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of 9
pro vyhledávání: '"Priore, Shawn"'
Autor:
Priore, Shawn, Oishi, Meeko
While techniques have been developed for chance constrained stochastic optimal control using sample disturbance data that provide a probabilistic confidence bound for chance constraint satisfaction, far less is known about how to use sample data in a
Externí odkaz:
http://arxiv.org/abs/2303.16981
Autor:
Priore, Shawn, Oishi, Meeko
We propose an open loop methodology based on sample statistics to solve chance constrained stochastic optimal control problems with probabilistic safety guarantees for linear systems where the additive Gaussian noise has unknown mean and covariance.
Externí odkaz:
http://arxiv.org/abs/2303.13036
Autor:
Priore, Shawn, Oishi, Meeko
While many techniques have been developed for chance constrained stochastic optimal control with Gaussian disturbance processes, far less is known about computationally efficient methods to handle non-Gaussian processes. In this paper, we develop a m
Externí odkaz:
http://arxiv.org/abs/2303.12295
Autor:
Priore, Shawn, Oishi, Meeko
This work proposes an open-loop methodology to solve chance constrained stochastic optimal control problems for linear systems with a stochastic control matrix. We consider a joint chance constraint for polytopic time-varying target sets under moment
Externí odkaz:
http://arxiv.org/abs/2302.01863
We propose an open loop control scheme for linear time invariant systems perturbed by multivariate $t$ disturbances through the use of quantile reformulations. The multivariate $t$ disturbance is motivated by heavy tailed phenomena that arise in mult
Externí odkaz:
http://arxiv.org/abs/2210.09479
We propose an open loop control scheme for linear systems with time-varying random elements in the plant's state matrix. This paper focuses on joint chance constraints for potentially time-varying target sets. Under assumption of finite and known exp
Externí odkaz:
http://arxiv.org/abs/2210.09468
We propose a method for open-loop stochastic optimal control of LTI systems based on Taylor approximations of quantile functions. This approach enables efficient computation of quantile functions that arise in chance constrained reformulations. We ar
Externí odkaz:
http://arxiv.org/abs/2110.03040
Publikováno v:
2022 American Control Conference (ACC).
We propose a method for open-loop stochastic optimal control of LTI systems based on Taylor approximations of quantile functions. This approach enables efficient computation of quantile functions that arise in chance constrained reformulations. We ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88b13a9884288bcd19065ba13f517e4e
https://doi.org/10.23919/acc53348.2022.9867580
https://doi.org/10.23919/acc53348.2022.9867580
Autor:
Alessandro Abate, Andrea Marin
This book constitutes the proceedings of the 18th International Conference on Quantitative Evaluation Systems, QEST 2021, held in Paris, France, in August 2021.The 21 full papers and 2 short papers presented together with 2 keynote papers were carefu