On Composable and Parametric Uncertainty in Systems Co-Design
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913985217953792 |
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| author | Huang, Yujun Furter, Marius Zardini, Gioele |
| author_facet | Huang, Yujun Furter, Marius Zardini, Gioele |
| contents | Optimizing the design of complex systems requires navigating interdependent decisions, heterogeneous components, and multiple objectives. Our monotone theory of co-design offers a compositional framework for addressing this challenge, modeling systems as Design Problems (DPs), representing trade-offs between functionalities and resources within partially ordered sets. While current approaches model uncertainty using intervals, capturing worst- and best-case bounds, they fail to express probabilistic notions such as risk and confidence. These limitations hinder the applicability of co-design in domains where uncertainty plays a critical role. In this paper, we introduce a unified framework for composable uncertainty in co-design, capturing intervals, distributions, and parametrized models. This extension enables reasoning about risk-performance trade-offs and supports advanced queries such as experiment design, learning, and multi-stage decision making. We demonstrate the expressiveness and utility of the framework via a numerical case study on the uncertainty-aware co-design of task-driven Unmanned Aerial Vehicles (UAVs). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Composable and Parametric Uncertainty in Systems Co-Design Huang, Yujun Furter, Marius Zardini, Gioele Systems and Control Optimization and Control Optimizing the design of complex systems requires navigating interdependent decisions, heterogeneous components, and multiple objectives. Our monotone theory of co-design offers a compositional framework for addressing this challenge, modeling systems as Design Problems (DPs), representing trade-offs between functionalities and resources within partially ordered sets. While current approaches model uncertainty using intervals, capturing worst- and best-case bounds, they fail to express probabilistic notions such as risk and confidence. These limitations hinder the applicability of co-design in domains where uncertainty plays a critical role. In this paper, we introduce a unified framework for composable uncertainty in co-design, capturing intervals, distributions, and parametrized models. This extension enables reasoning about risk-performance trade-offs and supports advanced queries such as experiment design, learning, and multi-stage decision making. We demonstrate the expressiveness and utility of the framework via a numerical case study on the uncertainty-aware co-design of task-driven Unmanned Aerial Vehicles (UAVs). |
| title | On Composable and Parametric Uncertainty in Systems Co-Design |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2504.02766 |