Andreas Völz and Knut Graichen},
journal= {arXiv preprint arXiv:2512.08446},
year = {2025}
}