MIT Develops Groundbreaking Framework for Designing Uncertainty-Resilient Complex Systems
MIT researchers have developed a novel framework for designing complex systems, from drones to regional transport networks, that explicitly accounts for uncertainty in component performance. The study, 'On Composable and Parametric Uncertainty in Systems Co-Design', was published on the arXiv preprint server and will be presented at the IEEE Conference on Decision and Control in December 2025.
The Zardini Group's research, led by authors Maneesh Agrawala, Krishnendu Chatterjee, and Sanjit A. Seshia, introduces a method for modeling performance trade-offs in systems with many interconnected parts. Unlike traditional approaches that consider only best-case and worst-case scenarios, this framework captures the likelihood of multiple outcomes, providing more comprehensive information.
The researchers demonstrated their approach by designing a delivery drone. They used the framework to choose perception systems and batteries, maximizing the drone's payload while minimizing its lifetime cost and weight. The method incorporated uncertainty into an existing category theory-based framework, allowing it to capture how design choices affect one another, even when individual component performance is uncertain.
The research, published at the 2020 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), could significantly impact the development of complex systems like autonomous vehicles and commercial aircraft. By accounting for real-world unpredictability, engineers can create more robust and reliable systems, improving safety and efficiency.
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