An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912755572801536 |
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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 |
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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 |