Conference Proceedings
A Computationally-Friendly Data-Driven Safety Filter for Control-Affine Discrete-Time Systems Subject to Unknown Process Noise
F Farokhi, AS Leong, I Shames, M Zamani
Proceedings of the American Control Conference | IEEE | Published : 2023
Abstract
A supervisory safety filter is developed to minimally modify nominal control inputs to a nonlinear control-affine discrete-time system to ensure satisfaction of potentially time-varying state and input constraints, i.e., safety constraints, with high probability. The system model is known while the environment model, i.e., distribution of additive Gaussian process noise, is unknown. State measurements are used to learn the statistics of the process noise. The safety filter employs a robust optimization problem involving tightening of the safety constraints based on the learned statistics and the corresponding confidence.
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Grants
Awarded by Multidisciplinary University Research Initiative
Funding Acknowledgements
[ "The work of F. Farokhi is supported by the Defence Science and Technology (DST) Group, Australia, via research contract ID10298.", "The work of I. Shames is supported by the Australian Government, via grant AUSMURIB000001 associated with ONR MURI N00014-19-1-2571." ]