Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

By Andrzej Banaszuk, Vladimir A. Fonoberov, T. Frewen, Marin Kobilarov, G. Mathew, Igor Mezi'c, A. Pinto, T. Sahai, H. Sane, Alberto Speranzon and 1 more. 2011

18 citations recorded by the source corpus.

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Statistics and Probability

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Citations in the corpus

MOPO: Model-based Offline Policy Optimization (2020, background): “Uncertainty quantification, a key ingredient to our approach, is critical to good performance in model-based RL both theoretically [63, 75, 44] and empirically [12, 7, 50, 39, 8], and in optimal control [62, 2, 34].”